dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-molecule data analysis was performed using the MAT-LAB application POLCAM-SR, which includes tools for localization, filtering, drift correction, and data visualization. The source code and installer are available on GitHub at https://github. com/ezrabru/POLCAM-SR. Diffraction-limited data analysis was performed using custom MATLAB scripts (MATLAB R2022a, Mathworks) and a napari (48) plugin called naparipolcam for processing and visualization of multi-dimensional polarization camera datasets. The source code and installation instructions for naparipolcam are available on GitHub at https://github. com/ezrabru/ napari-polcam. Refer to Supplementary Note. for a detailed description of the image analysis pipelines. SYTOX orange on cover glass. Glass coverslips (VWR collection, 631- 0124) were plasma cleaned for 1 h (argon plasma cleaner, PDC-002, Harrick Plasma.
For each of them, the corresponding high-level GO categories as well as the most represented GO-term are indicated. Four of those clusters are related to "response to stress", and more precisely to "response to wounding". Three other clusters are related to "immune system processes". 6 Among the remaining ones are found biological processes involved in "blood vessel development", "regulation of plasma lipoprotein particle levels", "cholesterol transport", "locomotion", "secretion", "negative regulation of peptidase activity" or more general catabolic processes like "carbohydrate derivative catabolic processes" and "NAD metabolic process". The amino acid sequences of all the proteins identified in the trout CF were blasted against the PhyloFish transcriptomic database  29. For each protein, the main tissue of expression of the corresponding contig was manually checked (Figure  2B , Table  S3 ).
Identification of salmonid specific CF proteins Extended egg viability is a salmonid-specific trait. In order to identify proteins conferring this specific egg conservation property to the salmonids CF, a proteomic comparison of three different salmonid species CF (Rainbow trout, Oncorhynchus mykiss; Atlantic salmon, Salmo salar and brown trout, Salmo trutta) to three non-salmonid teleost species OF (Goldfish, Carassius auratus; Common carp, Cyprinus carpio; and Pikeperch, Sander lucioperca) was performed. Together, 511 proteins were identified in Atlantic salmon CF, 388 in brown trout CF, 876 in goldfish OF, 423 in common carp OF and 392 in pikeperch OF (Table  S1 ). To facilitate inter-species comparisons, all identified proteins were mapped to teleost common ancestors using the Genomicus  30  database in order to identify groups of orthologous proteins.
Sialic acid synthases are responsible for the formation of sugars on glycoproteins in eukaryotic cells and are involved in fertilization, embryogenesis, and differentiation  [31] [32] [33]. The identified Nansa protein has a NeuB domain and a SAF NeuBlike domain. NeuB is the catalytic domain involved in N-acetylneuraminic acid (Neu5Ac) in procaryotes, whereas SAF NeuB-like domain would allow the binding to substrates. SAF NeuB-like domain is part of the superfamily domain SAF which also includes the domains similar to fish antifreeze proteins according to the NCBI conserved domain database  34. According to Phylofish database  29  , the contig corresponding to Nansa is mainly expressed in the ovary (Table  S3 ). Using quantitative RT-PCR we observed that nansa was strongly and predominantly expressed in the ovary, but also in the testis (Figure  5A ).
Identification of candidate CF proteins involved in egg conservation More than a comprehensive proteomic characterization of the rainbow trout CF, the main goal of this study was to identify proteins conferring its exceptional egg preservation ability to salmonid CF. We first used an evolutionary approach to identify proteins specifically enriched in salmonid CF. To our knowledge, this is the first study comparing the proteomic composition of CF/OF in different fish species. The main challenge was to find a way to perform inter species comparisons. To address this issue, all identified proteins were mapped to teleost common ancestors using the Genomicus database that provides syntenysupported orthology assignments. Then, species were separated in 2 groups: salmonid species and non-salmonid species.
Outliers over 3 times the standard deviation were excluded from calculations as technical errors. Unsupervised clustering analysis was performed on transformed qRT-PCR data using complete linkage with correlation distance (Pearson correlation) for drug, treatment, and brain region (Cluster 3. 0 [https://www. encodeproject. org/software/cluster/] and Java TreeView 3. 0 [https://jtreeview. sourceforge. net/]). Results for qRT-PCR analysis and discarded or missing (undetected) values are detailed in Tables S3, S5, S7, and S9. Details for mouse lines, drugs, toxin ablation and optogenetic manipulation, methods of qRT-PCR analysis, immunohistochemistry, and cell counting are available in the Supplement. The list of primers can be found in Table  S1.
