dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Estonian Genome Center  and  Andres Metspalu  for access to the  data of Georgian, German, Hungarian, Latvian, Lithuanian , and Ukrainian samples. We thank  Doron Behar  for early access to the  Ashkenazi Jew ,  Karaite Jew , and  Lemba Jew data. We acknowledge  Elza Khusnutdinova  for early access to Avar,  Besserman, Cirkassian, Kabardin, Karelian, Komi, Mordovian, Udmurt , and Vepsa data. We thank  Rusudan Khukhunaishvili  and  Sophiko Tskvitinidze  for early access to Adjarian data. We thank  Oleg Balanovsky  for access to Cossack,  Coast Crimean Tatar ,  Mountain Crimean Tatar , and  Steppe Crimean Tatar data. All these samples will be published in dedicated papers. We thank  Lena Khushniarevich  for  Belarusian Tatar  samples and  Vladimir Ferak  for some of the Roma samples.
Analyzed data We assembled  [11, 16, 17, 20, 22, 24, 26, 33, [81] [82] [83]  a genome-wide dataset of 25,732 worldwide individuals genotyped with different Illumina platforms. Of these, 25,455 were retrieved from publicly available and controlled access resources. In order to increase our resolution in identifying the source of analyzed individuals, we added 277 samples from 35 Eurasian populations. Genotype data for 89 samples are available at http://evolbio. ut. ee/. The remaining samples will be available in dedicated future publications. More detailed information are reported in Data S1A and S1B and Figure  S1. The obtained dataset was filtered using PLINK ver. 1. 9  [73]  to include only SNPs and individuals with a successful genotyping rate >97%, retaining a total of 251,548 autosomal markers.
We used KING to remove one random individual from pairs with kinship parameter higher than 0. 0884  [74]. The final dataset was therefore composed of 17,722 individuals from 261 populations  [11, 16, 17, 20, 22, 24, 26, 33, [81] [82] [83]  (Data S1A and S1B; Figure  S1 ). Of these, 11,607 individuals belonging to 22 admixed American populations were treated as 'recipients', while the remaining 6,115 samples from 239 source populations were considered 'donors'.
Sex-biased admixture evaluation We intersected SNPs from the X chromosome that were present in both our main datasets and in the 1000 Genomes Project samples. Three admixed American groups (Mexican, Maya, and Mayas) were removed because they did not include any genotypes for chromosome X. We revised and imputed sex assignments based on X chromosome data using the --impute-sex command in PLINK. A male or female call is made when the rate of homozygosity is > 80% and < 20%, respectively. Individuals for which sex imputation was ambiguous were removed and heterozygous SNPs in male X chromosomes were set as missing. After this step, only samples and positions with a genotyping rate R 97% were retained: 5,227 SNPs in a total of 15,353 individuals. The same set of individuals was extracted from the filtered autosomal dataset with 258,720 SNPs.
Subsequently, we performed LD pruning (--indep-pairwise 200 50 0. 2) in both X chromosome and autosomal datasets, resulting in a total of 2,519 and 116,912 SNPs, respectively. We ran separate unsupervised ADMIXTURE (version 1. 3. 0 [31] ) analysis for the two datasets using K values = 3 and 10 independent runs. We used the option '--haploid = 'male:23' in order to properly treat male individuals and chose the best run according to the highest value of log likelihood. Finally, we performed paired Wilcoxon tests in order to test for significant differences between the ancestry proportions observed in the autosomes versus the X chromosome and used Bonferroni correction for multiple-testing (adjusted p value < 0. 05). We evaluated similarities in the autosomal/X chromosome ratio distribution by applying a Wilcoxon distribution, and reported the p value in Figure  3  and Data S2D.
ACKNOWLEDGMENTS. This work was supported by grants from the  Israel Science Foundation  ( 1479/16 , to E. E. ) and from the  Israel Science Foundation  ( 1082/17 ),  Israel Ministry of Agriculture  ( 15-37-0003 ), and the  National Science Foundation United States-Israel Binational Science Foundation Molecular and Cellular Biosciences Program  (2015839, to Z. R. ). E. E. is the incumbent of the Ernst and  Kaethe Ascher Career Development Chair  and thanks the Ascher foundation for their support. Y. B. thanks the  Yale Slifka Center ,  Schusterman Travel Grant , and the  Richard Goodman Yale-Weizmann Exchange Program  for their support. Z. R. is an incumbent of the  Hella and Derrick Kleeman Chair of Biochemistry. The electron microscopy studies were conducted at the Irving and  Cherna Moskowitz Center for Nano and Bio-Nano Imaging at the Weizmann Institute of Science.
