dataesr/scientific-paragraphs-categorization huggingface dataset:
Even if we do not explicitly propose a priori regrouping or modification of the dataset, some of those species should be Recovery in the population abundance of sensitive fish species, FC2 -Proportion of large fish (Large Fish Index) or FW3 -Size composition in fish communities  (OSPAR, 2017). We can also note the interest of the data produced to identify certain elements of the remarkable diversity that is all the more appreciable with the improvement in the quality of species determination. In particular, the detection of rare or new species in the study area are valuable data for characterizing regional biodiversity and judging the evolution of continental shelf communities. The strength of this series also lies in the additional data (hydrological, other biological compartments) acquired simultaneously and offering an increasingly complete panorama of the ecosystems of the continental shelves in the fall period.
Torpedo marmorata Torpedo nobiliana Confusion between these 2 species is very likely, only the name T. marmorata was used in the database until 2019. For some part of the time series, the occurrence of Argentina sphyraena in the deeper area is doubtful. Small A. silus individuals may have been confused with A. sphyraena. Confusion with A. thori is unlikely, but confusion between A. imperialis and A. laterna may have existed during the series; the criteria have been refined since 2019. Although described in the study area but rare E. albidus not easily identified; criteria better defined since recent years but remains difficult to sort with very high abundances of the much more frequent E. encrasicolus Labrus bergylta, L. mixtus species rarely caught and possible confusion during the whole time series https://doi.
Acknowledgements We would like to thank all the participants in the Evhoe fishing surveys and in particular the pioneers of the fisheries survey in the bay of Biscay,  A. Maucorps ,  R. Chevalier , J. -C. Quéro ,  A. Laurec , and the successive heads of mission and observation managers who have been on board since 1987 :  Jean-Charles Poulard ,  A. Charuau ,  R. Bellail ,  J. -C. Mahé ,  J. -P. Léauté ,  M. Salaün ,  J. Dimeet ,  L. Pawlowski ,  E. Duhamel ,  N. Caroff ,  J. -J. Rivolaen ,  L. Bouche ,  S. Martin ,  L. Le Ru ,  D. Le Roy  and more generally the staff of the  IFREMER 's research units STH, EMH, HMMN and Marbec We would like to thank all the sailors of GENAVIR, operator of the Thalassa, as well as the " French Oceanographic Fleet " for the technical and organizational support of these fishing surveys.
Data availability The updated EVHOE dataset is provided on the SEANOE platform  (Laffargue et al. 2021 , https://doi. org/10. 17882/80041) which includes automatic duplication to the EMODnet marine data portal (https://www. emodnet-ingestion. eu). The raw collected data were currently banked on an IFREMER's internal general database (Harmonie) collecting in a standardized way the whole data flow of IFREMER fisheries information system (https://sih. ifremer. fr/Donnees). Moreover, the data were annually reported to the ICES database DATRAS (http://www. ices. dk/marine-data/data-portals/Pages/DATRAS. aspx). Data analysis and illustration: P. Laffargue 1 , D. Delaunay Dataset preparation and processing: V. Badts, O. Berthele, A. S. Cornou, P. Laffargue.
This procedure was carried out for five different conditions: 100 and 250 individuals of L. sericata or C. vicina in conspecific groups, along with 125 individuals of both species (250 individuals in total) in the heterospecific group. Six repetitions, which never ran simultaneously, were performed for each condition (i. e. , 5 boxes with 5 different conditions were used per test run). Of these, another Lille study by  Scanvion et al. (2018)  provided the developmental data of L. sericata in conspecific conditions. Experimental developmental data were analysed separately for the two studied species and at two different scales. First, development rate during feeding and post-feeding stages were compared at an individual scale. This allowed a qualitative approach on the developmental rhythm for each individual larva and direct comparison across all the larvae studied.
Reads were filtered and trimmed to remove adapter-derived or low-quality bases using Trimmomatic and checked again with FASTQC. Illumina reads were aligned to Drosophila reference genome (dm6 Ensembl release 70) with Hisat2. Read counts were generated for each annotated gene using HTSeq-Count. RPKM (Reads Per Kilobase of exon per Megabase of library size) values were calculated using Cufflinks. Reads normalization, variance estimation and pair-wise differential expression analysis with multiple testing correction was conducted using the R Bioconductor DESeq2 package. Heatmaps and hierarchical clustering were generated with 'pheatmap' R package. GO enrichment analyses were performed using Genomatix. The RNA-seq data were deposited on GEO under the accession number GSE152416.
