dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of mutations in circulating human SARS-CoV-2 RBDs All 15,371,428 spike sequences on GISAID  [10]  as of 14-April-2023 were downloaded and aligned, following approach in the work of Starr et al. [31]. Sequences from non-human origins and with lengths outside [1260, 1276] were removed. They were then aligned via mafft  [32]  and sequences containing unicode errors, gap or ambiguous characters were removed. Overall, we retained 11,976,984 sequences. RBD amino-acid mutations were enumerated compared to the reference Wuhan-Hu-1 SARS-CoV-2 RBD sequence (Genbank MN908947, residues N331-T531). We then filtered to remove all sequences with RBD not matching any of the possible intermediates between Wuhan Hu-1 and Omicron BA. 1, and obtained 1121 unique oberved RBDs (representing 21% of the initial dataset).
Logistic Regression For the purpose of logistic regression analysis, we utilized the intersecting data obtained from "Methods" RBD Fitness and RBD Binding Affinity. This accumulated dataset comprises 1118 unique RBDs observed in the GISAID database  [33] , for which the 𝐾 𝐷 values have been experimentally determined. We further partitioned this data based on the presence or absence of the S:T478K mutation within the sequence, resulting in two distinct subsets. Ratio between system temperature 𝑇 𝑠 (corresponding to state of RBD in host) and experimental temperature 𝑇 𝑒 (corresponding to temperature of experiments conducted in work from Moulana et al. [8, 9] , leading to Δ𝐺 = 𝑅𝑇 𝑒 𝑙𝑛(𝐾 𝐷 )) was treated as a hyperparameter T (simply refered as "temperature"), that's to say as a parameter whose value is chosen before the fit is done.
Model fitting was performed with least square regression (scipy. optimize package) on a randomly selected subset comprising 80% of the existing variants and then evaluated on the remaining 20% for testing absence of overfitting. To obtain the logistic regression coefficients in Figure  2d , we fitted our model on the whole dataset of existing variants (for which we have fitness coming from population data). We computed error bars as the standard deviation of coefficients of the model fitted on 10 non-overlapping subsets of existing variants.
Epistasis analysis Epistasis describes how mutation interactions can affect fitness 𝐹. If there is no epistasis then fitness can be described as a linear combinations of the presence of each mutation 𝑋 𝑚 ∈ {𝑂, 1}, leading to a first order epistatic model: 𝐹 = ∑︁ 𝑖 𝑐 𝑖 𝑋 𝑖 (6) If we consider pairwise epistatic interactions between mutated sites, we get second-order epistatic model: 𝐹 = ∑︁ 𝑖 𝑐 𝑖 𝑋 𝑖 + ∑︁ 𝑖< 𝑗 𝑐 𝑖 𝑗 𝑋 𝑖 𝑋 𝑗 (7) 𝑐 𝑖 are considered as "first-order" epistatic coefficient while 𝑐 𝑖, 𝑗 are called "second-order" epistatic coefficients as they are due to epistatic interaction between mutated sites 𝑖 and 𝑗. To make sure the linear model does not overfit and can generalize on unseen data, we implemented a 10-fold cross-validation strategy (dataset split: 90% / 10%) and identified a linear model involving first and second order coefficients as described in Equation  7 gives a better representation of data than a first order model (𝑅 2 = 0.
Acknowledgements This work is supported by  NIH   R35GM139571  (to E. I. S). V. M. was supported by award no. T32GM144273  from the  National Institute of General Medical Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institute of General Medical Sciences  or  National Institutes of Health. We would like to thank  Vaibhav Upadhyay ,  Krishna Malella ,  Zechen Zhang  and  Junlang Liu  for useful discussions. We gratefully acknowledge all data contributors, i. e. , the Authors and their Originating laboratories responsible for obtaining the specimens, and their Submitting laboratories for generating the genetic sequence and metadata and sharing via the  GISAID Initiative , on which this research is based.