Data availability. The draft genome sequence of K. schroeteri H01 obtained in this whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under the accession number VHHR00000000. The version described in this paper is the first version, VHHR01000000. The raw sequencing data have been deposited in the same database under the accession number SRR9323812. The BioSample and BioProject numbers are SAMN12090546 and PRJNA549517, respectively.
ACKNOWLEDGMENTS We acknowledge the contribution of the  Normandy Network of Microbial Metagenomics (RNAmb) , supported by  Région Normandie and European Union (FEDER). The work of N. D. D. , M. V. Z. , S. V. M. , V. K. P. , and A. V. G. was partially supported by the  Ministry of Science and Higher Education of the Russian Federation.
In both cases, MN residues appear to be gating residues, oscillating between rigid and flexible states along the MD trajectory and controlling ligand migration in the vicinity of the active site. Since its initial developments a few decades ago, NMR spectroscopy has turned out to be a powerful tool for exploring the structure and dynamics of proteins in solution  [18,37 -39]. In particular, proteic entries in the Protein Data Bank  [40]  (PDB) produced by solution NMR often provide a conformational ensemble containing between 10 and 30 structural models instead of a single structure. In this work, we used these conformational ensembles to investigate the mechanical properties in five globular protein families using CG simulations performed by ProPHet. The conformational variations in the experimental NMR ensembles are sufficient to induce large mechanical changes between models from a single PDB entry.
NMR conformational ensembles We searched the PDB for proteins where NMR conformational ensembles, comprising a minimum of 10 models, are available for at least three different species. This led to the selection of 19 PDB entries belonging to five protein families with various folds listed in table 1: globins, dihydrofolate reductase (DHFR), cyclophilin A (CPA), peptidyl-tRNA hydrolase (PTH), and parvulin. Each NMR ensemble comprises between 10 and 40 models, and the most representative ones (between two and seven models) were selected using the OLDERADO webserver, http:// www. ebi. ac. uk/pdbe/nmr/olderado/  [41]. If the NMR structure was not listed online, we used the Ensemble cluster tool, which employs the same method as OLDERADO  [42] , in the UCSF-Chimera program, https://www. cgl. ucsf.
edu/chimera/ [43] , to determine the most representative models. Both the ChannelsDB database, https://webchemdev. ncbr. muni. cz/ChannelsDB/ index. html [44] , and the CASTP webserver, http://sts. bioe. uic. edu/castp/index. html?2r7g  [45] , were used to identify tunnel and cavity lining residues within all these protein structures.
Following a procedure developed during earlier work on globins  [35]  and hydrogenases  [36] , we compared the rigidity profiles obtained for the selected representatives models. The models extracted from the NMR conformational ensembles do not present important structural variations, with Ca RMSDs between two models from the same PDB entry being always inferior to 3 A ˚(except for the structure of parvulin domain from Staphylococcus aureus, 2jzv, which presents both a flexible N-terminal tail and a flexible loop). However, these small structural changes are nonetheless sufficient to induce noticeable variations in the mechanical properties of a limited number of residues. We made a systematic pairwise comparison of all the rigidity profiles produced for the representative models from a single PDB entry and, for each residue in the protein, kept the maximum value that could be observed for its force constant variation.
Sequence alignments Sequences from PDB entries belonging to the same protein family were aligned using the online MUSCLE tool, https://www. ebi. ac. uk/Tools/msa/muscle/  [50] , with the default parameters. Despite the important mechanical similarity that can be observed in the rigidity profiles within a single protein family, the corresponding sequences display relatively low identities ranging from 15 to 82% (table  1 ). MS residues that were identified using the protocol described in the previous section are highlighted in bold and colour along the protein sequences in figure  3a  and all throughout the electronic supplementary material.
Globins Four NMR structures were selected in the PDB for the globin family: myoglobin (Mgb) from Physeter catodon (PDB entry 1myf), truncated haemoglobin (Tr. Hb) from S. sp. PCC 6803 (1mwb) and the a and b chains from human haemoglobin, Hb (2m6z). The force constant profiles for the main representative model of each protein are shown in figure  1. Similar to what has been observed in previous studies  [35, 46] , the analogous aspects of the profiles reflect the common globin fold, with a-helices appearing as grouped rigidity peaks along the protein sequence. The max(Dk) profiles resulting from the systematic pairwise comparison of the force constants calculated for different representative models for a given NMR conformational ensemble are shown in figure  2  and residues with max(Dk) values above the threshold given in the Material and Methods section (indicated by the red horizontal line) are defined as MS residues, since their mechanical properties are the most impacted by the small conformational variations observed between the NMR models.
