dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability DNA sequence data have been deposited in the European Genome-Phenome Archive (EGA; https://ega-archive. org) under overarching accession EGAD00001008032. Human DNA sequence data from a previous study 8 are deposited in the EGA (accession EGAD00001004192). Processed mutation calls and other data used in the analyses have been deposited in Zenodo (https://doi. org/10. 5281/zenodo. 5554777). Raw mortality data used to estimate lifespan (Species360 Data Use Approval Number 60633) cannot be publicly shared, as Species360 is the custodian (not the owner) of their members' data. Raw data are accessible through Research Request applications to Species360. Once Species360 grants access to data, they are intended only for and restricted to use in the project they were approved for and for a single publication.
Approximately 7 3 10 5 CD56 bright NK cells were sorted from each HD. Cytofluorimetric data were analyzed with Flow Jo (version 10. 6. 0; BD Biosciences). Cell lines and cytotoxicity assays K562 (a human erythroleukemic cell line) were grown in suspension in 10% FBS-supplemented RPMI 1640, and A549 (a human lung adenocarcinoma cell line) were grown in adhesion in 10% FBS (Gibco)-supplemented Dulbecco modified Eagle medium. For NK cell cytotoxicity assay against K562, target cells were labeled with Green Cell Tracker (Invitrogen, Thermo Fisher Scientific) according to the manufacturer's instructions and 10 4 cells were mixed at varying ratios with NK cells. After 4 hours, cells were stained with 5 mg/mL propidium iodide (Sigma Aldrich) and analyzed by flow cytometry. Specific lysis was calculated as the percentage of propidium iodide 1 cells among total Green Tracker 1 cells, minus the background level (as determined by target cells incubated without effectors).
RNA isolation, library construction, sequencing, and analysis Total RNA was prepared from freshly isolated NK cells, T cells, monocytes, cultured NK cells, or sorted and cultured CD56 bright NK cells with an RNeasy Plus Micro Kit (Qiagen, Hilden, Germany). For RNA-sequencing of sorted CD56 bright NK cells, 3 biological replicates were generated for each condition (Dex and control). The DNA libraries were generated with the TruSeq stranded mRNA prep kit (Illumina, San Diego, Calif), and RNA-sequencing was performed with a NextSeq 500 (Illumina; paired-end reads 2 3 75 with 30 mol/L reads per sample). The fastq files were assessed with the fastqc program (http://www. bioinformatics. babraham. ac. uk/projects/fastqc) and cutadapt (https://journal. embnet. org/index. php/ embnetjournal/article/view/200) was used to remove the adapter sequence (if present) and the very short reads (reads length <20).
We also measured PDCD1 expression in freshly isolated T cells and monocytes as positive and negative controls, respectively. While PDCD1 transcript was undetectable in monocytes, it was measurable in unstimulated NK cells, confirming that it is expressed in steady-state conditions (Fig  5, A ). IL-12-, IL-15-, IL-18-, and Dex-stimulation induced an increase of PDCD1 transcript in NK cells after 4 days to a level that did not further increase at 6 days, and which was around 10-fold lower than that in T cells (Fig  5, A ). Similar results were obtained using either GAPDH or ACTB as housekeeping gene (data not shown). Analyzing by Western blot PD-1 expression, we found that the total PD-1 amount was increased on the combined stimulation of IL-12, IL-15, IL-18, and Dex already at 4 days and then further at 6 days, to a level almost similar to that of freshly isolated T cells (Fig 5  , B ).
at contrasted altitudes and temperatures, are widely studied, but data contributing to their molecular phenotyping are still scarce. A global quantitative proteomics approach using isobaric stable isotope labeling (Tandem Mass Tags, TMT) was performed on Ws and Col-0. Plants have been hydroponically grown at 16 h/8 h (light/dark cycle) at 23 • C day/19 • C night for three weeks. A TMT labeling of the proteins extracted from their shoots has been performed and showed a differential pattern of protein abundance between them. These results have allowed identifying several proteins families possibly involved in the differential responses observed for Ws and Col-0 during plant development and upon environmental changes. In particular, Ws and Col-0 mainly differ in photosynthesis, cell wall-related proteins, plant defense/stress, ROS scavenging enzymes/redox homeostasis and DNA/RNA binding/transcription/translation/protein folding.
