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Bibliography on: Ecological Informatics

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ESP: PubMed Auto Bibliography 26 Dec 2024 at 01:45 Created: 

Ecological Informatics

Wikipedia: Ecological Informatics Ecoinformatics, or ecological informatics, is the science of information (Informatics) in Ecology and Environmental science. It integrates environmental and information sciences to define entities and natural processes with language common to both humans and computers. However, this is a rapidly developing area in ecology and there are alternative perspectives on what constitutes ecoinformatics. A few definitions have been circulating, mostly centered on the creation of tools to access and analyze natural system data. However, the scope and aims of ecoinformatics are certainly broader than the development of metadata standards to be used in documenting datasets. Ecoinformatics aims to facilitate environmental research and management by developing ways to access, integrate databases of environmental information, and develop new algorithms enabling different environmental datasets to be combined to test ecological hypotheses. Ecoinformatics characterize the semantics of natural system knowledge. For this reason, much of today's ecoinformatics research relates to the branch of computer science known as Knowledge representation, and active ecoinformatics projects are developing links to activities such as the Semantic Web. Current initiatives to effectively manage, share, and reuse ecological data are indicative of the increasing importance of fields like Ecoinformatics to develop the foundations for effectively managing ecological information. Examples of these initiatives are National Science Foundation Datanet projects, DataONE and Data Conservancy.

Created with PubMed® Query: ( "ecology OR ecological" AND ("data management" OR informatics) NOT "assays for monitoring autophagy" ) NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)

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RevDate: 2024-12-24

Wang T, Campbell C, Stockdale AJ, et al (2025)

Distinct virologic trajectories in chronic hepatitis B identify heterogeneity in response to nucleos(t)ide analogue therapy.

JHEP reports : innovation in hepatology, 7(1):101229.

BACKGROUND & AIMS: The dynamics of HBV viral load (VL) in patients with chronic hepatitis B (CHB) on nucleos(t)ide analogue (NA) treatment and its relationship with liver disease are poorly understood. We aimed to study longitudinal VL patterns and their associations with CHB clinical outcomes.

METHODS: Utilising large scale, routinely collected electronic health records from six centres in England, collated by the National Institute for Health and Care Research Health Informatics Collaborative (NIHR HIC), we applied latent class mixed models to investigate VL trajectory patterns in adults receiving NA treatment. We assessed associations of VL trajectory with alanine transaminase, and with liver fibrosis/cirrhosis.

RESULTS: We retrieved data from 1,885 adults on NA treatment (median follow-up 6.2 years, IQR 3.7-9.3 years), with 21,691 VL measurements (median 10 per patient, IQR 5-17). Five VL classes were identified from the derivation cohort (n = 1,367, discrimination: 0.93, entropy: 0.90): class 1 'long term suppression' (n = 827, 60.5%), class 2 'timely virological suppression' (n = 254, 18.6%), class 3 'persistent moderate viraemia' (n = 140, 10.2%), class 4 'persistent high-level viraemia' (n = 44, 3.2%), and class 5 'slow virological suppression' (n = 102, 7.5%). The model demonstrated a discrimination of 0.93 and entropy of 0.88 for the validation cohort (n = 518). Alanine transaminase decreased variably over time in VL-suppressed groups (classes 1, 2, 5; all p <0.001), but did not significantly improve in those with persistent viraemia (classes 3, 4). Patients in class 5 had twofold increased hazards of fibrosis/cirrhosis compared with class 1 (adjusted hazard ratio, 2.00; 95% CI, 1.33-3.02).

CONCLUSIONS: Heterogeneity exists in virological response to NA therapy in CHB patients, with over 20% showing potentially suboptimal responses. Slow virological suppression is associated with liver disease progression.

IMPACT AND IMPLICATIONS: Treatment recommendations for people living with chronic hepatitis B virus (HBV) infection are becoming less stringent, meaning that more of the population will be eligible to receive therapy with nucleos(t)ide analogue agents. We explored outcomes of HBV treatment in a large UK dataset, describing different responses to treatment, and showing that the viral load is not completely suppressed after 1 year in about one in five cases, associated with an increased risk of liver complications. As treatment is rolled out more widely, patients and clinicians need to be aware of the potential for incomplete virologic responses. The findings can support the identification of high-risk individuals, improve early fibrosis and cirrhosis prediction, guide monitoring and preventive interventions, and support public health elimination goals.

RevDate: 2024-12-24
CmpDate: 2024-12-24

Mendoza JN, Prūse B, Ciriaco A, et al (2024)

Fishery and ecology-related knowledge about plants among fishing communities along Laguna Lake, Philippines.

Journal of ethnobiology and ethnomedicine, 20(1):108.

BACKGROUND: Ethnobotanical knowledge about plant roles in fisheries is crucial for sustainable resource management. Local ecological knowledge helps understand dynamics of the lake ecosystem. Fishers use plants based on availability and characteristics while adapting to the changes in the environment. Studying fishery related uses of plants and algae and the challenges interconnected with them from local perspectives can provide insights into their beneficial uses and impacts to the ecosystem.

METHODS: The study investigates the botanical knowledge of three fishing villages in Laguna Lake or Laguna de Bay (LB), Philippines, including Buhangin, Sampiruhan, and Mabato-Azufre, each with varying degrees of industrialization. The ethnobotanical study, which gathered 27 interviews between June 2022 and July 2024, included plant collection with the help of local collaborators, including local fishers as research guides.

RESULTS: Fishers in LB highlighted positive and negative plant-fishing interactions. The most frequently mentioned plant applications were fish habitat and fish hiding places. Fish food, spawning sites, conservation, and a number of challenges such as navigational concerns and aquaculture fish deaths had been previously reported in local use reports. The remaining observations provide new insights into plant-fishing interactions, including indicators of food quality and food sources for fish, the decrease in the action of waves, and how plants help in improving the quality of the water.

CONCLUSION: These results highlight that the knowledge of fishers regarding the ecosystem in which they conduct their fishing activities provides baseline information about the positive and negative relationships between plants and fishing activities in the region, which is vital for further understanding its biodiversity and ecosystem interactions. It is crucial to consider fisher knowledge and involve them as equal partners in conservation efforts of LB.

RevDate: 2024-12-23
CmpDate: 2024-12-24

Chunduri JR, SP Sagar (2025)

Insect Brain Proteomics: A Case Study of Periplaneta americana.

Methods in molecular biology (Clifton, N.J.), 2884:99-118.

Insects are known invertebrate species with economic, ecological, pathological, and medicinal value, as well as closely associated with human populations. Entomophagy and entomotherapy are future promising prospects largely attributable to the abundant availability, high protein content, and climatic sustainability of insects. In particular, the insect brain is an important system with a secluded, compact, and protective exoskeleton. It is immunologically privileged and capable of producing a robust immune response against pathogens. It is also a source of materials that initiate key activity throughout the body. Proteomic interrogation of Periplaneta americana enables understanding the role of this insect in the fields of food and pharmacology. Proximate analyses of P. americana highlights its richness in proteins. Here we perform a simple proteomic analysis to study the brain proteome of P. americana. The processes applied during the study include gel-based isolation and separation of proteins, followed by NanoLC-MS (Orbitrap) analyses and bioinformatic interrogation of the data. The results demonstrated that this insect proteome comprises antimicrobial proteins, allergens, and proteins required for metabolic processes.

RevDate: 2024-12-23

Blair ME, Noguera-Urbano EA, Ochoa-Quintero JM, et al (2024)

Software codesign between end users and developers to enhance utility for biodiversity conservation.

Bioscience, 74(12):867-873.

Creating software tools that address the needs of a wide range of decision-makers requires the inclusion of differing perspectives throughout the development process. Software tools for biodiversity conservation often fall short in this regard, partly because broad decision-maker needs may exceed the toolkits of single research groups or even institutions. We show that participatory, collaborative codesign enhances the utility of software tools for better decision-making in biodiversity conservation planning, as demonstrated by our experiences developing a set of integrated tools in Colombia. Specifically, we undertook an interdisciplinary, multi-institutional collaboration of ecological modelers, software engineers, and a diverse profile of potential end users, including decision-makers, conservation practitioners, and biodiversity experts. We leveraged and modified common paradigms of software production, including codesign and agile development, to facilitate collaboration through all stages (including conceptualization, development, testing, and feedback) to ensure the accessibility and applicability of the new tools to inform decision-making for biodiversity conservation planning.

RevDate: 2024-12-22
CmpDate: 2024-12-22

Yuan H, Hicks P, Ahmadian M, et al (2024)

Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata.

Briefings in bioinformatics, 26(1):.

Reusing massive collections of publicly available biomedical data can significantly impact knowledge discovery. However, these public samples and studies are typically described using unstructured plain text, hindering the findability and further reuse of the data. To combat this problem, we propose txt2onto 2.0, a general-purpose method based on natural language processing and machine learning for annotating biomedical unstructured metadata to controlled vocabularies of diseases and tissues. Compared to the previous version (txt2onto 1.0), which uses numerical embeddings as features, this new version uses words as features, resulting in improved interpretability and performance, especially when few positive training instances are available. Txt2onto 2.0 uses embeddings from a large language model during prediction to deal with unseen-yet-relevant words related to each disease and tissue term being predicted from the input text, thereby explaining the basis of every annotation. We demonstrate the generalizability of txt2onto 2.0 by accurately predicting disease annotations for studies from independent datasets, using proteomics and clinical trials as examples. Overall, our approach can annotate biomedical text regardless of experimental types or sources. Code, data, and trained models are available at https://github.com/krishnanlab/txt2onto2.0.

RevDate: 2024-12-21
CmpDate: 2024-12-21

De Wint FC, Nicholson S, Koid QQ, et al (2024)

Introducing a global database of entomopathogenic fungi and their host associations.

Scientific data, 11(1):1418.

Pathogens significantly influence natural and agricultural ecosystems, playing a crucial role in the regulation of species populations and maintaining biodiversity. Entomopathogenic fungi (EF), particularly within the Hypocreales order, exemplify understudied pathogens that infect insects and other arthropods globally. Despite their ecological importance, comprehensive data on EF host specificity and geographical distribution are lacking. To address this, we present EntomoFun 1.0, an open-access database centralizing global records of EF-insect associations in Hypocreales. This database includes 1,791 records detailing EF species, insect host taxa, countries of occurrence, life stages of hosts, and information sources. EntomoFun 1.0 is constructed based on 600 literature sources, as well as herbarium specimens of the Royal Botanical Gardens, Kew. This database is intended to test hypotheses, identify knowledge gaps, and stimulate future research. Contents of the EntomoFun 1.0 database are visualized with a global map, taxonomic chart, bipartite community network, and graphs.

RevDate: 2024-12-21

Jakobsson M, Mohammad R, Karlsson M, et al (2024)

The International Bathymetric Chart of the Arctic Ocean Version 5.0.

Scientific data, 11(1):1420.

Knowledge about seafloor depth, or bathymetry, is crucial for various marine activities, including scientific research, offshore industry, safety of navigation, and ocean exploration. Mapping the central Arctic Ocean is challenging due to the presence of perennial sea ice, which limits data collection to icebreakers, submarines, and drifting ice stations. The International Bathymetric Chart of the Arctic Ocean (IBCAO) was initiated in 1997 with the goal of updating the Arctic Ocean bathymetric portrayal. The project team has since released four versions, each improving resolution and accuracy. Here, we present IBCAO Version 5.0, which offers a resolution four times as high as Version 4.0, with 100 × 100 m grid cells compared to 200 × 200 m. Over 25% of the Arctic Ocean is now mapped with individual depth soundings, based on a criterion that considers water depth. Version 5.0 also represents significant advancements in data compilation and computing techniques. Despite these improvements, challenges such as sea-ice cover and political dynamics still hinder comprehensive mapping.

RevDate: 2024-12-20
CmpDate: 2024-12-21

Zhang M, Sun Y, Lan Y, et al (2024)

Multiomics joint analysis reveals the potential mechanism of differences in the taproot thickening between cultivated ginseng and mountain-cultivated ginseng.

BMC genomics, 25(1):1228.

Panax ginseng is an important medicinal plant in China and is classified into two types: cultivated ginseng (CFCG) and mountain-cultivated ginseng (MCG). The two types of genetic varieties are the same, but the growth environments and management practices are different, resulting in substantial differences in their taproot morphology. Currently, there is a paucity of research on the internal mechanisms that regulate the phenotypic differences between cultivated ginseng and mountain-cultivated ginseng. In this study, we explored the potential mechanisms underlying their phenotypic differences using transcriptomic and metabolomic techniques. The results indicate that the taproot thickening of CFCG was significantly greater than that of MCG. Compared with MCG-4, MCG-10, and MCG-18, the diameters of the taproots of CFCG-4 increased by 158.96, 81.57, and 43.21%, respectively. Additionally, the contents of sucrose and starch in the taproot, as well as TRA and DHZR, were markedly elevated. Transcriptome analysis revealed that compared with MCG of different age groups, genes associated with starch and sucrose metabolism pathways (PgSUS1, PgSPS1, PgSPS3, and PgglgC1) were significantly upregulated in CFCG-4, whereas genes involved in the phenylpropanoid biosynthesis pathway (PgPER3, PgPER51, and PgPER12) were significantly downregulated in CFCG-4. This imbalance in the metabolic pathways suggests that these genes play crucial roles in ginseng taproot thickening. PgbHLH130 and PgARF18 may be key regulators of transcriptional changes in these pathways. These findings elucidate the molecular mechanisms governing ginseng taproot thickening, and have important implications for enhancing the overall quality and value of ginseng.

RevDate: 2024-12-23
CmpDate: 2024-12-23

Redhead D, McElreath R, CT Ross (2024)

Reliable network inference from unreliable data: A tutorial on latent network modeling using STRAND.

Psychological methods, 29(6):1100-1122.

Social network analysis provides an important framework for studying the causes, consequences, and structure of social ties. However, standard self-report measures-for example, as collected through the popular "name-generator" method-do not provide an impartial representation of such ties, be they transfers, interactions, or social relationships. At best, they represent perceptions filtered through the cognitive biases of respondents. Individuals may, for example, report transfers that did not really occur, or forget to mention transfers that really did. The propensity to make such reporting inaccuracies is both an individual-level and item-level characteristic-variable across members of any given group. Past research has highlighted that many network-level properties are highly sensitive to such reporting inaccuracies. However, there remains a dearth of easily deployed statistical tools that account for such biases. To address this issue, we provide a latent network model that allows researchers to jointly estimate parameters measuring both reporting biases and a latent, underlying social network. Building upon past research, we conduct several simulation experiments in which network data are subject to various reporting biases, and find that these reporting biases strongly impact fundamental network properties. These impacts are not adequately remedied using the most frequently deployed approaches for network reconstruction in the social sciences (i.e., treating either the union or the intersection of double-sampled data as the true network), but are appropriately resolved through the use of our latent network models. To make implementation of our models easier for end-users, we provide a fully documented R package, STRAND, and include a tutorial illustrating its functionality when applied to empirical food/money sharing data from a rural Colombian population. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

RevDate: 2024-12-20
CmpDate: 2024-12-20

Townsend HA, Rosenberger KJ, Vanderlinden LA, et al (2024)

Evaluating methods for integrating single-cell data and genetics to understand inflammatory disease complexity.

Frontiers in immunology, 15:1454263.

BACKGROUND: Understanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible.

METHODS: We present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of three recent algorithms, based on pathways (scGWAS), single-cell disease scores (scDRS), or both (scPagwas), according to accuracy/sensitivity and interpretability. While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,173 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools' resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas. Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls.

RESULTS: We first found that single-cell based tools scDRS and scPagwas called superior numbers of supported cell states that were overlooked by scGWAS. While scGWAS and scPagwas were advantageous for gene exploration, scDRS effectively accounted for batch effect and captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5' scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohn's disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases.

RevDate: 2024-12-20
CmpDate: 2024-12-20

Oskolkov N, Sandionigi A, Götherström A, et al (2024)

Unraveling the ancient fungal DNA from the Iceman gut.

BMC genomics, 25(1):1225.

BACKGROUND: Fungal DNA is rarely reported in metagenomic studies of ancient samples. Although fungi are essential for their interactions with all kingdoms of life, limited information is available about ancient fungi. Here, we explore the possibility of the presence of ancient fungal species in the gut of Ötzi, the Iceman, a naturally mummified human found in the Tyrolean Alps (border between Italy and Austria).

METHODS: A robust bioinformatic pipeline has been developed to detect and authenticate fungal ancient DNA (aDNA) from muscle, stomach, small intestine, and large intestine samples.

RESULTS: We revealed the presence of ancient DNA associated with Pseudogymnoascus genus, with P. destructans and P. verrucosus as possible species, which were abundant in the stomach and small intestine and absent in the large intestine and muscle samples.

CONCLUSION: We suggest that Ötzi may have consumed these fungi accidentally, likely in association with other elements of his diet, and they persisted in his gut after his death due to their adaptability to harsh and cold environments. This suggests the potential co-occurrence of ancient humans with opportunistic fungal species and proposes and validates a conservative bioinformatic approach for detecting and authenticating fungal aDNA in historical metagenomic samples.

RevDate: 2024-12-19

Wong SY, Machado-de-Lima NM, Wilkins D, et al (2024)

Fine-scale landscape heterogeneity drives microbial community structure at Robinson Ridge, East Antarctica.

The Science of the total environment, 958:177964 pii:S0048-9697(24)08121-X [Epub ahead of print].

Life at Robinson Ridge, located in the Windmill Islands region of East Antarctica, is susceptible to a changing climate. At this site, responses of the vegetation communities and moss-beds have been well researched, but corresponding information for microbial counterparts is still lacking. To bridge this knowledge gap, we established baseline data for monitoring the environmental drivers shaping the soil microbial community on the local 'hillslope' scale. Using triplicate 300-m long transects encompassing a hillslope with wind-exposed arid soils near the top, and snowmelt-sustained-moss beds at the bottom, we assessed the fine-scale heterogeneity of the soil environmental and microbial properties. Moist, low-lying, and vegetated soils exhibited higher soil fertility and unique biodiversity, with taxa adapted to thrive in moist conditions (i.e., Tardigrada, Phragmoplastophyta, Chloroflexi) and those that have previously demonstrated strong specificity for moss species (i.e., Fibrobacterota, Mucoromycota and Cyanobacteria) dominating. In contrast, elevated soils with limited moisture and nutrients were dominated by metabolically diverse phyla like Actinobacteriota and Ascomycota. Significant differences in microbial communities were observed at both hillslope (50-300 m) and fine spatial scales, as small as 0.1 m. Vertical heterogeneity was observed with higher abundances of Cyanobacteria and micro-algae in surfaces compared to subsoil, potentially indicating early biocrust formation. Stochastic and deterministic processes governing phylogenetic assembly were linked to soil positional groups and microbial domains rather than soil depth. Gradient Forest modeling identified critical environmental thresholds, such as ammonia, manganese, and sulphur, responsible for drastic community changes following level alterations. This reinforces the existence of strong niche preferences and distinct distribution patterns within the local microbial communities. This study highlights the need for finer-scale investigations considering site topography to better understand the relationship between environmental drivers and local microbiota. Ultimately, these insights enable us to understand environmental drivers and predict Antarctic ecosystem responses, helping safeguard this fragile environment.

