dataesr/scientific-paragraphs-categorization huggingface dataset:
Rainfall Comparison Metric (RCM) RCM on the other hand is a more practical metric and is computed based on the highest rainfall value present in the dataset and in each simulation member: RCM = 1 3 3 j=1 max[R Λ(obs(j)) ] -1 10 10 k=1 max[R Λ(sim(k),j) ] max[R Λ(obs(j)) ] (12) where Λ(obs(j)) = L obs(j) δ(j) ; Λ(sim(k), j) = L sim(k) δ(j) ; δ(j) =1 day,1 hour,6 minutes for j = 1, 2, 3, the indices j, k have the same meaning as in MCM. Lower values of RCM imply that the extreme behaviour of simulations are closer to that of the observed data. But RCM is sensitively dependent on scale and P ,D,T. Therefore, as shown in Figure. 9 the RCM values are larger for cases where P D is larger. This might be due to the fact that the datasets used (since they are of shorter lengths) are not actually representative of these specific P ,D,T values that correspond to rainfall events that are more extreme (since probability of observing rarer events is higher in larger datasets).
4 shows the UM parameters estimated using the 9 different datasets, while Fig. 4 shows the DTM based estimation procedure. The parameters for the first scaling regime and second scaling regimes are denoted by the subscripts 1 and 2 respectively. Although 3 different scaling breaks and 6 different pairs of α,C 1 values are empirically estimated (3 pairs for each scaling regime) for each region, for simulating a reference rainfall 5 https://doi. org/10. 5194/hess-2021-580 Preprint. Discussion started: 22 November 2021 c Author(s) 2021. CC BY 4. 0 License. scenario that corresponds to rainfall observed in the corresponding region only 1 scaling break and 2 pairs of α,C 1 values(1 pair for each scaling regime) are necessary (since these values are not too dependent on the dataset used, this choice is justified).
, 2011)  using T 1 transgenic seeds selected by fluorescence (FAST;  Shimada et al. , 2010). Red light imaging of cell death (Landeo  Villanueva et al. , 2021)  was conducted using a Fusion FX system (Vilber Lourmat St e, Coll egien, France). Intensity of cell death reactions was measured using IMAGEJ, and data were normalized to a 0-1 range.
Acknowledgements This work was funded by  GRC  grant  STU 642-1/1  ( Deutsche Forschungsgemeinschaft, DFG)  to JS. Furthermore, JS is grateful for support through the  Leibniz price  from the  DFG  and the  Alfried Krupp von Bohlen und Halbach Stiftung , awarded to  Ulla Bonas. DL and JEP acknowledge support from The  Max-Planck Society and Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )  SFB-1403-414786233. SLV was financed by the  Peruvian Council for Science, Technology and Technological Innovation (CONCYTEC)  and its executive unit  FONDECYT. We are grateful to  Bianca Rosinsky  for taking care of plant growth facilities and growing plants. We thank  Bart Thomma  for providing Avr4/Cf4 and Avr9/Cf9 expression constructs and  Sebastian Schornack  for discussion of BAK1 orthologs.
Data availability The data that support the findings of this study are available within the article and Supporting Information. ALPHAFOLD2 models were deposited in ModelArchive (www. modelarchive. org) with identifiers ma-k5om5 (AtEDS1-AtSAG101), ma-ewx53 (AtEDS1-AtSAG101-AtNRG1) and ma-cf7k5 (SlEDS1-SlSAG101).
Leucocytes were further purified through Ficoll-Paque gradient. CD8 + cells were enriched using BD IMag Mouse CD8 + T Lymphocyte Enrichment Set (BD Bioscience, 558471), which employed a negative selection method that pulled down CD8 -immune cells with magnetic beads conjugated with an antibody cocktail, followed by stimulation with phorbol 12-myristate 13-acetate (100 ng/mL, Sigma) and ionomycin (1 μg/mL, Sigma) for 3 hours. RNA extraction was performed in both the bead and flowthrough (CD8 + T cell-enriched) fractions to assess the purification efficiency and the expression of ISGs. RNA-Seq and data analysis. RNA-Seq was performed on RNA from 4 cell culture replicates per treatment. Sequencing and raw data processing were conducted at the IMB Genomics Core and IMB Bioinformatics Service Core at the Academia Sinica (Taipei, Taiwan).
