dataesr/scientific-paragraphs-categorization huggingface dataset:
Improving metatranscriptomic assignments Metatranscriptomic reads were generated from soil samples collected along an elevation gradient spanning 400 m of elevation in the Alps 28,  79, 80. Brie y, short soil cores were taken and preserved in LifeGuard (Qiagen, Hilden, Germany) in 2015 and 2017. RNA was extracted with an RNeasy PowerSoil Total RNA Kit (Qiagen) from the samples. RNA sequencing libraries were prepared of each RNA extracts with a NEBNext Ultra RNA Library Prep Kit (Frankfurt am Main, Germany), and 8 gigabases of each library were sequenced at Novogene (UK) on an Illumina NovaSeq6000 sequencer in a 150 bp paired-end reaction. Reads were trimmed of adapters with Trimmomatic  72  and assigned with kraken2  73  to a custom database containing all bacterial, plant and fungal reference genomes from NCBI (accessed on 15.
1. 2023), and the 232 soil invertebrate genomes. We visualised the richness of soil invertebrates along the elevation gradient at the genus level. As nucleotide sequence counts are not normally distributed and they are frequently overdispersed 81 , we evaluated differences in community composition among the study years and habitats, and along the elevation with a model-based analysis of multivariate abundance data  82. We visualised differences in community composition with non-metric multidimensional scaling. Community analyses were performed in R v4. 2. 2 83. Building the phylogeny using metazoan BUSCO genes We searched for BUSCO genes with the metazoan_obd10 database (v4. 1. 4) to generate a single phylogeny of the 232 soil invertebrate genomes and a selection of 118 publicly available invertebrate RefSeq genomes from NCBI (downloaded on 16.
09. 2021). The RefSeq genomes were included if they a) were from the same taxon group as our specimens, b) served to shorten the evolutionary distance between taxa in the tree. More speci cally, we included any chromosome-level Protostomia genomes (excluding Insecta), genomes of any assembly quality for species within our 14 taxonomic groups of interest, and some additional speci c outgroups (two Echinodermata, three Rotifera, a Priapulida, Machilis hrabei and Drosophila albomicans). We found 141 metazoan BUSCO genes which were present in at least 75% of the genome assemblies (Supp. Tables  1, 4 ). The phylogenetic approach is based on the https://github. com/mag-wolf/BUSCO-to-Phylogeny pipeline. We aligned these with Mafft (v7. 481 84 ) with 1000 iterative re nements. These gene alignments were then concatenated into a supermatrix using FASCONCAT (v1.
Estimating effective population size Using mlRho (v2. 9) we estimated theta directly from the genome data by making use of the genome-wide heterozygosity in the reference individual. This proxy measure of effective population size was calculated individually for each genome assembly with at least 4X coverage, as recommended by Haubold et al. 95  Annotating repeat content In addition several genome properties (i. e. , GC content, BUSCO gene content, genome size and effective population size), and because repeat content is particularly relevant for explaining genome size variation among species, we also annotated the repetitive elements. Species-speci c repeat libraries were constructed using the automated RepeatModeler (v2. 0. 1) pipeline with LTR Structural discovery pipeline activated  96.
Ecological trait data To connect the 232 new genome assemblies with ecological traits of the respective species, we rst gathered existing functional trait data from Edaphobase (https://portal. edaphobase. org/) and from literature. We focussed on a) body length (minimum female adult body length for nematodes, and mean body length for all other taxa) as a proxy for body size, b) reproduction mode, and c) known occurrences in different soil habitat types (based on level 2 hierarchies described by the Coordination of information on the environment (CORINE)  47  ). We provide this collected information as an additional database resource, in Supp. Table  1.
