dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by the  François Rabelais University of Tours  and funded by grants from the  European Commission  ( SyntheGeneDelivery 018716 ), the  CNRS , the  French MENRT , the  AFM  (Grants  14020 ,  14509  and  15519 ), the  GR CNRS 2157  and  La Ligue Nationale  contre le cancer. Solenne Bire held a doctoral fellowship from the  French MENRT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In mammals, clear identification of active and null domains is evident, while Polycomb and Hp1 domains, if exists, are likely to be smaller than 1MB in scale, making their detection using current maps difficult. The correlations between 3C domain structure and epigenomic datasets pave the way to better integrative models in epigenomics. The notoriously complex and indirect correlations between the many measurable aspects of epigenomic structure can now be overlaid on top of 3C contact maps, putting epigenetic and regulatory marks onto a model reflecting short-range and long-range contacts. The a priori independence of 3C maps from other epigenomic profiling techniques, and its two-dimensional natural matrix structure, suggest these maps can become a standard basis for epigenomic exploration, even before the precise physical basis of their domain structure is fully resolved.
Acknowledgements  Giacomo Cavalli  is supported by grants from the  European Research Council  ( ERC-2008-AdG No 232947 ), the  CNRS , the  European Network of Excellence EpiGeneSys , the  Agence Nationale de la Recherche , and by the  Association pour la Recherche sur le Cancer. Amos Tanay is supported by grants from the  European Research Council  ( ERC-2012-StG No 309706 ), the  European Network of Excellence EpiGeneSys , and the  Israeli Science Foundation  ( ISF 711313 ). AT is an incumbent of the  Robert Edward and Roselyn Rich Manson Career Development Chair.
Manual Gene Annotation and Bioinformatic Analysis Comparative analyses were performed using the fully sequenced genomes of four representatives of the M. extorquens species; strain CM4 (GenBank accession no. CP001298, CP001299, CP001300), strain AM1 (GenBank accession no. CP001511, CP001512, CP001513 and CP001514), strain DM4 (GenBank accession no. FP103042 and FP103043 and FP103044), strain PA1 (GenBank accession no. CP000908) and strain BJ001 (GenBank accession no. CP001029, CP001030 and CP001031)  [5]. Putative orthology relationships were operationally defined by gene pairs from different genomes satisfying an alignment threshold of at least 40% amino acid sequence identity (aa Id) over at least 80% of the length of the smallest encoded protein. The search for conserved genes clusters was performed as previously described  [11].
Manual validation of automatic gene annotations on pCMU01 plasmid (also known as pCMU01) was performed using the relational database  [18]  Microscope web interface (MethyloScope) https:// www. genoscope. cns. fr/agc/mage/wwwpkgdb/Login/log. php?pid = 26). Insertion sequence (IS) annotations were done as previously described  [11]. IS elements were given names of type ''ISMch3'', with ''Mch'' for Methylobacterium extorquens degrading chloromethane.
Proteome Image Analysis Differential analysis was performed using ImageMaster 2D Platinum software (v. 6. 0, GE Healthcare). Six gels were grouped in two classes of three independent gels depending on the two compared conditions (chloromethane or methanol growth conditions). Gels were matched with one reference gel (master gel) following spot detection. For each spot, the relative volume corresponded to the normalized volume of the spot compared to the normalized volume of the entire gel coloration. Statistical analysis was performed by calculating the Student t value for each spot, as well as a ratio value defined as the mean of the relative volume of the spot obtained in the different replicates for growth with chloromethane divided by the mean of the relative volumes obtained for growth with methanol.
Spots with a Student t value higher than 1. 9 (corresponding to a p-value of ,0. 1) and ratios $2. 0 or #22. 0 were analyzed by mass spectrometry. DIGE images were analyzed with DeCyder software (v. 7. 0, GE Healthcare). A total of twelve images obtained from 4 gels (three images each) were analyzed. Student's t test was used to determine differential abundance of proteins. In this procedure, the p-values were corrected for false discovery rate  [20]. Spots with a p-value ,0. 01 and ratios $2. 0 or #22. 0 were considered to be differentially abundant.