Briefly, samples were quantified with Ribogreen (Life Technologies) and RNA integrity was checked with a Bioanalyzer 2100 (Agilent). (RIN > 8; OD 260/280; and OD 260/230 > 1. 8) RNA libraries were prepared with the TruSeq Stranded mRNA Library Preparation Kit (Illumina) according to the manufacturer's instructions using 2 μg of RNA per sample. Sequencing was performed using an Illumina NextSeq 500 High Output Kit (75 cycles, 400 million total reads) on an Illumina NextSeq 500 instrument. Raw data was processed using the Qiagen CLC Genomics Workbench (v. 10. 1. 1). Raw sequencing reads were trimmed by removing adapter sequences, low-quality sequences (Phred quality score of < 20), and sequences with lengths greater than 25 bp. Quality control for individual samples were examined. Sequencing reads were mapped to the mouse genome assembly (mm 10) from the University of California, Santa Cruz with the following parameters: mismatches = 2, minimum fraction length = 0.
9, minimum fraction similarity = 0. 9, and maximum hits per read = 5. Normalization and calculation of expression values were performed using the Differential Gene Expression for RNA-Seq Tool in the Qiagen CLC Genomics Workbench v10. Normalization of gene expression was based on transcripts per kilobase million (RPKM). Statistical analysis of differential gene expression was calculated by generalized linear model implemented in the EdgeR package in R, accounting for differences in library size between samples  (30). A significance cutoff for differentially expressed genes of FDR < 0. 001 was used to determine differentially expressed genes. RNA-Seq data have been deposited in the Gene Expression Omnibus database under the accession number GSE169630. Gene Ontology (GO) analyses were performed with DAVID (https://david.
Acknowledgments This work was supported by  NIH  grants  R01DK064750  and  R01DK113791  (to CHL) and funds  105-2917-I-564-089  from the  Ministry of Science and Technology, Taiwan, ROC  (to YHL). The authors thank  Gokhan Hotamisligil  of the  Sabri Ulker Center  for assistance in the confocal microscopy; the  IMB Genomics Core and Bioinformatics Service Core of the Academia Sinica  for RNA sequencing/data analysis; and  Ryan K. Alexander  for generation of reagents not included in the final version of the manuscript. Address correspondence to:  Chih-Hao Lee ,  Department of Molecular Metabolism, Harvard T. H. Chan School of Public Health, 665 Huntington Ave, Building 1, Room 409, Boston, Massachusetts 02115, USA. Phone:  617. 432. 5778 ;Email: clee@hsph. harvard. edu. DJ's present address is: L'institut du thorax, INSERM,  CNRS ,  UNIV Nantes & CHU Nantes, Nantes, France.
While wind is non manipulable, emission sources are and our framework could also serve as a stepping-stone to evaluate potential interventions to control emissions-if a source is shut-down in the North-East of Paris, would wind blowing from this direction influence less specific air pollutant concentrations? We took great care to make our work fully reproducible to help researchers implement but also improve and criticize our approach. Data and detailed R codes are available at the following address: https://osf. io/7x23u/.
Data We built a dataset combining daily time series of air pollutant concentrations and weather parameters in Paris over the 2008-2018 period. We choose to carry out an analysis at the daily level as it done in studies on the acute health effects of air pollution  (3, 4, 6). First, we obtained hourly air quality data from AirParif, the local air quality surveillance organization. Figure  2  displays the location of the selected measuring stations. Using a 2. 5% trimmed mean, we first averaged at the daily level the concentrations (µg/m 3 ) of background measuring stations for NO 2 , O 3 and PM 10. For a given day, if more than 3 hourly readings were missing, the average daily concentration was set to missing. The proportion of missing values for stations ranged from 2. 8% up to 9. 1%. We also computed the average daily concentrations of PM 2.
5  We then retrieved meteorological data from the single monitoring station located in the South of the city and ran by the French national meteorological service Météo-France. We extracted daily observations on wind speed (m/s), wind direction (measured on a 360°wind rose where 0°is the true North), the average temperature (°C), and the rainfall duration (min). Weather parameters had very few missing values (e. g. , at most 2. 5% of observations were missing for the rainfall duration). Finally, to avoid working with a reduced sample size, we imputed missing values for all variables but PM 2. 5. There were no clear patterns in the missingness of NO 2 , O 3 and PM 10 concentrations. We used the chained random forest algorithm implemented by the R package missRanger  (33). A small simulation exercise showed that it had good performance for imputing NO 2 concentrations (the absolute difference between observed and imputed values was equal to 3.
