dataesr/scientific-paragraphs-categorization huggingface dataset:
Electrophysiology recordings on cell culture Coverslips of transfected neurons were placed in a Ludin Chamber on an inverted motorized microscope (Nikon Eclipse Ti). Extracellular recording solution was composed of the following (in mM): 110 NaCl, 5. 4 KCl, 1. 8 CaCl2, 0. 8 MgCl2, 10 HEPES, 10 D-Glucose, 0. 001 Tetrodotoxin and 0. 05 Picrotoxin (pH 7. 4; 245 mOsm). Patch pipettes were pulled using a horizontal puller (P-97, Sutter Instrument) from borosilicate capillaries (GB150F-8P, Science Products GmbH) to resistance of 4-6 MΩ and filled with intracellular solution composed of the following (in mM): K-gluconate, 10 HEPES, 1. 1 EGTA, 3 ATP, 0. 3 GTP, 0. 1 CaCl2, 5 MgCl2 (pH 7. 2; 230 mOsm). Transfected neurons were identified under epifluorescence from the GFP signal. Recordings were performed using an EPC10 patch clamp amplifier operated with Patchmaster software (HEKA Elektronik).
Whole-cell voltage clamp recordings were performed at room temperature and at a holding potential of -70 mV. Unless specified otherwise, all chemicals were purchased from Sigma-Aldrich except for drugs, which were from Tocris Bioscience. Data were collected and analysis of miniature EPSCs were performed using a software developed by Andrew Penn, the matlab script is available on MATLAB File Exchange, ID: 61567; http://uk. mathworks. com/matlabcentral/fileexchange/61567-peaker-analysis-toolbox).
Bicuculline (20 µM) was added to the aCSF to block inhibitory currents and DNQX (100 nM) was added to control epileptic activity. Series resistance was always lower than 20 mΩ. Paired-pulse ratio was determined by delivering two stimuli 50 ms apart and dividing the peak response to the second stimulus by the peak response of the first one. For recordings aEPSCs, extracellular CaCl2 was substituted to equimolar SrCl2. aEPSCs evoked within 500 ms after the stimulation, were analyzed off-line with Mini Analysis software (Synaptosoft). In all cases, at least 20 sweeps per recording were analysed. with a detection threshold set at 5 pA.
Co-localization analysis Co-localization analysis was performed using custom written program in Matlab (Mathworks, UK). Manders' coefficients were chosen as co-localization measure because they do not depend on the relative intensity difference between two component images, therefore bypassing alteration in labeling efficiency of different cellular structures. Here we perform pairwise analysis between coincidental objects observed in two image components. Thus, our Manders' coefficients represent fraction of the intensity belonging to co-localizing super-resolution pixels of a given object. Manders' coefficients are calculated using following equations: Where i is a pixel index, 1 and 2 stands for two image components and n is the number of pixels in an object for which coefficient is evaluated.
ACKNOWLEDGEMENTS We acknowledge  E. Gouaux  for the anti-GluA2 antibody;  J-B Sibarita  and  Corey Butler  for providing single particle analysis software,  M. Sainlos  and  I. Gauthereau  for anti-GFP nanobody production;  C. Breillat  and  E. Verdier  for cell culture and plasmid production;  M. Goillandeau  and  Andrew Penn  for mEPSC analysis software ( Detection Mini ). This work was supported by funding from the  Ministère de l'Enseignement Supérieur et de la Recherche  ( ANR NanoDom ),  Centre National de la Recherche Scientifique, FRM  to BC, ERC Grant nano-dyn-syn to DC. SyMBaD - ITN Marie Curie , Grant Agreement n° 238608 -7 th  Framework Program  of the  EU.
Hi-C data For Drosophila data analysis, we used publicly available high-throughput chromatin conformation capture (Hi-C) data of embryonic Kc167 cells from Gene Expression Omnibus (GEO) accession GSE62904  (17). We also used Kc167 Hi-C data from GEO accession GSE89112  (18). Hi-C data were binned at 1, 2 and 5 kb resolutions. For human data analysis, we used publicly available Hi-C data of lymphoblastoid GM12878 cells from GEO accession GSE63525  (12). We used Hi-C data binned at 10, 40 and 100 kb resolution.
