dataesr/scientific-paragraphs-categorization huggingface dataset:
Annotation of the Reference Genome To recover TE fragments and TE genomic sequence occupancy, the reference genome assembly was masked using RepeatMasker and the above TE database  (-gccalc, -s, -a,cutoff 200, -no_is, -nolow, -norna, -u; v 1. 332)   (Bailly-Bechet et al. 2014). Gene density was computed from a run of augustus (-species¼fly, -strand¼both, -genemodel¼complete; v2. 5. 5)  (Stanke et al. 2008)  as the number of genes completely within nonoverlapping genomic windows of 200 kb. For comparison, the same pipeline was applied to D. melanogaster assembly (release 6). Promer was used to generate alignments between D. melanogaster and D. suzukii masked assemblies and establish syntenic relationships (MUMmer v3. 23)  (Kurtz et al. 2004 ). The promer output was filtered out using the delta-filter module to obtain a one-to-one mapping of reference to query (-q, -r).
A file containing alignment coordinates for alignments of minimum length 100 bp, and in which overlapping alignments were merged, was generated with the show-coords module (-b, -L 100, -r). Because the abundance of repeated sequences and the use of masked assemblies may result in multiple small alignments, alignments separated by less than 20 kb were merged using a custom script. Note that only alignments involving the 2L, 2R, 3L, 3R, X, and four chromosomes of D. melanogaster were kept at this step, and if a D. suzukii contig aligned to several D. melanogaster chromosomes only the best pair was conserved (i. e. , the pair producing the longest alignment). Graphical visualization of the results was produced using Circos  (Krzywinski et al. 2009).
Fly Samples and Pool Sequencing Pool-sequencing (PoolSeq) data originate from  Olazcuaga et al. (2020) , where the detailed associated protocol is described. Briefly, adult wild flies were sampled between 2013 and 2016 from 22 localities of both native and invasive areas (fig. 3A )  (Fraimout et al. 2017). Six samples were collected in the native Asian area, more precisely in four Chinese and two Japanese localities. The remaining 16 samples were chosen to be representative of two separate invasion roads: the American invasion road and the European invasion road. The American invasion road is represented by one Hawaiian sample, one Brazilian sample, and six samples from the United States. The European invasion road corresponds to two German samples, four French samples (including one from La R eunion Island), one Italian sample, and one Spanish sample.
For each population sample, DNA extraction was performed from the thoraxes of 50-100 flies and used to prepare PE libraries (insert size of $550 bp). PE sequencing was achieved using an HiSeq 2500 from Illumina to obtain 2 Â 125 bp reads. Reads were trimmed using the trim-fastq. pl script in the PoPoolation package (-min-length 75, -quality-threshold 20; v1. 2. 2)  (Kofler et al. 2011).
TE Frequencies and Abundances To obtain TE insertion frequencies and abundances in PoolSeq samples a calling of TEs was done using PoPoolationTE2  (Kofler et al. 2016) , the reference genome, and the newly constructed database. To make sure that no reads from TE sequences could map on the masked assembly, TE reads were simulated, and mapped on the masked assembly and aligned positions were also masked. Reads simulation was performed using the script create-reads-for-tesequences. py (Kofler et al. 2016 ): reads of 125 bp reads, coverage of 1,024 X per TE sequence in the database. Because we do not expect a split read-based TE calling tool such as PoPoolationTE2 to accurately call for insertions shorter than the insert size, TE sequences shorter than 500 bp were removed before calling. Moreover, as PoPoolationTE2 filters out insertions with reads mapping on more than one family, families with cross-mapping were grouped in pseudofamilies.
Two families were brought together if at least 1% of reads from one sequence of the first family were mapped on a sequence of the second family (read simulation: 125 bp reads, coverage of 100X per consensus). The classification of each pseudofamily corresponds to that of the families that compose it. In case of divergent classification between families of a pseudofamily, this pseudofamily was classified as unknown. One exception has been made for a pseudofamily regrouping ten families: one annotated as an I element and nine as Helitron; this pseudofamily was considered as Helitron. Concerning the TE calling, reads were mapped using bwa bwasw (v0. 7. 17)  (Li and Durbin 2010)  and PE information restored using the se2pe script provided with the PoPoolationTE2 package (v1. 10. 04) (Kofler et al.