RevDate: 2024-12-19

Duan HN, Hearne G, Polikar R, et al (2024)

The Naïve Bayes Classifier ++ for Metagenomic Taxonomic Classification-Query Evaluation.

Bioinformatics (Oxford, England) pii:7928842 [Epub ahead of print].

MOTIVATION: This study examines the query performance of the NBC ++ (Incremental Naive Bayes Classifier) program for variations in canonicality, k-mer size, databases, and input sample data size. We demonstrate that both NBC ++ and Kraken2 are influenced by database depth, with macro measures improving as depth increases. However, fully capturing the diversity of life, especially viruses, remains a challenge.

RESULTS: NBC ++ can competitively profile the superkingdom content of metagenomic samples using a small training database. NBC ++ spends less time training and can use a fraction of the memory than Kraken2 but at the cost of long querying time. Major NBC ++ enhancements include accommodating canonical k-mer storage (leading to significant storage savings) and adaptable and optimized memory allocation that accelerates query analysis and enables the software to be run on nearly any system. Additionally, the output now includes log-likelihood values for each training genome, providing users with valuable confidence information.

AVAILABILITY: Source code and Dockerfile are available at http://github.com/EESI/Naive_Bayes.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online, and databases are available at Zenodo records #11657719 and #11643985.

RevDate: 2024-12-19
CmpDate: 2024-12-19

Zhang Z, Li Q, Li H, et al (2024)

Integrative multi-omics analysis reveals the contribution of neoVTX genes to venom diversity of Synanceia verrucosa.

BMC genomics, 25(1):1210.

BACKGROUND: Animal venom systems are considered as valuable model for investigating the molecular mechanisms underlying phenotypic evolution. Stonefish are the most venomous and dangerous fish because of severe human envenomation and occasionally fatalities, whereas the genomic background of their venom has not been fully explored compared with that in other venomous animals.

RESULTS: In this study, we followed modern venomic pipelines to decode the Synanceia verrucosa venom components. A catalog of 478 toxin genes was annotated based on our assembled chromosome-level genome. Integrative analysis of the high-quality genome, the transcriptome of the venom gland, and the proteome of crude venom revealed mechanisms underlying the venom complexity in S. verrucosa. Six tandem-duplicated neoVTX subunit genes were identified as the major source for the neoVTX protein production. Further isoform sequencing revealed massive alternative splicing events with a total of 411 isoforms demonstrated by the six genes, which further contributed to the venom diversity. We then characterized 12 dominantly expressed toxin genes in the venom gland, and 11 of which were evidenced to produce the venom protein components, with the neoVTX proteins as the most abundant. Other major venom proteins included a presumed CRVP, Kuntiz-type serine protease inhibitor, calglandulin protein, and hyaluronidase. Besides, a few of highly abundant non-toxin proteins were also characterized and they were hypothesized to function in housekeeping or hemostasis maintaining roles in the venom gland. Notably, gastrotropin like non-toxin proteins were the second highest abundant proteins in the venom, which have not been reported in other venomous animals and contribute to the unique venom properties of S. verrucosa.

CONCLUSIONS: The results identified the major venom composition of S. verrucosa, and highlighted the contribution of neoVTX genes to the diversity of venom composition through tandem-duplication and alternative splicing. The diverse neoVTX proteins in the venom as lethal particles are important for understanding the adaptive evolution of S. verrucosa. Further functional studies are encouraged to exploit the venom components of S. verrucosa for pharmaceutical innovation.

RevDate: 2024-12-18

Ma X, Zheng G, Xu C, et al (2024)

A global product of 150-m urban building height based on spaceborne lidar.

Scientific data, 11(1):1387.

Urban building height, as a fundamental 3D urban structural feature, has far-reaching applications. However, creating readily available datasets of recent urban building heights with fine spatial resolutions and global coverage remains a challenging task. Here, we provide a 150-m global urban building heights dataset around 2020 by combining the spaceborne lidar (Global Ecosystem Dynamics Investigation, GEDI), multi-sourced data (Landsat-8, Sentinel-2, and Sentinel-1), and topographic data. The validation results revealed that the GEDI-estimated building height samples were effective compared to the reference data (Pearson's r = 0.81, RMSE = 3.58 m). The mapping product also demonstrated good performance, as indicated by its strong correlation with the reference data (Pearson's r = 0.71, RMSE = 4.73 m). Compared with the currently existing datasets, it holds the ability to provide a spatial resolution (150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This product will boost future urban studies across many fields, including environmental, ecological, and social sciences.

RevDate: 2024-12-19
CmpDate: 2024-12-19

Alvarado M, Gómez-Navajas JA, Blázquez-Muñoz MT, et al (2024)

The good, the bad, and the hazardous: comparative genomic analysis unveils cell wall features in the pathogen Candidozyma auris typical for both baker's yeast and Candida.

FEMS yeast research, 24:.

The drug-resistant pathogenic yeast Candidozyma auris (formerly named Candida auris) is considered a critical health problem of global importance. As the cell wall plays a crucial role in pathobiology, here we performed a detailed bioinformatic analysis of its biosynthesis in C. auris and related Candidozyma haemuli complex species using Candida albicans and Saccharomyces cerevisiae as references. Our data indicate that the cell wall architecture described for these reference yeasts is largely conserved in Candidozyma spp.; however, expansions or reductions in gene families point to subtle alterations, particularly with respect to β--1,3--glucan synthesis and remodeling, phosphomannosylation, β-mannosylation, and glycosylphosphatidylinositol (GPI) proteins. In several aspects, C. auris holds a position in between C. albicans and S. cerevisiae, consistent with being classified in a separate genus. Strikingly, among the identified putative GPI proteins in C. auris are adhesins typical for both Candida (Als and Hyr/Iff) and Saccharomyces (Flo11 and Flo5-like flocculins). Further, 26 putative C. auris GPI proteins lack homologs in Candida genus species. Phenotypic analysis of one such gene, QG37_05701, showed mild phenotypes implicating a role associated with cell wall β-1,3-glucan. Altogether, our study uncovered a wealth of information relevant for the pathogenicity of C. auris as well as targets for follow-up studies.

RevDate: 2024-12-18

Reynolds SA, Beery S, Burgess N, et al (2024)

The potential for AI to revolutionize conservation: a horizon scan.

Trends in ecology & evolution pii:S0169-5347(24)00286-6 [Epub ahead of print].

Artificial Intelligence (AI) is an emerging tool that could be leveraged to identify the effective conservation solutions demanded by the urgent biodiversity crisis. We present the results of our horizon scan of AI applications likely to significantly benefit biological conservation. An international panel of conservation scientists and AI experts identified 21 key ideas. These included species recognition to uncover 'dark diversity', multimodal models to improve biodiversity loss predictions, monitoring wildlife trade, and addressing human-wildlife conflict. We consider the potential negative impacts of AI adoption, such as AI colonialism and loss of essential conservation skills, and suggest how the conservation field might adapt to harness the benefits of AI while mitigating its risks.

RevDate: 2024-12-18
CmpDate: 2024-12-18

Shitindo M (2024)

Interactions between wild pigs and the spread of disease.

eLife, 13: pii:105293.

Tracking wild pigs with GPS devices reveals how their social interactions could influence the spread of disease, offering new strategies for protecting agriculture, wildlife, and human health.

RevDate: 2024-12-18

Winders S, Yoo L, Heitkemper M, et al (2024)

Multilevel Factors and Sleep in Adults With Inflammatory Bowel Disease: A Qualitative Study.

Crohn's & colitis 360, 6(4):otae075 pii:otae075.

BACKGROUND: This study aimed to describe the patient-reported factors that impact sleep among individuals with inflammatory bowel disease (IBD), aligning with the Social Ecological Model of Sleep. This addresses the gap in IBD sleep research, which predominantly focuses on individual-level factors and their impact on sleep.

METHODS: Adults (ages 18-65) with IBD were recruited online through ResearchMatch in June 2023. Participants filled out survey questions on their demographic characteristics, health history, sleep, and IBD-related symptoms. Content analysis was conducted on 2 open-ended questions about factors that impacted their sleep.

RESULTS: This analysis included 163 adults with IBD (M = 39 years of age, 76.7% White, 91.4% non-Hispanic or Latino, 66.9% female, and 83.4% active IBD) who answered open-ended questions with comments about their sleep. Most participants indicated an individual-level factor impacted their sleep quality (85.3%, n = 139), categorized into 5 subthemes: Mental health, health, behavior and choices, physiology, and attitudes. Additionally, participants (43.6%, n = 71) mentioned social-level factors divided into 7 subthemes: Family, work, home, neighborhood, social network, and school. A smaller group of participants (17.2%, n = 28) mentioned societal-level factors designated into 4 subthemes: Natural environment and geography, technology, 24/7 society, and economics.

CONCLUSIONS: This study highlights the need for tailored sleep interventions for those with IBD that consider not only disease activity but also mental health, family, work, and the natural environment. IBD clinics should prioritize sleep health using an interdisciplinary approach to holistically address the unique needs of those with IBD.

RevDate: 2024-12-17
CmpDate: 2024-12-17

Radice VZ, Hernández-Agreda A, Pérez-Rosales G, et al (2024)

Recent trends and biases in mesophotic ecosystem research.

Biology letters, 20(12):20240465.

Mesophotic ecosystems (approx. 30-150 m) represent a significant proportion of the world's oceans yet have long remained understudied due to challenges in accessing these deeper depths. Owing to advances in underwater technologies and a growing scientific and management interest, there has been a major expansion in research of both (sub)tropical mesophotic coral ecosystems and temperate mesophotic ecosystems. Here, we characterize the recent global trends in mesophotic research through an updated release of the 'mesophotic.org' database (www.mesophotic.org) where we reviewed and catalogued 1500 scientific publications. In doing so, we shed light on four major research biases: a gross imbalance in (a) the geographical spread of research efforts, differences in (b) the focal depth range and (c) research fields associated with study organisms and research platforms, and (d) the lack of temporal studies. Overall, we are optimistic about the future of mesophotic research and hope that by highlighting current trends and imbalances, we can raise awareness and stimulate discussion on the future directions of this emerging field.

RevDate: 2024-12-18
CmpDate: 2024-12-18

Graham AL, RR Regoes (2024)

Dose-dependent interaction of parasites with tiers of host defense predicts "wormholes" that prolong infection at intermediate inoculum sizes.

PLoS computational biology, 20(12):e1012652 pii:PCOMPBIOL-D-24-00323.

Immune responses are induced by parasite exposure and can in turn reduce parasite burden. Despite such apparently simple rules of engagement, key drivers of within-host dynamics, including dose-dependence of defense and infection duration, have proven difficult to predict. Here, we model how varied inoculating doses interact with multi-tiered host defenses at a site of inoculation, by confronting barrier, innate, and adaptive tiers with replicating and non-replicating parasites across multiple orders of magnitude of dose. We find that, in general, intermediate parasite doses generate infections of longest duration because they are sufficient in number to breach barrier defenses, but insufficient to strongly induce subsequent tiers of defense. These doses reveal "wormholes" in defense from which parasites might profit: Deviation from the hypothesis of independent action, which postulates that each parasite has an independent probability of establishing infection, may therefore be widespread. Interestingly, our model predicts local maxima of duration at two doses-one for each tier transition. While some empirical evidence is consistent with nonlinear dose-dependencies, testing the predicted dynamics will require finer-scale dose variation than experiments usually incorporate. Our results help explain varied infection establishment and duration among differentially-exposed hosts and elucidate evolutionary pressures that shape both virulence and defense.

RevDate: 2024-12-18
CmpDate: 2024-12-18

Champion C, Momal R, Le Chatelier E, et al (2024)

OneNet-One network to rule them all: Consensus network inference from microbiome data.

PLoS computational biology, 20(12):e1012627 pii:PCOMPBIOL-D-23-01356.

Modeling microbial interactions as sparse and reproducible networks is a major challenge in microbial ecology. Direct interactions between the microbial species of a biome can help to understand the mechanisms through which microbial communities influence the system. Most state-of-the art methods reconstruct networks from abundance data using Gaussian Graphical Models, for which several statistically grounded and computationnally efficient inference approaches are available. However, the multiplicity of existing methods, when applied to the same dataset, generates very different networks. In this article, we present OneNet, a consensus network inference method that combines seven methods based on stability selection. This resampling procedure is used to tune a regularization parameter by computing how often edges are selected in the networks. We modified the stability selection framework to use edge selection frequencies directly and combine them in the inferred network to ensure that only reproducible edges are included in the consensus. We demonstrated on synthetic data that our method generally led to slightly sparser networks while achieving much higher precision than any single method. We further applied the method to gut microbiome data from liver-cirrothic patients and demonstrated that the resulting network exhibited a microbial guild that was meaningful in terms of human health.

RevDate: 2024-12-17
CmpDate: 2024-12-17

Bernadou A, R Jeanson (2024)

Randomness as a driver of inactivity in social groups.

PLoS computational biology, 20(12):e1012668 pii:PCOMPBIOL-D-24-00988.

Social insects, such as ants and bees, are known for their highly efficient and structured colonies. Division of labour, in which each member of the colony has a specific role, is considered to be one major driver of their ecological success. However, empirical evidence has accumulated showing that many workers, sometimes more than half, remain idle in insect societies. Several hypotheses have been put forward to explain these patterns, but none provides a consensual explanation. Task specialisation exploits inter-individual variations, which are mainly influenced by genetic factors beyond the control of the colony. As a result, individuals may also differ in the efficiency with which they perform tasks. In this context, we aimed to test the hypothesis that colonies generate a large number of individuals in order to recruit only the most efficient to perform tasks, at the cost of producing and maintaining a fraction of workers that remain inactive. We developed a model to explore the conditions under which variations in the scaling of workers' production and maintenance costs, along with activity costs, allow colonies to sustain a fraction of inactive workers. We sampled individual performances according to different random distributions in order to simulate the variability associated with worker efficiency. Our results show that the inactivity of part of the workforce can be beneficial for a wide range of parameters if it allows colonies to select the most efficient workers. In decentralised systems such as insect societies, we suggest that inactivity is a by-product of the random processes associated with the generation of individuals whose performance levels cannot be controlled.

RevDate: 2024-12-17

Bao Y, Jia F, Geng Y, et al (2024)

Uncovering the Differed Susceptibility of Fusarium oxysporum (Fo32931 and FocII5) to Fungicide Phenamacril: From Computational and Experimental Perspectives.

Journal of agricultural and food chemistry [Epub ahead of print].

Fo32931 and FoCII5 are two subtypes of Fusarium oxysporum (Fo), a pathogenic filamentous fungus. Phenamacril (PHA), a Fusarium-specific fungicide that targets myosin I, exhibits significant hyphal growth inhibition in Fo32931 but shows weak resistance in FocII5, despite only two amino acid differences in the PHA-binding pocket of myosin I. In this study, we aim to elucidate the molecular basis for the differential sensitivity ofF. oxysporum myosin I variants (FoMyoI[32931] and FoMyoI[cII5]) to phenamacril through computational methods and biochemical validation. The results suggest that phenamacril functions as an allosteric inhibitor for FoMyoI[32931], inhibiting the large oscillation of the converter lever domain (CLD) upon ATP binding and promoting the opening of the outer cleft, further impairing protein function. PHA significantly reduced the coupling between the CLD, especially the converter, and the catalytic center, diminishing the response of the CLD to the motor domain in FoMyoI[32931]. From the residue mutation experiment, we found that the S418T substitution in FoMyoI[cII5] is the key to the reduced phenamacril sensitivity of FocII5. According to the microscale thermophoresis (MST) assay and pocket conformation analysis, the S418T mutation disturbs the orientation of pocket residues, especially Lys537, leading to a looser pocket and reduced interaction between Lys537 and phenamacril, which lowers the binding affinity of FoMyoI[cII5] for phenamacril. These findings provide deeper insights into the reasons for the lower sensitivity of FoCII5 to phenamacril from both molecular and structural perspectives and will also guide the design of novel inhibitors against resistant Fusarium spp., like FoCII5.

RevDate: 2024-12-17
CmpDate: 2024-12-17

Liu R, Zhang P, Bai J, et al (2024)

Integrated Transcriptomic and Proteomic Analyses of Antler Growth and Ossification Mechanisms.

International journal of molecular sciences, 25(23): pii:ijms252313215.

Antlers are the sole mammalian organs capable of continuous regeneration. This distinctive feature has evolved into various biomedical models. Research on mechanisms of antler growth, development, and ossification provides valuable insights for limb regeneration, cartilage-related diseases, and cancer mechanisms. Here, ribonucleic acid sequencing (RNA-seq) and four-dimensional data-independent acquisition (4D DIA) technologies were employed to examine gene and protein expression differences among four tissue layers of the Chinese milu deer antler: reserve mesenchyme (RM), precartilage (PC), transition zone (TZ), cartilage (CA). Overall, 4611 differentially expressed genes (DEGs) and 2388 differentially expressed proteins (DEPs) were identified in the transcriptome and proteome, respectively. Among the 828 DEGs common to both omics approaches, genes from the collagen, integrin, and solute carrier families, and signaling molecules were emphasized for their roles in the regulation of antler growth, development, and ossification. Bioinformatics analysis revealed that in addition to being regulated by vascular and nerve regeneration pathways, antler growth and development are significantly influenced by numerous cancer-related signaling pathways. This indicates that antler growth mechanisms may be similar to those of cancer cell proliferation and development. This study lays a foundation for future research on the mechanisms underlying the rapid growth and ossification of antlers.

RevDate: 2024-12-17
CmpDate: 2024-12-17

Zhang KL, Leng YN, Hao RR, et al (2024)

Adaptation of High-Altitude Plants to Harsh Environments: Application of Phenotypic-Variation-Related Methods and Multi-Omics Techniques.

International journal of molecular sciences, 25(23): pii:ijms252312666.

High-altitude plants face extreme environments such as low temperature, low oxygen, low nutrient levels, and strong ultraviolet radiation, causing them to adopt complex adaptation mechanisms. Phenotypic variation is the core manifestation of ecological adaptation and evolution. Many plants have developed a series of adaptive strategies through long-term natural selection and evolution, enabling them to survive and reproduce under such harsh conditions. This article reviews the techniques and methods used in recent years to study the adaptive evolution of high-altitude plants, including transplantation techniques, genomics, transcriptomics, proteomics, and metabolomics techniques, and their applications in high-altitude plant adaptive evolution. Transplantation technology focuses on phenotypic variation, which refers to natural variations in morphological, physiological, and biochemical characteristics, exploring their key roles in nutrient utilization, photosynthesis optimization, and stress-resistance protection. Multiple omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have revealed genes, regulatory pathways, and metabolic networks associated with phenotypic variations at the genetic and molecular levels. At the same time, the limitations and deficiencies of current technologies used to study plant adaptation to high-altitude environments were discussed. In addition, we propose future improvements to existing technologies and advocate for the integration of different technologies at multiple levels to study the molecular mechanisms of plant adaptation to high-altitude environments, thus providing insights for future research in this field.