ChIP-seq data For Drosophila data analysis, we used publicly available protein-binding profiles of Kc167 cells (except for Pnr whose data were from 6-8 h embryos). ChIP-seq data for CP190, Su(Hw), dCTCF and BEAF-32 were obtained from GEO accession GSE30740  (19). ChIP-seq data for Barren (condensin I), Cap-H2 (condensin II), Chromator, Rad21 (cohesin), GAF and dTFIIIC were obtained from GEO accession GSE54529  (11). ChIP-seq data for Fs(1)h-L were obtained from GEO accession GSE42086  (20). ChIPseq data for Ttk69k were obtained from GEO accession GSE34698  (21). ChIP-seq peak calling was done using MACS 2. 1. 0 with default parameters for all proteins (https: //github. com/taoliu/MACS). ChIP-chip peaks for Pnr were directly downloaded from  (22). For human data analysis, we used publicly available binding peaks of 73 chromatin proteins (Rad21, CTCF, YY1, ZBTB33, MAZ, JUND, ZNF143, EZH2, ATF2, ATF3, BATF, BCL11A, BCL3, BCLAF1, BHLHE40, BRCA1, CEBPB, CFOS, CHD1, CHD2, CMYC, COREST, E2F4, EBF1, EGR1, ELF1, ELK1, FOXM1, GABP, IKZF1, IRF4, MAX, MEF2C, MTA3, MXI1, NFATC1, NFE2, NFIC, NFKB, NFYA, NFYB, NRF1, NRSF, P300, PAX5, PBX3, PML, POL2, POL3, POU2F2, RFX5, RUNX3, RXRA, SIN3A, SIX5, SMC3, SP1, SPI1, SRF, STAT1, STAT3, STAT5, TBLR1, TBP, TCF12, TCF3, TR4, USF1, USF2, WHIP, ZEB1, ZNF274 and ZZZ3) of GM12878 cells from ENCODE  (23).
DNA motifs To scan the genome for motif occurrences, we used Find Individual Motif Occurrences (FIMO) with default parameters and with position-specific priors (PSPs) to improve the identification of true motif occurrences  (24). GM12878 DNase data from ENCODE were used as PSPs  (23). The motif information was taken either from the litterature (using consensus motif) or from JASPAR database (http:// jaspar. genereg. net/). For Drosophila data analysis, we used transcription factor-binding site (TFBS) motifs from the JASPAR database. For some proteins, we used instead motif consensuses from the litterature: BEAF-32 (CGATA)  (25) , dCTCF (AGGTGGCG)  (26) , Su(Hw) (TGCATATTT)  (27) , GAF (GAGAGA)  (28) , ZW5 (GCTGMG)  (29) , DREF (TATCGATA)  (30) , M1BP (GGTCACACT)  (31) , Ttk69k (GGTCCTGC)  (32) , dTFIIIC A box (TGGN NNAGNNG), Pita (GGTTNNNNNNNNNGCT)  (29) , ZIPIC (AGGGNTG)  (29) , Ibf (ATGTANAA)  (33) , Elba (CCAATAAG)  (34)  and Zelda (CAGGTAG)  (35).
PAGE 4 OF 10 The model is available in the R package 'HiCblock' which can be downloaded from the Comprehensive R Archive Network (https://cran. r-project. org/web/packages/ HiCblock/index. html). For the negative binomial regression, model ␤s are learned by iterative weighted least squares (glm. nb function from MASS R package with default parameters). For the Poisson lasso regression, model ␤s are learned by cyclical coordinate descent and lambda parameter is estimated with 10-fold crossvalidation (cv. glmnet function from glmnet R package with default parameters).