Searching for core metazoan genes As a rst-look into the functional capacities of the soil invertebrates in our study, we searched the genomes for potential loss of protein-coding genes. Using 11 species from across the Metazoa tree of life (Supp Table  5 ) which were part of the Orthologous MAtrix database (OMA 100 ), we computed a list of 1482 core metazoan genes which were common to at least 9 of these 11 species using DCC (https://github. com/BIONF/dcc2) and precomputed ortholog groups from the OMA DB. We then searched for orthologs of these 1482 metazoan core genes among the more complete (> 50% BUSCO completeness) soil invertebrate genomes (n = 177) with fDOG-Assembly (https://github. com/BIONF/fDOG/tree/fdog_goes_assembly). fDOG-Assembly performs targeted, featureaware ortholog search without the need for annotated genomes as the starting point.
Due to the taxonomic breadth of our dataset, six separate ortholog searches were performed, each using the three most closely-related reference species with protein annotations available (Supp. Table  6 ). Genes without orthologs in all investigated species were excluded from the following analyses. The resulting phylogenetic ortholog pro les were visualised with PhyloPro le (v1. 8. 6 101 ) and clustered according to the euclidean distance of the presence and absence patterns of the ortholog groups. Hence, after visual inspection of the ortholog pro les, we were able to identify patches of core metazoan genes which were missing from certain groups. We tested for gene ontology (GO) enrichment of the potentially missing genes using the InterProScan database 102 and the function runTest from the topGO package (v2.
Acknowledgements This work is a result of the  LOEWE Centre   for Translational Biodiversity Genomics  funded by the  Hessen State Ministry of Higher Education, Research and the Arts (HMWK). This project contributes to the  Soil Invertebrate Genome Initiative  (https://tbg. senckenberg. de/sigi/), a liated with the Earth BioGenome Project. Special thanks to  Damian Baranski ,  Jürgen Otte  and  Jörg Müller  for DNA extractions, to  Astrid König  for extraction and preparation of nematodes and tardigrades from Senckenberg cultures and soils, to  Lena Bonassin  and  Jade Tessien  for help with organising the datasets and barcodes, to  Prof. Dr. Florian Grundler  ( INRES Molekulare Phytomedizin, Bonn University, Germany ) for thousands of J2 juveniles of Heterodera schachtii and Meloidogyne incognita in ethanol from their cultures, and to  Magnus Wolf  for the original BUSCO-to-phylogeny pipeline.
Data availability Vouchers are deposited in the collections of the Senckenberg Museum of Natural History Görlitz (SMNG), Germany. Draft assemblies and raw les are accessible through the ENA/NCBI project PRJEB57698. 28S and COI barcodes are publicly available at dx. doi. org/10. 5883/DS-TBGMI. Genome metadata can be accessed at the Genomes on a Tree (https://goat. genomehubs. org/projects/METAINVERT).
Background Although depression is a major issue among farming population, to date, there have been few studies on specific agricultural activities. We aimed to investigate whether, among the entire French farm manager (FM) workforce, certain agricultural activities are more strongly associated with depression than others. Methods This nationwide retrospective cohort study used data from an administrative health database available to the TRACTOR project. This database pertains to the entire French agricultural workforce (overseas workers not included). Data were analyzed from January 2021 to December 2022. All FMs that worked at least once over the period 2002-2016 were included. The outcome measure was the association between 26 agricultural activities and the risk of depression measured as hazard ratios (HRs) after adjusting for age, sex, and pre-existing medical comorbidities.
Added value of this study To the best of our knowledge, this is the first study using data from insurance health databases pertaining to the entire French farm manager population (overseas workers not included) that investigated the associations between 26 agricultural activities and depression, overall, and by sex category. Increased risks of depression were observed for several activities. Cow farming, poultry and rabbit farming, dairy farming, and mixed farming were the activities the most at risk of depression within the farm manager population. Implications of all the available evidence Depression surveillance using administrative health data is an alternative and a complementary approach to traditional cohort studies, which can be more resource-intensive. Findings from this study will be of interest to policy makers and public health practitioners because they can help to determine potential agricultural activities for which further studies could be needed.