Immunostaining, in situ hybridization, and microscopy Staged larvae and pupae were used for dissection from timed embryos. Immunostainings were as described before  (Kulkarni et al. , 2011). Images were captured with Zeiss LSM 510 confocal or Zeiss LSM 880 confocal microscopes and analyzed using Zen black processing software and ImageJ. For in situ hybridization, lymph glands were fixed in 4% paraformaldehyde for 15 min, washed three times for 15 min each in PBST, and pre-incubated for 1 hr at 60˚C in hybridization buffer (HB: 50% Formamide, 2Â SSC, 1 mg/ml Torula RNA, 0. 05 mg/ml Heparin, 2% Roche blocking reagent, 0. 1% CHAPS, 5 mM EDTA, 0. 1% Tween 20). Lymph gland preparations were then incubated overnight at 60˚C with DIG-labeled RNA probe, followed by incubation for 1 hr in HB and for 30 min in 50% HB-50% PBST at 60˚C and three washes 15 min each in PBST.
Image processing and analysis For representative images, projections were made from complete Z-stacks and stitched manually to reconstitute the whole lymph gland. For Phalloidin, medial slices were stitched manually to avoid interference with the cardiac tube. Images were processed uniformly for brightness and contrast using Adobe Photoshop Elements 14. White dotted lines indicate lymph gland lobe boundaries, and yellow asterisks indicate pericardial cells. Complete Z-stacks were considered for all analysis. GFP mean fluorescence intensity (MFI) was measured using ImageJ software. The area to be measured for each lobe was marked with the help of the select tool, followed by intensity measurements. DAPI cell counts analysis was performed using 3D object counter module in ImageJ software. For cell cycle analysis 3D images were reconstructed for lymph gland lobes using complete Z-stacks with the help of IMARIS software.
GFP + , RFP + cells were analyzed using the spots module in IMARIS, and spots co-localization module was used to identify GFP + RFP +. For analysis of markers using 3D images, DAPI positive nuclei were counted by using the spots module in IMARIS. For membrane and cytoplasmic markers, surface module was used for 3D rendering. Distance between the 3D surface and spot was defined, with the help of modules -Find spots closer to surface and Find spots away from surface; number of nuclei that were positive or negative for a particular marker were determined. Quantifications were performed for the primary, secondary, and tertiary lobes individually.
Acknowledgements We thank the  Drosophila Hemocyte Biology  and  European Drosophila Research Conference  (EDRC) meeting participants for valuable discussions and feedback;  Drosophila community  for fly stocks and antibodies;  National Centre for Biological Sciences, Fly Facility  for stocks; JNCASR Imaging facility and our laboratory members for valuable inputs and suggestions. This work was funded by the  Indo-French Centre for the Promotion of Advanced Research (IFCPAR/CEFIPRA)  grant to MI and LW; MI's work was also supported by  SERB  grant,  J C Bose  award project and  Jawaharlal Nehru Centre for Advanced Scientific Research. LW was supported by grants from the  Agence Nationale pour la Recherche and Fondation ARC.
Data availability RNA-seq data has been deposited in GEO under the accession number GSE152416. All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures  1, 3, 4 , 5, 6, 7 and Figure 1-figure supplement 1, 2, Figure 4figure supplement 1, 2, Figure 5-figure supplement 1, 2, Figure 7-figure supplement 1. The following dataset was generated: Author(s) Year Dataset title Dataset URL Database and Identifier Waltzer L, Renaud Y 2021 Transcriptomic analysis of Drosophila larval lymph glands https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE152416 NCBI Gene Expression Omnibus, GSE152416.
Visual Predictive Check (VPC) to assess whether the current model describes the central tendency and variability in the observed data by comparing the observed median and quantiles to the median and quantiles of the simulated dataset is given in Fig. 5. Inspection of VPC indicates that the full model was able to replicate the central tendency and variation in observed data. The individual predicted and observed plasma concentration for the six horses and the three modalities of administration are given in Fig. 6. Visual inspection of the 18 plots indicates that the selected model was able to predict individual disposition curves for the three modalities of administration. Bootstrap estimates of typical values of the primary structural parameters of the model (thetas), the secondary parameters (steady state volume of distribution, mean residence time, half-life and bioavailability) and their associated coefficient of variation as a measure of precision of their estimation are given in Table  1.