RevDate: 2024-12-17

Chaligava O, Zinicovscaia I, Peshkova A, et al (2024)

Major and Trace Airborne Elements and Ecological Risk Assessment: Georgia Moss Survey 2019-2023.

Plants (Basel, Switzerland), 13(23): pii:plants13233298.

The study, carried out as part of the International Cooperative Program on Effects of Air Pollution on Natural Vegetation and Crops, involved collecting 95 moss samples across the territory of Georgia during the period from 2019 to 2023. Primarily samples of Hypnum cupressiforme were selected, with supplementary samples of Abietinella abietina, Pleurozium schreberi, and Hylocomium splendens in cases of the former's absence. The content of 14 elements (Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, S, Sr, V, and Zn) was detected using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES), while the Hg content was determined using a Direct Mercury Analyzer. To identify any relationships between chemical elements and to depict their sources, multivariate statistics was applied. Principal component analysis identified three main components: PC1 (geogenic, 43.4%), PC2 (anthropogenic, 13.3%), and PC3 (local anomalies, 8.5%). The results were compared with the first moss survey conducted in Georgia in the period from 2014 to 2017, offering insights into temporal trends of air quality. Utilizing GIS, a spatial map illustrating pollution levels across Georgia, based on the Pollution Load Index, was generated. The Potential Environmental Risk Index emphasized significant risks associated with mercury and cadmium at several locations. The study highlights the utility of moss biomonitoring in assessing air pollution and identifying hotspots of contamination. The findings from this study could be beneficial for future biomonitoring research in areas with varying physical and geographical conditions.

RevDate: 2024-12-16

Khosravi M, Mojtabaeian SM, MA Sarvestani (2024)

A Systematic Review on the Outcomes of Climate Change in the Middle-Eastern Countries: The Catastrophes of Yemen and Syria.

Environmental health insights, 18:11786302241302270.

The Middle East is facing serious climate change challenges, rendering it as one of the most affected regions worldwide. This paper aimed to investigate the outcomes of climate change in the Middle East. In 2024, a qualitative study was conducted employing a methodology that integrated systematic review for data collection and thematic analysis for data analysis. Such integration of the approaches provided valuable insights into the findings within the literature in a comprehensive and categorized format. PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews were searched for relevant studies published between 2000 and 2024. The quality of these studies was assessed using the AACODS (Accuracy, Coverage, Objectivity, Date, Significance) checklist. The data extracted from the included studies underwent a thematic analysis utilizing Braun and Clarke's methodology. After completing the screening process, a total of 93 papers were deemed suitable for inclusion in the study. The quality assessment of these selected studies demonstrated a notably high standard, particularly in terms of authority, accuracy, coverage, objectivity, and significance. Moreover, minimal levels of bias were observed within the included studies. Subsequent thematic analysis of the findings from the systematic review identified 6 overarching themes: "Human Health Outcomes," "Animal Health Outcomes," "Plant Health Outcomes," "Ecological Outcomes," "Economic Outcomes," and "Political Outcomes." The study revealed ecological outcomes as the most prevalent consequences of climate change in the Middle East, including alterations in habitat distribution, temperature increase, water scarcity, and more. The outcomes seemed to be interconnected, exacerbating each other. Yemen and Syria had faced severe consequences, leading to political unrest and humanitarian crises in which Yemen ranking among the most water-stressed nations globally, while Syria contending with millions of displaced individuals living in dire conditions.

RevDate: 2024-12-15

Mu X, Chen C, Fan Q, et al (2024)

Removal and ecological impact of sulfamethoxazole and N-acetyl sulfamethoxazole in mesocosmic wetlands dominated by submerged plants: Plant tolerance, microbial response, and nitrogen transformation.

The Science of the total environment, 958:178034 pii:S0048-9697(24)08191-9 [Epub ahead of print].

Sulfamethoxazole (SMX) and its human metabolite N-acetylsulfamethoxazole (N-SMX) are frequently detected in aquatic environments, posing potential threats to freshwater ecosystem health. Constructed wetlands are pivotal for wastewater treatment, with plant species serving as key determinants of pollutant removal efficiency. In this study, wetlands dominated by three submerged plants (Myriophyllum verticillatum, Vallisneria spiralis, Hydrilla verticillata) were respectively constructed to investigate the removal of SMX and N-SMX, and the impact on wetland ecology regarding plant tolerance, microbial response, and nitrogen transformation. Results showed that wetlands removed N-SMX (82.3-99.8 %) more effectively than SMX (54.3-80.2 %), with the wetland dominated by Myriophyllum verticillatum showing the highest removal efficiency. However, high concentrations (5 mg/L) of SMX and N-SMX significantly reduced NH4[+]-N and TN removal (p < 0.05), accompanied by shifts in microbial communities, especially a decreased abundance of Proteobacteria and key nitrogen-transforming genes. A total of 22 different ARGs (antibiotic resistance genes) were detected. SMX significantly increased the relative abundance of sulfonamide resistance genes (sul1, sul2) (p < 0.05), while major denitrifying genera, such as Thiobacillus, which were not the primary hosts of these genes, showed a significant negative correlation with sul1 and sul2 (p < 0.05). This study provides a reference for ecological remediation of wetlands in response to antibiotic contamination.

RevDate: 2024-12-14
CmpDate: 2024-12-14

Movahed E, Gandomkar F, M Ameri (2024)

Impact of Climatic Factors on the Incidence of Urban Leishmaniasis Using Geographic Information System.

Iranian biomedical journal, 28(7):91.

INTRODUCTION: The present study aimed to evaluate the effect of climatic factors on the rate of urban cutaneous leishmaniasis in the Sar Asiyab area of Kerman using a geographic information system from 2016 to 2021.

MATERIALS AND METHODS: The sample size in this descriptive-analytical cross-sectional study included patients suffering from urban cutaneous leishmaniasis who lived in Kerman City, Sar Asiyab region, from 2016 to 2021, using the census method.

RESULTS: The study involved 332 patients with cutaneous leishmaniasis. Of these, 36.7% were under 15 years old, and 6.4% were over 60. A statistically significant difference was observed between patients' mean and standard deviation in each season a year in Kerman (Sar Asiyab) (p = 0.03). The highest incidence rate of cutaneous leishmaniasis was in 2017, and the lowest one was in 2020.

CONCLUSION AND DISCUSSION: Considering the high incidence of leishmaniasis in 2016 and the significant difference in the seasons, all climatic factors should be determined simultaneously. Additionally, the geographical distribution of the disease should be assessed from various epidemiological and ecological aspects in 2016, considering the seasons.

RevDate: 2024-12-15
CmpDate: 2024-12-15

Kan-Lingwood NY, Sagi L, Mazie S, et al (2025)

Genotyping Error Detection and Customised Filtration for SNP Datasets.

Molecular ecology resources, 25(1):e14033.

A major challenge in analysing single-nucleotide polymorphism (SNP) genotype datasets is detecting and filtering errors that bias analyses and misinterpret ecological and evolutionary processes. Here, we present a comprehensive method to estimate and minimise genotyping error rates (deviations from the 'true' genotype) in any SNP datasets using triplicates (three repeats of the same sample) in a four-step filtration pipeline. The approach involves: (1) SNP filtering by missing data; (2) SNP filtering by error rates; (3) sample filtering by missing data and (4) detection of recaptured individuals by using estimated SNP error rates. The modular pipeline is provided in an R script that allows customised adjustments. We demonstrate the applicability of the method using non-invasive sampling from the Asiatic wild ass (Equus hemionus) population in Israel. We genotyped 756 samples using 625 SNPs, of which 255 were triplicates of 85 samples. The average SNP error rate, calculated based on the number of mismatching genotypes across triplicates before filtration, was 0.0034 and was reduced to 0.00174 following filtration. Evaluating genetic distance (GD) and relatedness (r) between triplicates before and after filtration (expected to be at the minimum and maximum respectively) showed a significant reduction in the average GD, from 58.1 to 25.3 (p = 0.0002) and a significant increase in relatedness, from r = 0.98 to r = 0.991 (p = 0.00587). We demonstrate how error rate estimation enhances recapture detection and improves genotype quality.

RevDate: 2024-12-16
CmpDate: 2024-12-15

Zhao L, Henriksen RA, Ramsøe A, et al (2025)

Revisiting the Briggs Ancient DNA Damage Model: A Fast Maximum Likelihood Method to Estimate Post-Mortem Damage.

Molecular ecology resources, 25(1):e14029.

One essential initial step in the analysis of ancient DNA is to authenticate that the DNA sequencing reads are actually from ancient DNA. This is done by assessing if the reads exhibit typical characteristics of post-mortem damage (PMD), including cytosine deamination and nicks. We present a novel statistical method implemented in a fast multithreaded programme, ngsBriggs that enables rapid quantification of PMD by estimation of the Briggs ancient damage model parameters (Briggs parameters). Using a multinomial model with maximum likelihood fit, ngsBriggs accurately estimates the parameters of the Briggs model, quantifying the PMD signal from single and double-stranded DNA regions. We extend the original Briggs model to capture PMD signals for contemporary sequencing platforms and show that ngsBriggs accurately estimates the Briggs parameters across a variety of contamination levels. Classification of reads into ancient or modern reads, for the purpose of decontamination, is significantly more accurate using ngsBriggs than using other methods available. Furthermore, ngsBriggs is substantially faster than other state-of-the-art methods. ngsBriggs offers a practical and accurate method for researchers seeking to authenticate ancient DNA and improve the quality of their data.

RevDate: 2024-12-13
CmpDate: 2024-12-13

Karim AAJ, Mahmud MZ, R Khan (2024)

Advanced vision transformers and open-set learning for robust mosquito classification: A novel approach to entomological studies.

PLoS computational biology, 20(12):e1012654.

Mosquito-related diseases pose a significant threat to global public health, necessitating efficient and accurate mosquito classification for effective surveillance and control. This work presents an innovative approach to mosquito classification by leveraging state-of-the-art vision transformers and open-set learning techniques. A novel framework has been introduced that integrates Transformer-based deep learning models with comprehensive data augmentation and preprocessing methods, enabling robust and precise identification of ten mosquito species. The Swin Transformer model achieves the best performance for traditional closed-set learning with 99.60% accuracy and 0.996 F1 score. The lightweight MobileViT technique attains an almost equivalent accuracy of 98.90% with significantly reduced parameters and model complexities. Next, the applied deep learning models' adaptability and generalizability in a static environment have been enhanced by using new classes of data samples during the inference stage that have not been included in the training set. The proposed framework's ability to handle unseen classes like insects similar to mosquitoes, even humans, through open-set learning further enhances its practical applicability employing the OpenMax technique and Weibull distribution. The traditional CNN model, Xception, outperforms the latest transformer with higher accuracy and F1 score for open-set learning. The study's findings highlight the transformative potential of advanced deep-learning architectures in entomology, providing a strong groundwork for future research and development in mosquito surveillance and vector control. The implications of this work extend beyond mosquito classification, offering valuable insights for broader ecological and environmental monitoring applications.

RevDate: 2024-12-13
CmpDate: 2024-12-13

Alipio K, García-Colón J, Boscarino N, et al (2025)

Indigenous Data Sovereignty, Circular Systems, and Solarpunk Solutions for a Sustainable Future.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 30:717-733.

Recent advancements in Artificial Intelligence (AI) and data center infrastructure have brought the global cloud computing market to the forefront of conversations about sustainability and energy use. Current policy and infrastructure for data centers prioritize economic gain and resource extraction, inherently unsustainable models which generate massive amounts of energy and heat waste. Our team proposes the formation of policy around earth-friendly computation practices rooted in Indigenous models of circular systems of sustainability. By looking to alternative systems of sustainability rooted in Indigenous values of aloha 'āina, or love for the land, we find examples of traditional ecological knowledge (TEK) that can be imagined alongside Solarpunk visions for a more sustainable future. One in which technology works with the environment, reusing electronic waste (e-waste) and improving data life cycles.

RevDate: 2024-12-13

Boyes D, Zilli A, University of Oxford and Wytham Woods Genome Acquisition Lab, et al (2024)

The genome sequence of the Grey Shoulder-knot, Lithophane ornitopus (Hufnagel, 1766).

Wellcome open research, 9:214.

We present a genome assembly from an individual male Lithophane ornitopus (the Grey Shoulder-knot; Arthropoda; Insecta; Lepidoptera; Noctuidae). The genome sequence is 508.6 megabases in span. Most of the assembly is scaffolded into 31 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 15.33 kilobases in length. Gene annotation of this assembly on Ensembl identified 18,397 protein coding genes.

RevDate: 2024-12-12

Baker KS, F Millerand (2024)

The Incremental Growth of Data Infrastructure in Ecology (1980-2020).

Ecology and evolution, 14(12):e70444.

After decades of growth, a research community's network information system and data repository were transformed to become a national data management office and a major element of data infrastructure for ecology and the environmental sciences. Developing functional data infrastructures is key to the support of ongoing Open Science and Open Data efforts. This example of data infrastructure growth contrasts with the top-down development typical of many digital initiatives. The trajectory of this network information system evolved within a collaborative, long-term ecological research community. This particular community is funded to conduct ecological research while collective data management is also carried out across its geographically dispersed study sites. From this longitudinal ethnography, we describe an Incremental Growth Model that includes a sequence of six relatively stable phases where each phase is initiated by a rapid response to a major pivotal event. Exploring these phases and the roles of data workers provides insight into major characteristics of digital growth. Further, a transformation in assumptions about data management is reported for each phase. Investigating the growth of a community information system over four decades as it becomes data infrastructure reveals details of its social, technical, and institutional dynamics. In addition to addressing how digital data infrastructure characteristics change, this study also considers when the growth of data infrastructure begins.

RevDate: 2024-12-11

Carter KR, Cavaleri MA, Atkin OK, et al (2024)

Photosynthetic responses to temperature across the tropics: a meta-analytic approach.

Annals of botany pii:7921674 [Epub ahead of print].

BACKGROUND AND AIMS: Tropical forests exchange more carbon dioxide (CO2) with the atmosphere than any other terrestrial biome. Yet, uncertainty in the projected carbon balance over the next century is roughly three-times greater for the tropics than other ecosystems. Our limited knowledge of tropical plant physiological responses, including photosynthetic, to climate change is a substantial source of uncertainty in our ability to forecast the global terrestrial carbon sink.

METHODS: We used a meta-analytic approach, focusing on tropical photosynthetic temperature responses, to address this knowledge gap. Our dataset, gleaned from 18 independent studies, included leaf-level light saturated photosynthetic (Asat) temperature responses from 108 woody species, with additional temperature parameters (35 species) and rates (250 species) of both maximum rates of electron transport (Jmax) and Rubisco carboxylation (Vcmax). We investigated how these parameters responded to mean annual temperature (MAT), temperature variability, aridity, and elevation, as well as also how responses differed among successional strategy, leaf habit, and light environment.

KEY RESULTS: Optimum temperatures for Asat (ToptA) and Jmax (ToptJ) increased with MAT but not for Vcmax (ToptV). Although photosynthetic rates were higher for "light" than "shaded" leaves, light conditions did not generate differences in temperature response parameters. ToptA did not differ with successional strategy, but early successional species had ~4 °C wider thermal niches than mid/late species. Semi-deciduous species had ~1 °C higher ToptA than broadleaf evergreen. Most global modeling efforts consider all tropical forests as a single "broadleaf evergreen" functional type, but our data show that tropical species with different leaf habits display distinct temperature responses that should be included in modeling efforts.

CONCLUSIONS: This novel research will inform modeling efforts to quantify tropical ecosystem carbon cycling and provide more accurate representations of how these key ecosystems will respond to altered temperature patterns in the face of climate warming.

RevDate: 2024-12-12
CmpDate: 2024-12-12

Osiecka AN, Bryndza P, Briefer EF, et al (2024)

Long distance calls: Negligible information loss of little auk social vocalisations due to high frequency propagation losses.

PLoS computational biology, 20(12):e1011961 pii:PCOMPBIOL-D-24-00367.

How well does the information contained in vocal signals travel through the environment? To assess the efficiency of information transfer in little auk (Alle alle, an Arctic seabird) calls over distance, we selected two of the social call types with the highest potential for individuality coding. Using available recordings of known individuals, we calculated the apparent source levels, with apparent maximum peak sound pressure level (ASPL) of 63 dB re 20 μPa at 1 m for both call types. Further, we created a sound attenuation model using meteorological data collected in the vicinity of the little auk colony in Hornsund, Spitsbergen. Using this model, we modelled the calls to reflect higher frequency filtering and sound level loss occurring during spherical spreading in perfect local conditions, down to the putative hearing threshold of the species, calculated to equal ASPL of signals "propagated" to roughly one kilometre. Those modelled calls were then used in a permuted discriminant function analysis, support vector machine models, and linear models of Beecher's information statistic, to investigate whether transmission loss will affect the retention of individual information of the signal. Calls could be correctly classified to individuals above chance level independently of the distance, down to and over the putative physiological hearing threshold. Interestingly, the information capacity of the signal did not decrease with its filtering and attenuation. While this study touches on signal properties purely and cannot provide evidence of the actual use by the animals, it shows that little auk signals can theoretically travel long distances with negligible information loss, and supports the hypothesis that vocalisations could facilitate long-distance communication in the species.

RevDate: 2024-12-11
CmpDate: 2024-12-11

Mortzfeld BM, Bhattarai SK, V Bucci (2024)

Novel class IIb microcins show activity against Gram-negative ESKAPE and plant pathogens.

eLife, 13: pii:102912.

Interspecies interactions involving direct competition via bacteriocin production play a vital role in shaping ecological dynamics within microbial ecosystems. For instance, the ribosomally produced siderophore bacteriocins, known as class IIb microcins, affect the colonization of host-associated pathogenic Enterobacteriaceae species. Notably, to date, only five of these antimicrobials have been identified, all derived from specific Escherichia coli and Klebsiella pneumoniae strains. We hypothesized that class IIb microcin production extends beyond these specific compounds and organisms. With a customized informatics-driven approach, screening bacterial genomes in public databases with BLAST and manual curation, we have discovered 12 previously unknown class IIb microcins in seven additional Enterobacteriaceae species, encompassing phytopathogens and environmental isolates. We introduce three novel clades of microcins (MccW, MccX, and MccZ), while also identifying eight new variants of the five known class IIb microcins. To validate their antimicrobial potential, we heterologously expressed these microcins in E. coli and demonstrated efficacy against a variety of bacterial isolates, including plant pathogens from the genera Brenneria, Gibbsiella, and Rahnella. Two newly discovered microcins exhibit activity against Gram-negative ESKAPE pathogens, i.e., Acinetobacter baumannii or Pseudomonas aeruginosa, providing the first evidence that class IIb microcins can target bacteria outside of the Enterobacteriaceae family. This study underscores that class IIb microcin genes are more prevalent in the microbial world than previously recognized and that synthetic hybrid microcins can be a viable tool to target clinically relevant drug-resistant pathogens. Our findings hold significant promise for the development of innovative engineered live biotherapeutic products tailored to combat these resilient bacteria.

