dataesr/scientific-paragraphs-categorization huggingface dataset:
If these data meet the conditions specified by the AMUNATCOLL administrator, they can be included in the general database and made publicly available to all system users. tity and coverage, sex, age, stage of development, and dimensions. An ecologist docu menting natural habitats has the option of selecting habitat types from the list provide or entering names according to individual classification. A special form is available fo descriptions of zoological samples, such as soil samples, litter, bird nests, and deadwood The geographic locations of the field observations saved in the application and the co lected samples can be displayed at all times on the map so that the user has a constan view of his or her location while working in the field. To deliver even more control ove fieldwork, the application can remember the user's course over the route.
API Interface Access to the AMUNATCOLL IT system takes place through an interface imple mented in REST (REpresentational State Transfer) technology (Figure  10 ). The portal an the mobile application use the same programming interface. Most of the offered function alities are available only to the logged-in user; therefore, access to individual interfac methods is secured with JSON Web Token (JWT). Selected information from the taxo nomic database is made available to external entities, primarily the GBIF. For this purpose the "BioCASe Provider Software" (BPS) service, which is compatible with the "Biologica Collection Access Service" [76], was established. This international network of biodive sity repositories combines specimen data from wildlife collections, botanical garden.
API Interface Access to the AMUNATCOLL IT system takes place through an interface implemented in REST (REpresentational State Transfer) technology (Figure  10 ). The portal and the mobile application use the same programming interface. Most of the offered functionalities are available only to the logged-in user; therefore, access to individual interface methods is secured with JSON Web Token (JWT). Selected information from the taxonomic database is made available to external entities, primarily the GBIF. For this purpose, the "BioCASe Provider Software" (BPS) service, which is compatible with the "Biological Collection Access Service" [76], was established. This international network of biodiversity repositories combines specimen data from wildlife collections, botanical gardens, zoos, and other research institutions around the world with information from huge observational databases, including the AMUNATCOLL database.
Conclusions Digital databases based on NHCs are already well established in the science of biodiversity. Large international data aggregators, such as the GBIF or Integrated Digitized Biocollections, are of global importance in facilitating access to biological data. A significant role is also played by small and medium-sized biodiversity collections, which on the one hand enable biodiversity research on a regional and local scale and on the other hand supply information to global databases. Such databases, apart from their scientific role, are also of great importance in the effective protection of biodiversity by developing areas of social activity such as education, citizen science, and environmental management. The social aspect and the related benefits for the wide group of stakeholders should be borne in mind whenever natural resources are being digitized and information systems are being designed to make them available.
 basing the metadata structure on international standards (ABCD 2. 06 and Darwin Core) and enabling the mechanism to transfer data to the GBIF database;  expanding the metadata structure with fields useful in education, popularization of biodiversity knowledge, and practical nature conservation;  considering physical specimens and multimedia documents collected directly under empirical environmental study in the digitization process;  implementing advanced search and analysis tools, especially a rich set of tools for spatial analysis, in the portal;  integrating the portal with the dedicated mobile application, intended for documenting natural observations directly in the field;  facilitating a space on the portal for user observations documented by means of a mobile application to work on NHCs;.
• basing the metadata structure on international standards (ABCD 2. 06 and Darwin Core) and enabling the mechanism to transfer data to the GBIF database; • expanding the metadata structure with fields useful in education, popularization of biodiversity knowledge, and practical nature conservation; • considering physical specimens and multimedia documents collected directly under empirical environmental study in the digitization process; • implementing advanced search and analysis tools, especially a rich set of tools for spatial analysis, in the portal; • integrating the portal with the dedicated mobile application, intended for documenting natural observations directly in the field; • facilitating a space on the portal for user observations documented by means of a mobile application to work on NHCs; • creating an open system for collectors wanting to deposit their digital specimens, documents, or observations in the AMUNATCOLL database.
Acknowledgments: We would like to thank the  Ministry of Funds and Regional Policy in Poland  for the financial support for the  AMUNATCOLL  project (Grant number  POPC. 02. 03. 01-00-0043/18 ). Special thanks go to the associates who supported us during the project implementation:  Magdalena Dylewska ,  Maria Jaraszkiewicz ,  Małgorzata Klimas  and  Dorota Obiegała.