2 µg/m 3 for an average concentration of 37. 6 µg/m 3 ) but was much less effective for imputing PM 10 concentrations (the absolute difference between observed and imputed values was equal to 6. 1 µg/m 3 for an average concentration of 23. 4 µg/m 3 ). As a robustness check, we also provide results of the proposed method we advocated in this article for a complete-case analysis. Once the data were imputed, we averaged the air pollutant concentrations at the city level as it the spatial level of analysis used in  (3, 4, 6). Further details on the data wrangling procedure and an exploratory analysis of the data can be found in the replication materials available at https://osf. io/ 7x23u/.
Performance of the Matching Procedure Our initial dataset consists in 4,018 daily observations, divided into 912 treated units and 3,106 control units. The matching procedure results in 271 pairs of matched treated-control units-only 30% of treated units could be matched to similar control units given the constraints we set. In the supplementary materials (),we show that the matched sample has different characteristics from the initial sample: days belong more the period ranging from May to October, their average temperature is higher and their relative humidity lower. In Figure  4 , we display how the balance of continuous and categorical covariates improves after the matching procedure. Blue dots represent either the absolute mean differences between treated and control units for continuous variables or the absolute difference in percentage points for categorical variables.
The difficulty to find similar treated and control units should make researchers interested in the acute health effects of air pollution worry that instrumental variable strategies exploiting wind patterns and based on multivariate regression models might suffer from extrapolation bias  (41, 22, 42). Further research is needed to better understand if improving the design stage of instrument variable studies with matching actually lead to different results  (43, 44). In the third step, we analyzed our matched data using Neymanian inference, a mode of inference specifically elaborated for randomized experiments. We found that compared to other wind directions, North-East winds could lead to relatively large increases in PM 10 and PM 2. 5 concentrations on the current and following days. In the final fourth step, we implemented a quantitative bias analysis.
Acknowledgments Airparif and  Metéo-France  helped us access the data we used in our analysis. Grégory Abbou provided valuable feedback on an early and different draft of the paper. We especially thank  Stéphane Shao  for developing the matching algorithm and  Marie-Abèle Bind  and  Alice Sommers  for formulating and advocating in several papers the causal inference pipeline we rely on. Map data copyrighted OpenStreetMap contributors and available from https://www. openstreetmap. org.
Tree selection Every tree in Angrignon, Ignace-Bourget and Marguerite-Bourgeoys parks had been previously geolocalized and identified to the species level. This information was accessible through the city database for urban trees (http://donnees. ville. montreal. qc. ca/dataset/arbres). We selected a total of 48 trees of five deciduous species (Table  1 ). Three species are native to the study area (Acer saccharinum L. , Tilia americana L. , Quercus rubra L. ) while two are exotics, from Europe (Acer platanoides L. , Tilia cordata Mill. ). These species are amongst the most abundant tree species in the city of Montreal where together they represent 37% of all the tree species of the public domain. In agreement with the city of Montreal administration, we only selected trees with a diameter at breast height (DBH) greater than 8 cm (mean ± SD: 18.
Acknowledgements This study has been carried out with financial support from (i) the  French National Research Agency (ANR)  in the frame of the Investments for the future Programme to BC, within the Cluster of  Excellence COTE  ( ANR-10-LABX-45 ), (ii) the  Conseil Franco-Québécois de Coopération Universitaire (FRQNT -Samuel-de-Champlain fund)  to BC and AP, and (iii) a  Discovery grant  to AP from the  Natural Sciences and Engineering Research Council of Canada. We thank  Charles Desroches ,  Elyssa Cameron ,  Christian Messier , summer interns at  UQAM, and the city of Montréal, Arrondissement du Sud-Ouest  for their help with field work. We also thank  Frédéric Barraquand  and  Benjamin Brachi  for their comments on the preliminary version of the paper. We thank  Luc Barbaro  and  Steve Frank  for their friendly reviews of early drafts of this paper.