RevDate: 2024-12-10

Zdouc MM, Blin K, Louwen NLL, et al (2024)

MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration.

Nucleic acids research pii:7919508 [Epub ahead of print].

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/.

RevDate: 2024-12-11
CmpDate: 2024-12-11

Wu H, Li Q, JC Wu (2024)

Bioinformatics-aided function exploration of GH29 fucosidases from human gut Parabacteroides.

Glycobiology, 34(12):.

Gut microbes produce α-l-fucosidases critical for utilizing human milk oligosaccharides, mucosal and dietary glycans. Although gut Parabacteroides have garnered attention for their impact on host health and disease, their CAZymes remain poorly studied. CAZome analysis of eleven gut Parabacteroides type strains revealed their capacity to degrade mucin O-glycans. Their abundance of GH29 fucosidases caught our attention, and we predicted the functional profiles of 46 GH29 fucosidases using in silico approaches. Our findings showed diverse linkages specificities and species-specific distributions, with over half of GH29 enzymes functioning as α1,3/4 fucosidases, essential for acting on Lewis antigen epitopes of mucin O-glycans. We further enzymatically validated 4 novel GH29 sequences from poorly characterized groups. PgoldGH29A (cluster37GH29BERT, GH29:75.1CUPP) does not act on tested natural substrates. PgoldGH29B (cluster1GH29BERT, GH29:84.1CUPP) functions as a strict α1,3/4 fucosidase. PgoldGH29C (cluster14GH29BERT, GH29:29.1CUPP) displays unprecedented substrate specificity for α1,2/3/4 disaccharides. PgoldGH29D (cluster4GH29BERT, GH29:6.2CUPP) acts on α1,2/3/4/6 linkages similar to enzymes from GH29:6.1CUPP but prefers disaccharides over trisaccharides. These results suggest that PgoldGH29B and PgoldGH29D can contribute to mucin O-glycan degradation via their α1,3/4 and α1,2 fucosidase activity, respectively, while the natural substrates of PgoldGH29A and PgoldGH29C may be irrelevant to host-glycans. These insights enhance our understanding of the ecological niches inhabited by gut Parabacteroides and may guide similar exploration in other intriguing gut microbial species.

RevDate: 2024-12-10
CmpDate: 2024-12-10

Mohammadi A, Adeli Z, Atafar Z, et al (2024)

Survey of PM10 Values in Ambient Air and Mapping with GIS in Maragheh and Urmia City.

Iranian biomedical journal, 28(7):73.

INTRODUCTION: The present study monitored particulate matter smaller than 10 microns (PM10) in ambient air in Maragheh and Urmia Cities.

METHODS AND MATERIALS: This study was conducted as a descriptive ecological study. A total of 30 sampling points were selected in each city, and PM10 values were measured using a portable dust measuring device. After determining the concentration of pollutants, mapping was performed using Arc GIS software to analyze the spatial trend of particulate matter in each city.

RESULTS: The results showed that the seasonal mean concentration of PM10 in Maragheh City ranged from 12 to 16 μg/m3, while in Urmia City, it ranged from 33 to 51 μg/m3. Additionally, the summer and winter seasons exhibited higher pollution levels in Maragheh and Urmia, respectively. According to the World Health Organization guidelines established in 2021, which recommend a maximum of 15 μg/m3 of PM10 over a 24-hour period, Maragheh City demonstrates cleaner air quality. In contrast, Urmia City experience unacceptable pollution levels on most days. An analysis of the spatial trends of PM10, based on pollutant mapping, revealed that pollution levels were higher at the city's entry and exit points, where traffic emissions are prevalent. In Urmia, the central, eastern, and western areas exhibited increased pollution due to vehicular traffic and fuel combustion during the cold months.

CONCLUSION AND DISCUSSION: This study demonstrates that PM10, a particulate matter associated with air quality, is significantly polluted in Urmia City, while the air quality in Maragheh was assessed as clean. The primary sources of these particles include vehicular traffic, the burning of fossil fuels, and dust carried by the wind. Therefore, additional research is recommended, along with the enhancement of green spaces and facilities.

RevDate: 2024-12-10
CmpDate: 2024-12-10

Amiri Z, Moradi A, E Mosa Farkhani (2024)

Predictors of Lesion Size among People with Cutaneous Leishmaniasis (2016-2021): A Multilevel Analysis.

Iranian biomedical journal, 28(7):67.

INTRODUCTION: Cutaneous leishmaniasis is one of Iran's most important endemic diseases and the second parasitic disease transmitted by arthropods after malaria in Iran. About 20,000 new cases of CL are reported from different parts of Iran annually. Since infectious disease risk factors operate simultaneously at multiple levels and ecological data are typically available at different geographic scales, multilevel modeling serves as a valuable tool for the epidemiological investigation of disease transmission. Given the prevalence of Leishmania disease in the population, this study was conducted to investigate the predictors of lesion size among individuals with cutaneous leishmaniasis.

METHODS AND MATERIALS: This population-based cross-sectional study was conducted using data from 7,433 patients with CL who visited health centers, clinics, outpatient facilities, and hospitals in Khorasan Razavi province, Iran, from 2016 to 2021. Variables related to CL were assessed using mixed or multilevel effects models with two levels of analysis: individual and city. Geographic Information Systems (GIS) techniques were utilized to map the distribution of CL cases in Mashhad City. Poisson multiple regression was used to investigate the relationship between climatic variables and leishmaniasis incidence. Data management and analysis were performed using Stata 11.

RESULTS: The mean age was significantly higher in the group with more extensive lesions (37.60 years) compared to the group with smaller lesions (32.10 years; p = 0.001). The incidence of CL varied across different cities during the study period. Binaloud City had the highest average annual incidence at 208.6 per 100,000 people, while Bakharz City had the lowest at 2.3 per 100,000. According to multilevel analysis, significant associations were found between lesion size and catching the disease from family members (AOR = 0.88; p = 0.23), female gender (AOR = 0.62; p = 0.001), and location of injury (AOR = 1.66; p = 0.001). Poisson regression found a statistically significant association between average humidity and the incidence rate ratio of CL.

CONCLUSION AND DISCUSSION: This study highlights the spatial heterogeneity in CL transmission across different cities in Northeast Iran. Larger lesion size was associated with intra-household transmission, female gender, and lesions on the lower limbs. Environmental factors, notably higher humidity levels, also significantly influenced the incidence rate of CL. Targeted interventions addressing household-level transmission, gender-specific risk factors, and climatic influences are crucial for effective disease control and prevention strategies.

RevDate: 2024-12-09
CmpDate: 2024-12-09

Lee HB, Nguyen TTT, Noh SJ, et al (2024)

Aspergillus ullungdoensis sp. nov., Penicillium jeongsukae sp. nov., and other fungi from Korea.

Fungal biology, 128(8 Pt B):2479-2492.

Eurotiales fungi are thought to be distributed worldwide but there is a paucity of information about their occurrence on diverse substrates or hosts and at specific localities. Some of the Eurotiales, including Aspergillus and Penicillium species, produce an array of secondary metabolites of use for agricultural, medicinal, and pharmaceutical applications. Here, we carried out a survey of the Eurotiales in South Korea, focusing on soil, freshwater, and plants (dried persimmon fruits and seeds of Perilla frutescens, known commonly as shiso). We obtained 11 species that-based on morphology, physiology, and multi-locus (ITS, BenA, CaM, and RPB2) phylogenetic analyses-include two new species, Aspergillus ullungdoensis sp. nov. and Penicillium jeongsukae sp. nov., and nine species that were known, but previously not described in South Korea, Aspergillus aculeatinus, Aspergillus aurantiacoflavus, Aspergillus croceiaffinis, Aspergillus pseudoviridinutans, Aspergillus uvarum, Penicillium ferraniaense, Penicillium glaucoroseum, Penicillium sajarovii, and one, Penicillium charlesii, that was isolated from previously unknown host, woodlouse (Porcellio scaber). We believe that biodiversity survey and identifying new species can contribute to set a baseline for future changes in the context of humanitarian crises such as climate change.

RevDate: 2024-12-09
CmpDate: 2024-12-09

Pillay R, Watson JEM, Hansen AJ, et al (2024)

Global rarity of high-integrity tropical rainforests for threatened and declining terrestrial vertebrates.

Proceedings of the National Academy of Sciences of the United States of America, 121(51):e2413325121.

Structurally intact native forests free from major human pressures are vitally important habitats for the persistence of forest biodiversity. However, the extent of such high-integrity forest habitats remaining for biodiversity is unknown. Here, we quantify the amount of high-integrity tropical rainforests, as a fraction of total forest cover, within the geographic ranges of 16,396 species of terrestrial vertebrates worldwide. We found up to 90% of the humid tropical ranges of forest-dependent vertebrates was encompassed by forest cover. Concerningly, however, merely 25% of these remaining rainforests are of high integrity. Forest-dependent species that are threatened and declining and species with small geographic ranges have disproportionately low proportions of high-integrity forest habitat left. Our work brings much needed attention to the poor quality of much of the forest estate remaining for biodiversity across the humid tropics. The targeted preservation of the world's remaining high-integrity tropical rainforests that are currently unprotected is a critical conservation priority that may help alleviate the biodiversity crisis in these hyperdiverse and irreplaceable ecosystems. Enhanced efforts worldwide to preserve tropical rainforest integrity are essential to meet the targets of the Convention on Biological Diversity's 2022 Kunming-Montreal Global Biodiversity Framework which aims to achieve near zero loss of high biodiversity importance areas (including ecosystems of high integrity) by 2030.

RevDate: 2024-12-09

Boyes D, Crowley LC, Hutchinson F, et al (2024)

The genome sequence of the Broad-barred Knot-horn, Acrobasis consociella (Hübner, 1813).

Wellcome open research, 9:429.

We present a genome assembly from one female Acrobasis consociella (the Broad-barred Knot-horn; Arthropoda; Insecta; Lepidoptera; Pyralidae). The genome sequence is 598.4 megabases in span. Most of the assembly is scaffolded into 31 chromosomal pseudomolecules, including the Z and W sex chromosomes. The mitochondrial genome has also been assembled and is 15.22 kilobases in length.

RevDate: 2024-12-08

He T, Xie J, Jin L, et al (2024)

Seasonal dynamics of the phage-bacterium linkage and associated antibiotic resistome in airborne PM2.5 of urban areas.

Environment international, 194:109155 pii:S0160-4120(24)00741-4 [Epub ahead of print].

Inhalable microorganisms in airborne fine particulate matter (PM2.5), including bacteria and phages, are major carriers of antibiotic resistance genes (ARGs) with strong ecological linkages and potential health implications for urban populations. A full-spectrum study on ARG carriers and phage-bacterium linkages will shed light on the environmental processes of antibiotic resistance from airborne dissemination to the human lung microbiome. Our metagenomic study reveals the seasonal dynamics of phage communities in PM2.5, their impacts on clinically important ARGs, and potential implications for the human respiratory microbiome in selected cities of China. Gene-sharing network comparisons show that air harbours a distinct phage community connected to human- and water-associated viromes, with 57 % of the predicted hosts being potential bacterial pathogens. The ARGs of common antibiotics, e.g., peptide and tetracycline, dominate both the antibiotic resistome associated with bacteria and phages in PM2.5. Over 60 % of the predicted hosts of vARG-carrying phages are potential bacterial pathogens, and about 67 % of these hosts have not been discovered as direct carriers of the same ARGs. The profiles of ARG-carrying phages are distinct among urban sites, but show a significant enrichment in abundance, diversity, temperate lifestyle, and matches of CRISPR (short for 'clustered regularly interspaced short palindromic repeats') to identified bacterial genomes in winter and spring. Moreover, phages putatively carry 52 % of the total mobile genetic element (MGE)-ARG pairs with a unique 'flu season' pattern in urban areas. This study highlights the role that phages play in the airborne dissemination of ARGs and their delivery of ARGs to specific opportunistic pathogens in human lungs, independent of other pathways of horizontal gene transfer. Natural and anthropogenic stressors, particularly wind speed, UV index, and level of ozone, potentially explained over 80 % of the seasonal dynamics of phage-bacterial pathogen linkages on antibiotic resistance. Therefore, understanding the phage-host linkages in airborne PM2.5, the full-spectrum of antibiotic resistomes, and the potential human pathogens involved, will be of benefit to protect human health in urban areas.

RevDate: 2024-12-07

Xie Y, Guo J, Fan Q, et al (2024)

High-density sampling reveals the occurrence, levels and transport flux of 15 polycyclic aromatic hydrocarbons derivatives (PAHs-d) along the Yangtze River.

The Science of the total environment, 958:177907 pii:S0048-9697(24)08064-1 [Epub ahead of print].

Polycyclic aromatic hydrocarbons derivatives (PAHs-d) have higher toxicity levels compared to its parent polycyclic aromatic hydrocarbons (PPAHs). Their partitioning in different media and large-scale transport patterns in rivers remain largely unknown. This study investigated the occurrence of 15 PAHs-d and 19 PPAHs in water and suspended particulate matter (SPM) of the Yangtze River between 2019 and 2020. The range of Σ15PAHs-d concentrations was 20.54 to 2010.03 ng·L[-1] in water and 0.62 to 29.80 μg·g[-1] in SPM. The primary PAHs-d components were 2,6-dimethylnaphthalene, 2-methylnaphthalene, and anthraquinone. The range of Σ19PPAHs concentrations in water and SPM was 34.89 to 739.53 ng·L[-1] and 0.37 to 204.62 μg·g[-1], respectively. And low-ring PAHs-d and PPAHs were more prevalent in water than SPM. Partitioning behaviors indicated that PAHs-d and PPAHs were more readily partitioned into water and SPM during normal and dry periods, respectively. The concentrations of PAHs-d saw significant changes in their spatial distribution, which rose in water and reduced in SPM in downstream of the Three Gorges Dam. This is due to the dam's blocking effect on sediment transport. Positive matrix factorization source analysis revealed biomass combustion upstream and vehicle emissions downstream as primary sources, shaped by the evolving energy consumption patterns of urban areas situated around the Yangtze River. The annual fluxes of PAHs-d in water and SPM of the Yangtze River were 90.40 t·yr[-1] and 11.95 t·yr[-1], representing 88.3 % and 11.7 % of the overall PAHs-d fluxes, respectively. The total fluxes of PAHs-d and PPAHs in water and SPM tended to increase spatially along the river, with growth rates exceeding 76 and 24 times, respectively. Interception within the Three Gorges Reservoir area has resulted in the differences in the concentration and transport distribution of PAHs-d and PPAHs upstream and downstream, which play important roles in reducing PAHs-d and PPAHs entry into the sea. Future studies on PAHs-d in Yangtze River basin tributaries and estuaries are essential.

RevDate: 2024-12-06
CmpDate: 2024-12-06

Zhang W, Jin Z, Huang R, et al (2024)

Multi-omics analysis reveals genetic architecture and local adaptation of coumarins metabolites in Populus.

BMC plant biology, 24(1):1170.

BACKGROUND: Accumulation of coumarins plays key roles in response to immune and abiotic stress in plants, but the genetic adaptation basis of controlling coumarins in perennial woody plants remain unclear.

RESULTS: We detected 792 SNPs within 334 genes that were significantly associated with the phenotypic variations of 15 single-metabolic traits and multiple comprehensive index, such as principal components (PCs) of coumarins metabolites. Expression quantitative trait locus mapping uncovered that 337 eQTLs associated with the expression levels of 132 associated genes. Selective sweep revealed 55 candidate genes have potential selective signature among three geographical populations, highlighting that the coumarins biosynthesis have been encountered forceful local adaptation. Furthermore, we constructed a genetic network of seven candidate genes that coordinately regulate coumarins biosynthesis, revealing the multiple regulatory patterns affecting coumarins accumulation in Populus tomentosa. Validation of candidate gene variations in a drought-tolerated population and DUF538 heterologous transformation experiments verified the function of candidate genes and their roles in adapting to the different geographical conditions in poplar.

CONCLUSIONS: Our study uncovered the genetic regulation of the coumarins metabolic biosynthesis of Populus, and offered potential clues for drought-tolerance evaluation and regional improvement in woody plants.

RevDate: 2024-12-06

Mendrik F, Hackney CR, Cumming VM, et al (2024)

The transport and vertical distribution of microplastics in the Mekong River, SE Asia.

Journal of hazardous materials, 484:136762 pii:S0304-3894(24)03343-0 [Epub ahead of print].

Rivers are primary vectors of plastic debris to oceans, but sources, transport mechanisms, and fate of fluvial microplastics (<5 mm) remain poorly understood, impeding accurate predictions of microplastic flux, ecological risk and socio-economic impacts. We report on microplastic concentrations, characteristics and dynamics in the Mekong River, one of the world's largest and polluting rivers, in Cambodia and Vietnam. Sampling throughout the water column at multiple localities detected an average of 24 microplastics m[-3] (0.073 mg l[-1]). Concentrations increased downstream from rural Kampi, Cambodia (344 km from river mouth; 2 microplastics m[-3,] 0.006 mg l[-1]), to Can Tho, Vietnam (83 km from river mouth; 64 microplastics m[-3], 0.182 mg l[-1]) with most microplastics being fibres (53 %), followed by fragments (44 %) and the most common polymer being polyethylene terephthalate (PET) or polyester. Pathways of microplastic pollution are expected to be from urban wastewater highlighting the need for improved wastewater treatment in this region. On average, 86 % of microplastics are transported within the water column and consequently we identified an optimum sampling depth capturing a representative flux value, highlighting that sampling only the water surface substantially biases microplastic concentration predictions. Additionally, microplastic abundance does not linearly follow discharge changes during annual monsoonal floods or mirror siliciclastic sediment transport, as microplastic concentrations decrease rapidly during higher monsoon flows. The findings reveal complex microplastic transport in large rivers and call for improved sampling methods and predictive models to better assess environmental risk and guide policy.

RevDate: 2024-12-06
CmpDate: 2024-12-06

Lee TY, Chen CH, Chen IM, et al (2024)

Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study.

Journal of medical Internet research, 26:e55635 pii:v26i1e55635.

BACKGROUND: Although significant research has explored the digital phenotype in mood disorders, the time-lagged and bidirectional relationship between mood and global positioning system (GPS) mobility remains relatively unexplored. Leveraging the widespread use of smartphones, we examined correlations between mood and behavioral changes, which could inform future scalable interventions and personalized mental health monitoring.

OBJECTIVE: This study aims to investigate the bidirectional time lag relationships between passive GPS data and active ecological momentary assessment (EMA) data collected via smartphone app technology.