Results Paral1 is a long intergenic non-coding RNA specifically expressed in mature white adipocytes. To identify lincRNA(s) expressed in adipose tissue and regulated during adipogenesis, we mined the NONCODE v3. 0 database (http://www. noncode. org) containing 36,991 lncRNAs, from which 9,364 lincRNAs could be identified by filtering out transcripts overlapping with RefSeq genes. Using NGS data from differentiating 3T3-L1 cells  21  , a well-established model for adipocyte differentiation, 406 lincRNAs from the NONCODE database displaying an increased density in H3K4me3 and H3K27ac ChIP-seq signals within +/-2. 5 kb from the TSS upon differentiation were identified (Supplemental Table  2 , Fig. 1A ). Additional filtering using PPARγ ChIP-Seq signals narrowed this list down to 3 lincRNAs, amongst which BC034902, hereafter termed Paral1 (PPARγ-activator RBM14-associated lincRNA 1), displayed the strongest levels of transcriptional activation marks (Fig.
Adipose tissue inflammation lowers Paral1 expression in murine models of obesity. Paral1 expression was therefore assessed in 2 mouse models of obesity. Epididymal (e)WAT from both leptin-deficient (ob/ob) and high-fat diet-fed (HFD) mice displayed clear signs of increased expression of inflammation-related pathways (Fig. 6A, B , Supplemental Fig. 16 ), in agreement with our previous studies  26. GSEA comparing gene expression patterns of Paral1-depleted 3T3-L1 cells at D7 to that of ob/ob and HFD mouse eWAT revealed that pathways related to oxidative phosphorylation were commonly dysregulated (Fig. 6A, B  and Supplemental Fig. 10 ). Epididymal WAT from obese mice displayed a markedly decreased expression of Paral1, in line with our data showing that depletion of Paral1 yields dysfunctional 3T3-L1 cells, (Fig.
Microarray analysis. Total RNA (100-300ng) from 3T3-L1 cells was processed for labelling, purification and hybridized to Affymetrix Genechip Mouse Genome 430 2. 0 or to Mouse Transcriptome Array 1. 0 according to the manufacturer's protocol. Raw data (available on the GEO website under the accession number GSE 97241) were pre-processed using the GCCN and SST algorithms (Expression Console, v1. 4. 1. Affymetrix). RMA background correction and gene-level probe set summarization were performed with the Partek Genomics Suite software (v6. 6, Partek Inc. ). Microarray analysis of leptin-deficient (ob/ob) mouse WAT has been described elsewhere  26. Microarray data from mouse WAT fed either a chow or a high fat diet were from the GEO dataset GSE21069  46. Human WAT RNAs were analysed as described  26.
Human gene symbols were attributed to each murine gene using the Orthologue Conversion software (https:// biodbnet-abcc. ncifcrf. gov/db/dbOrtho. php)  47 and resulting files were analysed with the GSEA software [Broad Institute, v2. 2. 2  48  ]. Pathway-enrichment scores were calculated with the GSEA pre-ranking tool and the KEGG Pathway gene set. Default parameters were used except for the permutation number (10,000) and the enrichment score statistic (weighted). The GEO dataset is available under the number GSE97241. Data mining and bioinformatics. Long non-coding RNA identification. Murine lncRNA sequences were extracted from the NONCODE database v. 3 (http://www. noncode. org/NONCODERv3/; mouse genome reference: mm9). Gene Expression Omnibus (GEO) datasets were: GSE84888 for differentiating human adipose stromal (hASC) and murine 3T3-L1 cells  21  and GSE92590 for isolated primary mouse adipocytes from WAT  49.
RNA-Seq data were from the GEO dataset GSE35724  50. ChIP-Seq data were analyzed using the Galaxy Cistrome platform  11. RNA-Seq and ChIP-Seq data were visualized using the Integrative Genomics Viewer software (IGV, v2. 3. 14, Broad Institute). Coding potential analysis and proteomic databases. Three softwares were used with default parameters to determine lincRNAs' coding potential (CPC, http://cpc. cbi. pku. edu. cn/)  51 ; CPAT, http://lilab. research. bcm. edu/cpat/ index. php)  52 and GenView2 (http://bioinfo. itb. cnr. it/~webgene/wwwgene. html). GAPDH and XIST RNA were used as representative of mRNA and lincRNA respectively. Potential ORFs were converted into protein sequences and matching peptides were searched in four protein databases [UniProtKB (SwissProt and TEMBL; http://www. uniprot.