2016). One unique pileup file was generated with all samples specifying a minimum mapping quality of 15. The remaining modules of PoPoolationTE2 were used as follows: identifySignatures:mode joint, -signature-window minimumSampleMedian,min-valley minimumSampleMedian, -min-count 2; updatestrand: -map-qual 15, -max-disagreement 0. 5; frequency; filterSignatures -min-coverage 10, -max-otherte-count 2,max-structvar-count 2; pairupSignatures -min-distance -200, -max-distance 300. The final output contained frequencies in the 22 populations for each called TE insertion. To allow an unbiased comparison across samples, the frequencies spectrum (supplementary figs. S8 and S9, Supplementary Material online) and estimates of TE abundance came from a slightly different pipeline. The following parameters were modified:target-coverage 30; identifySignatures -mode separate.
Genome-Wide Association Study with TE Family Abundance All genome scans were performed using BayPass (v2. 2)  (Gautier 2015; Olazcuaga et al. 2020) , a package aiming at identifying markers evolving under selection and/or associated with population-specific covariates, taking into account the shared history of the populations. For each SNP/InDel previously called in these PoolSeq samples  (Olazcuaga et al. 2020) , we estimated 83 Bayes factors (BFs), reflecting their association with the number of insertions per HG of 83 families/pseudofamilies (based on a linear regression model). The 83 chosen TE families/pseudofamilies were those displaying an amplitude of variation of at least three insertions per HG across the complete data set. To improve computing time BayPass was run on data subsets.
The 83 families with an amplitude of variation of at least three insertions per HG between populations were considered. The population structuring of genetic diversity is summarized by the scaled covariance matrix of population allele frequencies (X) estimated with BayPass, one autosomal subset randomly chosen was used (the correlation of the posterior means of the estimated X elements across SNP subsamples had previously been verified,  Olazcuaga et al. 2020). Partial Mantel tests were conducted using the R package ecodist  (Goslee and Urban 2007). P values were further adjusted to account for multiple testing applying the Benjamini-Hochberg correction  (Benjamini and Hochberg 1995).
2018 ). In accordance with Jeffrey's rule, a BF superior to 20 deciban (dB) was considered as decisive evidence supporting an association  (Jeffreys 1961). XtX and C 2 estimates came from one single run and simulation were used to determine a significance threshold. The R function simulate. baypass() provided within the BayPass package was used to simulate read count data (nsnp ¼ 10,000, pi. maf ¼ 0). We used the physical coverage estimated from the pileup file using the module stat-coverage of PoPoolationTE2  (Kofler et al. 2016). BayPass was run on this simulated data set to estimate the null distribution of the XtX and the C 2 statistics. An insertion was considered as overly differentiated (for XtX) or associated with the tested contrast (for C 2 ) if the corresponding statistics exceeded the 99.
Data Availability The reference assembly used in this article is available on NCBI website (https://www. ncbi. nlm. nih. gov/genome/ 18317?genome_assembly_id¼910956)  (Paris et al. 2020). Population sequencing data are available online (SRA repository under the BioProject accession number PRJNA576997)  (Olazcuaga et al. 2020). The generated TE database and scripts are available in a github (https://github. com/vmerel/Dsu-TE).
Accordingly, at day 16 many genes pertaining to pathways such as fatty acid metabolism, degradation or lipolysis were downregulated in cells treated with EX527, most of them implicated in PPAR signaling (Figure  3B , S4A, Table  S3 ). Amongst these, the adipokine genes leptin (LEP), leptin receptor (LEPR), and adiponectin (ADIPOQ) showed significatively lower levels in EX527 treated cells (Table  S3 ). Interestingly, SIRT1 inhibition upregulated transcripts enriched for adhesion and locomotion, critical processes during the onset of the adipogenesis, including the fibronectin gene FN1, which inhibits adipocyte maturation(38) (Figure  3C , S4B. Table  S3 ). Accordingly, gene set enrichment analysis (GSEA) identified adipogenesis and apical junction as the topranked hallmarks enriched in untreated and EX527 treated cells respectively (Figure  3D, E ).
Slides were washed twice during 10 min. in water, and mounted in vectashield mounting media (Vector Labs, cat. no. H-1000). The images were captured with the camera Axiocam EEc 5s coupled to a ZEISS Primovert microscope, using a 20X magnification. Lipid droplets were quantified using the ImageJ software, by converting RGB to 8-bit grayscale images, and then using the "analyze particles" plug-in as described  (68). Four frames per slide were used for image analyses and quantification (n=3 biological replicates with 4 technical replicates). Quantitative real-time PCR: Total RNA was extracted from MSCs using TRIzol™ Reagent (Invitrogen cat. no. 15596018) following the manufacturer´s instructions. cDNA was obtained by retrotranscription of 1 μg of total mRNA with iScript cDNA synthesis kit (Bio-Rad) according to the manufacturer's instructions.