Health data from 2012 to 2016 were used as follow-up period, with January 1st 2012 as the baseline time point (i. e. , time zero), and December 31st 2016 as the follow-up end. Health data pertained to drug prescription, and to chronic diseases/long-term illnesses (LTIs) for which FMs are entitled to fee exemption, and the full coverage of health care expenditures. There was no missing data regarding the variables of interest for this study. Data were obtained and managed from October 2018 to December 2020,  18  and analyzed from January 2021 to December 2022. Administrative health data were not available after 2016. The French independent administrative authority protecting privacy and personal data approved this study. All methods were performed in accordance with the relevant guidelines and regulations.
Role of the funding source This work was partially supported by MIAI@Grenoble Alpes [ANR-19-P3IA-0003, 2019], and was supported within the STOP project (Searching and Tracking Occupational factors to Prevent suicide in agriculture) by MSA [MSA-2020-STOP, 2020]. The funding sources had no role in the study design and conduct of the study; in the collection, management, analysis, and interpretation of data; in the writing of the report; preparation, review, or approval of the article; or in the decision to submit the paper for publication. The Note: MA: main analysis, SA1: first sensitivity analysis, SA2: second sensitivity analysis, SA3: third sensitivity analysis, SA4: fourth sensitivity analysis, VIF: variance inflation factor. a The analysis was adjusted on sex only for "both sexes"; otherwise, the sex was used to conduct subgroup analyses.
26, 28, 29 Most studies are from the Agricultural Health Study in the US, where a history of pesticide poisoning as well as high cumulative exposure to pesticides were associated with depression. 1, 13, 26, 28, 29 We found a 10% excess risk of depression for male winegrowers, but not in other activities involving a frequent or high pesticide use such as arboriculture, or crop farming. We did not study depression risks related to specific pesticide compounds since these data were not collected by MSA. Regarding viticulture, besides potential pesticide exposure, other risk factors and stressors could play a role in the positive associations observed, such as weather conditions (e. g. , drought), quality and safety of food products, time pressure, or plant and fruit diseases (e. g. , mildew).
Strengths and limitations of this work Strengths of the study include a large nationwide population-based cohort from administrative data, with many exposed cases and completeness of available data (more than one million farmers for this study vs. <10,000 in the literature). While administrative data are increasingly used to conduct research on depression and inform health services and health policy,  7  to the authors' knowledge, this is the first study using data from insurance health databases pertaining to the entire French FM population. Only persons under medical care can be identified with administrative health data, but less than one-third of depression diagnosis are recorded by physicians,  6  which likely reduce the identification of depression in health records. We identified depression cases using ICD-10 codes and ATD prescriptions, which is not comparable to the real illness incidences, and could misestimate risk estimation.
It is possible that their absence could bias the estimated effects, and confounds/masks the genuine relationship between agricultural activities and depression. In addition, regarding available confounders, their accuracy was sometimes limited, so the possibility of residual confounding cannot be excluded. Findings should therefore be considered carefully. Only an indirect exposure estimation was possible using activities from administrative databases, with no information on chemical, physical, or biological agents that could be encountered/used by FMs. Some activities were not descriptive enough to provide the best risk estimation possible, in particular for activities that are highly heterogeneous in nature, such as crop farming. Linking MSA data to other external sources (e. g. , cohort studies, crop exposure matrices) could help to address some of the aforementioned limitations.
Patient consent was not required as the research team accessed fully anonymized data only, which were collected as part of routine health insurance purpose activities by the MSA. In addition, results were reported at a large collective scale (i. e. , farming activity level) and measures were undertaken to prevent the risk of reidentification of individuals. Following CNIL instructions, MSA is required to make bill posting in each of its 35 offices and to communicate yearly to all of its insured individuals about the goals, advancements and achievements of the TRACTOR project.