Analysis of SAM-seq over nucleosomes Nucleosome positioning was inferred using MNase libraries previously published  [24, [36] [37] [38]. MNaseq datasets were aligned to the Col-CEN reference genome assembly  [39]  and libraries with high mappability efficiency and median fragment lengths distribution of ~147 bp were retained. Paire-end reads were mapped to the Col-CEN assembly using Bowtie v2. 2. 5  [40]  with the following parameters: -N 1 --end-to-end --no-mixed --no-discordant. The resulting BAM files were sorted with samtools 1. 6, and then converted to bigwig files using deeptools v3. 5. 1  [41]  --binSize 1 --normalizeUsing RPGC --extendReads --effectiveGenomeSize 131559676 --ignoreForNormalization chrC chrM. The obtained bigwig files were pooled together to yield a smoothing effect.
seq and ABI5 ChIPseq data were obtained from GEO bioprojects GSE85203, GSE96994, GSE139460, GSE190317, GSE207391, GSE60143. Publicly available Maize ATAC-seq and DAP-seq were obtained from GEO bioprojects GSE60143 and GSE120304. All data generated or analysed during this study are included in this published article. Detailed SAMseq protocols for Arabidopsis and maize are publicly available at dx. doi. org/10. 17504/protocols. io. 8epv5x1xdg1b/v1 and dx. doi. org/10. 17504/protocols. io. kqdg3x25zg25/v1, respectively.
Acoustic analyses of cries All cries were acoustically analysed. This included 18 cries from 18 babies (9 girls and 9 boys) recorded in the discomfort context and 18 cries from 18 babies (9 girls and 9 boys) recorded in the pain context. To analyse the acoustic structure of cries, we used a series of custom-built scripts  (Charlton, Zhihe, & Snyder, 2009; Reby & McComb, 2003)  in PRAAT DSP package v. 6. 0. 31  (Boersma & Weenink, 2018). These procedures have previously been successfully applied to characterise acoustic variation in babies' cries  (Gustafsson, Levréro, Reby, & Mathevon, 2013; Koutseff et al. , 2018; Reby et al. , 2016). The program batch processed the analyses in distinct procedures (PRAAT commands are indicated in brackets) that extracted 15 acoustic features (listed and defined in Table  S1 ).
Acoustic analyses of cries We first used a Principal Component Analysis (PCA) to reduce the dimensionality of the acoustic dataset, and examine clustering among cry parameters. Missing data (5%) occurred when one vocal parameter in a given cry could not be measured mainly due to a high proportion of deterministic chaos. To consider a complete dataset, an imputation method was performed to process the missing data through the functions estim_ncpPCA and imputePCA from the missMDA R package (version 1. 10, see Josse & Husson, 2016 for more details). Prior to executing the PCA, data were log-transformed for each category. We retained the principal components of the PCA with eigenvalues >1 (Kaiser's criterion). To compare the acoustic structure of natural discomfort cries and natural pain cries, and to test for sexual dimorphism in cry structure, the scores of the retained principal components were confirmed for normality (Kolmogorov-Smirnov test) and used as input variables in the ANOVA (if residuals were normally distributed) or Wilcoxon signedrank tests (if residuals were not normally distributed).
For playback experiment 1, the model was run using a log link function with a binomial distribution. For the second and third playback experiments, models were run using a Poisson family distribution. The identity of listeners was included as a random factor in all GLMMs to avoid pseudoreplication and account for noise from systematic individual differences in ratings. We used Akaike Information Criteria (AIC) to determine the best fitting models. GLMMs were performed using the function glmer of the 'lme4' package  (Bates, Sarkar, Bates, & Matrix, 2007). The estimated marginal means were obtained using the the 'modelbased' package  (Makowski, Lüdecke, & Ben-Shachar, 2020)  and vetted by SPSS 24 (IBM). The figures were obtained using the package 'ggplot2'  (Wickham, Chang, & Wickham, 2016).
(B) Sex attributed by adult participants (28 men and 28 women) to each of five mean pitch re-synthesised cry variants (x-axis: mean f o variants = 338, 390, 440, 492 and 543 Hz; y-axis: sex attribution). The mean pitch level of the cry influences the sex attributed by the listener, with higher pitched cries more likely to be attributed to girls than to boys. (C) Examples of re-synthesised variants from the same cry exemplar. Data derive from estimated marginal means of GLMMs. Fig. 3. Effect of cry pitch on perceived distress by adult listeners (Playback experiment 2). (A) Distress rating by adult participants (28 men and 28 women) listening to natural cries of unfamiliar babies. Cry context is a significant predictor of distress ratings. (B) Distress ratings by adult participants (28 men and 28 women) for each of 10 mean pitch re-synthesised cry variants (x-axis: mean f o variants of cries recorded during a bath (discomfort context) and during a vaccination (pain context); yaxis: mean distress rating.