Flow cytometric analysis of adult tissues Cells were centrifuged at 320g for 7 min, resuspended in 4°C FACS Buffer (PBS 1X, 2mM EDTA, 0. 5% BSA), plated in multi-well round-bottom plates and immunolabelled for FACS analysis. After 15 min incubation with purified anti-CD16/32 (FcγRIII/II) diluted 1/50, antibody mixes were added and incubated for 30 min. When appropriate, cells were further incubated with streptavidin conjugates for 20 min. Samples were acquired using a Beckman Coulter Cytoflex S or a BD Fortessa cell analyser. All data was analysed using FlowJo 10. 5. 3 (BD). Cell density was calculated using the following formula: Cells/g of tissue = (.
Library preparation was performed using the ClonTech SMARTer Stranded RNA-SEq Kit V2 for mammalian pico RNA input (Takara) and RNA sequencing was performed using HiSeq 2500 sequencer with 50bp read lengths (Illumina). Differential expression analysis. DESeq2 (v_1. 22. 2)  46  Wald test was used to assess differential expression between groups. The input data consists of the raw count matrix where each row indicates the transcript, each column indicates the sample and each cell indicates the number of reads mapped to the transcript in the sample. After independent DESeq2 filtering  46  , P-values for selected genes were adjusted for multiple comparisons correction using Benjamini-Hochberg correction for FDR at threshold of P<0. 05 and no minimum LFC threshold was applied. Gene set enrichment analyses.
Gene set enrichment analysis (GSEA) (functional scoring method (FSC)  47  ) was conducted using the runGSA() function in piano R package  48  was performed on the ranked list of genes. For this, the result table from DESeq2 was ordered by the -log10(adjusted pvalue) from the Wald statistic test. Mouse Gene Symbol and Entrez IDs were added using the org. Mm. eg. db (v_3. 7. 0) and AnnotationDbi (v_1. 44. 0) Bioconductor Packages, using the Ensembl genes as keys, and duplicated entries identifiers were then collapsed by keeping the one with the lowest adjusted p-value. Gene set collections from the mouse version of the Molecular Signatures Database MSigDB v6 were downloaded from http://bioinf. wehi. edu. au/software/MSigDB/ Visual representation. Heatmap were made using R package pheatmap (v_1.
Sections where then incubated with Ethanol 95% for 5 min, Ethanol 100% for 2x5 min, and mounted in Eukitt mounting medium. Brightfield Images were then acquired with an Olympus VS120 Slide Scanner at a 20X magnification. Quantifications were done using the Fiji open-source software. Images were thresholded using a global thresholding algorithm (MaxEntropy) and the fraction of stained area was quantified. Eight to ten 0,34mm 2 FOV were quantified per animal, in 5 animals per group. For the evaluation of lipid deposition, 12 µm liver sections from WT and TRIF Lps2 mice were stained with Oil Red O (Sigma) as per the manufacturer's instructions. Brightfield Images were acquired with an Olympus VS120 Slide Scanner at a 40X magnification, within 24 hours after the staining. Quantifications were done using the Fiji open-source software.
Acknowledgments We thank  Ana Cumano ,  François Schweisguth ,  Geneviève Millon  and  Molly Ingersoll  for reading the manuscript,  Frédérique Vernel-Pauillac  for the Trif mutant animals and the ageing and longevity  Pasteur transversal program  for scientific support. We thank the  Plateforme de Cytometrie  ( Sophie Novault ) for advice and help with cell sorting, and the  Transcriptome and EpiGenome, BioMics, Center for Innovation and Technological Research of the Institut Pasteur for NGS. This work was supported by recurrent funding from the  Institut Pasteur , the  CNRS, and Revive  ( Investissement d'Avenir ;  ANR-10-LABX-73 ) and by an  ERC   investigator award  ( 2016-StG-715320 ) from the  European Research Council  to E. G. P. E. G. P. also acknowledges financial support from the  Fondation Schlumberger  ( FRM FSER 2017 ) and the  Emergence(s) program  from  Ville de Paris  ( 2016 DAE 190 ).