RNA-seq data processing. Human Reference genome and gene model annotation files were downloaded from genome website browser (NCBI/UCSC/Ensembl) directly. Indexes of the reference genome was built using STAR and paired-end clean reads were aligned to the Homo sapiens assembly GRCh38/hg38, with the STAR aligner v2. 5  (69). STAR uses the method of Maximal Mappable Prefix (MMP) which can generate a precise mapping result for junction reads. HTSeq v0. 6. 1 was used to count the read numbers mapped of each gene. Afterwards, FPKM of each gene was calculated based on the length of the gene and reads count mapped to it  (70). Differential expression analysis between conditions (three biological replicates per condition) was performed with the DESeq2 R package (2_1. 6. 3), which uses a model based on the negative binomial distribution  (32).
The resulting P-values were adjusted using the Benjamini and Hochberg's approach for controlling the False Discovery Rate (FDR). Genes with an adjusted P-value <0. 05 were assigned as differentially expressed. The Venn diagrams were prepared using the function vennDiagram in R based on the gene list for different groups, or with Venny V 2. 1 (https://bioinfogp. cnb. csic. es/tools/venny/). Differentially expressed genes were subjected to functional analyses using the "Compute Overlaps" tool to explore overlap with the CP (Canonical Pathways) and the GO:BP (GO biological process) gene sets at the MSigDB (molecular signature database). The tool is available at: https://www. gsea-msigdb. org/gsea/msigdb/annotate. jsp, and estimates statistical significance by calculating the FDR q-value. This is the FDR analog of the hypergeometric P-value after correction for multiple hypothesis testing according to Benjamini and Hochberg.
Gene set enrichment analysis (GSEA) was performed using GSEA v. 4. 0. 3. (71)  to determine the enrichment score within the Hallmark gene set collection in MSigDB v7. 0(72), selecting the Signal2Noise as the metric for ranking genes. The findMotifs. pl program in the HOMER software (73) was used for motif discovery and enrichment, searching within the genomic regions encompassing 300 Kb upstream and 50 Kb downstream the TSS, and selecting 6-8 bp for motif length. Motif enrichment is calculated by findMotifs. pl using the cumulative hypergeometric distribution. All RNA-seq raw and processed data will be made publicly available at Gene Expression Omnibus (GEO) under the accession number GSE178615 Two-photon excited fluorescence lifetime imaging (FLIM) and third harmonic generation (THG). Imaging was performed on a laser scanning microscope (TriMScope, Lavision Biotec, Germany).
Analysis of the Fluorescence Lifetime microscopy images. Intensity images were analysed with Fiji-ImageJ (NIH, Bethesda, MD, USA). All FLIM data was processed and analysed with SimFCS (developed by the laboratory for Fluorescence Dynamics https://www. lfd. uci. edu/globals/ ) and with a Matlab (Mathworks, Natick, MA, USA) custom written software. FLIM data were transformed by using FFT and plotted in the phasor plot as previously described (74)  (75)  (see Supplementary material). Briefly, the coordinates g and s in the phasor plot were calculated from the fluorescence intensity decay of each pixel of the image (Figure  S7F ) by using the transformations defined in the Supplementary material (equation 1 and 2). We applied an intensity threshold to eliminate the background of the cellular medium and a median filter on the g and s images to reduce the variance of the phasor location without decreasing the spatial resolution of the image.
Subcellular segmentation of lipid droplets, cell NADH, mitochondria and nucleus and cytoplasm. Image processing and segmentation was performed by a Matlab custom written software. The principles of the segmentation are illustrated in Figure  S1B. A threshold (2. 87 ns) was applied in the τm lifetime image to automatically separate lipid droplets and NADH of the cell. Pixels with longer lifetime were assigned to a lipid mask while pixels with shorter lifetime were highlighted to the NADH cell mask. The τm threshold was determined empirically to match the lipid mask border with the THG signal of the lipid droplet (Figure  1B ). Then the mask of NADH cells was used to calculate fB_NADH of the same ROI or cell. For quantification, we used the average values of fB_NADH and lipid ratio. To quantify the lipid droplets in ROIs or in single cells, we calculated the ratio between the number of pixels of the lipid mask and the total number pixels of the cell: Single cell analysis was performed manually.