thaliana and S. aureus only). In particular, by modulating the shape and size of the cavities and tunnels leading to the protein active site, MN residues can impact an enzyme's substrate selectivity and catalytic efficiency. Therefore, they represent an interesting target for the fine tuning of protein function. Conformational ensembles built from crystallographic structures can bring a lot of information regarding protein dynamics  [87] [88] [89]. In order to see whether these could also be used with this approach, we performed mechanical calculations and a pairwise comparison of the rigidity profiles on crystallographic structures of DHFR from three variants, using the PDB IDs listed in  [90] : DHFR from B. anthraci: chain A from 4alb, 4ale, 4elf, 4elg, 4elh (five structures), human DHFR, chains A and B from 2w3a, 2w3b and 2w3 m (six structures), DHFR from E.
coli, chain a from 1rx1, 1rx2, 1rx3, 1rx4, 1rx5, 1rx6, 1rx7, 1rx8, 1rx9 (nine structures). The resulting force constant variation profiles lead to the same MN as when using NMR ensembles, except for Ty121 (using the human sequence numbering), which is no longer an MS residue for any of the three species (see electronic supplementary material, figure  SI-2 ). This loss in the MN definition is not negligible, since this position has been shown to be functionally important for DHFR from S. aureus and M. tuberculosis  [65, 66]. Another issue with ensembles of crystallographic structures is the choice of representative structures which will be used for mechanical calculations (ideally, one should retain around four to six models to limit the computational cost of the approach). In the dataset listed in  [90] , at least 45 structures are retained for each protein.
ZZW-115 Is Active in Some Type of Cancers Since NUPR1 is overexpressed in several (if not all) tumors, we have evaluated the effect of treating cellular lines derived from several tumors with increasing concentrations of ZZW-115. Treatment of cells such as U87 (glioblastoma), A375 and B16 (melanoma), U2OS and SaOS-2 (osteosarcoma), HT29, SK-CO-1, and LS174T (colon cancer), H1299 and H358 (lung cancer), HepG2 (hepatocarcinoma), PC-3 (prostate), THP-1 (acute monocytic leukemia), Daudi (lymphoma), Jurkat (acute T cell leukemia), and MDA-MB-231 (breast cancer), demonstrated that ZZW-115 was efficient to kill these tumor cells with IC50 values in the range from 0. 42 µM (Hep2G cells) to 7. 75 µM (SaOS-2 cells). These data suggest that ZZW-115 could be potentially active for treating cancers from various tissues by targeting NUPR1.
ZZW-115 Is Active in Some Type of Cancers Since NUPR1 is overexpressed in several (if not all) tumors, we have evaluated the effect of treating cellular lines derived from several tumors with increasing concentrations of ZZW-115. Treatment of cells such as U87 (glioblastoma), A375 and B16 (melanoma), U2OS and SaOS-2 (osteosarcoma), HT29, SK-CO-1, and LS174T (colon cancer), H1299 and H358 (lung cancer), HepG2 (hepatocarcinoma), PC-3 (prostate), THP-1 (acute monocytic leukemia), Daudi (lymphoma), Jurkat (acute T cell leukemia), and MDA-MB-231 (breast cancer), demonstrated that ZZW-115 was efficient to kill these tumor cells with IC 50 values in the range from 0. 42 µM (Hep2G cells) to 7. 75 µM (SaOS-2 cells). These data suggest that ZZW-115 could be potentially active for treating cancers from various tissues by targeting NUPR1.
(Right panel) Nuclear magnetic resonance data indicated that ZZW-115 binds to NUPR1 in the residue Thr68  [35, 37] , located within the NLS. Thus, pharmacological inhibition of NUPR1 hampered the interaction with importin and its translocation to the nucleus (as showed in this immunofluorescence). After ZZW-115 treatment, NUPR1 was located in the perinuclear and cytoplasmic area, and inhibition of the nuclear activity of NUPR1 induced tumor growth arrest.