Funding This work was financially supported by a research grant from the  French National Research Agency  ( BABYCRY ANR-19-CE28-0014-01 ) to NM, the  University of Lyon IDEXLYON  grant as part of the ' Programme Investissements d'Avenir'  ( ANR-16-IDEX-0005 ) to DR, the program  IDEXLYON  ( ANR-16-IDEX-0005 ) to FL, the  Institut Universitaire de France (NM) , and the  University of Saint-Etienne.
Few computational tools have been developed for the identification of ceRNAs but are not adapted to the identification of ce-circRNAs. This constraint is primarily due to their input requirements, which accept only two types of RNA dataset simultaneously (i. e. miRNA and mRNA expression data), and to their predictions databases restricted to interactions between miRNA and mRNA  [11, 12]. Moreover, the studies focusing on circRNA-miRNA-mRNA networks have not developed a computational tool that automates the search for large-scale ce-circRNA  [13, 14, 15, 16, 17, 18, 19]. Two circRNA functional annotation databases predicting sponge mechanisms involving circRNAs in several tissues have been developed, but they do not allow users to query their own transcriptomic datasets on a large scale, they most often limite queries to circRNA identifiers or sequences  [20, 21].
We evaluated Cirscan on public multi-level transcript expression dataset from two cancers: colorectal cancer  [24]  and hepatocarcinoma cancer  [25]  and showed that Cirscan was able to retrieve previously described sponge mechanisms in the top ranked circRNA-miRNA-mRNA subnetworks, as well as identifying novel circRNA sponge candidates. In summary, Cirscan provides a user-friendly tool to identify and visualize circRNA-miRNA-mRNA networks with potential sponge mechanisms from users human multi-level transcript expression data, and may provide novel therapeutic targets. Cirscan Shiny app is freely available on Gitlab (https://gitlab. com/geobioinfo/ cirscan Rshiny). The scripts used in this paper are also provided on Gitlab (https: //gitlab. com/geobioinfo/cirscan paper).
1B. Step 1), 2) Calculation of a sponge score for each circRNA-miRNA-mRNA subnetwork (Fig. 1B. Step 2). Cirscan outputs the top ranked circRNA sponge candidates based on the ranked sponge scores. It also enables the visualization of the identified sponge mechanisms as networks and biological enrichment analysis of the genes in the networks of interest (Fig. 1C ). A video tutorial and an example dataset are available online to guide the user along the different steps of the application (https://gitlab. com/geobioinfo/cirscan Rshiny).
Input files Cirscan requires two types of CSV files from the user. First, multi-level transcript expression data are required as input files, including circRNA, mRNA and optionally miRNA expression matrices. If the user does not have miRNA expression data, miRNA signatures for different types of cancer (respectively 32 and 20 from the TCGA consortium (https://www. cancer. gov/tcga) and CCLE database (www. broadinstitute. org/ccle,  [26] )) are internally available in Cirscan. There is no constraint concerning the technology used to obtain the expression matrices (array or sequencing), which in return requires the user to properly normalize his data beforehand. Each expression matrix needs to be normalized (e. g. RMA for expression array, TPM for RNA-seq data) and log-transformed, and a minimum of 3 samples per condition is required to ensure reliability of the downstream analysis.
org/ccle,  [26] ) datasets. CircRNAs identifiers must correspond to the human circRNAs referenced in circBase ("hsa circ xx xxxxx")  [27] , human miRNAs from miRBase ("hsa-miR-xxx" or "hsa-let-xxx")  [28] , and mRNAs must be referenced as official gene symbols. Furthermore, the user must provide another CSV file corresponding to the sample annotation file. This file must contain two columns: samples with the sample names referenced in the RNA expression matrices and conditions with the corresponding condition it refers to (condition 1, condition 2), knowing that the first condition refers to the control condition. The file format required by Cirscan is detailed in the Home Panel of the Shiny application and in the example dataset provided in the Interaction Ranking Panel.
Identification of sponge mechanisms In-house miRNA-target interaction database A miRNA-target interaction occurs between the seed sequence of the miRNA and a complementary sequence called the miRNA Recognition Element (MRE) on the target (circRNA or mRNA). We used TargetScan  [29]  to predict miRNA-circRNA and miRNA-mRNA interactions, providing an affinity score for each interaction. For miRNA-circRNA interactions, we modified the affinity score calculated by TargetScan (context+ score,  [30] ) by excluding the 3' pairing feature since it is not relevant for circRNA. To reduce false positive predictions, we set a cutoff for the context+ score based on its distribution in experimentally validated interactions extracted from the ENCORI database  [31]. The cutoff was defined as the 95th percentile of the context+ score distribution for interactions supported by at least two CLIP-seq and degradomeseq experiments [see more details in Supplementary Material and Figure  S1 ].