Acknowledgments This article represents independent research funded by the  Mutualité Sociale Agricole (MSA)  [MSA-2020-STOP, 2020], and  MIAI@Grenoble Alpes  [ ANR-19-P3IA-0003 , 2019]. The funding sources had no role in the study design and conduct of the study; in the collection, management, analysis, and interpretation of data; in the writing of the report; preparation, review, or approval of the article; or in the decision to submit the paper for publication. The authors thank the MSA, their representatives and their chosen intermediaries within the framework of the  TRACTOR  project and the  STOP  project for their help to understand and access MSA data. The authors also thank the  ANSES (French Agency for Food, Environmental and Occupational Health & Safety)  for its support regarding the  TRACTOR  project.
Total RNA extraction, RNA-sequencing and data analysis The samples of L. edodes hyphae grown on oak wafers and corn stalks were snap-frozen in liquid N 2 stored at -80°C, and then sent to BGI Genomics (Shenzhen, China) for RNA extraction and sequencing. Three biological replicates were performed. The extraction of total RNA and the examination of concentration, quality, and purity were performed following the manufacturer's protocol from BGI Genomics. Frontiers in Microbiology 03 frontiersin. org Then, the construction of eighteen cDNA libraries and RNA-Seq sequencing were performed at BGI Genomics according to their standard Illumina protocols. The 150-bp long paired-end reads were generated on the Illumina HiSeq X-ten sequencer. The Analysis of similarity (ANOSIM), a non-parametric multivariate statistical test, was performed to test the difference among the spatial-temporal groups on different substrates using package vegan.
The differential expression analysis was performed using DESeq2  (Love et al. , 2014). Genes with a fold change ≥2 and a false discovery rate (FDR) <0. 01 were designated as differentially expressed genes (DEGs). DEGs were functionally annotated by blast search against the databases of Pfam, Swiss-Prot, NR, KEGG, GO, KOG, Interpro, carbohydrate-active enzymes (CAZymes) as previously described  (Chen et al. , 2016). The expression of several enzyme genes related to lignocellulose degradation was tested by qRT-PCR along the colonization process on substrates according to a previous study  (Cai et al. , 2017)  and the genespecific primers are shown in Supplementary Table  S1. The RNA-seq data reported in this paper have been deposited in the Genome Sequence Archive  (Chen et al. , 2021)  in the National Genomics Data Center (Members, CNCB-NGDC, 2022) (GSA: CRA010424).
Considering the strikingly different appearance of hyphae on oak wafers and corn stalk slices, we assessed the transcript level of ribosomal protein genes. A slightly higher transcription level on corn stalk, suggests more active cellular protein synthesis and thus may contribute to the different hyphal morphology and growth rate (Supplementary Data S1). Besides, most genes encoding the oligopeptide transporters (OPT7 and OPT3) and urea transporter (DUR3) in response to N limitation were significantly up-regulated at the late degradation stage for both substrates, particularly on oak wafer (Supplementary Figure  S7 ; Supplementary Data S1). This result suggested that more available N was released from the decomposed corn stalk  (Vanden Wymelenberg et al. , 2009; Nicolas et al. , 2019).
There is a growing consensus that to effectively adapt to climate change, cities need userfriendly tools and reliable high-resolution biophysical and socio-economic data for analysis, mapping, modeling, and visualization. This study examines the availability of various types of information used in climate adaptation plans of 40 municipalities with a population of less than 300,000 people in the United States and France, probing into the choice and usage of relevant information by small municipalities. We argue that non-climatic spatial data, such as population demographic and socioeconomic patterns, urban infrastructure, and environmental data must be integrated with climate tools and datasets to inform effective vulnerability assessment and equitable adaptation planning goals. Most climate adaptation plans examined in this study fail to address the existing structural inequalities and environmental injustices in urban infrastructure and land use.