ACKNOWLEDGMENTS AND DISCLOSURES This work was supported by the  Région Centre  ( ARD2020   Biomédicament  -Grant No. GPCRAb [to JLM and JAJB]);  ERA-NET Neuron  (to JLM and JAJB); the  FNRS  (Grant Nos. 23587797 ,  33659288 , and  33659296  [to AdKdE]);  WELBIO  (Grant No. 30256053  [to AdKdE]); the  Fondation Simone et Pierre Clerdent 2018 Prize  (to AdKdE); the  Fondation ULB  (to AdKdE); a fellowship from the  FRS-FNRS (Belgium)  (to ADG); the  Institut National de la Santé et de la Recherche Médicale  (to JLM and JAJB); the  Centre National de la Recherche Scientifique  (to JLM and JAJB); the  Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement  (to JLM and JAJB); and the  Université de Tours  (to JLM and JAJB). JLM, AdKdE, and JAJB designed the experiments.
A potential strategy for implementing genomic selection in small populations consists of sharing genomic and phenotypic datasets for developing across-country genomic prediction of breeding values. The quality of these genomic evaluations depends on the genetic relationship among the populations from the countries involved. In this context, this study evaluated the feasibility of an international genomic evaluation for Alpine and Saanen goat populations from 4 countries involved in the European SMARTER project, including results of genetic parameters across countries. The strong genetic correlations estimated between countries for rear udder attachment and fat content are encouraging for future international genomic evaluations.
, 2013; Rupp et al. , 2016; Teissier et al. , 2019; Massender et al. , 2022a,b). However, the size of the reference populations is often small and combining datasets from different countries could increase the prediction reliability of genomic breeding values, especially for lowly heritable or difficult-to-measure traits. Therefore, France, Italy, Switzerland, and Canada, which are part of the H2020 SMARTER Project (www. smarterproject. eu), shared phenotypes, genotypes, and pedigree datasets for both Alpine and Saanen dairy goat breeds to evaluate the feasibility of an acrosscountry genomic evaluation for these 2 goat breeds. A first study has confirmed the exchange of genetic material between these.
, , 2017)). The main objective of this study was to estimate (co)variance components for 2 milk production traits (i. e. , lactation milk yield [MYI] and average milk fat content  [FCO] ), and an udder type trait (i. e. , rear udder attachment  [RUA] ), in the Alpine and Saanen goat breeds from France, Canada, Italy, and Switzerland. These selected traits represent the main breeding goals in the countries involved and different levels of trait heritability. The 4 countries shared MYI; Canada, France, and Italy shared RUA; and Italy and France shared FCO records. This study was exempt from formal institutional animal care and use approval because data were recorded in each country according to normal practices (agronomic or veterinary practice) in commercial farms. Table  1  presents a description of the phenotypic records shared by each country.
Raw phenotypes (instead of de-regressed breeding values) were shared to enable the use of common statistical methods for calculating breeding values based on datasets from different countries. Milk production records of goats born between 2000 and 2010 for France and between 2002 and 2015 for Italy were retained. Phenotypes recorded from 1978 to 2018 and from 1988 to 2019 were available for Canada and Switzerland, respectively. In addition, only data from goats whose sires had at least 6 offspring were selected. The RUA was recorded in Canada, France, and Italy based on a scale from 1 to 9  (Manfredi et al. , 2001). The mean of this score, for the 2 breeds (weighted by the number of phenotypes available for each breed), was close between Canada (5. 60) and Italy (5. 75) and a little weaker in France (4.
, 2022). The 4 countries also shared data from animals genotyped using the Goat SNP50 BeadChip (Illumina Inc. , San Diego, CA). A quality control was performed on these genotypes using the PLINK 1. 9 software  (Purcell et al. , 2007) , which consisted of excluding SNPs with a call rate lower than 0. 90 or a minor allele frequency lower than 0. 01, and animals with call rate lower than 0. 90. The quality control was done within breed and country and the data were subsequently merged. A total of 9,855 animals and 50,578 SNPs were kept for further analyses. The genotypes of French animals represented about 50% of the total amount of genotypes in both breeds. Swiss genotyped animals comprised 21% and 8% of the Alpine and Saanen animals, respectively. These proportions were of similar magnitude for Italian animals (18% of Alpine and 8% of Saanen), but were 13% and 25% for Canadian Alpine and Saanen animals, respectively.