For miRNA-mRNA interactions, we used the affinity score provided by TargetScan that incorporates additional criteria relevant to miRNA-mRNA interactions that minimize false positive predictions, such as 3' UTR length, ORF length, and probability of conserved targeting between species (context++ score,  [29] ) [see more details in Supplementary Material]. In total, the miRNA-target interaction database includes 1,244,932 miRNA-mRNA interactions and 31,045,182 miRNA-circRNA interactions.
Visualization of the sponge mechanisms of interest The identified circRNA-miRNA-mRNA subnetworks involving potential sponge mechanisms can be visualized in the Network Visualization Panel using the visNetwork R package (v2. 0. 8, https://github. com/datastorm-open/visNetwork). It is possible to visualize a network by selecting an interaction from the score matrix or by specifying the name of an RNA of interest: nodes correspond to the different types of RNAs (orange miRNAs, green mRNAs, and pink circRNAs) and edges to the miRNA-target interactions. The edge thickness is proportional to the sponge scores (the higher the value, the thicker the edge and the stronger the interaction affinity) and the RNA node of interest is larger than the others.
Cirscan performance evaluation We evaluated Cirscan on two public multi-level transcript expression datasets from colorectal cancer (CRC) (n=20, 10 normal and 10 tumor samples)  [24]  and hepatocarcinoma (HCC) (n=14, 7 normal samples and 7 tumor samples)  [35]  in order to identify sponge mechanisms active in normal or tumor conditions. For these two cancers, we retrieved sponge mechanisms referenced in the NcPath database  [36] , as well as described by Chen et al. [24]  and in a recent review by  [37]. We selected sponge mechanisms involving circRNAs for which a circBase identifier exists and a reference is associated, i. e. 30 sponge mechanisms described in the literature in colorectal cancer and 93 in hepatocarcinoma. The tables of sponge mechanisms are provided on our Gitlab web page (https://gitlab.
com/geobioinfo/cirscan paper). Using the Cirscan tool on the CRC dataset pre-processed [see more details in Supplementary Material], we identified 12,850 potential circRNA-miRNA sponge mechanisms (i. e. 1,413 unique circRNAs) [Table  available  at https://gitlab. com/ geobioinfo/cirscan paper as well as Supplementary File 1]. Among them, 3 sponge mechanisms were already described in the literature in CRC  [38, 39]  and significantly enriched in the top ranked sponge mechanisms given by Cirscan (GSEA enrichment  [40] , p-value = 4. 04e-07) (Figure  2 ). Similar results were observed using the TCGA CRC miRNA signature available internally in Cirscan (p-value = 1. 51e-06) [Table  available  at https://gitlab. com/geobioinfo/cirscan paper as well as Supplementary File 2]. The same approach was applied to the HCC circRNA and mRNA transcriptomic datasets and the TCGA HCC miRNA signature available internally in Cirscan, as no miRNA expression dataset was generated by the same authors [see more details in Supplementary Material].
We identified 16,190 potential circRNA-miRNA sponge mechanisms (i. e. 2,089 unique circRNAs) [Table  available  at https://gitlab. com/ geobioinfo/cirscan paper as well as Supplementary File 3] including 3 sponge mechanisms already described in the literature in HCC  [41, 42, 43]  that were also significantly enriched in the top ranked sponge mechanisms (p-value = 0. 03) (Figure  2 ). This significant enrichment of the sponge mechanisms identified in colorectal cancer and hepatocarcinoma in the literature among the top candidates of Cirscan confirms the reliability of our tool.
Identification of known and novel sponge mechanisms in colorectal cancer Using the public colorectal cancer dataset, Cirscan identified the hsa circ 0000977:hsa-miR-135b-5p subnetwork, described in the literature by Ding et al. [38] , at rank 18/12,850 out of all identified mechanisms (in the top 1%) and rank 17/5,468 out of all mechanisms identified in the normal condition (Figure  3. A). This result was consistent with the study of Ding et al. [38] , as the authors describe this mechanisms as downregulated in the tumor condition. The KEGG biological term enrichment analysis of the mRNAs of this subnetwork revealed biological pathways associated with cell proliferation and differenciation involving the tumor suppressor genes APC and SMAD2 (Figure  3. B). Among the sponge mechanisms involved in the tumor condition and described in the literature, Cirscan identified the hsa circ 0001806:hsa-miR-193a-5p subnetwork (Figure  3.