Acknowledgments The IPSL-CM6 experiments were performed using the HPC resources of TGCC under the allocation  2021-A0100107732  (project  gencmip6 ) provided by  GENCI  ( Grand Equipement National de Calcul Intensif ). This work bene ted from the technical support and discussions with several researchers, in particular,  Daniel Goll  and  Sebastien Nguyen of LSCE (France) ,  Matteo Mastropierro of Ca'Foscari University (Italy) ,  Jörg Schwinger  of  Bjerknes Centre for Climate Research (Norway) and Nico Bauer of Potsdam Institute for Climate Impact Research (Germany).
Due to the absence of genuine gender effects, claims with significant repercussions for language processing have been proposed, namely, that any type of competitive or facilitative priming effect underlying the selection of grammatical gender cannot be observed experimentally because gender is likely to be selected automatically. Hence, and contrary to the predictions of both WEAVER++ and the IN model, the GCE would not be located at the level of gender node selection. The scenario with bound morphemes could be thus explained by assuming that they are processed through phonological transformations. The mixed results for Romance languages are also troublesome and might constitute either unfavorable evidence for the claim of automatic gender selection (i. e. , the GCE in Portuguese), or controversial evidence supporting different mechanisms that operate gender selection depending on the language family (i.
Literature search We conducted a comprehensive and systematic search of two online databases: APA PsycINFO and Web of Science (WoS). The search included the key words: "gender congruency", "grammatical gender", and "picture word interference", the first of these on its own, and the other two combined. We obtained 76 results from APA PsycINFO and 85 results from WoS. 4  From a total of 161 studies, we deleted duplicates with the RefWorks® citation software and obtained 100 different studies of interest. By reviewing the titles, the abstracts, the keywords, plus the full-text of the studies where necessary, we applied the following criteria for inclusion: a) The study constitutes an empirical exploration of grammatical gender representation and processing during the lexical access of nouns, and entirely avoids the inclusion of nouns with natural or semantic gender in the stimuli list (e.
1F -H and fig. S2E ). In the fibrotic liver, scRNA analyses revealed that CLDN1 was differentially expressed in a subpopulation of diseased hepatocytes at the epithelial-stromal interface (Fig. 1E-F ) and correlated with grade of de-differentiation towards a progenitor or bile-duct like cell type  (16)  (p=3. 75 x 10 -8 , fig. S2F ). Within the mesenchymal cell compartment in fibrotic liver, CLDN1 was highly expressed by mesothelial cells (p<0. 0001, U-test, GSE136103, Fig. 1E  and S2G) that have previously been described as fibrosis-associated fibroblast progenitor cells  (18, 19)  (fig. S2H ). Taken together, our studies show that CLDN1 is robustly expressed in hepatocytes, liver progenitor cells, cholangiocytes, mesenchymal cells including aHSCs/HLMFs and mesothelial cells. The upregulation of CLDN1 expression in fibrotic liver and its association with disease progression among different etiologies suggests a functional role in liver fibrosis.
Human subjects and patient cohorts. For transcriptomic analyses and ex vivo perturbation studies human liver tissue samples were obtained from patients who had undergone liver resections for HCC, colorectal cancer metastasis, or cholangiocellular carcinoma between 2014 and 2022 at Strasbourg University Hospitals, France (DC-2016-2616 and RIPH2 LivMod IDRCB 2019-A00738-49, ClinicalTrial NCT04690972). All patients provided written informed consent and the protocol followed the ethical principles of the declaration of Helsinki and was approved by the local independent ethics committees. For CLDN1 expression analysis, liver tissues were obtained from local biobanks of the Department of Imaging & Pathology, KU Leuven, the University Hospital Geneva, Pathology at Hvidovre University Hospital, Denmark or Indivumed, Hamburg.
Demographic data and clinical characteristics of patients enrolled are summarized in table S1-S2 and S5-S6, respectively. Datasets of clinical cohorts with chronic liver disease (GSE34798, GSE83148, GSE49541), chronic kidney disease (GSE11585 and GSE60685), IPF (GSE2052, GSE53845, GSE24206) were selected following comprehensive database analysis, where we identified CLDN1 as part of the microarray data. GSE34798, GSE83148, GSE49541, GSE34798, GSE115857, GSE53845 and GSE24206 were analysed using shinyGEO  (47)  with expression values shown as log2 expression. All other microarray data were analysed using signal intensity values. Liver scRNA-seq data (GSE124395 and GSE136103) and snRNA-seq data (GSE185477) were obtained as Seurat objects and investigated. Bioinformatic and statistical analyses.