Mass Spectrometry Protein Identification The procedure of Muller et al. [16]  was followed for spot identification, with minor adjustments. Mass spectrometry analyses were performed in reflector positive mode on a Biflex III (Bruker-Daltonik GmbH, Bremen, Germany) matrix-assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOF TOF) and on an Autoflex III Smartbeam (Bruker-Daltonik GmbH, Bremen, Germany) matrix-assisted laser desorption/ ionization time-of-flight mass spectrometer (MALDI-TOF TOF). A saturated solution of a-cyano-4-hydroxycinnamic acid in 50% water/50% acetonitrile was used as matrix for MALDI mass measurement on the Biflex III. Peptide mass fingerprinting data (PMF) and peptide fragment fingerprinting data (PFF) were combined by Biotools 3 software (Bruker Daltonik) and transferred to the search engine MASCOT (Matrix Science, London, UK).
[5,11] database (http://www. genoscope. cns. fr/agc/mage). bProbability-based mowse score calculated using MASCOT software (Matrix Science, London, UK); error refers to mass accuracy; coverage refers to the percentage of the protein sequence covered by the matched peptides. cSpots indicated as ''CH 3 Cl'' were only detected in the proteome of M. extorquens CM4 grown with chloromethane. Spots indicated as ''+'' were more abundant in chloromethane-grown cultures (or less abundant in methanolgrown cultures). Spots indicated as ''2'' were more abundant in methanol-grown cultures (i. e. less abundant in chloromethane-grown cultures). Factors of differential abundance were defined as follows:++(d NL, non linear pI range used in 2D-DIGE experiments. eOnly found in strain CM4 (among the 8 Methylobacterium strains for which the complete genome sequence is known;[5,11]) and localized on plasmid pCMU01.
Genetic and environmental determinants of altered cellular function, disease state, and drug response are increasingly studied using time-resolved transcriptomic profiles. While it is widely acknowledged that the rate of biological processes may vary between individuals, data analysis approaches that go beyond evaluating differential expression of single genes have so far not taken this variability into account. To this end, we introduce here a robust multi-gene data analysis approach and evaluate it in a biomarker discovery scenario across four publicly available datasets. In our evaluation, existing methods perform surprisingly poorly on time-resolved data; only the approach taking the variability into account yields reproducible and biologically plausible results. Our results indicate the need to capture gene expression between potentially heterogeneous individuals at multiple time points, and highlight the importance of robust data analysis in the presence of heterogeneous gene expression responses.
The same holds true for most differential expression approaches applied to time series data, which by default typically ignore the time heterogeneity between the sample series. Figure  1  illustrates this weakness of the 'group average'-based approaches (Fig. 1  upper path), and the alternative 'individual correlation'based approach we introduce here (Fig. 1  lower path). Data shown is from a study of gene expression in responders and nonresponders to interferon/ribavirin therapy to Hepatitis C  [25]. The strong pairwise correlation within each individual is lost in the average-based approach, whereas it is clearly exposed by the individual correlation-based approach. To systematically examine the differences between group average-and individual correlation-based approaches, we evaluated them using a biomarker discovery scenario across four different published datasets (Table  1 ).
Additionally, we evaluated an alternative strategy of concatenating the time series measurements across individuals within the groups, as applied earlier  [26, 27]. More specifically, we considered here six different approaches: (i) conventional differential expression analysis using the Bioconductor limma package  [28] ; (ii) differential co-expression analysis after group averaging using the popular WGCNA approach or (iii) the more recent DCp method; (iv) differential co-expression analysis after concatenating the time series within the groups using WGCNA; (v) differential co-expression analysis on individual correlations using WGCNA or (vi) an alternative approach we introduce here, which we name Dynamically Coexpressed Neighborhoods (DCeN), that takes into account also the neighbor-wise co-expression changes instead of a simple total connectivity of a gene.
Consistency We assessed the reproducibility of the top-ranked genes in the human hepatitis C virus (HCV) treatment dataset  [25]  across independent subsamples of two to six replicates (Fig. 2  and Fig. S1 ). Throughout all numbers of replicates tested, the reproducibility of our DCeN method was significantly higher than that of the current state-of-the-art gene ranking methods based on differential expression or differential co-expression (Fig. 2 ; Wilcoxon signed rank test, p,0. 01). In general, the individual correlation-based approach improved markedly also the performance of WGCNA when compared to the previously applied average-based and concatenation-based approaches, although its reproducibility remained significantly lower than that of DCeN. We also tested whether subtracting the gene-wise average intensity from each individual before concatenation would improve the concatenation-based approach but we did not observe any significant effect (Fig.