Despite no marked difference in species richness, we found a higher phylogenetic diversity in proximity to the city, but a higher functional diversity on the more isolated reef. Composition differences between coastal areas were associated with different frequencies of reef fish families. Because of a partial reference database, eDNA only partly matched those detected with UVC, but eDNA surveys nevertheless provided rapid and robust species occurrence responses to contrasting environments. eDNA metabarcoding coupled with functional and phylogenetic diversity assessment can serve the management of coastal habitats under increasing threat from global changes.
, 2018). Environmental DNA (eDNA) metabarcoding is rapidly developing and can now identify species assemblages from water samples containing trace DNA from organisms in the environment  (Pedersen et al. , 2015). When combined with a genetic reference database, eDNA metabarcoding provides an inventory of species composition in aquatic systems that often better recovers elusive and cryptic species of monitoring focus  (Deiner et al. , 2015; Harrison et al. , 2019; Polanco Fernández et al. , 2021). Studies of eDNA on coral reefs have shown a strong ability for biodiversity detection showing capacity to match inventories from traditional surveys  (Polanco Fernández et al. , 2021; Sigsgaard et al. , 2020; West et al. , 2021). Beyond inventories, eDNA could allow rapid quantification of biodiversity and ecosystem quality indices which, in combination with functional or phylogenetic information, may help monitor shifts in ecosystem processes and states  (Holman et al.
| ObiTools filtering analyses for taxonomic assignments and comparison of the two areas We applied a first bioinformatic workflow that optimizes the ability to detect identified taxonomic entities. The sequencing reads were processed to remove errors and analyzed using programs implemented in the ObiTools package (http://metab arcod ing. org/obitools, Boyer et al. , 2016)  following a published protocol  (Valentini et al. , 2016). The forward and reverse reads were assembled using the ILLUMINAPAIREDEND program using a minimum score of 40 and retrieving only joined sequences. The reads were then assigned to each sample using the NGSFILTER software. A separate data set was created for each sample by splitting the original data set in several files using OBISPLIT. After this step, we analyzed each sample individually before merging the taxon list for the final ecological analysis.
Strictly identical sequences were clustered together using OBIUNIQ. Sequences shorter than 20 bp, or with occurrences lower than 10, were excluded using the OBIGREP program. The OBICLEAN program was then run within a PCR product. We discarded all sequences labeled "internal" that correspond most likely to PCR substitutions and indel errors. Taxonomic assignment of the remaining sequences was performed using the program ECOTAG the sequences extracted from the release 142 (standard sequences) of the European Nucleotide Archive (ENA). Taxonomic assignments were corrected as follows to be more conservative: For an identification match >98% identity, we validated a species level, for a 96-98% match, genus level if available and for a 90-96% match, family level if possible. Considering the wrong assignments of a few sequences to the wrong sample due to tag-jumps  (Schnell et al.
| Taxonomic functional and phylogenetic indicators from eDNA Using the fish identification outputs from the ObiTools pipeline, we computed taxonomic, functional and phylogenetic indices of the structure of the fish assemblages for the two coastal areas. We collected functional traits using online databases  (Fishb ase. org; Froese & Pauly, 2021 , Robertson & Van Tassell, 2015). We compiled five traits linked to diverse ecological functions: the minimum and maximum depth (m), the position in the water column divided into six categories ("pelagic", "bathypelagic", "benthopelagic", "demersal", "benthic", and "bathydemersal") indicating habitat, the trophic level and the maximum body size associated with food acquisition, mobility, and predation functions. Sequences attributed to the species were directly associated with the corresponding functional traits.
For sequences assigned at the genus or family level by ObiTools, we randomly selected from the list of the regional fish species, one species belonging to the same genus or family along with its associated traits. The random selection was performed 100 times resulting in 100 traits matrices. For each trait matrix and each coastal area, we computed the community mean of continuous trait values and the proportion for categorical traits repeated across all 100 matrices by using the cmw function of the weimea R package  (Zeleny, 2018). We also computed the standard deviation of those measures. Moreover, we computed 100 distance matrices using Gower's distance, which allows continuous and categorical traits  (Gower, 1971) , that we calculated by using the function funct. dist of the mFD R package  (Magneville et al.