METHODS: Between March 2020 and May 2022, we recruited 45 participants (mean age 42.3 years, SD 12.1 years) who were followed up for 6 months: 35 individuals diagnosed with mood disorders referred by psychiatrists and 10 healthy control participants. This resulted in a total of 5248 person-days of data. Over 6 months, we collected 2 types of smartphone data: passive data on movement patterns with nearly 100,000 GPS data points per individual and active data through EMA capturing daily mood levels, including fatigue, irritability, depressed, and manic mood. Our study is limited to Android users due to operating system constraints.

RESULTS: Our findings revealed a significant negative correlation between normalized entropy (r=-0.353; P=.04) and weekly depressed mood as well as between location variance (r=-0.364; P=.03) and depressed mood. In participants with mood disorders, we observed bidirectional time-lagged associations. Specifically, changes in homestay were positively associated with fatigue (β=0.256; P=.03), depressed mood (β=0.235; P=.01), and irritability (β=0.149; P=.03). A decrease in location variance was significantly associated with higher depressed mood the following day (β=-0.015; P=.009). Conversely, an increase in depressed mood was significantly associated with reduced location variance the next day (β=-0.869; P<.001). These findings suggest a dynamic interplay between mood symptoms and mobility patterns.

CONCLUSIONS: This study demonstrates the potential of utilizing active EMA data to assess mood levels and passive GPS data to analyze mobility behaviors, with implications for managing disease progression in patients. Monitoring location variance and homestay can provide valuable insights into this process. The daily use of smartphones has proven to be a convenient method for monitoring patients' conditions. Interventions should prioritize promoting physical movement while discouraging prolonged periods of staying at home.

RevDate: 2024-12-05

Vojteková J, Janizadeh S, Vojtek M, et al (2024)

Prediction of potential occurrence of historical objects with defensive function in Slovakia using machine learning approach.

Scientific reports, 14(1):30350.

In this article, we aim at the prediction of possible locations of already defunct historical objects with a defensive function (HODFs) in Slovakia, which have not been found and documented so far, using three machine learning methods. Specifically, we used the support vector machine, k-nearest neighbors, and random forest algorithms, which were trained based on the following five factors influencing the possible occurrence of HODFs: elevation, distance from a river, distance from a settlement, lithological rock type, and type of representative geoecosystems. Training and testing datasets were based on a database of already documented 605 HODFs, which were divided into 70% of training samples and 30% of testing samples. All of the three models reached the AUC-ROC value over 0.74 based on the testing dataset. The best performance was recorded by the random forest predictive model with the AUC-ROC value equal to 0.79. The results of the random forest model were also validated with the recently documented HODFs via the archeological research.

RevDate: 2024-12-05
CmpDate: 2024-12-05

Ganie PA, Posti R, Bharti VS, et al (2024)

Erosion landscape characterization in the Himalayan basin: insights from geospatial data and multi-criteria evaluation.

Environmental monitoring and assessment, 197(1):29.

In regions characterized by mountainous landscapes, such as watersheds with high elevations, steep inclines, and rugged terrains, there exists an inherent susceptibility to water-induced soil erosion. This susceptibility underscores the importance of identifying areas prone to erosion to mitigate the loss of valuable natural resources and ensure their preservation over time. In response to this need, the current research employed a combination of four multi-criteria decision-making (MCDM) models, namely TOPSIS-AHP, VIKOR-AHP, ARAS-AHP, and CODAS-AHP, for the identification of areas susceptible to soil erosion within the Himalayan River basin of Nandakini, Uttarakhand, India. This identification was facilitated through the utilization of remote sensing and geospatial technologies. The study considered a total of 19 prioritization parameters that included morphological, topo-hydrological, climatic, and environmental factors specific to the Nandakini catchment for the purpose of prioritization modeling. The adoption of morphometric parameters in depicting the geological structures and hydrodynamic behavior of the river basin proves to be a crucial approach in locales where hydrological data may be scarce. The investigation delineated twenty watersheds within the catchment by employing SRTM DEM, SOI toposheets, and Geographic Information Systems (GIS), calculating the catchment's total area to be approximately 540.98 km[2]. The analysis determined that the catchment is classified as a 6th-order catchment, exhibiting mainly a sub-dendritic to dendritic drainage pattern. It was identified that the catchment is vulnerable to flooding and subsequent gully erosion due to the slow movement of surface runoff. Furthermore, the catchment's elongated shape and the compactness coefficient suggest a delayed peak runoff. The drainage texture ranged from very coarse to coarse, and the relief characteristics highlighted that the watersheds within the catchment possess a high relief ratio, thereby increasing their erosion vulnerability. Topo-hydrological indices revealed significant topographic variability and spatial differences in water availability and erosion potential across the basin. The efficacy of the MCDM models was evaluated through the Spearman's correlation coefficient test, alongside indices of intensity and percentage of change, to validate the findings. The ARAS-AHP and CODAS-AHP models were found to exhibit superior efficiency and higher accuracy relative to the other methods assessed. The insights gained from the ARAS-AHP and CODAS-AHP models are instrumental in the development of strategies for sustainable catchment management plans and inform decision-making processes regarding water resources management within the catchment.

RevDate: 2024-12-06
CmpDate: 2024-12-06

Devadhasan A, Kolodny O, O Carja (2024)

Competition for resources can reshape the evolutionary properties of spatial structure.

PLoS computational biology, 20(11):e1012542 pii:PCOMPBIOL-D-24-00596.

Many evolving ecosystems have spatial structures that can be conceptualized as networks, with nodes representing individuals or homogeneous subpopulations and links the patterns of spread between them. Prior models of evolution on networks do not take ecological niche differences and eco-evolutionary interplay into account. Here, we combine a resource competition model with evolutionary graph theory to study how heterogeneous topological structure shapes evolutionary dynamics under global frequency-dependent ecological interactions. We find that the addition of ecological competition for resources can produce a reversal of roles between amplifier and suppressor networks for deleterious mutants entering the population. We show that this effect is a nonlinear function of ecological niche overlap and discuss intuition for the observed dynamics using simulations and analytical approximations. We use these theoretical results together with spatial representations from imaging data to show that, for ductal carcinoma, where tumor growth is highly spatially constrained, with cells confined to a tree-like network of ducts, the topological structure can lead to higher rates of deleterious mutant hitchhiking with metabolic driver mutations, compared to tumors characterized by different spatial topologies.

RevDate: 2024-12-05

Qiu Y, He Z, Yu X, et al (2024)

Analysis of factors influencing groundwater drought in the Loess zone of China.

iScience, 27(10):110929.

Understanding the characteristics and factors influencing groundwater resources is important for regional water resources management. The Gravity Recovery and Climate Experiment (GRACE)-based groundwater conditions were used to analyze the spatiotemporal characteristics of and the factors influencing groundwater storage (GWS) distribution in the Loess zone of the Yellow River Basin. The results revealed that the spatiotemporal distribution of GWS anomalies in the Loess zone of China was best explained by the first three components of the empirical orthogonal function (EOF), representing 85.6% of the total variance. The normalized difference vegetation index (NDVI) was significantly correlated with groundwater drought (p < 0.05). In addition, NDVI and evapotranspiration (ET) were the dominant factors influencing groundwater drought. NDVI was the dominant influencing factor in 67% and 80% of the total study area between 2002-2014 and 2015-2021, respectively. This study provides important guidance for a future ecological restoration plan in the Loess zone.

RevDate: 2024-12-04

Rodman KC, Bradford JB, Formanack AM, et al (2024)

Restoration treatments enhance tree growth and alter climatic constraints during extreme drought.

Ecological applications : a publication of the Ecological Society of America [Epub ahead of print].

The frequency and severity of drought events are predicted to increase due to anthropogenic climate change, with cascading effects across forested ecosystems. Management activities such as forest thinning and prescribed burning, which are often intended to mitigate fire hazard and restore ecosystem processes, may also help promote tree resistance to drought. However, it is unclear whether these treatments remain effective during the most severe drought conditions or whether their impacts differ across environmental gradients. We used tree-ring data from a system of replicated, long-term (>20 years) experiments in the southwestern United States to evaluate the effects of forest restoration treatments (i.e., evidence-based thinning and burning) on annual growth rates (i.e., basal area increment; BAI) of ponderosa pine (Pinus ponderosa), a broadly distributed and heavily managed species in western North America. The study sites were established at the onset of the most extreme drought event in at least 1200 years and span much of the climatic niche of Rocky Mountain ponderosa pine. Across sites, tree-level BAI increased due to treatment, where trees in treated units grew 133.1% faster than trees in paired, untreated units. Likewise, trees in treated units grew an average of 85.6% faster than their pre-treatment baseline levels (1985 to ca. 2000), despite warm, dry conditions in the post-treatment period (ca. 2000-2018). Variation in the local competitive environment promoted variation in BAI, and larger trees were the fastest-growing individuals, irrespective of treatment. Tree thinning and prescribed fire altered the climatic constraints on growth, decreasing the effects of belowground moisture availability and increasing the effects of atmospheric evaporative demand over multi-year timescales. Our results illustrate that restoration treatments can enhance tree-level growth across sites spanning ponderosa pine's climatic niche, even during recent, extreme drought events. However, shifting climatic constraints, combined with predicted increases in evaporative demand in the southwestern United States, suggest that the beneficial effects of such treatments on tree growth may wane over the upcoming decades.

RevDate: 2024-12-05
CmpDate: 2024-12-05

Madden WG, Jin W, Lopman B, et al (2024)

Deep neural networks for endemic measles dynamics: Comparative analysis and integration with mechanistic models.

PLoS computational biology, 20(11):e1012616 pii:PCOMPBIOL-D-24-00893.

Measles is an important infectious disease system both for its burden on public health and as an opportunity for studying nonlinear spatio-temporal disease dynamics. Traditional mechanistic models often struggle to fully capture the complex nonlinear spatio-temporal dynamics inherent in measles outbreaks. In this paper, we first develop a high-dimensional feed-forward neural network model with spatial features (SFNN) to forecast endemic measles outbreaks and systematically compare its predictive power with that of a classical mechanistic model (TSIR). We illustrate the utility of our model using England and Wales measles data from 1944-1965. These data present multiple modeling challenges due to the interplay between metapopulations, seasonal trends, and nonlinear dynamics related to demographic changes. Our results show that while the TSIR model yields similarly performant short-term (1 to 2 biweeks ahead) forecasts for highly populous cities, our neural network model (SFNN) consistently achieves lower root mean squared error (RMSE) across other forecasting windows. Furthermore, we show that our spatial-feature neural network model, without imposing mechanistic assumptions a priori, can uncover gravity-model-like spatial hierarchy of measles spread in which major cities play an important role in driving regional outbreaks. We then turn our attention to integrative approaches that combine mechanistic and machine learning models. Specifically, we investigate how the TSIR can be utilized to improve a state-of-the-art approach known as Physics-Informed-Neural-Networks (PINN) which explicitly combines compartmental models and neural networks. Our results show that the TSIR can facilitate the reconstruction of latent susceptible dynamics, thereby enhancing both forecasts in terms of mean absolute error (MAE) and parameter inference of measles dynamics within the PINN. In summary, our results show that appropriately designed neural network-based models can outperform traditional mechanistic models for short to long-term forecasts, while simultaneously providing mechanistic interpretability. Our work also provides valuable insights into more effectively integrating machine learning models with mechanistic models to enhance public health responses to measles and similar infectious disease systems.

RevDate: 2024-12-05
CmpDate: 2024-12-05

Groote-Woortmann W, Korbel K, GC Hose (2024)

STYGOTOX: A Quality-Assessed Database of (Eco)Toxicological Data on Stygofauna and Other Aquatic Subterranean Organisms.

Environmental toxicology and chemistry, 43(12):2492-2500.

We have compiled the toxicity data on stygofauna and other aquatic subterranean organisms in one (eco)toxicological database. A total of 46 studies were found, containing 472 toxic endpoints covering 43 different stressors. These compounds were tested on subterranean organisms from four phyla, 12 orders, 24 genera, and 55 species. The studies included were published between 1976 and December 2023 using fauna collected in 13 different countries. The suitability of the studies was assessed to indicate the completeness of reporting and their suitability for use in hazard and risk assessment. This compilation provides a valuable source of data for future development of toxicity testing protocols for groundwater organisms, and to support decision-making, ecological risk assessments and the derivation of water quality criteria for the protection of groundwater ecosystems. Environ Toxicol Chem 2024;43:2492-2500. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

RevDate: 2024-12-03

Peltier DMP, Carbone MS, Ogle K, et al (2024)

Decades-old carbon reserves are widespread among tree species, constrained only by sapwood longevity.

The New phytologist [Epub ahead of print].

Carbon reserves are distributed throughout plant cells allowing past photosynthesis to fuel current metabolism. In trees, comparing the radiocarbon (Δ[14]C) of reserves to the atmospheric bomb spike can trace reserve ages. We synthesized Δ[14]C observations of stem reserves in nine tree species, fitting a new process model of reserve building. We asked how the distribution, mixing, and turnover of reserves vary across trees and species. We also explored how stress (drought and aridity) and disturbance (fire and bark beetles) perturb reserves. Given sufficient sapwood, young (< 1 yr) and old (20-60+ yr) reserves were simultaneously present in single trees, including 'prebomb' reserves in two conifers. The process model suggested that most reserves are deeply mixed (30.2 ± 21.7 rings) and then respired (2.7 ± 3.5-yr turnover time). Disturbance strongly increased Δ[14]C mean ages of reserves (+15-35 yr), while drought and aridity effects on mixing and turnover were species-dependent. Fire recovery in Sequoia sempervirens also appears to involve previously unobserved outward mixing of old reserves. Deep mixing and rapid turnover indicate most photosynthate is rapidly metabolized. Yet ecological variation in reserve ages is enormous, perhaps driven by stress and disturbance. Across species, maximum reserve ages appear primarily constrained by sapwood longevity, and thus old reserves are probably widespread.

RevDate: 2024-12-04
CmpDate: 2024-12-04

Yao M, Ren A, Yang X, et al (2025)

Unveiling the influence of heating temperature on biofilm formation in shower hoses through multi-omics.

Water research, 268(Pt B):122704.

Shower systems provide unique environments that are conducive to biofilm formation and the proliferation of pathogens. The water heating temperature is a delicate decision that can impact microbial growth, balancing safety and energy consumption. This study investigated the impact of different heating temperatures (39 °C, 45 °C, 51 °C and 58 °C) on the shower hose biofilm (exposed to a final water temperature of 39 °C) using controlled full-scale shower setups. Whole metagenome sequencing and metaproteomics were employed to unveil the microbial composition and protein expression profiles. Overall, the genes and enzymes associated with disinfectant resistance and biofilm formation appeared largely unaffected. However, metagenomic analysis revealed a sharp decline in the number of total (86,371 to 34,550) and unique genes (32,279 to 137) with the increase in hot water temperature, indicating a significant reduction of overall microbial complexity. None of the unique proteins were detected in the proteomics experiments, suggesting smaller variation among biofilms on the proteome level compared to genomic data. Furthermore, out of 43 pathogens detected by metagenomics, only 5 could actually be detected by metaproteomics. Most interestingly, our study indicates that 45 °C heating temperature may represent an optimal balance. It minimizes active biomass (ATP) and reduces the presence of pathogens while saving heating energy. Our study offered new insights into the impact of heating temperature on shower hose biofilm formation and proposed optimal parameters that ensure biosafety while conserving energy.

RevDate: 2024-12-03

Tian T, Zhang X, Zhang F, et al (2024)

Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.

Frontiers in microbiology, 15:1510139.

INTRODUCTION: The integration of artificial intelligence (AI) in pathogenic microbiology has accelerated research and innovation. This study aims to explore the evolution and trends of AI applications in this domain, providing insights into how AI is transforming research and practice in pathogenic microbiology.

METHODS: We employed bibliometric analysis and topic modeling to examine 27,420 publications from the Web of Science Core Collection, covering the period from 2010 to 2024. These methods enabled us to identify key trends, research areas, and the geographical distribution of research efforts.

RESULTS: Since 2016, there has been an exponential increase in AI-related publications, with significant contributions from China and the USA. Our analysis identified eight major AI application areas: pathogen detection, antibiotic resistance prediction, transmission modeling, genomic analysis, therapeutic optimization, ecological profiling, vaccine development, and data management systems. Notably, we found significant lexical overlaps between these areas, especially between drug resistance and vaccine development, suggesting an interconnected research landscape.

DISCUSSION: AI is increasingly moving from laboratory research to clinical applications, enhancing hospital operations and public health strategies. It plays a vital role in optimizing pathogen detection, improving diagnostic speed, treatment efficacy, and disease control, particularly through advancements in rapid antibiotic susceptibility testing and COVID-19 vaccine development. This study highlights the current status, progress, and challenges of AI in pathogenic microbiology, guiding future research directions, resource allocation, and policy-making.

RevDate: 2024-12-02
CmpDate: 2024-12-02

Jeong D, Hyun JY, Marchenkova T, et al (2024)

Genetic insights and conservation strategies for Amur tigers in Southwest Primorye Russia.

Scientific reports, 14(1):29985.

Southwest Primorye hosts approximately 9% of the remaining wild Amur tiger population and represents hope for the revival of tigers in Northeast China and the Korean peninsula. Decades of conservation efforts have led to a significant increase in population size, from less than 10 individuals surviving in the region in 1996 to multiple folds today. However, while the population size has recovered since the mid-1900s, the effects of genetic depletion on evolutionary potential are not easily reversed. In this study, a non-invasive genetic analysis of the Amur tiger subpopulation in Southwest Primorye was conducted using microsatellite loci and mitochondrial genes to estimate genetic diversity, relatedness, and determine the impact of historical demographic dynamics. A total of 32 individuals (16 males, 15 females, and 1 unidentified sex) were identified, and signs of bottlenecks were detected, reflecting past demographic events. Low genetic variation observed in mitochondrial DNA also revealed genetic depletion within the population. Most individuals were found to be closely related to each other, raising concerns about inbreeding given the small population size and somewhat isolated environment from the main population in Sikhote-Alin. These findings emphasize the urgent need to establish ecological corridors to neighboring areas to restore genetic diversity and ensure the conservation of the Amur tiger population in Southwest Primorye.

RevDate: 2024-12-03
CmpDate: 2024-12-03

Tinsley E, Froidevaux JSP, G Jones (2024)

The location of solar farms within England's ecological landscape: Implications for biodiversity conservation.

Journal of environmental management, 372:123372.