Variance-covariance components were computed via Gibbs sampling, using the gibbs1f90 software  (Misztal et al. , 2002). The analyses were run using either only phenotypic and pedigree information or also incorporating genomic information. A multitrait animal model was fitted for each trait and breed, where each country was considered as a different trait. The systematic effects included in the statistical models resembled the ones used in routine genetic evaluations in each country. For milk production traits, the models included the animal genetic and permanent environment as random effects. The fixed effects for France included an interaction of herd, year, and parity; an interaction effect of age at kidding, parity, year, and region; an interaction effect of month of kidding, parity, year, and region; and an interaction effect of the length of the dry period, parity, year, and region.
The fixed effects for Italy included the interaction effect of herd, year, and season of kidding. For Switzerland, the fitted fixed effects were parity and an interaction effect of year and season, whereas herd was considered as a random effect. The fixed effects for Canada included herd, parity, and year. For RUA, the models contained the random animal genetic effect. The fixed effects included in the models for analyzing the French datasets were the interaction effect of herd, year, and parity; the interaction effect of age at scoring-parity and year; and the interaction effect of lactation stage at the scoring-parity by year. The fixed effects fitted for the Italian datasets included an interaction between DIM and parity, classifier, herd-season of classification, and age as linear and quadratic covariates.
The models for the Canadian datasets also included an interaction of herd, year, and classifier as a random effect, and parity, age, and DIM as fixed effects. Figure  1  shows the average heritability estimate for each trait obtained in bivariate analyses when including genomic information. We obtained several heritability estimates for a specific country as we ran all 2-by-2 country combinations and the estimates were similar regardless of the analyses. The heritability estimates had an average standard error (SE) of 0. 01 with a minimum of 0. 00 and a maximum of 0. 04. An average difference of 0. 05 points was observed between heritability estimates from pedigree and genomic analyses. For France, the heritability estimates for the Alpine and Saanen breeds were close to the estimates routinely used in the genetic evaluations, except for MYI, with a slightly lower heritability of about 0.
Dairy phenotypes (quantity and quality of milk) are collected based on the same protocols with sampling intervals of 4 or 5 wk along the lactations and for all parities. However, phenotypes from Italy are precorrected and the fixed effects included in the genetic models are also different, which could explain these differences in the genetic correlations. It was surprising that the covariances for MYI across countries were not properly estimated, except between France and Italy. This might be due to the weak genetic connection between dairy goat populations from the 4 countries, except between France and Italy; unbalanced amount of data across datasets (France contributed 88% and 85% of the data for the Alpine and Saanen breeds, respectively); and different data collection protocols across the countries with different heritability estimates and routinely genetic evaluation models.
1. 1 Figure 1. Screening of residues located along the extracellular domain (ECD) of m5-HT3AR as potential anchors for fluorescent probes and establishment of four reporter sensors. A. Left panel: visualization of the residues mutated, TAMRA-labelled and tested for variation of current and fluorescence on the Apo Cryo-EM structure of m5-HT3A (from Basak et al. , 2018), in lateral (zoom on the ECD) and top view with two subunits in cartoon representation and the three others represented in surface mode (PDB: 6BE1). Middle panel: representation of the two isomers of the labeling fluorescent probe mixture used in this study, MTS-TAMRA (5(6)carboxytetramethylrhodamine methanethiosulfonate). Right panel: representation of currents evoked by 50-100 µM of 5-HT on the mutants and of the absolute values of variation of fluorescence (difference between the baseline fluorescence and steady-state fluorescence upon 5-HT perfusion) recorded simultaneously.
Introduction Detecting where selection acts in the genome, along with the mechanisms at play, is a major research goal in evolutionary genetics. Many evolutionarily-important traits are complex and polygenic, meaning they are affected by multiple variants present genome-wide  (Falconer and Mackay 1996). Selection acting on these traits will incur allele-frequency changes acting on these variants simultaneously. Recent years have seen major interest in polygenic selection research, aided by the appearance of large genome datasets that can identify many variants with small effect sizes, and new methods developed specifically for detecting it (reviewed by  Pritchard and Di Rienzo (2010) ;  Pritchard et al. (2010) ;  Sella and Barton (2019) ;  Barghi et al. (2020) ;  Fagny and Austerlitz (2021) ).