, 2022). We applied a principal coordinates analysis (PCoA) on each of the 100 distance matrices and computed the corresponding multivariate functional spaces. We selected the most appropriate number of axes following the framework proposed by  Maire et al. (2015)  that evaluates the quality of the functional space based on the deviation between the original trait-based distance and the final Euclidean distance. We used the quality. fspaces function from the mFD R package for both the computing of the PCoA, the multivariate functional spaces and their quality evaluation  (Magneville et al. , 2022). From the PCoA, we computed the functional richness (FRic) that represents the volume of functional space defined by the convex envelope of all species in a given community  (Mouillot et al. , 2013; Villeger et al.
, 2017). We quantified the richness dimension by calculating Faith's phylogenetic diversity index (PD) that corresponds to the overall amount of evolutionary history in a sampled community  (Faith, 1992 ) by using the pd. query function of the PhyloMeasures R package  (Tsirogiannis & Sandel, 2015). We computed the divergence facet using two indices, the phylogenetic Mean Pairwise Distance (MPD) corresponding to the average phylogenetic distance among species and the phylogenetic Mean Nearest Taxonomic Distance (MNTD) that measures the average phylogenetic distance among the closest relatives species within a community  (Tucker et al. , 2017). We extracted the tree cophenetic matrix for the MPD et MNTD calculation by using the cophenetic function of the stats R package (R Core Team, 2021). Then, we assessed the regularity facet by calculating the variance of the phylogenetic distance among species (VPD index) and the variance of the phylogenetic distance among the closest relative species within a community (VNTD;  Tucker et al.
, 2017). We produced phylogenetic richness accumulation curves across filtration samples by randomly selecting the samples among all possible permutations, and we measured the PD values. To investigate the relationship between the phylogenetic richness and the considered number of samples, we fitted a generalized additive model. We tested the robustness of the phylogenetic and functional metrics to the random assignment of a representative species to a genus or family detected by eDNA. Using the UVC data set with taxonomic resolution at the species level, we first calculated the mean functional richness (FRic) and the mean phylogenetic diversity (PD) across all the sites (gamma-diversity) with the resolved species-level information. Then, we degraded this data set by successively removing 10% to 90% of the species that we randomly replaced by their genus or their family.
, 2012). We computed the mean and the standard deviation of all the previous indices. All the β-diversity indices were computed using the "Betapart" R package  (Baselga & Orme, 2012). To visually represent the differences in eDNA composition between the sampling stations in Willemstad and Valentijnsbaai, we used a PCoA on the Jaccard distance matrix. We reported the explained deviance of each axis and mapped the ordination values in the geographic space of the PCoA. We further performed a PCoA on the UVC samples which we compared with the eDNA ordination.
| SWARM clustering analyses for MOTU identification We applied a second bioinformatic workflow to cluster sequences into taxonomic units without requiring a complete reference database to estimate richness and MOTUs composition  (Marques et al. , 2020). We used the sequence clustering SWARM algorithms that group multiple variants of sequences into MOTU (Molecular Operational Taxonomic Units;  Mahé et al. , 2014 , Rognes et al. , 2016). Reads were assembled using VSEARCH  (Rognes et al. , 2016)  and then demultiplex and trimmed using CUTADAPT  (Martin, 2011) , and clustering was performed using SWARM  (Mahé et al. , 2014)  with a minimal distance of 1 between each cluster. The clustering algorithm uses sequence similarity and abundance patterns to delineate meaningful entities, by grouping together sequence variants.
Once MOTUs are generated, the most abundant sequence within each cluster is used as a representative sequence for taxonomic assignment. Then, a post-clustering curation algorithm  (LULU, Frøslev et al. , 2017)  was applied to curate the data. The taxonomic assignment was performed using the ECOTAG program against the NCBI database. The taxonomic level of assignments was determined by the result of the ECOTAG program and the percentage of similarity between the sequences in the sample and those in the reference database. We corrected the taxonomic levels by applying the same thresholds as the pipeline using the ObiTools. Cleaning filters were then applied to remove sequences most likely corresponding to errors and non-specific amplifications: (i) removal of amplicons with less than 10 reads per PCR replicate, (ii) removal of the non-specific amplifications (non-fish), (iii) removal of all sequences found in only one PCR in the entire data set and (iv) removal tag-jumps and indexhopping (as described above).