In addition, we only validated the proteins that were present in the three biological and technical replicates. In cases of redundancy, shared peptides were assigned to the protein sequence with the most matching peptides; thus, adhering to principles of parsimony. The UniProt as well as the ProtAnnDB databases were used to annotate the proteins (https://www. uniprot. org/, http:// www. polebio. lrsv. ups-tlse. fr/ProtAnnDB/index. php)  [21]. ProtAn-nDB gives access to multiple bioinformatic programs to predict the sub-cellular localization of proteins and functional domains. The DAPs were identified based on t test with p-value less than 0. 05 and with a fold change >1. 7 or <0. 6. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://www. proteomexchange.
org/) via the PRIDE  [22]  partner repository with the dataset identifier PXD022441 an https://doi. org/10. 6019/PXD022441. Altogether, 6383 non-redundant proteins were identified (Table  S1 ): 3307 could be quantified in Col-0 and Ws and 265 were DAPs between Ws and Col-0 (Figure  2A ). Among them, only 30 proteins were over-accumulated in Ws as compared to Col-0, whereas 235 were under-accumulated (Figure  2B ). The DAPs were distributed into 14 functional categories according to their functions or putative functions (Figure  3  and Tables  S2  and S3 ). The number of functional categories for the over-accumulated proteins was smaller (11 DAPs) than for under-accumulated proteins (14 DAPs). Thus, proteins involved in protein degradation, cytoskeleton, and as yet unknown function, were only observed among the under-accumulated proteins.
The protein families are assigned based on the information available in the Uniprot and Gene Ontology databases and reclassified according to literature information to the latitude of origin  [25]. Differences observed for photosynthesis DAPs between Col-0 and Ws (Tables  S1  and S2 ) could also be explained by the differences between their developmental stages, the Ws rosettes were smaller than those of Col-0 and Ws plants were starting to develop their floral stem  [3]. Cell-wall related proteins also exhibited great differences between Ws and Col-0 (Figure  3  and Tables  S2  and S3 ). These results can be explained by the fact that Col-0 and Ws initially grew at high and low temperature, respectively. Cell wall plays a major role to modify the cell and plant shapes during development  [26]  and in response to environmental changes  [27].
The isolation width for the frontal cortex samples was set to 0. 7 m/z with no offset. The top 10 fragment ions for each peptide MS2 were notched out with an isolation width of 2 m/z and co-fragmented to produce MS3 scans analyzed in the MS at a nominal resolution of 50,000 after higher-energy collision dissociation (HCD) fragmentation at a normalized collision energy of 65%. Raw data files from the Orbitrap Fusion were processed using Proteome Discoverer (version 2. 4). Protein identification was achieved by comparing the MS data to the TAIR database (www. arabidopsis. com). Parameters were specified as follows: trypsin enzyme, two miscleavages allowed, minimum peptide length of six amino acids, TMT tags on lysine residues and peptide N-termini (+229. 162932 Da), carbamidomethylation of cysteine residues (+57.
ACKNOWLEDGMENTS M. L. , M. I. , C. R. and K. P. are thankful the  CRRBM (Centre de Ressources Régionales en Biologie Moléculaire, Amiens, France ) for using plant growth chamber and the  French Ministry of Research  for the funding of M. L. PhD work. E. J. and H. S. C. wish to thank the  CNRS  and the  Paul Sabatier-Toulouse 3 University  for supporting their work. The publication received financial support from the  University of Picardie Jules Verne (UPJV).
2019 , Dallas and Santini 2020 , Holt 2020). If these factors dominate over macro-environmental constraints on abundance, then abundance will be poorly predicted using a species distribution modelling approach. Additionally, it is not clear whether there is always a strong linear link between predicted habitat suitability, or occurrence probability, and species local abundance  (Vanderwal et al. 2009, Dallas and Hastings 2018). If indirectly predicting abundance from habitat suitability is ineffective, this demands us to explore more directly predicting abundance using species distribution models. At present, the expected predictive power when modelling abundance in relation to environmental conditions is poorly understood and not quantitatively reviewed over large datasets and a varied set of modelling frameworks.