Overall biological relevance To systematically assess the overall biological relevance of each computational method, we applied them to two publicly available gene expression time series datasets in which the biological importance of many genes had been independently assessed using RNA interference (RNAi) or gene deletions. In the LPS data set, the transcriptome of mouse dendritic cells was profiled at multiple time points after stimulation with various toll-like receptor ligands, and without stimulation. Independently, the biological role of 125 transcription factors had been probed and validated using RNAi and a set of signature genes  [29]. We used each computational analysis method to rank the validated factors, and then quantified an average regulatory effect of the topranked factors on the basis of the RNAi data; the regulatory effect of a factor was defined as the percentage of the signature genes that were identified as its targets (Fig.
014; Fig. 3b ). In these data, also the individual correlation-based WGCNA produced significant enrichment (permutation test, most significant p = 0. 003). Again, no statistically significant enrichment was observed for the other methods (permutation test, p. 0. 05 at all top list sizes from 1 to 1000). Overall, the traditional differential expression or differential coexpression measures performed surprisingly poorly, whereas the individual correlation-based approaches improved the detections. Only DCeN identified significant numbers of functionally relevant genes in both datasets, while individual correlation-based WGCNA performed well only in the CDC13 data.
Detection of specific co-expression patterns Finally, we performed a detailed manual analysis using DCeN on the human T helper 2 (Th2) cell differentiation dataset  [32]. Human activated CD4+ T cells were profiled with and without polarization towards Th2 with IL-4. Among the 115 top-ranked  genes for differential co-expression (permutation test, p,0. 05) were several factors with previously assigned functions specific to Th2 cells, such as ITK, ICOS, LEF1, and GATA3, a well-known master regulator of Th2 cell differentiation, as well as genes that have only more recently been proposed to play an important role in Th2 cell differentiation, such as SOCS2 and STAT3  [33] [34] [35] [36] [37] [38]. Also, the strong enrichment of the DCeN genes for immune response (19%, false discovery rate FDR,10 25 ) and regulation of apoptosis (18%, FDR,10 24 ) was consistent with the different Th cell subsets playing a critical role in immune response and having different susceptibility to apoptosis.
Discussion Our results suggest surprising significant weaknesses of state-ofthe-art data analysis approaches to detect differential co-expression from grouped time-resolved transcriptomic data. In our evaluation across the few published datasets of this type, the DCeN method, which we introduced here, was the only approach to consistently extract significant numbers of genes corroborated in independent studies, and highlighted dynamic association of known immune regulators under corresponding experimental conditions. The insights enabled by the global measurement of dynamic patterns across groups of individuals will make this type of data increasingly attractive, and will certainly create a broader basis for studies such as this in the future. Nonetheless, the observed difference in performance is striking.
DE, WGCNA, and DCp Differential expression (DE) was determined using the Bioconductor limma package  [28]. Genes were ranked according to the moderated F-test. Weighted Gene Co-expression Network Analysis (WGCNA)  [42]  and Differential Co-expression Profile analysis (DCp)  [22]  were applied using the R package DCGL following the instructions provided in the package manual  [43]. DCp was recently suggested to improve the detection of differentially coexpressed genes, whereas WGCNA showed the best performance among the popular existing algorithms in the same study  [22].
Datasets used for evaluation The human hepatitis C virus (HCV) dataset  [25]  used for the reproducibility analysis included gene expression profiles of peripheral blood mononuclear cells from Caucasian American patients with chronic HCV infection. Gene expression was profiled at six time points after initiation of treatment. The preprocessed data on 30 patients with measurements at each time point was downloaded  [44] , including 17 patients showing good (marked or intermediate) response and 13 patients showing poor response. The mouse lipopolysaccharide (LPS) dataset  [29]  includes gene expression profiles of mouse dendritic cells exposed to LPS. Gene expression was profiled in duplicate series at nine time points after stimulation with LPS, and in a non-stimulated control series. The normalized data was downloaded from GEO (GSE17721).