, 2015). The MOTU data set comprises the occurrence of 79 MOTUs in 19 samples. The UVC data set comprises the occurrence of 58 species in 32 samples. For this analysis, we excluded species that occurred in fewer than 5 sampling units and no more than n-2 sampling units to avoid spurious and unidentifiable environmental responses for species with few data  (Ovaskainen & Abrego, 2020). For both the UVC and the MOTU, we also fitted a random effect associated with each sample to ensure latent variables (e. g. , species' associations) are fitted in HMSC  (Ovaskainen & Abrego, 2020). To strictly compare with the eDNA data, we both fitted a UVC model with the same number of samples as eDNA and a second model with all 32 samples. In all models, we used the sampling unit by species matrix as the response variable (i.
e. , the n × n s "Y" of HMSC; see  Ovaskainen et al. , 2017)  propagated with species occurrence or absences (0 or 1). We used a probit regression in all analyses. We included a single fixed effect of the anthropic area as our species by covariate matrix (i. e. , the n × n c "X" of HMSC; see  Ovaskainen et al. , 2017). We estimated a species-specific regression parameter to contrast their occurrences in the two areas. For the MOTU data, we further fitted a transect-level random effect to control for unexplained variation among sampling units (e. g. , 2 × 30 L water filtrations per transect). We used the R package "Hmsc"  (Tikhonov et al. , 2020)  to fit our model assuming default prior distributions  (Ovaskainen & Abrego, 2020). We sampled the prior distribution with four Markov Chain Monte Carlo (MCMC) chains each run for 37,500 interactions of which the first 12,500 were removed as burn-in.
The chains were thinned by 100 to obtain 1000 posterior samples in total. We ensured model convergence by evaluating the potential scale reduction factors (e. g. ,  Gelman & Rubin, 1992). We evaluated the explanatory power of our models for each species by comparing the observed and predicted occurrences using area under receiver-operator curve (AUC;  Pearce & Ferrier, 2000)   and Tjur's R 2 (Tjur, 2012) statistics. Due to the limited number of replicates in our study, we did not expect good predictive (out-of-sample) power and, therefore, only report model explanatory power (within-sample prediction). We evaluated the proportion of MOTUs and species that exhibit positive or negative responses to anthropic areas with 95% credible intervals of coefficients non-overlapping 0, assessed the continuity of these responses across eDNA metabarcoding MOTU and UVC data sets, and we computed the phylogenetic signal of the estimated coefficients for both eDNA and UVC.
| DISCUSS ION A variety of human activities can impact coral reefs directly and indirectly, resulting in their degradation  (Pandolfi et al. , 2003)  and a decline in fish diversity associated with this habitat  (Graham et al. , 2011). In particular, the coral cover of Curaçao has been steadily decreasing over the last decades  (Jackson et al. , 2014)  although to a lesser extent than most other islands in the Caribbean and with variations between different coastal stretches of the island (Waitt Institute, 2017). Here, using eDNA, we showed differences in species functional and phylogenetic compositions between two coastal areas on the Southern side of the island of Curaçao. By comparing two reef stretches under different degrees of anthropogenic pressures, we showed how eDNA combined with species features from associated databases and advanced modeling approaches can deliver ecological indices that can inform ecosystem status.
Management toward the preservation of coral reef ecosystems requires monitoring approaches that can be quickly deployed in the field  (Obura et al. , 2019)  and we demonstrated that eDNA metabarcoding provides as rich fish assemblage information as UVC in terms of taxa samples, but which requires significantly less sampling time and resource in the field. Yet, remaining gaps in the reference database still limit the information provided by eDNA and prevent making an accurate description of species assemblages. In our case, some species recorded by UVC were not recovered with eDNA because we lacked reference information. To exploit the potential of eDNA metabarcoding in species detection, a vast effort is needed to improve the taxonomic coverage of reference databases  (Schenekar et al. , 2020).