More samples generally improve species distribution model performance by being less geographically and environmentally biased  (Wisz et al. 2008 ) and should similarly improve abundance model performance  (Yañez-Arenas et al. 2014 ). Yet, these effects have not been tested. Here, we aim to provide practical guidance on applying statistical approaches to predict species abundance and identify factors most affecting predictive performance. We compare 68 abundance-based species distribution models fitted for two standardised abundance datasets containing more than 800 marine and terrestrial vertebrate species and over 800 000 abundance observations. We test model interpolative (within-sample) and extrapolative (out-ofsample) performance. We ask how statistical framework and model complexity, and species and data characteristics, affect.
Spatial abundance data We obtained standardised estimates of species abundance across large regions for birds and shallow-water reef fishes from the Breeding Bird Survey of the USA (BBS) and Reef Life Survey (RLS), respectively (for detailed sampling schemes, see  Pardieck et al. 2019  for birds and Edgar and Stuart-Smith 2014 for fishes). For birds, abundance data comprise of 3-min counts of individuals sighted and heard within a 400-m stop radius along a transect of 50 stops. We summed bird species abundance across 50 stops within a sampled year and meanaveraged abundances for a given species in a repeated site across the years 2014-2018. We aggregated abundances across years to better generalise our results to the structure of most abundance datasets, whereby yearly values across broad geographic regions are unlikely to be available (see the Supporting information for exploration of this assumption).
We filtered out all samples that did not meet BBS-established weather, date, time, route completion, randomised sampling and sampling protocol criteria (i. e. using BBS data with a run type of 1). For fishes, abundance data are counts of individuals sighted along 50-m-long underwater transects (summed across 2 × 5-m-wide blocks on either side of the transect line). We meanaveraged RLS abundance estimates across multiple transects within sites, with sites defined as sets of transects <200 m apart  (Edgar and Stuart-Smith 2014, Cresswell et al. 2017 ). As such, where sites are surveyed in multiple years, abundances were mean-averaged. We filtered sites geographically between 3°S to 50°S and 110°E to 165°E to select for Australian and Indo-Pacific survey locations where sampling effort was most intensive and comprehensive in the RLS dataset.
For both BBS and RLS datasets, we removed species without full scientific names and fewer than 50 abundance records. We required species absences for two-stage models and abundance-absence models. We generated absences for each species by taking observations where species were present and finding all observations within a 1000-km buffer where species were not present. A lack of observed presence is not necessarily a 'true absence' but instead suggests species were undetectable with a reasonable sampling effort (Guillera-Arroita 2017). We analysed a total of 264 474 observations of 385 species in 3890 sites for birds in the BBS dataset and 567 669 observations of 495 species in 2137 sites for reef fishes in the RLS dataset.
Covariates We matched site locations to gridded environmental variables representing climate, biogeochemistry, land-use, depth, habitat area and human populations, retaining only variables with expected a priori relationships with abundance (see the Supporting information for details). Because of the high number of similar climate-related variables, and to avoid multicollinearity in these, we first applied robust principal component analysis (PCA) using package pcaMethods (ver. 1. 76. 0; Stacklies et al. 2007 ) which is shown to be a good approach to reduce multicollinearity in species distribution models (SDM)  (Cruz-Cárdenas et al. 2014 , De Marco and Nóbrega 2018 , Osorio-Olvera et al. 2020). Furthermore, we focused on predictive power to ensure our results were more robust to potential multicollinearity.
We ran a separate robust PCA on 19 variables characterising climates across the bird survey locations (bio1-bio19) and on 15 variables characterising climatic and biogeochemical properties across the fish survey locations (mean, minimum and maximum of pH, salinity, chl-a, net primary productivity, degree heating weeks; indicated in the Supporting information). For each dataset, we retained three principal components, explaining 87. 8% and 77. 8% variation, respectively, and used these principal component scores as predictor variables to summarise the dominant climate and biogeochemical regimes of the data in each set of models (three PCA variables for birds and three PCA variables for fishes; Supporting information). In addition to the climatological variables, we also included additional environmental variables as predictors in our model that we expected to act independently.