Pharmacokinetic model Data analyses were carried out using Phoenix WinNonlin 8. 0 (Pharsight Corporation St Louis, MO, USA). Data sets obtained from the three modalities of administration (IV, eye drops and infusion) were analyzed simultaneously using a Non-Linear Mixed Effect model (NLME). For infusion, plasma concentrations at the first sampling time (collected at about 0. 5 h post-administration) was rather high and variable. According to the clinician having carried out the infusion, it is likely that an initial spurious atropine bolus dose occurred when activating the infusion pump. Thus, these first initial values were discarded from data analysis. A two-compartment structural model (Fig. 10 ) was selected based on the Likelihood Ratio Test (LRT), the Akaike Information Criterion (AIC) and inspection of different diagnostic plots.
We used a Laplacian method that is appropriate when data below limit of quantification (BQL) are handled by the model. A bootstrap method was used to estimate precision parameters. From the model parameters, different secondary parameters were estimated: Bioavailability (from 0 to 1) was estimated from the corresponding typical values (eye drop and infusion) value as: F ¼ EXP ilogit ð Þ 1 þ EXP ilogit ð Þ ð5Þ The terminal slope (β) of the atropine plasma concentration-time course was described as: β ¼ 0:5• Cl d V c þ Cl d V t þ Cl V c - Cl d V c þ Cl d V t þ Cl V c 2 -4 Cl d V t • Cl V c " # 0:5 " # ð6Þ With Cl, Cl d , V c and V t as previously defined. The terminal half-life (t 1/2β ) of the plasma atropine concentration-time course was described as: t 1=2β ¼ ln 2 β ð7Þ The apparent volume of distribution at steady stare (V ss ) and the mean residence time (MRT) for atropine after an IV administration was computed as: V ss ¼ V c þ V t ð8Þ MRT ¼ V ss Cl ð9Þ Using the developed model, plasma concentration profiles corresponding to different atropine dosage regimen scenario were simulated (infusion at 2, 4, 8 μg per 24 h and drops (0.
Within this framework, we used the data of saproxylic beetle communities of three protected areas in the Iberian Peninsula dominated by mature evergreen Quercus species. The objective was two-fold: on the one hand, to assess the relationship between the taxonomic and functional diversity patterns, and on the other, to test the sensitivity of saproxylic beetle functional diversity to possible species loss  (Petchey & Gaston, 2002; Cadotte et al. , 2011; Parisi et al. , 2018). Taxonomic diversity may not necessarily be a reliable surrogate for the functional diversity of saproxylic groups, so both approaches should be used as complementary tools in conservation studies. In fact, since saproxylic beetles are strongly affected by environmental filters relating to habitat and microhabitat (e. g.
within-species variations  (Fountain-Jones et al. , 2015). A stereomicroscope (Leica M205C) and leica Application Suite software version 4. 8 were used for the measurements. After calculating each trait's mean per species, it was standardised using body size, following the study by  Barton et al. (2011). The single phenological trait (the beetles' active period or active months) was calculated as the number of months that the adult beetles of each species were captured; it was thus used as a surrogate for the number of months that the beetles were active  (Moretti et al. , 2017). The trophic trait was the abundance of each species in the feeding guild  (Watts & Mason, 2015; Moretti et al. , 2017; Table 2). Finally, regarding the surrogates for the physiological traits, we measured the parks' air temperature (Table  2 ).
Data analysis To verify the inventory completeness of our sampling, both for each park and for all the parks together, we calculated the sample coverage estimator, which measures the proportion of the total number of individuals in a community that belongs to the species represented in the sample  (Chao & Jost, 2012). We used the 'SPADE' package in r version 3. 4. 3 (Chao & Shen, 2010; Chao et al. , 2016; r Core Team, 2017). To analyse the differences, between the parks, in the patterns of diversity metrics for taxonomic diversity and functional diversity, we calculated two and six metrics for each park, respectively. Due to the data's non-normality, we looked for   (Forsythe, 1987; Ribera & Nilsson, 1995; Cunningham & Murray, 2007; Barton et al. , 2011; Fountain-Jones et al. , 2017; Gillespie et al.