Some U. S. states, such as California, provide state-level open access peer-reviewed cross-disciplinary data and collaboration opportunities to stakeholders, including infrastructure managers, municipal planners, community-based organizations, state agencies, scientists and climate experts, educators, and the public via Cal Adapt  [7]. These databases provide state-wide data on temperature, rainfall, wind, soil moisture, and ocean conditions, as well as maps, risk and vulnerability analyses, assessments, and long-term projections and scenarios. They can be combined with socio-economic variables and non-meteorological data such as agricultural production, health trends, human settlement in high-risk areas, and road and infrastructure maps for the delivery of goods, depending on user needs and other relevant information.
Climate Adaptation Plans This inquiry on the role of multidisciplinary data and tools available for municipal climate adaptation planning is informed by the analysis of climate adaptation plans and vulnerability assessment reports developed by urban and rural municipalities with populations less than 300,000 people in the U. S. and France. To investigate the content, sources, and scale of climatic and non-climatic tools, services, and data used by the local communities we examined 40 published climate adaptation plans (23 in the U. S. and 17 in France) of small cities, towns, and counties. The selection of planning documents for the U. S. part of the dataset is described in detail in  [31] , while the selection of both U. S. adaptation plans and French PCAETs (Plan Climat Air Energie Territorial) is the most recent update of our earlier dataset published in [27] and  [32].
Although most climate adaptation planning methodologies recommend that municipalities engage community members in their vulnerability assessments and climate adaptation planning, opportunities for meaningful engagement of local citizens and especially vulnerable groups are quite low. One possible reason for this may be the lack of inclusive user-friendly collaborative engines tailored to non-expert participants, connecting local communities with relevant climate services and tools. A promising example of such a platform outside of our study areas is the Climate Just platform in the UK  [144] , connecting users and producers through high-resolution mapping of community vulnerability to climate change. Involvement of broad coalitions of various groups of stakeholders including citizens, schools, universities, environmental organizations, and private firms in the co-creation and analysis of knowledge is possibly the only realistic way to bridge the gap between the national and state-scale data providers and the city-scale and neighborhoodscale data needs.
Cities need the best common standards, which are currently lacking, but not necessarily common data sources or guidelines. (b) Centralized approaches to data monitoring for climate adaptation planning often fail to provide information at relevant temporal and spatial scales. Produced by different agencies and groups of experts, these databases are often hard to integrate and downscale. Decentralized interdisciplinary monitoring networks equipped with digital applications allowing local citizens to engage in knowledge production may offer promising alternatives. (c) Climate-adaptation design tools for local governments should prioritize climate justice in all adaptation contexts and sectors. GIS-based online mapping tools and mobile applications are very helpful in visualization and analysis of spatial correlations between income, race, environmental justice issues, and various dimensions of vulnerability to climate impacts, helping to inform difficult conversations about resource allocations in climate adaptation planning.
e. , area not too disrupted by the replacement of the landrace populations with new exogenous cultivars promoted by the formal plant breeding organisations. Here we present an analysis of the genetic bases of adaptation to climate changes based on sampling across time, in an autogamous crop species, rice, of major significance for the world food security. Our study place was the tropical forest and the Sudanian savannah areas of Guinea in West Africa, not much affected by the Green Revolution. The interval between the two sampling times was approximately 30 years. First, using local historical meteorological data we analysed and confirmed the reality of climate changes in the target area. Second, we analysed and confirmed significant phenological differences between the populations of the Asian rice (Oryza sativa) and of the African rice (Oryza glaberrima) sampled across time.
The passport data for Collect-1 samples included the village name, the variety name, the species (O. sativa or O. glaberrima) and, occasionally, the rice growing ecosystem  (upland, hydromorphic, lowland)  and the type of collect (field/granary, bulk/panicle). Approximately 40% of the accessions were collected in the field. To prepare the Collect-2 campaign, the geographic coordinates of a large share of the villages visited during Collect-1 was retrieved using Google-Earth software. The collect team borrowed the track of Collect-1 campaign but did not limit its collect to the Collect-1 villages, especially as some villages could not be found. The passport data for samples from Collect-2 included the village name and its geographic coordinates, the variety name and tentative species membership, the rice growing ecosystem and the type of collect.