A-D) OCR was measured at days 4 (A), 8 (B), 12 (C) or 16 (D) after adipogenic induction in hMSC, in the absence or presence of the indicated treatments. with sequential addition of oligomycin (Oligo, complex V inhibitor), FCCP (a protonophore), and Rotenone/antimycin A (Rot/AA, complex III inhibitor), E-F) Mitochondrial bioenergetic parameters calculated from extracellular flux analyses: basal respiration, maximal (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. respiratory capacity, spare respiratory capacity, and ATP production. Two-way ANOVA followed by Tukey´s post test. * p <0. 05, ** p <0. 01, *** p <0. 001. I-L) ECAR was measured after serial addition of oligomycin and FCCP. Data is presented by mean ±SEM. AD: adipogenic induced cells; NAD+ adipogenic induced cells treated with 5 mM NAD + ; FK866: adipogenic induced cells treated with 1 nM FK866; EX527: adipogenic induced cells treated with 50 µM EX527.
MSC: untreated, undifferentiated hMSC. Dataset S1 (separate file). Summary of mapping results. Sample name: sample identification; Total reads: total clean reads suitable for analysis; Total mapped: numbers of reads being mapped on the genome; Uniquely mapped reads: numbers of reads being mapped to a single position of the genome; Multiple mapped reads: numbers of reads being mapped to more that one genomic sites; Total mapping rate: (mapped reads)/(total reads)*100; Uniquely mapping rate: (uniquely mapped reads)/(total reads)*100; Multiple mapping rate: (multiple mapped reads)/(total reads)*100. Dataset S2 (separate file). List of differentially expressed genes from NAD+-treated cells genes and their functional analyses. Dataset S3 (separate file): List of DE genes in from EX527-treated cells at terminal differentiation (day 16) and their functional analyses.
ACKNOWLEDGEMENTS We thank all members of  Aguilar-Arnal and Chiara Stringari laboratories  for helpful discussions and advice. We thank  Pierre Mahou ,  Isabelle Lamarre  and  Emmanuel Beaurepaire  for scientific discussions and technical help. We are thankful to  Marcia Bustamante Zepeda ,  MSc , and  Miguel Tapia Rodríguez , at the IIBo, for technical assistance. We are thankful to  Dr. Alfonso Leon-del-Rio  laboratory at the IIBo and particularly to  Salvador Ramírez Jiménez, MSc , for kindly sharing reagents and equipment. This work was supported by  Human Frontier Science Program (HFSP)  under the contract  RGY0078/2017 ChroMet  to CS and LA-A, the  Agence Nationale de la Recherche (ANR)  under contracts  ANR-10-INBS-04   France BioImaging  and  ANR-11-EQPX-0029 Morphoscope2  to CS, and grant  PAPIIT IN210619  from  Universidad.
Procedure Participants were recruited via social media and poster advertisements placed in different buildings at the university. Students willing to participate were invited to visit the laboratory to receive more details about the study design and sign the informed consent. The EMA phase lasted 14 days. Participants received three daily semi-random prompts (between 9:30-14:00, 14:00-18:30, and 18:30-23:00) to complete the momentary assessment through the data collection program Qualtrics. To prevent backfilling, participants were given sixty minutes to access the survey; after that period of time, the assessment was marked as missing. During the entire study, participants could contact a researcher on the team to resolve technical issues. At the end of the study, participants were invited to return to the laboratory for a debriefing session.
Statistical analyses The datasets of the analyses and the R code are contained in an open-access file available on the OSF website at https://doi. org/10. 17605/OSF. IO/TEUBR. The data analytic strategy followed three steps that are similar to the steps found in previous ecological momentary assessment studies on reciprocal influences between emotions and actions  [29, 36, 53]. In an initial data preparation step, all the variables of interest were person-mean-centred to enable the examination of within-individual processes. Then, to analyse the relationships between variables assessed at two consecutive time points (t0 and t1), data were lagged. This meant deleting assessments that were not directly preceded or followed by another completed assessment (n = 558). Consequently, each row of the data frame analysed contained participants' responses to two consecutive assessments (see the 'Data.
csv' file at https://doi. org/10. 17605/OSF. IO/TEUBR). The first aim of the study was to examine the effect of the PE felt at a given time on the subsequent implementation of positive strategies. To this end, a series of linear mixed-effects models containing one random intercept per participant were estimated using maximum likelihood with the R lmerTest package  [54]. Linear mixed-effects models were computed to take into account the hierarchical nature of the data. In this step, six models were computed (i. e. , one per strategy). The dependent variable entered in each model was the change in the strategy of interest from t0 to t1, whereas the main independent variable was PE at t0. Two other independent variables were also included to neutralize their possible confounding effects: the use of each strategy at t0 and PE at t1.