significant differences using the nonparametric Kruskal-Wallis test  (Ostertagová et al. , 2014; De La Riva et al. , 2017). For taxonomic diversity, we calculated species richness (S) and the number of effective species using Hill numbers, or true diversity of order 1 ( 1 D), since it weights each species according to its frequency in the sample  (Jost, 2006; Scheiner, 2012). True diversity was calculated as the exponent of the Shannon index using the 'SPADE' package in r  (Chao & Shen, 2010). For functional diversity, we calculated functional richness, functional richness after removing the effect of the number of species, functional evenness, functional divergence, functional redundancy (after removing the effect of the number of species), and functional rarity. Functional richness is the amount of trait space occupied by the species present within a community  (Mason et al.
e. ≥ or ≤) than the observed value  (Götzenberger et al. , 2016). For the null model, we used the 'randomizeMatrix' function of the 'picante' package in r  (Kembel et al. , 2010; r Core Team, 2017). In order to determine the strength and direction of the relationships between the two taxonomic metrics and the five functional metrics (without considering the functional richness dependent on taxonomic richness), we performed Spearman rank correlation tests  (Vieira et al. , 2013; Morelli et al. , 2018). They were conducted for each protected area separately and also for the three protected areas together (at a regional level). In addition, to uncover how the loss of species (reduction in species richness) may influence the functional richness and redundancy of the communities for each protected area, we applied three species extinction scenarios, the first with a random loss of species.
ACKNOWLEDGEMENTS We are grateful for the support and dedication for determining the saproxylic beetle species by Calmont B. ,  Castro A. , Courtin O. , Lencina J. L. , Liberti G. , Otero J. C. , Rose O. , Soldati F. , Stuben P. , Verdugo A. , Vienna P. P. , Viñolas A. , and  Zapata J. L. In addition, we would like to thank the translation service of ' Centro Superior de Idiomas' of the University of Alicante  for their help with the revision and correction of the language. Financial support was provided by the research Project ' Ministerio de Ciencia e Innovación y fondos EUFEDER'  ( CGL201231669 ), ' Ministerio de economía, industria y competitividad ' ( CGL2016-78181-R ), and ' Generalitat Valenciana'  ( PROMETEO/2013/034  and  AICO 2020/192 ). This research is part of  Diana Pérez-Sánchez PhD  studies, funded by  'Ministerio de Educación, Cultura y Deporte ', through the fellowship  FPU14/03721.
Taxonomic variations in the fungal microbiome after the treatment and relapse phases. Taxonomic analysis of the baseline microflora showed the alpha-diversity of the fungal population to be significantly lower (p ≤ 0. 001) in the healthy scalp (HB: Healthy scalp Baseline) compared to the dandruff scalp (DB: Dandruff scalp Baseline) (Fig. S1a ). A high abundance of M. globosa (p ≤ 0. 0001) was observed in HB (16. 23%) compared to DB (6. 41%) (Fig. S1b-d ). A strikingly high proportion of OTUs corresponding to uncharacterized Malassezia spp. was observed. One of these OTUs belonged to uncultured species of Malassezia (> 95% identity with Uncultured Malassezia, Genbank ID-KC785585. 1) and others belonged to unknown Malassezia species (also at > 95% identity), of which, six OTUs (sequences provided in Supplementary Text) showed an identity of ≥ 85% with M.
The custom ITS1 database prepared previously  4  and Greengenes database v13_5 were used as a reference for fungal and bacterial taxonomic assignment, respectively  52. α-diversity was calculated using the Shannon index metrics and observed species after rarefying from 100 sequences at a step size of 6,000 for V3 as well as for ITS1 amplicons using QIIME v1. 9. Pielou's evenness was calculated to identify the distribution of species with respect to their proportion in each sample groups using the R-package  53. Weighted UniFrac distances were measured for the bacterial population, and not for fungal samples, due to the highly variable nature of ITS1 sequences, which makes them difficult to interpret informative and meaningful phylogenetic information  54, 55. For the taxonomic assignment of de novo OTUs, sequences were aligned against the respective databases using BLAT, and the assignment was performed using Lowest Common Ancestor (LCA) algorithm  52, 56.