A set of 125 candidate regulators were validated in the original study using RNAi and a set of signature genes  [29]. In the present study, the regulatory effect of a candidate regulator was defined as the percentage of the signature genes that were identified as its targets at 95% confidence in terms of both gene-specific and perturbation-specific noise. The yeast CDC13 dataset  [30]  includes gene expression profiles of three wild-type yeast strains and three strains carrying the temperature sensitive cdc13-1 mutation. Gene expression was profiled initially at 23uC and then at additional four time points after a temperature shift to 30uC. The data was preprocessed as described earlier  [45]. In two separate studies, the same laboratory carried out genome-wide screens for deletion mutants showing genetic interactions with the cdc13-1 mutation  [31, 46].
Here, we focused on the interactions that were reproducibly identified in both of the screens. The human T helper 2 (Th2) cell differentiation dataset  [32]  includes gene expression profiles of human CD4+ T cells activated (Th0), or activated and polarized towards Th2 with IL-4. Gene expression was profiled in triplicate series in naı ¨ve Th precursor (Thp) cells and at nine time points after activation and initiation of polarization. Here, we used those seven time points that had gene expression data from each replicate. The quantile-normalized probe-level data was transformed into probe set-level signal logratios between each non-Thp sample and the corresponding Thp sample using the probe-level expression change averaging procedure  [47] , as described in GEO (GSE18017). Time series data is used to construct a separate gene co-expression network for each individual case and control.
For a particular gene (the gray node), only those neighbors are retained that are consistently co-expressed across the replicates in at least one group (solid edges). These neighbors are then used to determine the changed and total neighborhoods of the gene. The changed neighborhood corresponds to the cumulative difference in the co-expression values of the gene between the two groups. The total neighborhood corresponds to the total connectivity of the gene across the groups. Finally, DCeN is defined as the proportion of the total neighborhood that is changed. See Materials and Methods for details of the procedure. doi:10. 1371/journal. pone. 0082340. g005 From the original preprocessed datasets, only those probe sets were retained that mapped to a unique Entrez ID. If multiple probe sets mapped to the same Entrez ID then the one with the highest overall intensity was selected  [48].
Acknowledgments We are grateful to  French "Ministère de l'Enseignement Supé rieur et de la Recherche" and Campus France "AVERROES"  for financial support to  Nathalie Legrave  and  Souhir Hamrouni , respectively, and to Pharmamar for financial help for the sponge collection. Thanks are also to the " Direction de l'Environnement de la Province Sud" in New Caledonia , for assistance in collecting the marine samples. Finally, we are grateful to  Clemence Ginet  for the additional assays on PBMC cells.
Epidemiological data 3. 3. 1. Animal rabies Between 2006 and 2011, NRL received 470 animal specimens and 231 (49. 1%) tested positive (Table  1 ). The majority of specimens originated from the urban area surrounding the capital, Antananarivo (Figure  1b ). Dogs represented more than 75% of specimens. Among the positive specimens, 195 (78%) were from dogs. PEP was administered to patients for 89 animal bites that were confirmed positive and for the remaining (30 cases), PEP was not initiated. Rabies circulation has been documented in 38 of the 111 districts (Figure  1b ). As part of the research program at the IPM, detection of lyssavirus was investigated in insectivorous and frugivorous bats, including two areas close to Antananarivo. No lyssavirus RNA was detected in the different individuals tested.
Tree topology was estimated using the best estimates among simultaneous Nearest Neighbour Interchanges and the approach relying on subtree pruning and regrafting. The support of the data for each internal branch of the phylogeny was estimated using non-parametric bootstrap. Bootstrap support of nodes discussed in the text is indicated. The tree is rooted between the Africa 2 and the Cosmopolitan lineages. The generated sequences have been deposited in GenBank under accession numbers KC787050 to KC787074, DQ420623 and U22854. dupreanum and Pteropus rufus (Figure  2 ) remains unknown and should be a focus of research. Both of these bats feed exclusively on fruit, pollen and nectar and live in colonies; the former making its day roost sites in caves and rock crevasses and the latter suspended from trees.