To this end, the dataset on which our analyses were based was restructured to obtain a data frame where each row contained a participant's responses to two consecutive assessments for one strategy category. In this restructured data frame, each pair of consecutive assessments completed consisted of three rows (one for the intensity of the use of attentional deployment strategies, one for the intensity of the use of cognitive change strategies and one for the intensity of the use of response modulation strategies; see the 'Data_ST. csv' file at https://doi. org/10. 17605/OSF. IO/ TEUBR). Then, the two types of linear mixed-effects models mentioned for the first and second aims of the study were computed again, but with slight modifications. A first model was designed to examine whether the effect of PE at t0 on the change in strategy use depended on the category of the strategy considered.
On the other hand, in A549 cells exposed to curcumin, a significant down-regulation in the expression of this gene has been identified  [77]. Potential role in viral and/or antiviral properties have been described for few other common miRNAs, such as let-7a-3p  [98] , miR-125a-5p  [99]  or miR-455-3p  [100]. Together with literature analysis, we also performed bioinformatic analysis to identify potential target genes of these 17 common miRNAs. Using MirWalk database (http://mirwalk. umm. uni-heidelberg. de) [101]  we identified 1734 potential targets with minimum free energy <-20kcal/mol indicating a stronger predicted pairing between the miRNA and the target mRNA. With the aim to identify potential pathways in which these target genes are involved in we performed pathway analysis using MirWalk database as well as GeneTrail2 database.
Interestingly, both databases showed that the potential target genes are involved in different cellular pathways, including those that have been previously identified as involved in regulation of SARS-CoV-2 replication  [56]. These pathways include EGFR signaling, focal adhesion kinase, integrin signaling, mTOR signaling, p38 MAPK signaling, PDGF receptor-like signaling, PI3K-Akt signaling and VEGF signaling, described to regulate entry of the virus, its efficient replication, inflammatory responses or viral survival in infected cells (Fig. 2 ). Moreover, comparison of the target genes with genomic data present in databases using Enrichr tool (https://maayanlab. cloud/Enrichr/) [102]  revealed significant homology with gene expression of healthy lung biopsy vs. SARS-CoV-2 infected lung, SARS-CoV-2 infection in lung tissue or A549 infected with SARS-CoV-2.
Corpus The corpus is a transcription of spontaneous speech produced by French mothers during several play sessions with their child, and available in the CHILDES database  (MacWhinney, 2000). The model used transcriptions of two mother/child pairs from the Lyon corpus (available at http://childes. psy. cmu. edu/data/Romance/French/Lyon. zip), Marie and Theotime, aged between 17 and 30 months during the recordings  (Demuth and Tremblay, 2008). The speech produced by the mothers of Marie and Theotime was extracted from the corpus, for a total of 58,241 utterances (265 K tokens). Each word of the corpus was then assigned a category (Part-of-Speech, or POS-tag) to evaluate the model's responses (by comparing the category predicted by the model with the actual category of the word). For the POS-tagging, we used the disambiguation grammar POST developed by Christophe Parisse that is integrated in the CLAN software (the program developed to exploit the CHILDES corpus;  MacWhinney, 2000).
Precision and Recall The precision (top) and recall (bottom) of the left context (red), right context (yellow), framing context (blue) and chance (black) model are presented in Figure  3 , with nouns on the left side and verbs on the right side. The x-axis in all graphs represents the different semantics seeds. We ran mixed effects models in R (R Core Team, 2013) with the package lme4  (Bates and Sarkar, 2007; Bates et al. , 2015). The statistical models we created aim to analyze the relation between our measures, precision and recall (precision [0-1], recall [0-1]) and the predictor variables: model type (model: baseline, right, left, framing), semantic seed size, (voc: V0, V1, V2, V3, V4, Vm), and the targets: nouns or verbs (n_v). Random intercepts and slopes for the 10 mini-corpora (the fold) were modeled for the predictor variables semantic seed size (voc) and noun or verb targets (n_v).