Other software used Data was processed and plotted in R 3. 6. 1 (R Core Team 2021). We also used the following R packages: cowplot (Wilke 2020); gplots  (Warnes et al. 2020) , plyr  (Wickham 2011) , RColorBrewer  (Neuwirth 2014) , ggplot  (Wickham 2016)  and tidyverse  (Wickham et al. 2019). Output PDFs were combined into single files using pdfjam (https://github. com/DavidFirth/pdfjam).
We then measured cell death following Dox (group#1), Dox+LPS (groups#1-3), Dox+Nig (groups#1, 2, 4), or Dox+LPS+Nig (groups#1-5) in the presence of these inhibitors added before the last treatment for each condition (Figures 6, S7-11). For each variant and for each treatment, we compared cell death in the presence of each of the inhibitors to vehicle only by fitting a linear mixed-effects model to AUC value databases (Figure  7 ). None of (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted September 22, 2023. ; https://doi. org/10. 1101/2023. 09. 22. 558949 doi: bioRxiv preprint the group#5 variants showed reduced sensitivity to any of the inhibitors, confirming the absence of gain-of-function of these variants.
Study approval The study was carried out in the setting of the ENFLAMAI protocol, which was previously  All NLRP3 amino-acids are numbered according to the Infevers database European nomenclature (https://infevers. umai-montpellier. fr/web/). 32  Their pathogenicity was classified by an International consortium of expert geneticists as previously described. 4  Cell culture Lenti-X TM 293T cells (Takara) were cultured at 0. 5-1x10 6 cells/ml in Dulbecco's modified Eagle's Medium (DMEM) GlutaMax TM -I supplemented with 1X penicillin/streptomycin (PS) and 10% Fetal Bovine Serum (FBS) (Gibco). NLRP3-deficient U937 cells have been previously described. U937 and U937 reconstituted NLRP3-deficient cells were cultured at 0. 25-1x10 6 cells/ml in Roswell Park Memorial Institute (RPMI) 1640 GlutaMax TM -I supplemented with 1X PS and 10% FBS (Gibco).
Recherche Médicale (Inserm) , the  Centre National de la Recherche scientifique (CNRS) ,  ENS Lyon ,  Université Claude Bernard Lyon 1 (UCBL1) , the  European Research Council  ( ERC-2013-CoG_616986  to BP), the  Agence Nationale de la Recherche  ( ANR-13-JSV3-0002-01  and  ANR-22-CE15-0032-01  to  BP ,  ANR-18-CE17-0001  " Action " and  ANR-18-CE15-0017 ) and through the " Investissements d'avenir" program  ( Institut Hospitalo-Universitaire Imagine ,  ANR-10-IAHU-01 ;  Recherche Hospitalo-Universitaire ,  ANR-18-RHUS-0010 ), the  Fondation pour la Recherche Médicale  (Equipe FRM  EQU202103012670  to FRL). We thank  Fabio Martinon  ( UNIL, Lausanne, Switzerland ) for the FLAG-NLRP3 constructs, and  Didier Trono  for the pMD2. G and pCMVR8. 74 ( EPFL, Lausanne, Switzerland ). We thank  Isabelle Durieux ,  Hermine Achard  and  Marie Guirguis  ( CIRI, Lyon, France ) for help with subcloning.
( which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted September 22, 2023. ; https://doi. org/10. 1101/2023. 09. 22. 558949 doi: bioRxiv preprint The cell death was observed at 10 time points ranging from 0 to 135 minutes by 15 minutes interval for each of the 34 NLRP3 variants and NLRP3 WT undergoing 5 treatments (i. e. untreated, Dox, Dox+LPS, Dox+Nig and Dox+LPS+Nig), and studied as a proportion of the whole cell population. The database was divided into 50 data subsets (5 treatments * 10 time points). For each subset we fitted a generalized linear mixed model (glmm), beta family with logit link producing 50 results for each variant. In our model, the NLRP3 variant type was set as the explanatory variable with the WT as the reference.
Experiments were independently repeated 2-8 times. IL-1b, IL-18 and TNF concentrations were measured for the 34 NLRP3 variants and NLRP3 WT upon 3 treatments (i. e. Dox, Dox+LPS, Dox+Nig). One dataset per treatment was created, and a linear mixed-effects model (lmm) was realized for each dataset to assess the dependence of the cytokine concentration on the variant. Each dataset (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted September 22, 2023. ; https://doi. org/10. 1101/2023. 09. 22. 558949 doi: bioRxiv preprint comprised 564 observations. The outcome in the model was the log1p (log1p(X)=log(1+X)) of the cytokine concentration given in pg/ml while the right side of the model was the NLRP3 variant.