A global energy transition to using sustainable renewable sources is being driven by global agreements. Simultaneously there is a call for increased biodiversity conservation. This creates a green-green dilemma, where the expansion of renewables could lead to the demise of biodiversity if not carefully assessed, managed and monitored. Recognition of the dilemma is central to the development of Sustainable Development Goals. It is therefore important to understand whether renewable energy sources such as solar farms are being sited in areas where they have minimal impact on biodiversity. If solar farms were sited with minimal impacts on biodiversity, we hypothesised that they would be less likely to be sited close to ecologically sensitive areas than near random points. We used Geographic Information System methods to explore the density of solar photovoltaic (PV) farms in England and assessed their siting relative to sensitive ecological features, including priority habitat types, designated sites, and land conservation initiatives. We compared the area of 25 sensitive ecological features around solar farms and random points across three spatial scales (100 m, 1000 m, and 6000 m radius scales). Solar farms were distributed throughout England, with the highest concentration in South West England. Solar sites were primarily surrounded by habitats with anthropogenic influences, such as agricultural and urban settings. Priority habitats, such as woodland, grassland, wetland and heathland, were more extensive around random points across spatial scales (except for woodland at the largest scale). Most designated sites were significantly more extensive around random points. We conclude that, under current planning regulations, solar sites in England are being placed appropriately with regard to sensitive ecological habitats, and are often sited in areas already impacted by farming and development. Adaptive planning should be implemented to ensure that the evolving research around biodiversity and solar farms is incorporated into decision making, and monitoring is completed across the lifespan of solar farms to assess impacts and effective mitigation.

RevDate: 2024-12-03
CmpDate: 2024-12-03

Konzen E, Delahay RJ, Hodgson DJ, et al (2024)

Efficient modelling of infectious diseases in wildlife: A case study of bovine tuberculosis in wild badgers.

PLoS computational biology, 20(11):e1012592 pii:PCOMPBIOL-D-24-00702.

Bovine tuberculosis (bTB) has significant socio-economic and welfare impacts on the cattle industry in parts of the world. In the United Kingdom and Ireland, disease control is complicated by the presence of infection in wildlife, principally the European badger. Control strategies tend to be applied to whole populations, but better identification of key sources of transmission, whether individuals or groups, could help inform more efficient approaches. Mechanistic transmission models can be used to better understand key epidemiological drivers of disease spread and identify high-risk individuals and groups if they can be adequately fitted to observed data. However, this is a significant challenge, especially within wildlife populations, because monitoring relies on imperfect diagnostic test information, and even under systematic surveillance efforts (such as capture-mark-recapture sampling) epidemiological events are only partially observed. To this end we develop a stochastic compartmental model of bTB transmission, and fit this to individual-level data from a unique > 40-year longitudinal study of 2,391 badgers using a recently developed individual forward filtering backward sampling algorithm. Modelling challenges are further compounded by spatio-temporal meta-population structures and age-dependent mortality. We develop a novel estimator for the individual effective reproduction number that provides quantitative evidence for the presence of superspreader badgers, despite the population-level effective reproduction number being less than one. We also infer measures of the hidden burden of infection in the host population through time; the relative likelihoods of competing routes of transmission; effective and realised infectious periods; and longitudinal measures of diagnostic test performance. This modelling framework provides an efficient and generalisable way to fit state-space models to individual-level data in wildlife populations, which allows identification of high-risk individuals and exploration of important epidemiological questions about bTB and other wildlife diseases.

RevDate: 2024-12-03
CmpDate: 2024-12-03

Li G, Wu M, Xiao Y, et al (2024)

Multi-omics reveals the ecological and biological functions of Enterococcus mundtii in the intestine of lepidopteran insects.

Comparative biochemistry and physiology. Part D, Genomics & proteomics, 52:101309.

Insect guts offer unique habitats for microbial colonization, with gut bacteria potentially offering numerous benefits to their hosts. Although Enterococcus has emerged as one of the predominant gut commensal bacteria in insects, its establishment in various niches within the gut has not been characterized well. In this study, Enterococcus mundtii was inoculated into the silkworm (Bombyx mori L.) to investigate its biological functions. Genome-based analysis revealed that its successful colonization is related to adherence genes (ebpA, ebpC, efaA, srtC, and scm). This bacterium did not alter the activities of related metabolic enzymes or the intestinal barrier function. However, significant changes in the gene expressions levels of Att2, CecA, and Lys suggest potential adaptive mechanisms of host immunity to symbiotic E. mundtii. Moreover, 16S metagenomics analysis revealed a significant increase in the relative abundance of E. mundtii in the intestines of silkworms following inoculation. The intestinal microbiome displayed marked heterogeneity, an elevated gut microbiome health index, a reduced microbial dysbiosis index, and low potential pathogenicity in the treatment group. Additionally, E. mundtii enhanced the breakdown of carbohydrates in host intestines. Overall, E. mundtii serves as a beneficial microbe for insects, promoting intestinal homeostasis by providing competitive advantage. This characteristic helps E. mundtii dominate complex microbial environments and remain prevalent across Lepidoptera, likely fostering long-term symbiosis between the both parties. The present study contributes to clarifying the niche of E. mundtii in the intestine of lepidopteran insects and further reveals its potential roles in their insect hosts.

RevDate: 2024-12-02
CmpDate: 2024-12-02

Monadjem A, Montauban C, Webala PW, et al (2024)

African bat database: curated data of occurrences, distributions and conservation metrics for sub-Saharan bats.

Scientific data, 11(1):1309.

Accurate knowledge of species distributions is foundational for effective conservation efforts. Bats are a diverse group of mammals, with important roles in ecosystem functioning. However, our understanding of bats and their ecological importance is hindered by poorly defined ranges, mostly as a result of under-recording. This issue is exacerbated in Africa by the ongoing rapid discovery of new species, both de novo and splits of existing species, and by inaccessibility to museum specimens that are mostly hosted outside of the continent. Here we present the African bat database - a curated set of 17,285 unique locality records of all 266 species of bats from sub-Saharan Africa, vouched for by specimens and/or genetic sequencing, and aligned with current taxonomy. Based on these records, we also present Maxent-based distribution models and calculate the IUCN Red List metrics for Extent of Occurrence and Area of Occupancy. This database and online visualization tool provide an important open-source resource and is expected to significantly advance studies in ecology, and aid in bat conservation.

RevDate: 2024-12-02

Nishimura H, Nawa N, Ogawa T, et al (2024)

Projections of future heat-related emergency hospitalizations for asthma under climate and demographic change scenarios: a Japanese nationwide time-series analysis.

Environmental research pii:S0013-9351(24)02405-8 [Epub ahead of print].

BACKGROUND: There is growing concern about climate impacts on human health. However, empirical evidence is lacking regarding future projections of heat-related asthma hospitalizations. This study aimed to project excess emergency hospitalizations for heat-related asthma exacerbation in Japan.

METHODS: Using Japanese nationwide administrative data from 2011 to 2019, we conducted an ecological time-series quasi-Poisson regression analysis to estimate the heat-related relative risk of emergency hospitalization for asthma over a lag of 0-3 days during the warm season (June to September). Heat exposure was defined as the region-specific daily mean temperature exceeding the locally defined minimum morbidity temperature percentile (MMP). Heat-related excess hospitalizations for asthma were projected under future climate and demographic change scenarios based on Shared Socioeconomic Pathways (SSPs).

RESULTS: We identified 75,829 emergency hospitalizations for asthma. The heat-related relative risk of hospitalization was 1.22 (95% confidence interval (CI): 1.12-1.33) at the 99th percentile temperature relative to the MMP, with the highest estimates for cases aged 0-14 years. Heat-related excess hospitalizations were projected to increase by 6.78 (95%CI: 5.84-7.67) times in 2091-2099 versus 2011-2019 along SSP5-8.5 when constant population structure was assumed. The increasing trend persisted even when the future population decline was considered (4.19 (95%CI: 3.53-4.85) times in 2091-2099 versus 2011-2019 under SSP5-8.5).

CONCLUSION: Future heat-related impacts on asthma exacerbation are expected to increase in Japan toward the end of this century, even when the future demographic change is considered. Our projections will contribute to resilient health systems adapting to ongoing climate change.

RevDate: 2024-12-02
CmpDate: 2024-12-02

Alfaro-Sánchez R, Richardson AD, Smith SL, et al (2024)

Permafrost instability negates the positive impact of warming temperatures on boreal radial growth.

Proceedings of the National Academy of Sciences of the United States of America, 121(50):e2411721121.

Climate warming can alleviate temperature and nutrient constraints on tree growth in boreal regions, potentially enhancing boreal productivity. However, in permafrost environments, warming also disrupts the physical foundation on which trees grow, leading to leaning trees or "drunken" forests. Tree leaning might reduce radial growth, undermining potential benefits of warming. Here, we found widespread radial growth reductions in southern latitude boreal forests since the 1980s. At mid latitudes, radial growth increased from ~1980 to ~2000 but showed recent signs of decline afterward. Increased growth was evident since the 1980 s at higher latitudes, where radial growth appears to be temperature limited. However, recent changes in permafrost stability, and the associated increased frequency of tree leaning events, emerged as a significant stressor, leading to reduced radial growth in boreal trees at the highest latitudes, where permafrost is extensive. We showed that trees growing in unstable permafrost sites allocated more nonstructural carbohydrate reserves to offset leaning which compromised radial growth and potential carbon uptake benefits of warming. This higher allocation of resources in drunken trees is needed to build the high-density reaction wood, rich in lignin, that is required to maintain a vertical position. With continued climate warming, we anticipate widespread reductions in radial growth in boreal forests, leading to lower carbon sequestration. These findings enhance our understanding of how climate warming and indirect effects, such as ground instability caused by warming permafrost, will affect boreal forest productivity in the future.

RevDate: 2024-12-02

Boyes D, Young MR, University of Oxford and Wytham Woods Genome Acquisition Lab, et al (2024)

The genome sequence of the Straw Grass-veneer moth, Agriphila straminella (Denis & Schiffermüller), 1775.

Wellcome open research, 9:433.

We present a genome assembly from an individual male Straw Grass-veneer moth, Agriphila straminella (Arthropoda; Insecta; Lepidoptera; Crambidae). The genome sequence has a length of 511.50 megabases. Most of the assembly is scaffolded into 26 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 15.36 kilobases in length. Gene annotation of this assembly on Ensembl identified 12,087 protein-coding genes.

RevDate: 2024-11-30

Asaaga FA, Shakeer I, Sriram A, et al (2024)

Ties that bind: understanding One Health networks and participation for zoonoses prevention and control in India.

One health outlook, 6(1):24.

BACKGROUND: Cross-sectoral collaborations as exemplified by the One Health approach, are widely endorsed as pragmatic avenues for addressing zoonotic diseases, but operationalisation remain limited in low-and-middle income countries (LMICs). Complexities and competing interests and agendas of key stakeholders and the underlying politico-administrative context can all shape outcomes of collaborative arrangements. Evidence is building that organised collaborations are complex political initiatives where different objectives; individual and institutional agendas need to be reconciled to incentivise collaborations.

METHODS: Drawing on a qualitative network analysis of published sources on 'One Health' stakeholders supplemented with 26 multi-scale (national-state-district level) key-informant interviews (including policymakers, disease managers and public health experts), this paper characterises the fragmented and complex characteristics of institutional networks involved in zoonoses prevention and control in India.

RESULTS: Our results highlight how the local socio-political and institutional contexts interact to modulate how and when collaborations occur (or not), the associated contingencies and stakeholder innovations in circumventing existing barriers (e.g. competing interests, distrust between actors, departmental bureaucracy) to cross-sector collaborations and zoonoses management. Aside from principal actors negotiating common ground in some instance, they also capitalised on political/institutional pressure to subtly 'manipulate' their subordinates as a way of fostering collaboration, especially in instances when the institutional and political stakes are high.

CONCLUSION: Altogether our findings suggest that cross-sectoral collaborations are by-product of political and institutional tinkering as long as individual actors and institutional interests converge and these dynamics must be embraced to embed meaningful and sustainable collaborations in local socio-political and administrative contexts.

RevDate: 2024-12-02
CmpDate: 2024-12-01

Wu Y, Wei C, Zhang Y, et al (2024)

Investigating intrinsic and situational predictors of depression among older adults: An analysis of the CHARLS database.

Asian journal of psychiatry, 102:104279.

BACKGROUND: This study aimed to investigate the intrinsic and situational predictors of depression under the health ecological model.

METHODS: Two waves (2011 and 2013) of survey data were collected from the CHARLS. A total of 5845 older adults (≧60) were included, and depression was defined as CESD-10 score ≧10. Random forest combined with interpretable methods were utilized to select important predictors of depression. Multilevel logit model was used to examine the associations of intrinsic and situational predictors with depression.

RESULTS: After a 2-year follow up, 1822 individuals (31.17 %) developed depression. Interpretable analyses showed that both intrinsic and situational variables were predictive for depression. Multilevel logit model showed that age, gender, number of chronic diseases, number of pain areas, life satisfaction, and toilet distance were significantly associated with depression.

CONCLUSION: Both intrinsic and situational factors were found to be associated with depression among community older population, highlighting their significance for early prevention from the perspective of public health.

RevDate: 2024-12-02
CmpDate: 2024-12-02

Burger J, Gochfeld M, Brown KG, et al (2025)

Using the National Land Cover Database as an indicator of shrub-steppe habitat: comparing two large United States federal lands with surrounding regions.

Journal of toxicology and environmental health. Part A, 88(1):1-19.

There is a need to assess whether ecological resources are being protected on large, federal lands. The aim of this study was to present a methodology which consistently and transparently determines whether two large Department of Energy (U.S. DOE) facilities have protected valuable ecological lands on their sites compared to the surrounding region. The National Land Cover Database (2019) was used to examine the % shrub-scrub (shrub-steppe) and other habitats on the DOE's Hanford Site (HS, Washington) and on the Idaho National Laboratory (INL), compared to a 10-km and 30-km diameter band of land surrounding each site. On both sites, over 95% is in shrub-scrub or grassland, compared to the surrounding region. Approximately 70% of 10 km and 30-km bands around INL, and less than 50% of land surrounding HS is located in these two habitat types. INL has preserved a significantly higher % shrub/scrub habitat than HS, but INL allows grazing on 60% of its land. HS has preserved a significantly higher % grassland than INL but no grazing on site is present. The methodology presented may be used to compare key ecological habitat types such as grasslands, forest, and desert among sites in different parts of the country. This methodology enables managers, resource trustees, and the public to (1) make remediation decisions that protect resources, (2) assess whether landowners and managers have adequately characterized and protected environmental resources on their sites, and (3) whether landowners and managers have protected the integrity of that land as well as its climax vegetation.

RevDate: 2024-11-29
CmpDate: 2024-11-29

Ma D, Huang Q, Wang Q, et al (2024)

Detection of spatiotemporal changes in eco-environmental quality based on RSEI and SG filtering and its driving force analysis: a case study in Sichuan Province, China.

Environmental monitoring and assessment, 196(12):1274.

Landsat images were extracted using Google Earth Engine (GEE) platform and optimized by Savitzky-Golay (SG) filtering. The Remote Sensing Ecological Index (RSEI) method was used to analyze the eco-environmental quality in Sichuan Province in recent 20 years. In addition, Theil-Sen median method and Mann-Kendall (MK) test were used to test the change trend of eco-environmental quality. Furthermore, drivers were evaluated by partial correlation analysis, 2D scatter plots, and t tests. The results showed that (1) in the past 20 years, the eco-environmental quality of Sichuan Province was on the rise, and the eco-environmental quality in the western region was better than that in the eastern region. The eco-environmental quality was positively correlated with forest and grassland types, and negatively correlated with cultivated land and urban and rural construction land types. (2) The eco-environmental quality of Sichuan Province is linearly correlated with the digital elevation model, but poorly correlated with slope and slope direction. In the range of slope 0° ~ 9° and southeast direction, the eco-environmental quality is the worst. (3) The eco-environmental quality of Sichuan Province was most significantly affected by soil moisture and sunshine hours. The study can help us to understand and assess the health of ecosystems in Sichuan Province, provide a scientific basis for protecting and improving the environment, and guide the formulation and implementation of environmental protection policies.

RevDate: 2024-11-28
CmpDate: 2024-11-28

Alvarez-Mamani E, Buettner F, Beltran-Castanon CA, et al (2024)

Exploratory analysis of metabolic changes using mass spectrometry data and graph embeddings.

Scientific reports, 14(1):29570.

Mass spectrometry (MS)-based metabolomics analysis is a powerful tool, but it comes with its own set of challenges. The MS workflow involves multiple steps before its interpretation in what is denominate data mining. Data mining consists of a two-step process. First, the MS data is ordered, arranged, and presented for filtering before being analyzed. Second, the filtered and reduced data are analyzed using statistics to remove further variability. This holds true particularly for MS-based untargeted metabolomics studies, which focused on understanding fold changes in metabolic networks. Since the task of filtering and identifying changes from a large dataset is challenging, automated techniques for mining untargeted MS-based metabolomic data are needed. The traditional statistics-based approach tends to overfilter raw data, which may result in the removal of relevant data and lead to the identification of fewer metabolomic changes. This limitation of the traditional approach underscores the need for a new method. In this work, we present a novel deep learning approach using node embeddings (powered by GNNs), edge embeddings, and anomaly detection algorithm to analyze the data generated by mass spectrometry (MS)-based metabolomics called GEMNA (Graph Embedding-based Metabolomics Network Analysis), for example for an untargeted volatile study on Mentos candy, the data clusters produced by GEMNA were better than the ones used traditional tools, i.e., GEMNA has [Formula: see text], vs. the traditional approach has [Formula: see text].

RevDate: 2024-11-28
CmpDate: 2024-11-28

Whyte M, Wambui KM, E Musenge (2024)

Nigeria's malaria prevalence in 2015: a geospatial, exploratory district-level approach.

Geospatial health, 19(2):.

This study used data from the second Nigeria Malaria Indicator Survey (NMIS) conducted in 2015 to investigate the spatial distribution of malaria prevalence in the country and identify its associated factors. Nigeria is divided into 36 states with 109 senatorial districts, most of which are affected by malaria, a major cause of morbidity and mortality in children under five years of age. We carried out an ecological study with analysis at the senatorial district level. A malaria prevalence map was produced combining geographic information systems data from the Nigeria Malaria Indicator Survey (NMIS) of 2015 with shape files from an open data-sharing platform. Spatial autoregressive models were fitted using a set of key covariates. Malaria prevalence in children under-five was highest in Kebbi South senatorial district (70.6%). It was found that poorest wealth index (β = 0.10 (95% CI: 0.01, 0.20), p = 0.04), mothers having only secondary level of education (β = 0.78 (95% CI: 0.05, 1.51), p = 0.04) and households without mosquito bed nets (β = 0.21 (95% CI: 0.02, 0.39), p = 0.03) were all significantly associated with higher malaria prevalence. Moran's I (54.81, p<0.001) showed spatial dependence of malaria prevalence across contiguous districts and spatial autoregressive modelling demonstrated significant spill-over effect of malaria prevalence. Maps produced in this study provide a useful graphical representation of the spatial distribution of malaria prevalence based on NMIS-2015 data. Clustering of malaria prevalence in certain areas further highlights the need for sustained malaria elimination interventions across affected regions in order to break the chain of transmission.

RevDate: 2024-11-27

Soares R, Azevedo L, Vasconcelos V, et al (2024)

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.

