dataesr/scientific-paragraphs-categorization huggingface dataset:
Total concentrations of immunoglobulins IgA, IgG and IgM in plasma, and IgA in saliva, were measured by ELISA with commercial kits (Bethyl laboratories Inc. , Bionova, Spain), following the manufacturer's instructions. Plasma samples were diluted 1:10,000, 1:50,000 and 1:500,000 to detect IgA, IgG and IgM, respectively, while saliva samples were diluted 1:100 to detect IgA. Samples, in duplicate, were quantified by interpolating their absorbance from the standard curves constructed with known amounts of each pig immunoglobulin class and corrected for sample dilution. Absorbance was read at 450 nm using an ELISA plate reader (Bio-Rad) and analysed using the Microplate manager 5. 2. 1 software (Bio-Rad). Acute-phase proteins. C-reactive protein (CRP) levels were measured in serum samples diluted 1:3000 by ELISA kit (Abcam Plc.
, Spain) following manufacturer's instructions. Haptoglobin (HP) concentration was measured in undiluted serum samples by colorimetric assay (Tridelta Development Limited, Ireland) following manufacturer's instructions. All samples were quantified in duplicate using standard curves constructed by plotting absorbance against CRP or HP concentration, respectively. Absorbance was read at 450 nm for CRP and 630 nm for HP using an ELISA plate reader (Bio-Rad) and analysed using the Microplate manager 5. 2. 1 software. Gamma-delta T cells (γδ T cells). Peripheral blood mononuclear cells (PBMCs) were separated from heparinised peripheral blood by density-gradient centrifugation with Histopaque-1077 (Sigma, Spain) at 450 g for 30 min. The cells were resuspended in RPMI 1640 medium supplemented with 5% foetal bovine serum (FBS) (Sigma, Spain), 1% Penicillin-Streptomycin (10,000 U/mL-10 mg/mL) and 1% l-Glutamine (200 mM) (Cultek, Spain).
For PBMCs staining, the monoclonal antibody APC Rat Anti-Pig γδ T Lymphocytes (MAC320 clone, BD Pharmigen, Spain) and the APC Rat IgG2a κ isotype control (R35-95 clone, BD Pharmigen, Spain) were used. Briefly, 10 6 PBMCs were stained with the primary-conjugated antibodies (1:100) in 1×PBS-1% FBS for 20 min at 4 °C. After two washes with 1×PBS-1% FBS at 4 °C, cells were resuspended in 1×PBS-1% FBS and analysed by flow cytometry using the MACSQuant Analyzer 10 Flow cytometer (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany) and the MACSQuantify sofware v2. 6 (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany). For automated flow cytometry analysis, files were imported in R environment (v3. 6. 1)  68 with the read. flowSet function implemented in flowCore package (v1. 50. 0)  69. Fluorescence was transformed using arcsinhTransform function.
Doublets were removed using gate_singlet function (flowStats package v3. 42. 0 70 ), margin events using boundaryFilter function (flowCore package v1. 50. 0 69 ). Gatings were then performed using gate_mindensity2 function (openCyto package v1. 22. 2  71  ) on FSC channel to remove FSC low events corresponding to debris, SSC high events corresponding to residual granulocytes and to gate γδ T cells as APC positive. Parameters were adjusted for each day of lab analyses on a representative sample pooling all the data into one using getGlobalFrame function. Phagocytosis assay. Phagocytosis assay was carried out in heparinized whole blood samples incubated with fluorescein (FITC)-labelled opsonized E. coli bacteria by using the Phagotest kit (BD Pharmigen, Spain) and according to the protocol recommended by the manufacturer.
Samples were analysed by flow cytometry using the MACSQuant Analyzer 10 Flow cytometer (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany) and the MACSQuantify sofware v2. 6 (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany). With this assay we analyzed the percentage of cells having performed phagocytosis as well as their mean fluorescence intensity (number of ingested bacteria). Phagocytosis assay analyses were performed in R (v3. 6. 0)  68. Doublets were removed using gate_singlet function (flowStats package v3. 42. 0  70  ), margin events using boundaryFilter function (flowCore package v1. 50. 0 69 ). Gatings were then performed using either gate_mindensity2 or gate_flowClust_2d functions (openCyto package v1. 22. 0  71  ) on propidium iodure (PI) channel to gate blood cells as PIhi and cells having performed phagocytosis as FITC+.