Human RNA-seq data was mapped using HISAT2 to the human genome hg19. Mouse RNA-seq data was mapped to the mouse genome mm10 and annotated using the Gencode vM15 gene annotation. Data from humanized mice were mapped similarly, but to an artificial genome consisting of all human (hg19) and mouse (mm10) chromosomes, and only reads mapping to human chromosomes were kept for further analysis as described  (48). Reads were counted with htseq-count, and a differentially expression analysis was performed with DESeq2 applying GENCODE 19. Gene Set Enrichment Analysis (GSEA) was used for unbiased pathway analysis using Molecular Signature Database (MSigDB)  (49). Unbiased assessment of HALLMARK  (49) , gene ontology and curated gene sets  (49)  were used for primary screening of clinically relevant signaling pathways and cell circuits in NASH liver tissue microarray data (GSE49541), that were then subsequently analyzed in RNA-seq data of our NASH mouse models.
DATA AND MATERIALS AVAILIBILITY All data associated with this study are available in the main text or the supplementary materials. Raw data from figures are in data file S1. Transcriptomic data reported in this paper have been deposited at the Gene Expression Omnibus database (GEO) with accession numbers GSE174542, GSE174543, and GSE206581. FRG mice are available under a material transfer agreement with Yecuris. Recombinant HCV strains and Huh7. 5 cell lines for infection studies are available under a material transfer agreement with Apath LLC.
5 LS Duplex Reagent Kit (ACD) followed by RNAscope amplification. Fast red chromogenic detection for detection of COL3A1 was performed first, followed by green chromogenic detection (ACD) for detection of CLDN1. Dihydrodipicolinate reductase (dapB), a bacterial gene, was used as a negative control probe. Sections were counterstained with haematoxylin. Images were acquired using Olympus UC90 camera and Olympus cellSense Entry 2. 3 imaging software. Staining quantification was performed using QuPath version 0. 3. 2 (54).
Modeling of the claudin-1/antibody complex. Generation of a CLDN1 structural model: To date the structure of CLDN1 has not been solved and no structure is available in the protein data bank (PDB)  (55). We therefore generated an atomistic model by homology modeling. Sequence analysis revealed that claudin-19 (CLDN19) has a sequence similarity of 57% with CLDN1 and was therefore selected as a template. A structural model of CLDN1 was generated and optimized using PRIME, a dedicated pipeline implemented in the Schrodinger suite for molecular modeling  (56, 57). Generation of the antibody model: The structure of the antibody was generated using the antibody modelling pipeline implemented in the Schrodinger suite for molecular modeling  (58) (59) (60). Molecular dynamics simulations: To explore conformational variability and dynamics of CLDN1, we performed extensive molecular dynamics simulations.
Retrogenix study. Retrogenix's cell microarray technology was performed as described  (73). Briefly, 5484 expression vectors, encoding both ZsGreen1 and a full-length human plasma membrane protein or a cell-surface tethered human secreted protein were arrayed in duplicate across 16 microarray slides ('slide-sets') for a primary screen. An expression vector (pIRES-hEGFR-IRES-ZsGreen1) was spotted in quadruplicate on every slide and was used to ensure that a minimal threshold of transfection efficiency had been achieved or exceeded on every slide. Human HEK293 cells (RRID: CVCL_0045) were used for reverse transfection/expression. The test antibody was added to each slide after cell fixation, giving a final concentration of 2 µg/mL. Detection of binding was performed by using AlexaFluor 647 labelled anti-human IgG Fc detection antibody (RRID: AB_2563330, #409320, BioLegend).
Fluorescent images were analyzed and quantitated (for transfection) using ImageQuant software. A protein 'hit' was defined as a duplicate spot showing a raised signal compared to background. Hits were classified as 'strong, medium, weak, or very weak', depending on the intensity of the duplicate spots. To confirm the hits and assess specificity, vectors encoding all hits identified in the primary screens, plus vectors encoding CD20 and EGFR, were arrayed and expressed in HEK293 cells (RRID: CVCL_0045) on new slides. Confirmation/specificity screens and analyses were carried out as for primary screening except that identical slides were treated, after cell fixation, with the test antibody individually at the same concentration as before (2 µg/mL), 1 µg/mL Rituximab biosimilar, or no test antibody/secondary only (n=2 slides per treatment).