Tandem mass spectra were processed with the Thermo Scientific Proteome Discoverer software v 1. 3. Spectra were searched against UniprotKB/Swiss-Prot mouse databases (version 09/2015) using the SEQUEST HT algorithm (v1. 3. 0. 339). The search was performed choosing trypsin as the cleaving enzyme with one missed cleavage site allowed. Precursor mass tolerance was 10 ppm, and fragment mass tolerance was 0. 1 Da. N-terminal acetylation, cysteine carbamidomethylation and methionine oxidation were set as variable modifications. Peptide identification was performed with the Percolator algorithm by selecting only peptides with a q-value < 0. 01, which corresponds to a false discovery rate (FDR) of 1%.
Animal experimentation. All animal experiments were approved by the ethical committee for animal experimentation of Institut Pasteur de Lille, Pierre and Marie Curie University and French Research Council guidelines. All methods were performed in accordance with the relevant French and European guidelines and regulations. Detailed procedures can be found elsewhere  8, 27. Human biopsies. Human tissue samples were provided by Dr P. Gélé and the Centre d'Investigations Cliniques (C. H. R. U. Lille, France). Human WAT samples were collected from patients undergoing abdominal surgery by laparoscopy or coelioscopy after informed consent was obtained. All procedures were approved by the C. H. R. U. Lille Ethical committee and were compliant to the French National Ethics Committee guidelines. Tissue samples from female patients (35-59 year-old) and are from the ABOS cohort (ClinicalTrials.
(D) Knockdown of Paral1 expression alters the expression of a specific subset of genes. The 3T3-L1 cell transcriptome was characterized by DNA microarray analysis as described in the Materials & Methods section. GSEA was performed against the KEGG database. NES: normalized enrichment score, FDR: false discovery rate), red: up-regulated after Paral1/1-siRNA treatment, green: down-regulated after Paral1/1-siRNA treatment. (E) GSEA enrichment plots of oxidative phosphorylation, TCA cycle, PPAR signaling pathway and cytokine-cytokine receptor signature genes in differentiating 3T3-L1 cells. Green curves depict the enrichment score for each gene (vertical black line) ranked along a heatmap (red to green, up-to down-regulated genes) indicating the observed fold change in Paral1/1-siRNA treated cells.
Acknowledgements We thank  E. Bouchaert  and  O. Ghali  ( University of Lille, France ) for their advices and help with the isolation and differentiation of MSC. We are very grateful to  D. Monté  ( Univ. Lille 1 ) for providing us with the CoAA expression vectors. The authors also thank the  Research Federation FRABio (Univ. Lille, CNRS ,  FR 3688 ,  FRABio, Biochimie Structurale et Fonctionnelle des Assemblages Biomoléculaires ) and the  CLIC-Imaging Mass Spectrometry  facility of the  PRISM laboratory  ( INSERM U1192 ,  Univ. Lille)  for the NanoLC-Orbitrap experiments. We are also grateful to  L. Casteilla ,  X. Maréchal ,  E. Arnaud ,  A. Carrière  and  S. Mandrup  for helpful discussion and data sharing. This work has been supported by  Fondation pour la Recherche Médicale  (Equipe labellisée, DEQ.
Numbers indicate intracellular ORO stain and intracellular triglycerides quantification relative to non-transfected (NT) cells for a representative experiment. (D) Knockdown of Paral1 expression alters the expression of a specific subset of genes. The 3T3-L1 cell transcriptome was characterized by DNA microarray analysis as described in the Materials & Methods section. GSEA was performed against the KEGG database. NES: normalized enrichment score, FDR: false discovery rate), red: up-regulated after Paral1/1-siRNA treatment, green: down-regulated after Paral1/1-siRNA treatment. (E) GSEA enrichment plots of oxidative phosphorylation, TCA cycle, PPAR signaling pathway and cytokine-cytokine receptor signature genes in differentiating 3T3-L1 cells. Green curves depict the enrichment score for each gene (vertical black line) ranked along a heatmap (red to green, up-to down-regulated genes) indicating the observed fold change in Paral1/1-siRNA treated cells.