Granulocytes, monocytes, and lymphocytes were gated based on their FSC SSC properties. Parameters were adjusted for each day of lab analyses on a representative sample pooling all the data into one using getGlobalFrame function. The following phagocytosis traits were quantified: percentage of total phagocytic cells (PHAGO_%), percentage of phagocytic cells among granulocytes (GRANU_PHAGO_%), monocytes (MON_PHAGO_%), and lymphocytes (LYM_PHAGO_%), mean fluorescence in FITC among the total phagocytic cells (PHAGO_FITC), and mean fluorescence in FITC among the granulocytes (GRANU_ PHAGO_FITC), monocytes (MON_PHAGO_FITC) and lymphocytes (LYM_PHAGO_FITC) that phagocyte. Nitric oxide. Total concentrations of Nitric Oxide (NO) were measured by colorimetric assay (Thermo Fisher Scientific, Spain) following manufacturer's instructions.
After incubation, extracted samples were centrifuged at 7000g for 2 min and 700 µl of supernatant was transferred to a new 1. 5 ml tube. The supernatant was then placed into a speed vac for 2 h to evaporate the methanol. The dried extracts were stored at -20 °C until analysis. Total concentrations of cortisol (CORT) were measured by ELISA kit (Cusabio Technology LLC. , Bionova, Spain) with dried samples reconstituted with 210 µl of sample diluent. Samples were quantified by reference to standard curves constructed with known concentrations of pig cortisol dilutions of the Standard. Absorbance was read at 450 nm using an ELISA plate reader (Bio-Rad) and analysed using the Microplate manager 5. 2. 1 software (Bio-Rad). Neutrophil to lymphocyte ratio. The neutrophil to lymphocyte ratio (NLR) was calculated as a ratio of NEU and LYM.
A filtered dataset of log-transformed data (most phenotypes) and raw-data (% of phagocytosis) was employed to perform further analyses. Subsequently, pairwise phenotypic correlations (r p ) among all analysed phenotypes were computed after adjusting for significant environmental factors, and a correlation network was built up with Cytoscape 72 , considering those Pearson's correlation coefficients with absolute value ≥ 0. 3. Estimation of genetic parameters. The heritability (h 2 ) , i. e. the proportion of phenotypic variance attributable to additive genetic effects, was estimated for the 30 immune, haematological and stress traits showed in Table  1. Variance components and the corresponding h 2 were estimated from an univariate animal model as follows: where Y is the vector of phenotypic observations of all individuals for the health-related trait (either log- transformed or raw data, depending upon the trait); β is the vector of systematic (fixed) effects on the trait, including effect of sex (2 levels) plus batch effects (6 levels) for most traits but for phagocytosis-related traits, for which the data of laboratory analysis (12 levels, two by batch) was considered instead; X is the corresponding incidence matrix; u is the vector of animal's genetic additive (random) effects on the trait, and Z the correspond- ing incidence matrix; and e is the vector of random residual terms.
The assumed distribution of additive genetic effects was u∼N(0,A σ 2 u ), where A is the numerator relationship matrix computed on the basis of pedigree (1388 individuals, five generations) and σ 2 u is the additive genetic variance; random errors were distributed as e ∼ N(0,I σ 2 e ). Estimation of the model variance components and the corresponding heritability (h 2 = σ 2 u / σ 2 u + σ 2 e ) for each trait was performed by REML using the aireml program included in the BGF90 package  73  ; the standard errors (SE) of the heritability estimates were computed by repeated sampling from their asymptotic normal distribution following  74  , thus obtaining the corresponding confidence intervals at 95% (CI95). Subsequently, pairwise genetic correlations (for each two traits combination) were estimated in a two-traits animal model described as follows in matrix notation: Y = Xβ + Zu + e Y t1 Y t2 = X t1 0 0 X t2 β t1 β t2 + Z t1 0 0 Z t2 u t1 u t2 + e t1 e t2 where Y t1 and Y t2 are the vectors of phenotypic observations for trait 1 and trait 2, respectively; β t1 and β t2 are the vectors of systematic (fixed) effects on each trait previously described, and X t1 and X t2 the correspondent incidence matrices; u t1 and u t2 are the vectors of animal genetic additive effects on trait 1 or trait 2 (random effects), and Z t1 and Z t2 the corresponding incidence matrices; finally e t1 and e t2 are the vectors of residual errors for each trait, and 0 a matrix of zeros, assumed independent.