Transcriptional profiling of TAMs To evaluate the impact of the tumor-microenvironment on PM phenotype, we performed global gene expression analysis using microarrays on bulk PM from naïve mice and at different time points during tumor progression. We isolated naïve F4/80 hi PM and TAMs at 5, 12 and 21 days during ID8 tumor development by flow cytometry (Fig. S2 ). Total RNA was extracted and samples were analyzed using MoGene 1. 0st microarrays. RMA normalised data were filtered and analyzed for variations in gene expression. The heatmap in figure  S2B  shows the 1000 most variable genes in the dataset. To extract differentially expressed genes (DEGs) between naïve PM and TAMs at the different time points, we used Anova with an adjusted p value and a threshold of 1. 5 fold change (FC). We then used Gene Ontology (GO) enrichment analysis to identify pathways affected in TAMs at different time points (Fig.
Tumor-induced macrophage reprogramming is STAT6 and PI3K-dependent. To further characterize the mechanisms behind increased IL-4 induced gene expression in the presence of ID8-CM and how this may relate to cholesterol efflux, we analyzed IL-4 receptor signaling pathways. First, we observed no increase in the expression levels of the IL-4 receptor (IL4RA) on macrophages treated with ID8-CM (data not shown), but immunofluorescent staining illustrated an increased intracellular clustering of the receptor that suggested endosomal accumulation, which previously has been shown to promote downstream signaling  (Kurgonaite et al. )  (Fig. S4 ). We therefore measured activation of signaling pathways downstream of the IL-4 receptor. IL-4 induced gene expression is regulated by JAK-mediated phosphorylation of the STAT6 transcription factor.
Interestingly, using an established gene set for tumoricidal phenotype (GSE26912), which was enriched in naïve PM compared to TAMs, we also observed an enrichment in Pik3cd -/-TAMs compared to wild-type cells, suggesting that these cells retained a more tumoricidal phenotype in the absence of PI3K activation (Fig. S5 ). Finally, to evaluate the role of cholesterol efflux in TAMs in vivo, we established ID8 tumors in mice with a myeloid-specific deletion of both ABCA1 and ABCG1 (Abca1/g1 ∆Lyz2 ). ID8 tumor progression was significantly impaired in Abca1/g1 ∆Lyz2 mice compared to littermate controls (Fig. 6H ). Furthermore, microarray analysis of TAMs sorted from these mice showed a significant downregulation of genes associated with the IL-4 dependent TAM phenotype and a positive enrichment for tumoricidal genes (Fig.
Subsequently, the cells were fixed with 4 % Antigenfix (DiaPath) for 10 minutes on ice and nuclei were stained with TO-PRO-3. The di-4-ANEPPDHQ lipid raft staining protocol was adapted from the one described by Owen et al. (Owen et al. , 2011). Briefly, culture medium was replaced with fresh, serum-free DMEM containing 2 µl of di-4-ANEPPDHQ (5 µM). Dishes were shaken to ensure good mixing. After 30 min incubation at 37 °C in a humidified 5 % CO2 atmosphere, cells were fixed with 4 % PFA. Fluorescence was measured by confocal microscopy (Zeiss LSM780 or Leica SP5X) and blindly analyzed with FIJI software. The corrected total cell fluorescence (CTCF) was measured for each cell in at least 6 different fields of view per well.
Acknowledgements We thank  Bernard Malissen  ( CIML, FR ) for Stat6 -/-mice. These studies were supported by grants to TL from: L' Agence Nationale de la Recherche (ANR) ;  ANR-09-MIEN-029-01 ,  ANR-10-BLAN-1302-01  and  European Research Council ; FP7/2007-2013 Grant agreement number  260753 , and institutional funding from  INSERM ,  CNRS  and  Aix-Marseille-Université. JRV was funded by  Marie Curie actions IEF  (No. 234823 ). PG was funded by the  French Ligue Nationale contre le Cancer (LNCC). AE was funded by the  Novo Nordisk Foundation  ( NNF14OC0008781 ). Microscopy facilities are supported by  ANR-10-INBS-04-01   France Bio Imaging. JLS is a member of the  Excellence Cluster ImmunoSensation. The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme   FP7/2077-2013  under  REA  grant agreement no.
Deposited Data GSE126079 This paper https://www. ncbi. nlm. nih. gov/geo/q uery/acc. cgi?acc =GSE126079 GSE126080 This paper https://www. ncbi. nlm. nih. gov/geo/q uery/acc. cgi?acc =GSE126080 GSE126098 This paper https://www. ncbi. nlm. nih. gov/geo/q uery/acc. cgi?acc =GSE126098.
Acknowledgment This work was supported by the  Regional Council of Bourgogne -Franche-Comté , the  "Fonds Européen de Développement Régional (FEDER) " and " Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)"-Brazil  [grant number  200101/2014-5  to  A. A. C. Jr. ]. The authors are deeply grateful to the  Plateau Technique DImaCell (Université Bourgogne Franche-Comté, Dijon, France)  and to  Camille Malnoury  for technical assistance with the two-photon fluorescence imaging and to  Raphaël Caire  for the support with the enzymatic analysis.