Antibody-antigen complexes challenge our understanding, as analyses to date failed to unveil the key determinants of binding affinity and interaction specificity. We partially fill this gap based on novel quantitative analyses using two standardized databases, the IMGT/3Dstructure-DB and the structure affinity benchmark. First, we introduce a statistical analysis of interfaces which enables the classification of ligand types (protein, peptide, and chemical; cross-validated classification error of 9. 6%) and yield binding affinity predictions of unprecedented accuracy (median absolute error of 0. 878 kcal/mol). Second, we exploit the contributions made by CDRs in terms of position at the interface and atomic packing properties to show that in general, VH CDR3 and VL CDR3 make dominant contributions to the binding affinity, a fact also shown to be consistent with the enthalpy-entropy compensation associated with preconfiguration of CDR3.
The Dataset and Data Curation: The IMGT/3Dstructure-DB We use the Ig-Ag complexes from the IMGT/3Dstructure-DB [http://www. imgt. org/3Dstructure-DB/ (20)], corresponding to the category IG/Ag for IMGT complex type. Each such complex is processed in order to identify canonical complexes involving one heavy chain, one light chain, and one ligand. Upon inspecting such cases, two decisions are made. First, on the antigen side, we retain three types only (peptide, protein, and chemical), due to the scarcity of cases involving other types. Moreover, we also remove complexes involving multiple ligands types. For the same reason, regarding species, complexes are assigned to three classes: human, mouse, and other. In total, 489 complexes are retained after filtering for missing data, inconsistencies, redundancy, ligand type, and species.
The Binding Affinity Benchmark Our affinity predictions exploit the structure affinity benchmark (SAB)  (23) , a manually curated dataset containing 144 cases, each described by three crystal structures (of the unbound partners and of the complex) and the experimentally measured binding affinity in controlled conditions. In this work, we split the SAB into two sets: 14 Ig-Ag cases defining the test set (Table  S3  in Supplementary Material) and 125 non-Ig-Ag cases defining the training set. Five complexes (among which 3 Ig-Ag) were removed from the SAB because only an upper bound on their K d was provided, or had too many missing atoms. Having learned a statistical model from the latter, we predict affinities for Ig-Ag complexes of the former. See details in the Supplementary Material section.
Statistical Methodology Since the performance of classifiers tested on the training data is overestimated and leads to classifiers with poor generalization abilities (overfitting), various schemes have been devised to obtain an estimate of the generalization error. We use the k-fold cross-validation where the dataset is randomly divided in k subsets of equal size, and k -1 subsets are alternatively used to classify the remaining one. At the end of this procedure, each sample has been predicted and the proportion of misclassified samples can be computed. Here, k is set to 5. Since the partition into training and test data used during this procedure is inherently random and may lead to non-representative results for a single run, we report median errors over 1,000 crossvalidation runs. In order to size the expected performance of a random classifier, we use a simple permutation test.
In order to compare these results to what could be expected from a null model, we take the average -∆G of the training dataset (10. 78 kcal/mol ±2. 84) as prediction for all complexes. This results in a median absolute error of 1. 03 kcal/mol, or equivalently, in a ratio for K d equal to 5. 7. The previous conclusions must therefore be mitigated, since a simple null model can show good, albeit less so, performances as well. In order to rationalize the varying accuracy of predictions depending on the complex, we compute the average distance d i between each Ig-Ag complex i and its 10 nearest neighbors in the training set. We also compute the standard deviation of the affinity values of these 10 nearest neighbors σ i (Figure  S10  in Supplementary Material). Both d i and σ i are weakly correlated to the absolute prediction error |e i | with Pearson's correlation coefficients of 0.