The (co)variance matrix of random genetic effects was defined as: where σ 2 u1 and σ 2 u2 are the additive genetic variance of traits 1 and 2, respectively, σ u1,u2 is the genetic covariance between the traits, and A is the numerator relationship matrix as defined above. Estimation of the (co)variance components of each pairwise analysis was also performed by REML using the aireml program included in the BGF90 package  73. Genetic correlation between traits were obtained as r g = σ u1,u2 σ u1 σ u2 , and the SE of the genetic correlation estimates were also computed following  74. DNA extraction and SNP genotyping. Genomic DNA was extracted from blood samples using the NucleoSpin Blood (Macherey-Nagel). DNA concentration and purity were measured in a Nanodrop ND-1000 spectrophotometer. The 432 animals were genotyped for 68,516 single nucleotide polymorphisms (SNPs) with the GGP Porcine HD Array (Illumina, San Diego, CA) using the Infinium HD Assay Ultra protocol (Illumina).
The false discovery rate (FDR) method of multiple testing described by Benjamini and Hochberg 77 was used to measure the statistical significance for association studies at genome-wide level with the p. adjust function of R. The significant association threshold was set at FDR ≤ 0. 1. Manhattan plots based on the significance of the associations across the whole genome were generated using the R package qqman  78. Comparative QTL analysis between our results and previous published data was performed by retrieving all pig QTL and association data on SSC11. 1 for health traits from the pigQTL database  79. Gene annotation and SNP functional prediction. Biomart software  80  was used to retrieve gene annotations from the Ensembl Genes 98 Database using the Scrofa11. 1 reference assembly, considering 1 Mb downstream/upstream of around the candidate chromosomal regions.
Furthermore, functional predictions of the significantly associated SNPs were performed with Variant Effect Predictor software  81. For functional categorisation of the annotated genes, data were analyzed through the use of IPA  82  (QIAGEN Inc. , https ://www. qiage nbioi nform atics. com/produ cts/ingen uityp athwa y-analy sis) to obtain gene ontologies (GO), biological functions, gene networks and canonical pathways in which the genes annotated in the associated regions were involved. Orthologous human gene names were retrieved from the Ensembl Genes 98 Database for functional categorisation when a pig gene name was not assigned to the gene stable id. Furthermore, information from Mouse Genome Database  83  and Genecards 84 was used to identify gene functions affecting the analysed phenotypes.
miRNA target prediction and functional annotation. Porcine mRNA 3′UTR sequences from the whole pig genome (assembly 11. 1) were downloaded from Ensembl Gene 100 database  85  , and Seqkit tool  86  was used to search 3′ UTR seed matches with the 7mer seed miRNA sequence. Subsequently, the obtained list of 3′ UTR was used to predict miRNA target using the RNA hybrid software  87  with the following criteria: energy threshold of no more than -25 kcal/mol and perfect match of 2-8 nt in the seed region. Enriched GO terms and pathways of the predicted miRNA target genes was performed with the ClueGO v2.
Acknowledgements The study was funded by grant  AGL2016-75432-R  awarded by the  Spanish Ministry of Economy and Competitivity (MINECO). Maria Ballester  is financially supported by a  Ramon y Cajal contract  ( RYC-2013-12573 ) from the  Spanish Ministry of Economy and Competitiveness. YRC was funded by  Marie Skłodowska-Curie  grant ( P-Sphere ) agreement No  6655919 530  (EU). The authors belong to Consolidated Research Group AGAUR, ref. 2017SGR-1719. We gratefully acknowledge to technical staff from IRTA and  Selección Batallé S. A  for their collaboration in the experimental protocols at farm and slaughterhouse, as well as to  Albert Bensaid  and  Virginia Aragón  for valuable discussions and comments on the study.
This decreased the global dose from 2. 2 to 0. 5 J/cm 2. In this set of experiments we added a third source of light: fluorescent tubes used at the same exposure parameters. This was done in order to have a point of comparison because most of the phototoxicity data found in the literature used fluorescent tubes. The retina was evaluated 22 h after the exposure and a week later in order to see the reversibility or not of the damages produced. Figure  3  shows the results concerning GFAP and Fig. 4  PKC zeta (protein kinase C zeta). In Figure  1. Oxidative stress response after a total dose of 2. 2 J/cm 2. Male Wistar rats aged 7 weeks were exposed to GaN-on-GaN (GoG) LED or conventional white LED (XE) (Xanlite XXX Evolution 5 W) for 9 and 10 h respectively at 1900 lx, receiving the estimated retinal dose of 2.