Gene expression Gene expression profiles were performed at the GeT-TRiX facility (GénoToul, Génopole Toulouse Midi-Pyrénées, Toulouse, France) using Agilent Sureprint G3 Mouse GE v2 microarrays (8 × 60K, design 074809) following the manufacturer's instructions. Data acquisition and statistical analyses were performed as previously described. 18  A correction for multiple testing was applied using the Benjamini-Hochberg procedure to control the false discovery rate (FDR). Probes with fold change (FC) > -1. 5 and FDR < -0. 05 were considered to be differentially expressed between conditions. The enrichment of Gene Ontology (GO) biological processes was evaluated using Metascape. 19  Data are available in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE228554.
For real-time quantitative polymerase chain reaction (RT-qPCR), 2 lg RNA samples were reverse-transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Table  S1  presents the SYBR Green assay primers. Amplifications were performed using an ABI Prism 7300 Real-Time PCR System (Applied Biosystems). RT-qPCR data were normalised to TATA-box-binding protein (Tbp) mRNA levels. Proton nuclear magnetic resonance ( 1 H-NMR)-based metabolomics Plasma samples and liver polar extracts were prepared and analysed using 1 H-NMR-based metabolomics. All spectra were obtained on a Bruker DRX-600-Avance NMR spectrometer (Bruker) on the AXIOM metabolomics platform (MetaToul). Details on experimental procedures, data pre-treatment and statistical analysis were described previously.
Multi-omics analyses Bidirectional correlations between plasma metabolites and hepatic transcripts were investigated using N-integration discriminant analysis with DIABLO, an algorithm that aims to identify a highly correlated multi-omics signature discriminating several experimental groups using the R package Mixomics v6. 10. 9. 20  We used two components in the models, and for the estimation of model parameters, the cross-validation procedure method was used. For the correlation networks, only correlations with a Spearman's rank correlation coefficient >0. 96 were plotted.
Analyses of plasma markers Alanine aminotransferase (ALT), phosphatase alkaline (ALP), total cholesterol, high-density lipoprotein (HDL-cholesterol), triglycerides, and free fatty acids were determined using an ABX Pentra 400 biochemical analyser (Anexplo facility, Toulouse, France). Blood glucose levels were measured from the vena cava with an AccuCheck Performa glucometer (Roche Diagnostics). Values for Fmo3 gene expression were calculated using the GEO2R tool for microarray data and using GREIN  21  for RNA sequencing data. Thrombi formation under flow Biochips microcapillaries (Vena8Fluoro+, Cellix) were coated with a collagen fibril suspension (50 lg/ml) and saturated with a solution of 0. 5% bovine serum albumin in phosphate-buffered saline without Ca 2+ /Mg 2+. Mouse blood was transferred into heparin (10 IU/ml), and DIOC6 (2 lM) was used to label platelets in whole blood.
Using a syringe pump (PHD-2000; Harvard Apparatus) to apply a negative pressure, labelled blood was then perfused through a microcapillary for indicated time at a wall shear rate of 1,500 s -1 (67. 5 dynes/cm 2 ). Thrombus formation was visualised with an ×40 oil immersion objective for both fluorescent and transmitted light microscopy; the light source was provided by Colibri (Zeiss) and was recorded in real-time (one frame every 20 s). Thrombi volumes were calculated by thresholding of the surface covered by thrombi on a slice of Zstack images using IMARIS software. 22  Other statistical analyses All univariate statistical analyses were performed using Graph-Pad Prism v. 9 (GraphPad Software, San Diego, CA, USA). Outliers were identified using the ROUT method. The Kolmogorov-Smirnov test of normality was applied to all data.
5D ). Next, we analysed Fmo3 hepatic expression in several publicly available gene expression datasets. In the first experiment, Car -/-mice were knocked-in with human CAR coding sequence  24  and were fed diets containing 0 (CTRL) or 1,000 ppm PB, an indirect activator of both mCAR and hCAR. 25  We found that PB-fed hCAR mice had significantly lower expression of hepatic Fmo3 mRNA compared with control mice (Fig. 5E ). The second study compared wild-type (WT) mice treated with TCPOBOP vs. COtreated mice and hCAR mice treated with CITCO (a specific agonist of hCAR) vs. CO-treated hCAR mice. 26  Both TCPOBOP and CITCO-treated mice had significantly lower Fmo3 hepatic mRNA compared with their relative controls (Fig. 5F ). Finally, the last two studies were conducted in chimeric mice with human hepatocytes treated with PB.