57 and -0. 57, respectively. Both coefficients are (weakly) significantly different from zero with p-values of 0. 0312 and 0. 03316, respectively. The correlation between |e i | and d i /σ i is higher, however, with a Pearson correlation coefficient equal to 0. 72 and a p-value of 0. 00363. This suggests that good binding affinity prediction can be obtained provided that sufficiently similar complexes are in the training set and that their affinity values are consistent with each other. Interestingly, this property also accounts for the good performances of the null model. We now discuss a few cases, focusing on complexes with similar ligands (Table  S3  in Supplementary Material). PDB entry 2VIR consists of an influenza hemagglutinin in complex with a neutralizing Ig. PDB entry 2VIS is the same Ig in complex with an escape mutant of the previous hemagglutinin differing by only one mutation (T131I).
This causes their -∆G to differ by almost 5 kcal/mol which corresponds to a factor ~4,800 on K d. These two complexes lie very close to each other in the parameter space (Figure  3 ), and our model unsurprisingly predict similar affinity values. However, they are only accurate for 2VIS (Figure  2 ). This lack of accuracy may be due to the inability of our model to capture large energetic contribution from few residues-a difficulty calling for the identification of hot spots (57, 58)-or may be due to a deficient modeling of dynamical properties  (38). PDB entries 1BVK, 1DQJ, 1MLC, 1P2C, and 1VFB all share the same ligand (hen egg lysozyme C EC 3. 2. 1. 17) with -∆G ranging from 19. 61 to 13. 63 kcal/mol. Focusing on the V region of heavy chains, 1P2C and 1MLC are fairly similar with 90. 5% identity; and 1BVK and 1VFB as well with 79.
3% identity. All other pairs share limited sequence identity with values between 44. 3 and 57. 5%. For the V region of light chains, sequence identity between 1P2C, 1MLC, and 1DQJ ranges between 93. 5 and 96. 3%. Sequence identity between 1BVK and 1VFB is 82. 2%. Sequence identity between other pairs ranges between 56. 1 and 61. 7%. PDB entries 1DQJ and 1VFB which have similar experimental affinities are located close to each other in the parameter space (Figure  3 ) and are both well predicted by our model (Figure  2 ). PDB entry 1MLC is also well predicted even though its experimental affinity is much lower than that of the previous entries. On the other hand, 1BVK and 1P2C are not so well predicted with errors between one and two orders of magnitude on K d , although the latter is located in the same region as 1DQJ and 1VFB.
Respective Contributions of the CDRs to the Interface, for VH and VL Domains In an Ig-Ag complex, it is generally believed that VH contributes more to the recognition than VL. With a BSA of VH strictly larger than that of VL for 430/489 complexes (~86%) (Figure  5A ), our analyses support this idea. To refine this view, we split the BSA into contributions by the CDRs within a V domain, observing a great deal of variation across the dataset, independent from the ligand type (Figures  5B, C ). A general observation is that the sum of contributions of CDR1 and CDR2 essentially matches that of CDR3 for both VH and VL. Consider the sum of the BSA of CDR1 and CDR2 on one hand, and the BSA of CDR3 on the other hand. The first quantity is larger than the second one for ~46% of the complexes for VH and for ~40% of the complexes for VL.
Our predictions of K d are accurate within two orders of magnitude for all but one complex and within one order of magnitude for 8 of them. Interestingly, these results stress the relevance of the overall approach, which exploits structural and functional similarities between the test set (the Ig-Ag complexes) and the training set (the SAB deprived from the Ig-Ag complexes). In fact, the high accuracy of our predictions shows that the binding affinity prediction problem could be partially solved using large databases of Ig-Ag complexes with binding affinity measurements. Our results on specificity analysis and affinity predictions are of immediate practical relevance in the context of Ig design and Ig-Ag docking. Docking is the problem of predicting the pose (i. e. , the static structure) and the affinity of a complex from the unbound partners  (64).