Most transcriptional alterations are DNA methylationindependent To understand the basis of such transcriptional alterations, we next focused our analyses on IDHwt glioma samples. We used paired RNA-seq and HM450K data from 8 IDHwt samples to concomitantly determine the DNA methylation and transcriptional changes in the 1578 affected genes. In these IDHwt samples, we could identify four main transcriptional defect types (Fig. 3A ): gain or loss of gene expression associated with CGI/promoter hypermethylation (referred to as "Meth+/Exp+" and "Meth+/Exp-"defects, respectively), and gain or loss of gene expression without changes in CGI/promoter methylation status (i. e. , the CGI/promoter remained unmethylated; referred to as "No Meth/Exp+" and "No Meth/Exp-" defects, respectively). More than 93% of aberrantly repressed genes did not display any significant DNA methylation alteration at their CGI/promoter (No Meth/Exp-).
About 47% of the affected genes showed gain of expression that was associated with DNA hypermethylation at their CGI/promoter in 6% of them (Meth+/Exp+). To evaluate the robustness of this classification, we first analyzed the HM450K data of all 55 IDHwt glioma samples of our cohort and confirmed that DNA hypermethylation was associated with genes classified in the Meth+/Exp+ and Meth+/Exp-groups, and absence of methylation with genes classified in the No Meth/Exp+ and No Meth/Exp-groups. The only exception was a subset of "methylable" genes that gained methylation in some samples (Fig. 3B ). Next, we concomitantly assessed, in 42 IDHwt glioma samples, the DNA methylation and expression by RT-qPCR of randomly selected genes from the Meth+/Exp+, No Meth/Exp+, and No Meth/Exp-groups. Genes in the Meth+/ Exp+ group were methylated and ectopically expressed in all analyzed IDHwt glioma samples.
CGI/promoter hypermethylation is associated with gain of expression Because bisulfite treatment cannot distinguish between methylation and 5-hydroxymethylation (5-hmC), we evaluated whether the DNA methylation level detected with the HM450K array at genes classified in the Meth+/Exp+ group could be explained by 5hmC deposition. By using publicly available 5hmC data for IDHwt tumors  (Johnson et al. 2016) , we found that CGI/promoter regions of Meth+/Exp+ genes were devoid of 5hmC (median level = 1. 2%), indicating that the signal detected with the HM450K array was attributable to DNA methylation (Supplemental Fig. S6A ). Note that a similar observation is made at the three other defect groups (Supplemental Fig. S7 ). To determine how DNA hypermethylation and expression gain could coexist in this group of genes, we next assessed their CGI/promoter chromatin signature using publicly available ChIP-seq data for IDHwt glioma cells.
S9D ). We also observed that, as in IDHwt samples, genes with a bivalent chromatin signature in ES cells and/or with a dynamic expression pattern during neural differentiation were more prone to be deregulated in IDHmut glioma (Supplemental Fig. S10 ). Then, to understand the molecular bases of these alterations, and in the absence of publicly available ChIP-seq data on IDHmut samples, we performed ChIP analyses at selected genes from the Meth+/Exp-group (PCSK6 and MAL) and No Meth/Expgroup (SH3GL3 and PCDH10) in five IDHmut samples. We found that H3K27me3 enrichment was associated with reduced or unchanged H3K4me3 and H3K9ac in function of the studied locus (Supplemental Fig. S11A ). This confirms that, like in IDHwt samples, gain of H3K27me3 and chromatin bivalency are, besides DNA methylation, the main hallmarks of repressed genes in IDHmut glioma cells.
Tumor resection was classified as gross total resection, when no enhanced contrast was detected 48 h postsurgery, or as partial resection, when enhanced contrast was still detected 48 h postsurgery. The demographic and clinical features are presented in Supplemental Table  S1. The related statistical analyses were performed using the Stata software, version 13 (StataCorp) and the R software, version 3. 3. 1 (R Core Team 2016). To test the prognostic value of the patients' characteristics in univariate analyses, OS curves were compared between groups using the log-rank test. Results are expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Validation cohorts, obtained from The Cancer Genome Atlas (TCGA) research network, were described in  Ceccarelli et al. (2016). We retained IDHmut (n = 415) and IDHwt (n = 134) samples for which matched DNA methylation (HM450K array) and RNA expression (RNA-seq) data were available.