Acknowledgements Authors express their gratitude to  Laurent Jonet  and  Julia Pardo  for cryosections, to  Cathy Claramonte  and  Gaëlle Choumager  for the animal care and to  Christophe Klein  for his help in confocal microscopy. This work was supported by  ADEME  (grant  UV-LED ), INSERM, CSTB and ENVA. The institutions did not participate in the design of the study, collection of data, analysis and interpretation of data or in writing the manuscript.
Protein-SqD-NPs Interactions The interaction between fluorescent SqD-NPs and mucin glycoproteins was studied via NTA by NanoSight (LM-10, Laser 532 nm) (Malvern, UK). NTA is able to determine the hydrodynamic diameter of NPs. Under random Brownian motions, NPs scatter light from a laser beam. The scattered light can be visualized by the NTA and captured by a video camera, to enable the software to track the movement of the NPs individually. Using the Stoke-Einstein equation, the hydrodynamic diameter of the particles can be determined. To study the protein-SqD-NP interaction, the Brownian motion of fluorescent NPs in water was compared to their movement in water containing non-fluorescent 0. 1% mucin solution (mucin from porcine stomach, type II, Sigma). For the measurements, used dilutions of the NPs, either in 0.
1% mucin solution or water, were 1:100 (aSq-NPs), 1:200 (cSq-NPs), and 1:50 (PEG750Sq-NPs). To detect only the movement of the SqD-NPs, the particles were fluorescently labeled, and the fluorescence mode of the NTA was used. Therefore, SqD-NPs were loaded with 0. 5% Nile red by coprecipitation; a detailed description of the method is in the Preparation of Drug-Loaded SqD-NPs section. As a vehicle control sample, the 0. 1% aqueous mucin solution was studied with and without the fluorescence filter, to show the ability of the filter to exclude optical interferences of the proteins (Supplementary Figure  9 , green line: with filter, gray line: without filter). Videos with a duration of 20-30 s were taken and analyzed by NanoSight Software NTA 3. 3. The results of the particle concentration as a function of particle size are reported as mean ± standard error (SE).
Acknowledgements G. K. is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR)-Projets blancs ;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC) ;  Cancéropôle Ile-de-France ;  Chancelerie des universités de Paris  ( Legs Poix ),  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Project Oncobiome ;  Fondation Carrefour ;  High-end Foreign Expert Program in China  ( GDW20171100085  and  GDW20181100051 ),  Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LeDucq Foundation ; the  LabEx Immuno-Oncology ; the  RHU Torino Lumière ; the  Seerave Foundation ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and the  SIRIC Cancer Research and Personalized Medicine (CARPEM).
Acknowledgments We thank  J. Ferreira  for providing the NMDAR STORM image. This research was supported by the  Centre National de la Recherche Scientifique ,  Agence Nationale de la Recherche  ( L. Groc ,  ANR DopamineHub ,  19-CE16-0003-04 ),  Fondation pour la Recherche Médicale  ( M. Petit-Pedrol ,  SPF201909009269 ),  Human Frontier Science Program  ( L. Groc ,  RGP0019/2016 ),  Conseil Régional d'Aquitaine , and  Labex Bordeaux BRAIN. The authors declare no competing financial interests. Submitted: 9 August 2020 Revised: 19 November 2020 Accepted: 20 November 2020.
SPECIES FACTS: Rose belongs to the Rosaceae family that comprises over 90 genera and 2,500 species, many among which are economically important crops  5. Fruit-producing Rosaceae crops include pear, apple, raspberries, strawberries and stone fruits such as peach/nectarine, apricot, plum, cherry and almond. The rose represents an ideal model plant to study many biological aspects for many of these Rosacea crops, because: (i) Rose shares many agronomic traits with Rosaceae tree crops (ii) Rose is easily propagated by cuttings (iii) Cuttings are able to flower the same year, an advantage over other Rosaceae crops requiring many years of growth before flowering (iv) The availability of omics resources, including transcriptomics database  [6] [7] [8]  and a very high-quality assembled and annotated Rose genome  4.
We describe ASAP (Assemble Species by Automatic Partitioning), a new method to build species partitions from single locus sequence alignments (i. e. barcode datasets). ASAP is efficient enough to split datasets as large 10 4 sequences into putative species in several minutes. Although grounded in evolutionary theory, ASAP is the implementation of a hierarchical clustering algorithm that only uses pairwise genetic distances, avoiding the computational burden of phylogenetic reconstruction. Importantly, ASAP proposes species partitions ranked by a new scoring system that uses no biological prior insight of intraspecific diversity. ASAP is a stand-alone program that can be used either through a graphical web-interface or that can be downloaded and compiled for local usage. We have assessed its power along with three others programs (ABGD, PTP and GMYC) on 10 real COI barcode datasets representing various degrees of challenge (from small and easy cases to large and complicated datasets).