Analytical design We analysed a large diversity of species abundance models that spanned a gradient in model complexity and different formulations of abundance data. Further, we assessed model performance for interpolation and extrapolation of crossvalidation scenarios (Fig. 1 ). Given that data requirements are a major challenge in fitting species abundance models, we chose species-level statistical models that were suitable for our goal of comparing predictive performance (i. e. not mechanistic, hierarchical or multispecies/joint/multivariate SDMs). In total, we fitted and evaluated 68 types of species abundance model (24 model frameworks by 3 response variable (abundance) formulations, less 4 models of zero-inflation that are not valid for abundance-only models = 68 models; see Supporting information for full model list).
Species and data characteristics We tested how characteristics of species abundance, frequency and data availability affected model performance. To explain variation in model performance among species, we calculated 1) the mean abundance of species when present; 2) the proportion of presence compared to absence records (within 1000 km of presence records) in the observational data (% occupied sites) and 3) the total number of presence records per species (overall observation number). Although the frequency of occurrence and the total number of presences are collinear in bird and fish datasets (rho = 0. 87, rho = 0. 67, respectively), we included both because unbiased estimates of coefficients are achieved through multiple regression  (Morrissey and Ruxton 2018). We log10 transformed and standardised predictor variables to have unit variance and removed outliers (points > 2 SD from the mean) from the response variables.
Next, we fitted multiple regressions that explained how the model evaluation metrics depended on our three measures of species characteristics. For simplicity, we present these results using D Spearman due to the high number of comparisons and the importance of model discrimination highlighted above. We first fitted a full model, including three two-way interactions between pairs of predictors. We performed backwards stepwise model selection and selected the model with the lowest Akakike information criterion (AIC) score using the R package MuMIn  (Burnham and Anderson 2002, Barton 2017). We plotted marginal effects by predicting model effects for a given variable across the mean value of all other model covariates. We fitted these models using phylogenetic generalised least squares using the R package caper using maximum likelihood to estimate Pagel's λ (Blomberg and Symonds 2014,  Orme et al.
50-0. 56) and P dispersion (0. 44-0. 51 to 1. 10); however, performance decreased when indicated by D intercept (1. 7-2. 2 to 5. 6-5. 9) and A mae (0. 64-0. 69 to 0. 88-0. 94; see full results in the Supporting information). Table  2. Summary of evaluation metrics of model performance for most discriminatory models comparing within-and out-of-sample crossvalidations, median and interquartile range (IQR) for all species within datasets are presented. A mae is the proportional error of estimated mean abundance compared to observed mean abundance having a target value of 0. D Pearson and D Spearman are correlation coefficients having a target value of 1. D intercept is the number of individuals predicted from a linear regression between observed and predicted at 0 observed individuals. D slope is the slope of this regression having a target value of 1.
Species and data characteristics The variation in model performance explained by species and data characteristics varied among performance metrics and was higher in general for within-sample (R 2 = 0. 04-0. 44) compared to out-of-sample cross-validations (R 2 = 0. 01-0. 33; Supporting information). All six evaluation metrics were affected by species or data characteristics in both birds and fishes (Supporting information). P intercept had the most variation explained by species and data characteristics in both birds and fishes (R 2 of 0. 42-0. 44). We present the example metric D Spearman , which had an R 2 between 0. 16 and 0. 33. The effects of species and data characteristics on D Spearman were highly consistent across within-and out-of-sample predictions and across both datasets (Fig. 5 , Supporting information).
Effect of species and data characteristics on D Spearman for Breeding Bird Survey (a-c) and Reef Life Survey (d-f ). Plots display marginal effects from multiple regressions fitted using phylogenetic generalised least squares for within-sample cross-validations. Lines represent mean predicted values. Shaded areas show uncertainty as mean ± (SE × 1. 96) of coefficient values. All effects are significant at an alpha of 0. 05, and interaction terms are only shown when significant. Full statistical results across all metrics, datasets and cross-validations are displayed in the Supporting information. See the Supporting information for effect of species and data characteristics on D Spearman in out-of-sample predictions. although many challenges remain. Here, we discuss our relative success and challenges in modelling abundance to better guide future applications.