A linear mixed-effects model (lmm) was fitted for each genotype undergoing a given treatment, in a dataset constructed by the extraction of genotype*treatment from all data. The model response variable was log10(AUC) and the explanatory variable was the inhibitors. As data diagnosis proved a lawful steady batch effect of the independent experiment, lmm modeling was used to correct for this batch effect technical bias in the data. The pvalues obtained were corrected by the Bonferroni approach to take into account results for the 4 treatments (Dox, Dox+LPS, Dox+Nig and Dox+LPS+Nig). For visualisation purpose, cut-offs were fixed to regression coefficients <-0. 1 with p-value<0. 05.
Identification of metabolic gene clusters (MGCs) through genome mining Our goal was to identify metabolic gene clusters using two genome mining tool approaches (PlantClusterFinder and PlantiSMASH), compare the results, and apply criteria to select high confidence MGCs (Fig. 2 ). To acquire a set of high-confidence MGCs, we consider results only from genomes with defined chromosomes. We used E2P2 v4. 0  [20]  and annotated protein sequences to identify enzymes associated with plant metabolic pathways and then used Pathway Tools v. 26  [23]  and the PathoLogic software with default settings to generate metabolic pathway databases. These predicted pathways were manually filtered to only include those present in plants. To predict metabolic gene clusters, we modified a method based on previous studies by Schläpfer et al.
[49]  and Chen et al. [11]  (see Fig. 2 ). The output file from E2P2 was used with Pathway Tools to create species-specific metabolic pathway databases. These databases were then exported and inputted into PlantClusterFinder (PCF) version 1. 3 (https://github. com/carnegie/PlantClusterFinder,  [49] , which identifies groups of metabolic genes located contiguously on the same scaffold using sliding window searching. Default parameters were used for PlantClusterFinder. Finally, we used PfamScan v. 1. 6 (https://github. com/gpertea/gsrc/blob/master/scripts/pfam_scan. pl;  [15]  to determine protein domains for all genes identified by PlantClusterFinder. We also used PlantiSMASH v. 1. 0, a computational pipeline that predicts plant MGCs using specific HMM profiles  [24] , to identify MGCs. We input the genome sequences and annotation files in GFF3 format and applied the dynamic cutoff parameter for analysis.
RNA-Seq data and coexpression analysis To determine if the predicted MGC genes were coexpressed, we constructed coexpression networks using transcriptome data for six Rubiaceae species: Coffea arabica, Coffea canephora, Coffea eugenioides, Gardenia jasminoides, Neolamarckia cadamba and Ophiorrhiza pumila. For this, we used 65 libraries from 4 RNA-Seq experiments available in the European Nucleotide Archives (ENA) and the National Genomics Data Center (NGDC). In Table  2 , we detail conditions of these experiments: For Coffea arabica, Coffea canephora, and Coffea eugenioides, we performed coexpression analysis using RNA-Seq of seeds at three developmental stages  [55]. The study generated 27 RNA-Seq libraries (3 species, 3 replicates, 3 seed stages). Seed stages corresponded to the  following phenological phases: ST5 (seeds from green fruits, peak of reserve deposition and start of endosperm hardening), ST6 (seeds during fruit veraison), and ST7 (seeds from mature cherry fruits with red pericarp).
The RNA-Seq experiments in the Gardenia jasminoides dataset  [39]  used peel and sarcocarp samples from both green and red fruits, collected in triplicates. The study resulted in 12 RNA-Seq libraries, which were grouped into four tissues for coexpression analysis: GFS (Sarcocarps of green fruits), GFP (Peels of green fruits), FS (Sarcocarps of red fruits) and FP (Peels of red fruits). In the Ophiorrhiza pumila dataset  [60] , the expression of two ERF transcription factors was suppressed in hairy roots through RNA interference. The study generated 6 RNA-Seq libraries (3 conditions, 2 replicates each), which were divided into the following codes for coexpression analysis: Gusi (Hairy roots transformed with GUS); ERF1i (Hairy roots with suppressed OpERF1); ERF2i (Hairy roots with suppressed OpERF2).