Journal of chemical information and modeling [Epub ahead of print].

Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and research applications is essential. In this study, a searchable, updated, curated, and downloadable database of cyanobacteria bioactive compounds was designed, along with a machine-learning model to predict the compounds' targets of newly discovered molecules. A Python programming protocol obtained 3431 cyanobacteria bioactive compounds, 373 unique protein targets, and 3027 molecular descriptors. PaDEL-descriptor, Mordred, and Drugtax software were used to calculate the chemical descriptors for each bioactive compound database record. The biochemical descriptors were then used to determine the most promising protein targets for human therapeutic approaches and environmental bioremediation using the best machine learning (ML) model. The creation of our database, coupled with the integration of computational docking protocols, represents an innovative approach to understanding the potential of cyanobacteria bioactive compounds. This resource, adhering to the findability, accessibility, interoperability, and reuse of digital assets (FAIR) principles, is an excellent tool for pharmaceutical and bioremediation researchers. Moreover, its capacity to facilitate the exploration of specific compounds' interactions with environmental pollutants is a significant advancement, aligning with the increasing reliance on data science and machine learning to address environmental challenges. This study is a notable step forward in leveraging cyanobacteria for both therapeutic and ecological sustainability.

RevDate: 2024-11-27

Boyes D, Crowley LM, University of Oxford and Wytham Woods Genome Acquisition Lab, et al (2021)

The genome sequence of the harlequin ladybird, Harmonia axyridis (Pallas, 1773).

Wellcome open research, 6:300.

We present a genome assembly from an individual female Harmonia axyridis (the harlequin ladybird; Arthropoda; Insecta; Coleoptera; Coccinellidae). The genome sequence is 426 megabases in span. The majority (99.98%) of the assembly is scaffolded into 8 chromosomal pseudomolecules, with the X sex chromosome assembled.

RevDate: 2024-11-27

Toghan A, Alduaij OK, Sanad MMS, et al (2024)

Scalable Engineering of 3D Printing Filaments Derived from Recycling of Plastic Drinking Water Bottle and Glass Waste.

Polymers, 16(22): pii:polym16223195.

The most significant challenge that the world is currently facing is the development of beneficial industrial applications for solid waste. A novel strategy was implemented to produce a composite with varying loadings of glass waste nanoparticles (GWNP) in 5, 10, and 15 wt.% with recycled polyethylene terephthalate drinking water bottle waste (RPET). This strategy was based on glass and drinking water bottle waste. An analysis was conducted to evaluate the performance of the composite as filaments for 3D printer applications. This study evaluated the effect of GWNP addition on the chemical structure, thermal and mechanical characteristics of the composite. The Fourier Transform Infrared (FTIR) spectra of the filament composites and RPET composites exhibited similarities. However, the mechanical strength and thermal stability of the filament composites were enhanced due to the increased GWNP content. Furthermore, the results indicated that the filament developed could be utilized for 3D printing, as demonstrated by the successful fabrication of the filament composite, including 5 wt.% GWNP, using a 3D printer pen. The production of filaments using GWNP and RPET matrix presents a cost-effective, high-yield, and ecologically beneficial alternative. The present study may pave the way for the future advancement and utilization of 3D printing filaments by treating hazardous waste and using more ecologically friendly materials in design applications.

RevDate: 2024-11-27

Maślanka P, R Korycki (2024)

Material, Aerodynamic, and Operational Aspects of Single-Skin Paraglider.

Materials (Basel, Switzerland), 17(22): pii:ma17225553.

The operating comfort of a paraglider is created by the aerodynamic parameters as well as the mass and packing volume of the wing. A classic paraglider has upper and lower covers. To reduce the material and manufacturing costs as well as protect the environment, it is possible to introduce a single-skin wing. This article conducts an analysis of a single-skin paraglider covered only with upper panels, whereas the lower cover is applied only at the leading and trailing edges. The analysis is theoretically oriented; aerodynamic and structural calculations were performed using the ANSYS environment. The single-skin structure was evaluated in terms of the predicted behavior during flight and the material's deformation under the influence of a specified pressure and the overloads acting on it. The results show that developing these structures may influence the creation of models with comparable aerodynamic characteristics to traditional ones. Additionally, the reduced masses and packing volumes of difficult-to-degrade materials are strongly correlated with saving costs and an ecological approach. No corresponding studies were found in the available literature. Thus, this presented analysis may result in a greater understanding and application of this paraglider type.

RevDate: 2024-11-27
CmpDate: 2024-11-27

Signore IA, Donoso G, Bocchieri P, et al (2024)

The Chilean COVID-19 Genomics Network Biorepository: A Resource for Multi-Omics Studies of COVID-19 and Long COVID in a Latin American Population.

Genes, 15(11): pii:genes15111352.

Although a lack of diversity in genetic studies is an acknowledged obstacle for personalized medicine and precision public health, Latin American populations remain particularly understudied despite their heterogeneity and mixed ancestry. This gap extends to COVID-19 despite its variability in susceptibility and clinical course, where ethnic background appears to influence disease severity, with non-Europeans facing higher hospitalization rates. In addition, access to high-quality samples and data is a critical issue for personalized and precision medicine, and it has become clear that the solution lies in biobanks. The creation of the Chilean COVID-19 Biorepository reported here addresses these gaps, representing the first nationwide multicentric Chilean initiative. It operates under rigorous biobanking standards and serves as one of South America's largest COVID cohorts. A centralized harmonization strategy was chosen and included unified standard operating procedures, a sampling coding system, and biobanking staff training. Adults with confirmed SARS-CoV-2 infection provided broad informed consent. Samples were collected to preserve blood, plasma, buffy coat, and DNA. Quality controls included adherence to the standard preanalytical code, incident reporting, and DNA concentration and absorbance ratio 260/280 assessments. Detailed sociodemographic, health, medication, and preexisting condition data were gathered. In five months, 2262 participants were enrolled, pseudonymized, and sorted by disease severity. The average Amerindian ancestry considering all participant was 44.0% [SD 15.5%], and this value increased to 61.2% [SD 19.5%] among those who self-identified as Native South Americans. Notably, 279 participants self-identified with one of 12 ethnic groups. High compliance (>90%) in all assessed quality controls was achieved. Looking ahead, our team founded the COVID-19 Genomics Network (C19-GenoNet) focused on identifying genetic factors influencing SARS-CoV-2 outcomes. In conclusion, this bottom-up collaborative effort aims to promote the integration of Latin American populations into global genetic research and welcomes collaborations supporting this endeavor. Interested parties are invited to explore collaboration opportunities through our catalog, accessible online.

RevDate: 2024-11-27
CmpDate: 2024-11-27

Aras SG, Runyon JR, Kazman JB, et al (2024)

Is Greener Better? Quantifying the Impact of a Nature Walk on Stress Reduction Using HRV and Saliva Cortisol Biomarkers.

International journal of environmental research and public health, 21(11): pii:ijerph21111491.

The physiological impact of walking in nature was quantified via continuous heart rate variability (HRV), pre- and post-walk saliva cortisol measures, and self-reported mood and mindfulness scores for N = 17 participants who walked "The Green Road" at Walter Reed National Military Medical Center in Bethesda, Maryland. For N = 15 of the participants, HRV analysis revealed two main groups: group one individuals had a 104% increase (mean) in the root mean square standard deviation (RMSSD) and a 47% increase (mean) in the standard deviation of NN values (SDNN), indicating an overall reduction in physiological stress from walking the Green Road, and group two individuals had a decrease (mean) of 42% and 31% in these respective HRV metrics, signaling an increase in physiological stresses. Post-walk self-reported scores for vigor and mood disturbance were more robust for the Green Road than for a comparable urban road corridor and showed that a higher HRV during the walk was associated with improved overall mood. Saliva cortisol was lower after taking a walk for all participants, and it showed that walking the Green Road elicited a significantly larger reduction in cortisol of 53%, on average, when compared with 37% of walking along an urban road. It was also observed that the order in which individuals walked the Green Road and urban road also impacted their cortisol responses, with those walking the urban road before the Green Road showing a substantial reduction in cortisol, suggesting a possible attenuation effect of walking the Green Road first. These findings provide quantitative data demonstrating the stress-reducing effects of being in nature, thus supporting the health benefit value of providing access to nature more broadly in many settings.

RevDate: 2024-11-27

Alrhmoun M, Sulaiman N, Haq SM, et al (2024)

Is Boiling Bitter Greens a Legacy of Ancient Crete? Contemporary Foraging in the Minoan Refugium of the Lasithi Plateau.

Foods (Basel, Switzerland), 13(22):.

Wild greens (WGs) play a significant role in Mediterranean diets (MDs), reflecting botanical and cultural diversities, mainly influenced by a complex conglomerate of local human ecologies. This study investigates local ecological knowledge (LEK) linked to traditional gathering and consumption of WGs in the Lasithi Plateau of eastern Crete, where human genetic studies one decade ago showed very peculiar patterns, hypothesising that the Minoan civilisation took refuge there before it disappeared. A field ethnobotanical study was conducted to document the diversity of WGs and their detailed local culinary uses in the Lasithi area by interviewing 31 study participants. Fifty-nine folk taxa (species and subspecies) were recorded, corresponding to fifty-eight botanical taxa. A quotation index was measured to assess the cultural significance of WGs in the study areas; logistic regression analysis was adopted to understand the impact of sensory classifications of WGs and their local cooking methods. Lasithi's foraging showed a notable prevalence of bitter-tasting WGs, which play a central role in local cognition and culinary practices. This bitterness aspect of WGs, potentially influenced by cultural preferences and genetic factors, probably suggests a connection to the ancient Lasithi's inhabitants, i.e., Minoan dietary habits. We found that bitterness is the predominant sensory attribute in Lasithi, characterising 45.76% of the WGs. These findings underscore the complex interplay between local ecologies and biodiversity, LEK, and dietary traditions, highlighting the importance of WGs in understanding the evolution of foraging and plant culinary diversities across the Mediterranean.

RevDate: 2024-11-26
CmpDate: 2024-11-27

Das VA, Gautam B, Yadav PK, et al (2024)

Computational approach to identify novel genomic features conferring high fitness in Bacillus atrophaeus CNY01 and Bacillus velezensis AK-0 associated with plant growth promotion (PGP) in apple.

BMC plant biology, 24(1):1127.

A comparative genomic analysis approach provides valuable information about genetic variations and evolutionary relationships among microorganisms, aiding not only in the identification of functional genes responsible for traits such as pathogenicity, antibiotic resistance, and metabolic capabilities but also in enhancing our understanding of microbial genomic diversity and their ecological roles, such as supporting plant growth promotion, thereby enabling the development of sustainable strategies for agriculture. We used two strains from different Bacillus species, Bacillus velezensis AK-0 and Bacillus atrophaeus CNY01, which have previously been reported to have PGP activity in apple, and performed comparative genomic analysis to understand their evolutionary process and obtain a mechanistic understanding of their plant growth-promoting activity. We identified genomic features such as mobile genetic elements (MGEs) that encode key proteins involved in the survival, adaptation and growth of these bacterial strains. The presence of genomic islands and intact prophage DNA in Bacillus atrophaeus CNY01 and Bacillus velezensis AK-0 suggests that horizontal gene transfer has contributed to their diversification and acquisition of adaptive traits, enhancing their evolutionary advantage. We also identified novel DNA motifs that are associated with key physiological processes and metabolic pathways.

RevDate: 2024-11-26
CmpDate: 2024-11-26

Baradaran M, Salabi F, Mahdavinia M, et al (2024)

ScorpDb: A Novel Open-Access Database for Integrative Scorpion Toxinology.

Toxins, 16(11): pii:toxins16110497.

Scorpion stings are a significant public health concern globally, particularly in tropical and subtropical regions. Scorpion venoms contain a diverse array of bioactive peptides, and different scorpion species around the world typically exhibit varying venom profiles, resulting in a wide range of envenomation symptoms. Despite their harmful effects, scorpion venom peptides hold immense potential for drug development due to their unique characteristics. Therefore, the establishment of a comprehensive database that catalogs scorpions along with their known venom peptides and proteins is imperative in furthering research efforts in this research area. We hereby present ScorpDb, a novel database that offers convenient access to data related to different scorpion species, the peptides and proteins found in their venoms, and the symptoms they can cause. To this end, the ScorpDb database has been primarily advanced to accommodate data on the Iranian scorpion fauna. From there, we propose future community efforts to include a larger diversity of scorpions and scorpion venom components. ScorpDb holds the promise to become a valuable resource for different professionals from a variety of research fields, like toxinologists, arachnologists, and pharmacologists. The database is available at https://www.scorpdb.com/.

RevDate: 2024-11-27
CmpDate: 2024-11-27

Tayyab M, Hussain M, Zhang J, et al (2024)

Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan.

Journal of environmental management, 371:123094.

Due to its diverse topography, Pakistan faces different types of floods each year, which cause substantial physical, environmental, and socioeconomic damage. However, the susceptibility of specific regions to different flood types remains unexplored. To the best of our knowledge for the first time, this study employed an integrated approach by leveraging a GIS-based Analytical Hierarchy Process (AHP), remote sensing, and machine learning (ML) algorithms, to assess susceptibility to three different types of flooding in Peshawar, Pakistan. The study first evaluated the degree of susceptibility to riverine, urban, and flash floods using the GIS-based AHP technique, and then employed ML models, (i.e., specifically Random Forest [RF] and Extreme Gradient Boosting [XG-Boost] to analyze multi-type flood susceptibility in the study region. The performance of the ML models was also evaluated, and the XG-Boost model outperforms RF, demonstrating a higher correlation coefficient (R[2] = 0.561-0.922) and lower mean absolute error (MAE = 0.042-0.354), and root-mean-square error (RMSE = 0.119-0.415) for both training and testing datasets. The superior performance of the XG-Boost was further confirmed by the higher value of the area under the curve (AUC) values, which is relatively higher (0.87) than that of the AHP (0.70) and RF (0.86) models. Based on the relative best performance, the XG-Boost model was chosen for further susceptibility assessment of different types of floods, and the generated flood susceptibility maps revealed that 20.9% of the total area is susceptible to riverine flooding, while 30.27% and 48.68% of the total area is susceptible to urban and flash flooding, respectively. The study's findings are significant, offering valuable insights for relevant stakeholders in guiding future flood risk management and sustainable land use plans in the study area.

RevDate: 2024-11-27
CmpDate: 2024-11-27

Li Z, Zhang Y, Peng B, et al (2024)

A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.

Nucleic acids research, 52(21):13447-13468.

Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design. Proficient in uncovering subtle and intricate patterns within extensive enhancer screening data, DREAM achieves cutting-edge sequence-based enhancer activity prediction and highlights critical sequence features implicating strong enhancer activity. Leveraging DREAM, we have engineered enhancers that surpass the potency of the strongest enhancer within the Drosophila genome by approximately 3.6-fold. Remarkably, these synthetic enhancers exhibited conserved functionality across species that have diverged more than billion years, indicating that DREAM was able to learn highly conserved enhancer regulatory grammar. Additionally, we designed silencers and cell line-specific enhancers using DREAM, demonstrating its versatility. Overall, our study not only introduces an interpretable approach for enhancer design but also lays out a general framework applicable to the design of other types of cis-regulatory elements.

RevDate: 2024-11-27
CmpDate: 2024-11-27

Sun Q, Zhang Z, Ping Q, et al (2024)

Insight into using multi-omics analysis to elucidate nitrogen removal mechanisms in a novel improved constructed rapid infiltration system: Functional gene and metabolite signatures.

Water research, 267:122502.

In this study, a laboratory-scale improved constructed rapid infiltration (imCRI) system with non-saturated and saturated layers was constructed, and corn cobs as solid carbon source were added to the saturated layer to enhance the removal of nitrogen. Combined analyses of metagenomics and metabolomics were conducted to elucidate the nitrogen removal mechanism in the imCRI system. The results showed that the hydraulic load significantly influenced the treatment performance of the imCRI system, and a hydraulic load of 1.25 m[3]/(m[2]⋅d) was recommended. Under optimal conditions, the imCRI system using simulated wastewater achieved average removal efficiencies of 97.8 % for chemical oxygen demand, 85.7 % for total nitrogen (TN), and 97.6 % for ammonia nitrogen. Metagenomic and metabolomic analyses revealed that besides nitrification and denitrification, dissimilatory nitrate reduction to ammonium (DNRA), anammox, etc., are also involved in nitrogen metabolism in the imCRI system. Although nitrification was the predominant pathway in the non-saturated layer, aerobic denitrification also occurred, accounting for 22.59 % of the TN removal. In the saturated layer, nitrogen removal was attributed to synergistic effects of denitrification, DNRA and anammox. Moreover, correlation analysis among nitrogen removal, functional genes and metabolites suggested that metabolites related to the tricarboxylic acid cycle generated from the glycolysis of corn cobs provided sufficient energy for denitrification. Our results can offer a promising technology for decentralized wastewater treatment with stringent nitrogen removal requirements, and provide a foundation for understanding the underlying nitrogen transformation and removal mechanism.

RevDate: 2024-11-27
CmpDate: 2024-11-27

Li D, Ping Q, Mo R, et al (2024)

Revealing synergistic mechanisms of biochar-assisted microbial electrolysis cells in enhancing the anaerobic digestion performance of waste activated sludge: Extracellular polymeric substances characterization, enzyme activity assay, and multi-omics analysis.

Water research, 267:122501.

Although biochar (BC)-assisted microbial electrolysis cells (MEC) has been shown to improve anaerobic digestion (AD) performance of waste activated sludge (WAS), the underlying mechanisms remain unclear. This study conducted an in-depth investigation into the mechanism based on analyses of extracellular polymeric substances (EPS) characteristics, enzyme activities and multi-omics. The results showed that compared with the control group, methane production improved by 16.73 %, 21.32 %, and 29.37 % in the BC, MEC, and BC-assisted MEC (BC-MEC) groups, respectively. The reconfiguration of the protein secondary structure increased the hydrophobicity of the EPS, thereby promoting microbial aggregation. In addition, partial least-squares path modeling (PLS-PM) and mantel test based on the enzyme activity and multi-omics analyses revealed that the promotional effect of MEC on the hydrolysis of WAS was superior to that of BC, while BC was more advantageous in promoting electron transfer and biofilm formation regulated by quorum sensing. The synergistic effects of BC and MEC were exemplified in the BC-MEC group. g_norank_Aminicenantales responsible for the hydrolysis of WAS was enriched (29.6 %), and the activities of hydrolytic enzymes including α-glucosidases and proteases were increased by 29.1 % and 43.6 %, respectively. Further, the expressions of genes related to acyl homoserine lactones (AHLs) and diffusible signal factor (DSF) in quorum sensing systems, as well as the genes related to hydrogenase involved in electron transfer (mbhJKL, hyfB-JR, hypA-F, and hoxFHUY), were up-regulated in the BC-MEC group. This facilitated electron transfer and microbial communication, consequently enhancing methane production. This research significantly advances the understanding of the mechanism by which BC-assisted MEC enhances AD performance and provides valuable insights into strategies for improving energy recovery from WAS.