Aspergilli genome data analysis Fungal homologues of AAL lectin from A. aurantia were searched in publicly available sequence databases. Both annotated and partially completed genomic data and data from other sources (non-redundant GenBank CDS translations+PDB +SwissProt+PIR+PRF) accessible through NCBI server were searched. Lectin sequence of desire was analyzed according to signal peptide (SignalP 4. 0  [23] ) and transmembrane region (HMMTOP 2. 0  [24] ) presence. Sequences of AFL homologues were aligned using ClustalW2. 0 [25].
Analytical ultracentifugation Sedimentation analysis was performed using ProteomeLab XL-A analytical ultracentrifuge (Beckman Coulter) equipped with An-60 Ti rotor. Before analysis, purified AFL was brought into experimental buffer by dialysis and the dialysate was used as an optical reference. Sedimentation velocity experiments were performed at various loading concentrations of AFL (0. 04 -0. 30 mg•ml -1 ) in 20 mM Tris/HCl, 300 mM NaCl, pH 7. 4. Sedimentation velocity experiments were conducted in a standard double-sector centerpiece cell loaded with 420 μl of protein sample and 430 μl of reference solution. Data were collected using absorbance optics at 20° C at a rotor speed of 42,000 rpm. Scans were performed at 280 nm at 6 min interval and 0. 003 cm spatial resolution in continuous scan mode.
The partial specific volumes of protein together with solvent density and viscosity were calculated from amino acid sequence and buffer composition, respectively, using the software Sednterp (http:// bitcwiki. sr. unh. edu). The sedimentation profiles were analyzed with the program Sedfit 13. 0  [26]. Continuous size-distribution model for non-interacting discrete species providing a distribution of apparent sedimentation coefficients was used. Sedimentation equilibrium experiments were performed at 20° C in a six-channel centerpiece cell loaded with 110 µl of AFL (0. 05 mg•ml -1 ) and 120 µl of reference solution (20 mM Tris/HCl, 150 mM NaCl, pH 7. 4). The sample was gradually spun at rotor speeds of 7,800 rpm, 13,300 rpm, and 23,000 rpm, respectively. After the equilibrium was achieved, data were collected at 280 nm by averaging 20 replicates with 0.
Glycan array Protein affinity towards biologically important oligosaccharides was determined using glycan array. The pure protein was labelled by fluorescent dye DyLight 488 NHS Ester (Thermo scientific) according to product manual and desalted on Zeba Spin Desalting Column (Thermo scientific). The prepared sample was sent to Consortium for Functional Glycomics (USA). Three different concentrations of AFL (1, 0. 2 and 0. 05 µg ml -1 ) were analyzed using PA_v4. 1 chip. Experimental data were normalized to percentages of the highest RFU value for each analysis and the percentages for each glycan at different lectin concentrations were averaged to obtain an average binding. The data were sorted according to their average binding and analysed.
Crystallization and data collection Sitting and hanging drop methods were utilized for screening and optimization of protein crystals, respectively. Freeze-dryed protein was dissolved in water and its activity after resolving was verified by SPR with immobilized Fuc. Various crystallization conditions were tested using crystallization kits from Molecular Dimensions and Qiagen. The final crystals were obtained at 20°C under conditions as follows: 2 µl of precipitant (200 mM CaCl 2 , 25% PEG 4K and 100 mM Tris, pH 8. 5) mixed with 1 µl of protein solution containing 15 mg ml -1 AFL and 19 mM methyl-α,L-selenofucoside. The crystal growth was initiated by streak-seeding using microcrystals obtained from the same conditions.
Structure determination The crystals were cryo-cooled at 100K after soaking them in 15% (v/v) glycerol. Diffraction data were collected at beam-line ID14-4 of the ESRF, Grenoble (France). The data collection and refinement statistics are listed in Table  S3. Collected images were processed using XDS  [28]  and converted to structure factors using the CCP4 programs  [29]. The structure of AFL/MeSeFuc was determined using single-wavelength anomalous dispersion at 0. 980 Å. Phasing was performed using HKL2Map  [30]  from the position of 26 Se atoms, corresponding to six selenio-ligands per protein monomer and one selenio-ligand located between two adjacent monomers. After autobuilding cycles using ArpWarp  [31] , refinement was performed using Refmac  [32]  alternated with manual model building in Coot  [33].