They were designed for multilocus data and are computationally (extremely) demanding. As a consequence, they have been mainly applied to datasets with limited number of sequences and species, and to well-studied groups, for which competing partitions of species have been proposed in the literature; they generally correspond to species complexes, typically in the grey zone  (De Queiroz, 2005). A second category of methods corresponds to exploratory ones, i. e. methods that propose de novo species partitions, typically from a single-locus, DNA-barcoding-like, datasets. Although sometimes criticized because a single gene tree poorly represents the species tree  (Degnan & Rosenberg, 2009; Nichols, 2001) , these methods are widely used, as they are easy to apply on DNA-barcoding datasets, even large, and precisely because they do not necessitate pre-defined species hypotheses.
The most popular ones are GMYC -General Mixed Yule-Coalescent model-  (Pons et al. , 2006) , PTP -Poisson Tree Process-  (Zhang, Kapli, Pavlidis, & Stamatakis, 2013) , both first developed in a maximum likelihood framework, and later extended to a Bayesian framework  (Reid & Carstens, 2012) , and ABGD -Automatic Barcode Gap Discovery-  (Puillandre, Lambert, Brouillet, & Achaz, 2012). GMYC and PTP take as input a phylogenetic tree and estimate rates of branching events to infer which part of the tree more likely follows a speciation model (the deepest part) and which part follows a coalescent model (subtrees of the shallowest part). The species partition is found by maximizing the likelihood of the transition between these two branching rates, GMYC in absolute time (hence the need for an ultrametric tree), PTP in mutational time at different nodes of the tree.
GMYC and PTP first inferred a single transition event between the two rates (speciation vs coalescent) and were later expanded to infer "multiple thresholds", allowing several transitions to occur in different subtrees  (Kapli et al. , 2017; Monaghan et al. , 2009). Contrary to the two previous methods, ABGD uses only pairwise genetic distances (no tree is inferred) and automatically identifies in their distribution the so-called "barcode gap". This gap marks the limit between the smaller intra-specific distances and the larger inter-specific distances. From the gap, a distance threshold is estimated and used to partition the samples into putative species. A coalescent model is used to identify the position of the most likely barcode gap, based on a maximal genetic intraspecific divergence P defined a priori by the user.
Consequently, users must provide a range of P in which ABGD identifies one or several barcode gaps and the method outputs the corresponding species partitions. For a single dataset, ABGD thus eventually proposes several partitions that correspond to different prior values P. In its recursive version, ABGD is applied on each group of the initial partition, and eventually splits them when internal barcode gaps are detected. The relative performances of these three exploratory methods, GMYC, PTP and ABGD, sometimes together with less used methods  (Flot, Couloux, & Tillier, 2010; Ratnasingham & Hebert, 2013)  have been compared in various taxa: mammals  (Derouiche, Vercammen, Bouhadad, & Fernandes, 2017) , amphibians  (Vacher et al. , 2017) , squamates  (Blair & Bryson, 2017) , fishes  (Ramirez et al.
, 2017) , echinoderms  (Boissin, Hoareau, Paulay, & Bruggemann, 2017) , insects  (Lin, Stur, & Ekrem, 2015) , spiders  (Ortiz & Francke, 2016) , crustaceans  (Larson, Castelin, Williams, Olden, & Abbott, 2016) , pycnogonids  (Dömel, Melzer, Harder, Mahon, & Leese, 2017) , rotifers  (Papakostas et al. , 2016) , annelids  (Decaëns et al. , 2016) , molluscs  (Fourdrilis et al. , 2016) , flatworms  (Scarpa et al. , 2017) , nemerts  (Leasi & Norenburg, 2014) , cnidarians  (Arrigoni et al. , 2016) , plants  (Lithanatudom et al. , 2017) , algae  (Zou et al. , 2016 ), lichens (Pino-Bodas, Burgaz, Teuvo, & Stenroos, 2018) , fungi  (Alors, Lumbsch, Divakar, Leavitt, & Crespo, 2016)  and foraminifera  (André et al. , 2014). Although the results obtained with the various methods often vary depending on dataset characteristics (e.