The negative control samples showed a high abundance of fungal genus Pachysolen and bacterial genus Actinotalea, which are commonly found in environments such as air and soil, and not associated with the skin or scalp  57, 58. Hence, the OTUs from these genera were excluded from the analysis. Shotgun metagenomic data analysis. Metagenomic reads with 60% bases above Q25 were considered for the analysis  49. For fungal metagenome, the bacterial contaminant reads were removed by alignment against the bacterial reference genomes retrieved from NCBI, and the human contaminant reads were removed using BMTagger  59. The remaining fungal reads from each sample were assembled independently into contigs using SOAPdenovo at a k-mer size of 75 bp. For bacterial metagenome, the human and fungal contaminant reads were removed by aligning the sequences using BLAT against human HG19 assembly and custom fungal genome database, respectively  60.
These fungal genes and genomes were downloaded from Aspergillus database, FungiDB (release-30), Fungal Genome Initiative-Broad Institute, Fungi Ensembl, Saccharomyces Genome Database, Candida Genome Database and NCBI to construct a custom fungal genome database  56, [61] [62] [63] [64] [65]. If any fungal gene sequences were not available in these databases, the gene sequences were extracted from the genome sequences based on the gtf information using SAMtools  66. The high-quality reads were assembled at a k-mer length of 47 (the k-mer length was estimated using kmerGenie 67 ) using SOAPdenovo, and the genes were predicted from the assembled contigs using MetaGeneMark  68, 69. The fungal contigs with ≥ 300 bp length were selected and fungal genes were predicted from scaffolds using AUGUSTUS  1, 68, 70.
In order to increase the coverage of genes from fungal genomes found in our dataset, a total of 2,421,207 CDS were added from 303 fungal genomes from Ensembl fungi database. For bacteria, an additional parameter of identity > 50% with ≥ 60% coverage or aligned length > 300 was used. The relative abundance of each KO was calculated by adding up the abundance of genes mapping to the same KO ID, which was then used to calculate the relative abundance of KO ID in each sample. A similar approach was used to calculate the relative abundance of eggNOGs in each sample. Non-redundant bacterial gene catalogue was generated by using CD-HIT, which consisted of 19,729,749 bacterial genes. A total of 207,763 genes were used as a reference fungal gene set consisting of non-redundant 27,937 genes from assembled metagenomes combined with CDS obtained from Ensembl Fungi genomes database  71.
In total, 587,400 bacterial and 81,395 fungal non-redundant genes were identified in the dataset (see supplementary methods for details on gene quantification). Taxonomic assignment of reads from metagenomic data. The fungal reference genomes were retrieved from National Centre for Biotechnology Information (NCBI). The archaeal and bacterial genomes were retrieved from NCBI and Ensembl database. The metagenomic reads were aligned to the reference fungal and bacterial genomes and the mapped reads were considered for further analysis  72, 73. KEGG assignment of genes. The KEGG v60 was updated by retrieving new sequences for KO IDs from the KEGG server  74. The bacterial and fungal genes were annotated by alignment against KEGG and eggNOG v4. 0 databases  [75] [76] [77] [78]. Protein sequences were assigned to eggNOG and KEGG orthologous groups based on the highest scoring hit containing at least one HSP (highest-scoring segment pair) above 60 bits and E-value ≤ 10 -6 as described previously  4.
The Spearman's Rank Correlation Coefficients with FDR adj. p-value were calculated to correlate clinical parameters with species. Species with ≥ 1% proportion in at least 20 samples were selected for the correlation analysis. Correlations were tested across all the phases to obtain the largest set of values for each parameter. Hierarchical clustering algorithm was used for clustering the highly-correlated pathways and species in the samples. Figures 2b, 3b, 5, 6, S7a and S7b were created using 'ggplot2' package in R version 3. 0  80. Figure  4  was created with 'heatmap2' function using 'gplots' package in R  79. The Figures  S4a  and S4b  were created using the 'boxplot' function from 'graphics' package in R  79.
Acknowledgements Authors would like to thank  Dr. Mukta Sachdev , Medical Director,  MS Clinical Research  for her support during the clinical study. We also acknowledge  Francois Pradier , Director,  L'Oréal Research and Innovation, India  for his support for the study and  Dr. Audrey Gueniche  for her help in interpreting the clinical results. We thank  Subarna Saha ,  Mahesh M Veeranagaiah  and  Prashant Hegde  for participating in preparing the clinical protocol and carrying out the clinical sampling. We thank the  NGS facility  and  HPC facility at IISER Bhopal  for providing the infrastructure to carry out the sequencing experiments and computational analysis, respectively. RS and DBD acknowledge  DST-INSPIRE  and  UGC (Govt. of India) , respectively for providing research fellowships.