RevDate: 2024-11-26
CmpDate: 2024-11-26

Roman-Ramos H, PL Ho (2024)

Current Technologies in Snake Venom Analysis and Applications.

Toxins, 16(11): pii:toxins16110458.

This comprehensive review explores the cutting-edge advancements in snake venom research, focusing on the integration of proteomics, genomics, transcriptomics, and bioinformatics. Highlighting the transformative impact of these technologies, the review delves into the genetic and ecological factors driving venom evolution, the complex molecular composition of venoms, and the regulatory mechanisms underlying toxin production. The application of synthetic biology and multi-omics approaches, collectively known as venomics, has revolutionized the field, providing deeper insights into venom function and its therapeutic potential. Despite significant progress, challenges such as the functional characterization of toxins and the development of cost-effective antivenoms remain. This review also discusses the future directions of venom research, emphasizing the need for interdisciplinary collaborations and new technologies (mRNAs, cryo-electron microscopy for structural determinations of toxin complexes, synthetic biology, and other technologies) to fully harness the biomedical potential of venoms and toxins from snakes and other animals.

RevDate: 2024-11-26

Arias M, Behrendt L, Dreßler L, et al (2024)

Testing the equivalency of human "predators" and deep neural networks in the detection of cryptic moths.

Journal of evolutionary biology pii:7908977 [Epub ahead of print].

Researchers have shown growing interest in using deep neural networks (DNNs) to efficiently test the effects of perceptual processes on the evolution of color patterns and morphologies. Whether this is a valid approach remains unclear, as it is unknown whether the relative detectability of ecologically relevant stimuli to DNNs actually matches that of biological neural networks. To test this, we compare image classification performance by humans and six DNNs (AlexNet, VGG-16, VGG-19, ResNet-18, SqueezeNet, and GoogLeNet) trained to detect artificial moths on tree trunks. Moths varied in their degree of crypsis, conferred by different sizes and spatial configurations of transparent wing elements. Like humans, four of six DNN architectures found moths with larger transparent elements harder to detect. However, humans and only one DNN architecture (GoogLeNet) found moths with transparent elements touching one side of the moth's outline harder to detect than moths with untouched outlines. When moths were small, the camouflaging effect of transparent elements touching the moth's outline was reduced for DNNs but enhanced for humans. Prey size can thus interact with camouflage type in opposing directions in humans and DNNs, which warrants a deeper investigation of size interactions with a broader range of stimuli. Overall, our results suggest that humans and DNNs responses had some similarities, but not enough to justify the widespread use of DNNs for studies of camouflage.

RevDate: 2024-11-25

Gianicolo E, Russo A, Di Staso R, et al (2024)

A municipality-specific analysis to investigate persistent increased incidence rates of childhood leukaemia near the nuclear power plant of Krümmel in Germany.

European journal of epidemiology [Epub ahead of print].

Increased incidence rates for childhood leukaemia have been reported in municipalities close to the nuclear power plant (NPP) Krümmel (Geesthacht, Germany). Methodological challenges arise when analysing this association at ecological level. They include the use of an appropriate reference population, unstable estimates of standardised incidence ratios (SIRs), and the potential role of prevailing winds. The aim of our study is to address these challenges. The German Childhood Cancer Registry provided data on leukaemia in children under 15 years (2004-2019). The German Federal Statistical Office provided the population data. The study region included all municipalities with ≥ 75% surface area within 50 kms from the Krümmel NPP. We calculated SIRs using national and regional reference rates. Smoothed incidence relative rates (IRRs) were calculated and mapped to check for potential patterns associated with prevailing winds. Overall 356 cases of childhood leukaemia were observed in the study region (321 municipalities) during 2004-2019. SIRs based on national reference rates show nearly no difference to those calculated using the regional rates as reference. Increased SIR and IRR were observed in Geesthacht (observed-cases = eight; SIR = 2.29; 95% confidence interval: 0.99-4.51. IRR = 1.80; 95% credibility interval: 0.88-2.79). The analysis of the IRR map does not show patterns associated with prevailing winds. Using a regional population as the reference, we found evidence that there may still be an increased risk for childhood leukaemia in Geesthacht. However, IRR estimates are uncertain and credibility intervals are compatible with the absence of elevated risk. The persistent evidence of risk of childhood leukaemia in Geesthacht warrants further epidemiological surveillance.

RevDate: 2024-11-25
CmpDate: 2024-11-25

Adderley-Heron K, P Chow-Fraser (2024)

Unsupervised classification of Blanding's turtle (Emydoidea blandingii) behavioural states from multi-sensor biologger data.

PloS one, 19(11):e0314291 pii:PONE-D-24-14991.

Classifying animal behaviors in their natural environments is both challenging and ecologically important, but the use of biologgers with multiple sensors has significantly advanced this research beyond the capabilities of traditional methods alone. Here, we show how biologgers containing an integrated tri-axial accelerometer, GPS logger and immersion sensor were used to infer behavioural states of a cryptic, freshwater turtle, the Blanding's turtle (Emydoidea blandingii). Biologgers were attached to three males and five females that reside in two undisturbed coastal marshes in northeastern Georgian Bay (Ontario, Canada) between May and July 2023. Raw acceleration values were separated into static and dynamic acceleration and subsequently used to calculate overall dynamic body acceleration (ODBA) and pitch. The unsupervised Hidden Markov Model (HMM) successfully differentiated five behavioural states as follows: active in water, resting in water, active out of water, resting in water, and nesting. Overall accuracy of the classification was 93.8%, and except for nesting (79%), all other behaviours were above 92%. There were significant differences in daily activity budgets between male and female turtles, with females spending a greater proportion of time active out of water, and inactive out of the water, while males spent a greater proportion of time active in water. These differences were likely a result of large seasonal life-history requirements such as nesting and mate finding. Accurate classification of behavioural states is important for researchers to understand fine-scale activities carried out during the active season and how environmental variables may influence the behaviours of turtles in their natural habitats.

RevDate: 2024-11-25
CmpDate: 2024-11-25

Sahak AS, Karsli F, MA Saraj (2024)

Evaluating the impact of urban sprawl on the urban ecological status using GIS and remote sensing from 2000 to 2021: a case study of Herat City, Afghanistan.

Environmental monitoring and assessment, 196(12):1246.

Urbanization often incurs environmental costs, as fertile agricultural and forested lands are converted into urban areas. Herat City is currently undergoing significant urban transformation. This research aims to assess the impact of urban sprawl on Herat City's urban ecological status during 2000, 2013, and 2021, using GIS and remote sensing. The urban expansion intensity index was used to measure urban sprawl. The Mean Remote Sensing Ecological Index (MRSEI), integrating known granulation entropy (KGE) and comprehensive distance-based ranking (COBRA) algorithms, was utilized to evaluate urban ecological status. The random forest (RF) supervised machine learning-based algorithm was used to classify the study area into four categories (Built-up, Bare-land, Water, and Vegetation). Findings indicate rapid development from 2000 to 2013, followed by moderate expansion until 2021. Urban ecological quality degradation is observed in various directions over time, with the southeast consistently demonstrating excellent status. Interestingly, while good and excellent urban ecological status decreases over two decades, poor and very poor conditions improve. The research underscores an inverse relationship between urban expansion intensity and ecological status, highlighting the need for improved strategies to mitigate environmental decline. These findings will inform Afghan governmental bodies and international organizations, enabling them to better address resource consumption, ecological disruptions, social inequalities, and foster sustainable development.

RevDate: 2024-11-25

Hakkenberg CR, Clark ML, Bailey T, et al (2024)

Ladder fuels rather than canopy volumes consistently predict wildfire severity even in extreme topographic-weather conditions.

Communications earth & environment, 5(1):721.

Drivers of forest wildfire severity include fuels, topography and weather. However, because only fuels can be actively managed, quantifying their effects on severity has become an urgent research priority. Here we employed GEDI spaceborne lidar to consistently assess how pre-fire forest fuel structure affected wildfire severity across 42 California wildfires between 2019-2021. Using a spatial-hierarchical modeling framework, we found a positive concave-down relationship between GEDI-derived fuel structure and wildfire severity, marked by increasing severity with greater fuel loads until a decline in severity in the tallest and most voluminous forest canopies. Critically, indicators of canopy fuel volumes (like biomass and height) became decoupled from severity patterns in extreme topographic and weather conditions (slopes >20°; winds > 9.3 m/s). On the other hand, vertical continuity metrics like layering and ladder fuels more consistently predicted severity in extreme conditions - especially ladder fuels, where sparse understories were uniformly associated with lower severity levels. These results confirm that GEDI-derived fuel estimates can overcome limitations of optical imagery and airborne lidar for quantifying the interactive drivers of wildfire severity. Furthermore, these findings have direct implications for designing treatment interventions that target ladder fuels versus entire canopies and for delineating wildfire risk across topographic and weather conditions.

RevDate: 2024-11-25
CmpDate: 2024-11-23

Li J, Weckwerth W, S Waldherr (2024)

Network structure and fluctuation data improve inference of metabolic interaction strengths with the inverse Jacobian.

NPJ systems biology and applications, 10(1):137.

Based on high-throughput metabolomics data, the recently introduced inverse differential Jacobian algorithm can infer regulatory factors and molecular causality within metabolic networks close to steady-state. However, these studies assumed perturbations acting independently on each metabolite, corresponding to metabolic system fluctuations. In contrast, emerging evidence puts forward internal network fluctuations, particularly from gene expression fluctuations, leading to correlated perturbations on metabolites. Here, we propose a novel approach that exploits these correlations to quantify relevant metabolic interactions. By integrating enzyme-related fluctuations in the construction of an appropriate fluctuation matrix, we are able to exploit the underlying reaction network structure for the inverse Jacobian algorithm. We applied this approach to a model-based artificial dataset for validation, and to an experimental breast cancer dataset with two different cell lines. By highlighting metabolic interactions with significantly changed interaction strengths, the inverse Jacobian approach identified critical dynamic regulation points which are confirming previous breast cancer studies.

RevDate: 2024-11-25
CmpDate: 2024-11-25

Zhao Y, Cordero OX, M Tikhonov (2024)

Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function.

PLoS computational biology, 20(11):e1012590 pii:PCOMPBIOL-D-24-00586.

Microbial communities play key roles across diverse environments. Predicting their function and dynamics is a key goal of microbial ecology, but detailed microscopic descriptions of these systems can be prohibitively complex. One approach to deal with this complexity is to resort to coarser representations. Several approaches have sought to identify useful groupings of microbial species in a data-driven way. Of these, recent work has claimed some empirical success at de novo discovery of coarse representations predictive of a given function using methods as simple as a linear regression, against multiple groups of species or even a single such group (the ensemble quotient optimization (EQO) approach). Modeling community function as a linear combination of individual species' contributions appears simplistic. However, the task of identifying a predictive coarsening of an ecosystem is distinct from the task of predicting the function well, and it is conceivable that the former could be accomplished by a simpler methodology than the latter. Here, we use the resource competition framework to design a model where the "correct" grouping to be discovered is well-defined, and use synthetic data to evaluate and compare three regression-based methods, namely, two proposed previously and one we introduce. We find that regression-based methods can recover the groupings even when the function is manifestly nonlinear; that multi-group methods offer an advantage over a single-group EQO; and crucially, that simpler (linear) methods can outperform more complex ones.

RevDate: 2024-11-25
CmpDate: 2024-11-25

Behera BP, Naik H, VB Konkimalla (2024)

Peptaloid: A Comprehensive Database for Exploring Peptide Alkaloid.

Journal of chemical information and modeling, 64(22):8387-8395.

Peptaloid is the first dedicated database for peptide alkaloid molecules, a unique class of naturally derived compounds known for their structural diversity and significant biological activities. Despite their promising potential in drug discovery and therapeutic development, research on peptide alkaloids has been limited by the absence of a comprehensive and centralized resource. Fragmented data across various sources have posed a significant challenge, underscoring the need for a specialized database to facilitate more efficient research and application. Peptaloid addresses this critical gap by providing a database with over 161,000 peptide alkaloid entries, each detailed with structural, physicochemical, and pharmacological properties. By leveraging advanced computational tools and machine learning, Peptaloid generates ADMET profiles, aiding in identifying and optimizing therapeutic candidates. Designed for versatility, the database supports various applications beyond drug discovery, including ecology and material sciences. Peptaloid (as a specialized database for peptide alkaloids) will play a crucial role in innovation and collaboration across scientific disciplines. Peptaloid is accessible at https://peptaloid.niser.ac.in.

RevDate: 2024-11-23
CmpDate: 2024-11-23

Ang'ang'o LM, Herren JK, Ö Tastan Bishop (2024)

Bioinformatics analysis of the Microsporidia sp. MB genome: a malaria transmission-blocking symbiont of the Anopheles arabiensis mosquito.

BMC genomics, 25(1):1132.

BACKGROUND: The use of microsporidia as a disease-transmission-blocking tool has garnered significant attention. Microsporidia sp. MB, known for its ability to block malaria development in mosquitoes, is an optimal candidate for supplementing malaria vector control methods. This symbiont, found in Anopheles mosquitoes, can be transmitted both vertically and horizontally with minimal effects on its mosquito host. Its genome, recently sequenced from An. arabiensis, comprises a compact 5.9 Mbp.

RESULTS: Here, we analyze the Microsporidia sp. MB genome, highlighting its major genomic features, gene content, and protein function. The genome contains 2247 genes, predominantly encoding enzymes. Unlike other members of the Enterocytozoonida group, Microsporidia sp. MB has retained most of the genes in the glycolytic pathway. Genes involved in RNA interference (RNAi) were also identified, suggesting a mechanism for host immune suppression. Importantly, meiosis-related genes (MRG) were detected, indicating potential for sexual reproduction in this organism. Comparative analyses revealed similarities with its closest relative, Vittaforma corneae, despite key differences in host interactions.

CONCLUSION: This study provides an in-depth analysis of the newly sequenced Microsporidia sp. MB genome, uncovering its unique adaptations for intracellular parasitism, including retention of essential metabolic pathways and RNAi machinery. The identification of MRGs suggests the possibility of sexual reproduction, offering insights into the symbiont's evolutionary strategies. Establishing a reference genome for Microsporidia sp. MB sets the foundation for future studies on its role in malaria transmission dynamics and host-parasite interactions.

RevDate: 2024-11-23
CmpDate: 2024-11-21

Cuesta-Aguirre DR, Malgosa A, C Santos (2024)

An easy-to-use pipeline to analyze amplicon-based Next Generation Sequencing results of human mitochondrial DNA from degraded samples.

PloS one, 19(11):e0311115.

Genome and transcriptome examinations have become more common due to Next-Generation Sequencing (NGS), which significantly increases throughput and depth coverage while reducing costs and time. Mitochondrial DNA (mtDNA) is often the marker of choice in degraded samples from archaeological and forensic contexts, as its higher number of copies can improve the success of the experiment. Among other sequencing strategies, amplicon-based NGS techniques are currently being used to obtain enough data to be analyzed. There are some pipelines designed for the analysis of ancient mtDNA samples and others for the analysis of amplicon data. However, these pipelines pose a challenge for non-expert users and cannot often address both ancient and forensic DNA particularities and amplicon-based sequencing simultaneously. To overcome these challenges, a user-friendly bioinformatic tool was developed to analyze the non-coding region of human mtDNA from degraded samples recovered in archaeological and forensic contexts. The tool can be easily modified to fit the specifications of other amplicon-based NGS experiments. A comparative analysis between two tools, MarkDuplicates from Picard and dedup parameter from fastp, both designed for duplicate removal was conducted. Additionally, various thresholds of PMDtools, a specialized tool designed for extracting reads affected by post-mortem damage, were used. Finally, the depth coverage of each amplicon was correlated with its level of damage. The results obtained indicated that, for removing duplicates, dedup is a better tool since retains more non-repeated reads, that are removed by MarkDuplicates. On the other hand, a PMDS = 1 in PMDtools was the threshold that allowed better differentiation between present-day and ancient samples, in terms of damage, without losing too many reads in the process. These two bioinformatic tools were added to a pipeline designed to obtain both haplotype and haplogroup of mtDNA. Furthermore, the pipeline presented in the present study generates information about the quality and possible contamination of the sample. This pipeline is designed to automatize mtDNA analysis, however, particularly for ancient samples, some manual analyses may be required to fully validate results since the amplicons that used to be more easily recovered were the ones that had fewer reads with damage, indicating that special care must be taken for poor recovered samples.

RevDate: 2024-11-22
CmpDate: 2024-11-22

Zhu J, Li Z, Yang J, et al (2024)

Ecological space management and control zoning of Giant Panda National Park from the perspective of ecosystem services and land use.

Scientific reports, 14(1):19951.

Since China proposed building a national park system in 2017, the establishment of a planning system for nature reserves, with national parks as the main body, is being actively promoted around the country. Among them, scientific ecological space management and control zoning (ESMCZ) is an important link in maintaining the ecological stability of national parks. How to zone national parks and how to improve the precision of zoning has become a new task for national parks. Therefore, this study takes the Giant Panda National Park as the study area, takes ecosystem services and land use/cover change as the research perspective, integrates the InVEST model, PLUS model and bayes belief network (BBN) model, and builds a set of ecological space management and control zoning (ESMCZ) spatial zoning framework based on raster scale, dividing the study area into strictly protected zone, ecological buffer zone, ecological control zone and controlled development zone. The results showed that: (1) The study area showed an increasing trend in water conservation, soil conservation and carbon storage from 2005 to 2020, and the habitat quality index was generally high. The spatial heterogeneity of ecosystem services in the study area was significant, and the effect of a single factor on ecosystem services was most pronounced. (2) Large variation in area for different land uses under natural development scenarios and ecological protection scenarios. In both scenarios, the area of cultivated land, the area of grassland and the area of unused land decrease relative to 2020, and the area of forested land, the area of water and the area of constructed land increase relative to 2020. (3) The Giant Panda National Park is divided into strictly protected zone, ecological buffer zone, ecological control zone and control development zone, of which the strictly protected zone have the largest area and the best ecosystem background condition, and the control development zone have the smallest area and the worst ecosystem background condition. (4) The ecological space management and control zoning (ESMCZ) framework provides a more refined method for the secondary zoning of nature reserves such as the Giant Panda National Park, which is valuable for the implementation of zoning and categorization management for ecological conservation in the Giant Panda National Park.

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This is a must read book for anyone with an interest in invasion biology. The full title of the book lays out the author's premise — The New Wild: Why Invasive Species Will Be Nature's Salvation. Not only is species movement not bad for ecosystems, it is the way that ecosystems respond to perturbation — it is the way ecosystems heal. Even if you are one of those who is absolutely convinced that invasive species are actually "a blight, pollution, an epidemic, or a cancer on nature", you should read this book to clarify your own thinking. True scientific understanding never comes from just interacting with those with whom you already agree. R. Robbins

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