2014 , Maclean et al. 2015 , Woods et al. 2015). Our sensitivity analysis indicates improved model fit with improved data resolution for some species, but not all, when using just one year of BBS data linked to a finer temporal resolution of climate data (Supporting information). This finding indicates species-specific behaviour (migratory versus non-migratory), mobility (sedentary or mobile, home-range size), life-cycle (hibernators versus year-round activity) and environmental niche characteristics (breadth, plasticity) could contribute to the resolution and windows of microclimatic data required to accurately estimate local abundances and occurrence  (Bennie et al. 2014 , Lembrechts et al. 2019 ). An additional problem, not present in occurrence-based models, is that the probability of sampling a system in an extreme abundance state is higher with more samples, leading to outlier points (i.
Acknowledgements -We thank the many  Reef Life Survey (RLS)  divers who participated in data collection and provided ongoing expertise and commitment to the program. We thank the  North American Breeding Bird Survey Dataset  for providing access to the data and the thousands of participants who annually perform and coordinate the survey. CW was supported by the  BiodivERsA grant Reef-Futures  ( SNF_184118 ). JT acknowledges  Research Council of Norway-funded project BiodivERsA (Reef-Futures , no. 295340 ). WWLC acknowledges funding support from the  Natural Sciences and Engineering Research Council of Canada  for the  BiodivERsA  project  Reef-Futures. Reef Life Survey uses the NCRIS-enabled Integrated Marine Observing System (IMOS) infrastructure for database support and storage , with support from  Antonia Cooper  and  Elizabeth Oh.
#62248). The slides were then mounted with Prolong Glass antifade reagent (Invitrogen, cat. #P36980 and scanned with AxioScan (Zeiss). Colocalization between FH and CK was analyzed using HALO Image Analysis software (Indica Labs). Patient stratification. The antibody Ox24 (which was produced for this study using a hybridoma from ATCC, a kind gift from Pierre Fabre Research Institute) was selected for FH staining. The specificity of Ox24 for FH was validated by staining of liver sections (Geneticist) and inhibition of the signal by preincubation with purified FH (Complement Technology, cat. #A317) (Supplementary Figure  S1A ). Since Ox24 recognizes also Factor H like 1 (FHL-1), a splice variant of FH containing the first 7 domains, the presence of full-length FH in ccRCC tumor sections was validated by additional FH antibodies, the Nterminal-specific antibody Quidel 2 (Quidel, cat.
6 µm sections of frozen mouse tumor tissues were fixed with cold acetone for 8 minutes and blocked with 5% BSA-TBS for 30 minutes. Tumor sections were then stained with a rabbit polyclonal anti-CD31 (Abcam, cat. #ab124432) or a goat polyclonal antiserum specific for FH (Quidel, cat. #A312) as primary antibodies and then goat anti-rabbit coupled to AF647 (Thermo Fisher Scientific, cat. #A-21245) or donkey anti-goat coupled to AF647 (Thermo Fisher Scientific, cat. #A-21447) as secondary antibodies. The nuclei were stained with DAPI. The slides were then mounted with Prolong Glass antifade reagent and scanned with AxioScan (Zeiss). Quantification of the FH or CD31 staining was analyzed using HALO Image Analysis software (Indica Labs).
RNA-seq The RNA of 3 independent biological replicates for each condition (siFH vs siC) was used for the RNAseq study. The libraries were prepared with NEBNext Ultra II Directional RNA Library Prep Kit (New England BioLab, cat. #E7760) according to the manufacturer's protocol: polyA purification using poly-T oligo attached magnetic beads, followed by 300-bp fragmentation with divalent cations under elevated temperature, synthesis of double-stranded cDNA and amplification by PCR. Sequencing was carried out on a paired-end 75 bp Illumina HiSeq 4000 instrument. The quality of FASTQ files was verified using FASTQC and then aligned to a reference genome using STAR tool. Once the data were mapped on the genome, HTSeqCount was used to count and assign reads to a given exon and generate a count table.
Before differential analysis, non-expressed genes with a count equal to 0 were removed. The differential expression analysis between siFH and siC was performed using R software and the DESeq2 package  [29]. Results were considered statistically significant at an adjusted p-value of < 0. 05. The top 50 most significantly up-or down-regulated genes were sorted by the adjusted p-value; volcano plots were produced, as was a heatmap using heatmap. 2 and EnhancedVolcano packages in R. Gene Ontology enrichment analysis was performed with WEV-based Gene SeT AnaLysis Toolkit (WebGestalt) with the GO biological process category. The raw data are available in GEO database under the accession number PRJNA720902.