dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Values of OPR and APR for each species and condition were compared with generalized linear models  (Hoffmeister et al. 2006 ). Since our results were a mixture of zeros and positive values, we used a Tweedie family (Poisson distribution), followed by pairwise comparisons, using the Tukey's post hoc test. All statistical analyses were performed with the R studio software (version 3. 3. 3) (R Core Team 2017).
Acknowledgements The research leading to these results received funding from the  European Union 's  Seventh Framework programme  for research, technological development and demonstration under Grant Agreement Number  613678  (DROPSA), the  Swiss Federal Office for the Environment and the Loterie Romande. We would like to thank  Nicolas Ris  ( INRA Sophia-Antipolis, France ),  Jinping Zhang  and  Renya Liao  ( MoA-CABI Joint Laboratory for Biosafety, Beijing, China ),  Hao Wu ,  Guohua Chen ,  Yan Liu ,  Yan Xiong ,  Chun Xiao  ( Yunnan Agricultural University, Kunming, China ) and  Masahito T. Kimura  ( University of Hokkaido, Japan ) for helping with the parasitoid collections. We thank  S. Fischer  ( Agroscope Changins, Switzerland ) for.
Data processing for heat source calculation As mentioned above in ''Introduction'' section, a zerodimensional (0D) version of the heat diffusion equation can be used when heat sources are homogeneous in the specimen; That is, when the strain fields are homogeneous (which is the case for the present experiments). This equation can be used to calculate the heat sources produced or absorbed by the material from the knowledge of the mean temperature changes. Equation  (1)  shows the relation between the heat source s (in W/m 3 ) and the mean temperature change h: s ¼ qC dh dt þ h s ð1Þ where q is the material density, C the specific heat and s a time constant characterizing the global heat exchanges with the environment of the specimen. The value of s was initially measured for each specimen by considering a natural return to ambient temperature, the specimen being placed in the jaws of the testing machine as for the mechanical tests.
Thermal and calorific analysis of the cyclic tests After some preliminary tests on various samples, cyclic loadings were applied to each type of leather at the two loading rates. It can be noted that, as is usual for full-field measurement techniques, a huge amount of data was recorded (temperature video files of between 50 Mo and 500 Mo per test, depending on the test duration). Figure  5  shows the temperature change and heat source variations for split leather A at the two displacement rates. Time periods corresponding to buckling are highlighted by gray-shaded rectangles in the graphs. The times corresponding to maximum stretching are indicated by vertical solid lines. The following observations can be made concerning this figure: • a global temperature increase is observed for both displacement rates.
Conclusions The mechanical response under tension of leathers has been studied in the scientific literature for decades. The present study aimed at providing new data concerning their thermomechanical response, namely the temperature changes caused by material stretching. For this purpose, IR thermography and heat source calculations were performed to analyze the response under tension of different leathers. Split leathers and grain leathers were compared during monotonous tests and cyclic tests. Beyond the mechanical responses, the following conclusions can be drawn from the calorific response of the tested specimens: • the two tested split leathers (cow and calf), as well as the grain pigskin leather, featured ''classic'' isentropic thermoelastic coupling. Typically, the materials absorbed heat when starting the loading under tension; • the tested grain calf leather, which was mechanically softened during the elaboration process and fed by a fatty acid during re-tanning, featured entropic thermoelastic coupling, such as in rubber-like materials.
Acknowledgements The authors thank the  National Center for Scientific Research (MRCT-CNRS  and  MI-CNRS ),  Rennes Metropole  and  Region Bretagne  for financially supporting this work. Authors also thank  Dr Mathieu Miroir ,  Dr Eric Robin ,  Mr Vincent Burgaud  and  Mr Mickae ¨l Lefur  for having designed the biaxial tensile machine, and  Dr Pierre-Olivier Bussiere  for the chemical analyses. The authors also thank the  Campus des Metiers et des Qualifications ''Design, Materiaux & Innovation''  for supporting this work.
ChIP-seq data analysis Raw Sp2 ChIP-exo reads were aligned using Subread version 1. 4. 3-p1 (39) against the Mus musculus genome assembly from Ensembl revision 74. Peak discovery was performed using MACE 1. 1 with default parameters except for "-fold ϭ 1. 5"  (40). Peaks were filtered to have at least 100 reads in WT MEFs, less than 50 reads in Sp2ko MEFs, and at least a 3-fold enrichment in WT MEFs over the Sp2ko control. Raw Pbx1, Prep1, and FLAG-Sp2 ChIP-seq reads were aligned using Subread (39) version 1. 4. 3-p1. Reads matching multiple locations were discarded during alignment. Peaks were called with MACS (41) version 1. 4. 0rc2 against an IgG control ChIP  (6). Filtered peaks were required to have at least 30 tags and a sequencing depth-corrected ratio over control of 3-fold. For motif search and heat maps, peaks were centered at their summits and fixed sized regions were extracted.
Acknowledgments-We thank Anna Mary Steitz, Irene Klassen, and Xenia Schmidt for their contribution to the acquisition and collection of data, and Iris Rohner for excellent technical assistance. Chiara Bruckmann and Franceso Blasi (IFOM, Milan, Italy) generously provided the bacterial expression vector for co-expression of GST-Pbx1 and Prep1. We thank  Alexander Brehm ,  Jaqueline Mermoud , and  Colin Dingwall  for discussion and critical reading of the manuscript.
RFD profiling RFD profiling of all cell lines was performed by OK-seq as described  (23). Briefly, after a 2 min pulse of replicative incorporation of the thymidine analogue 5-ethynyl-2deoxyuridine (EdU), DNA was purified, heat-denatured, Okazaki fragments were size-purified on sucrose gradients, biotin-labelled at EdU sites by click-chemistry, captured on streptavidin-coated magnetic beads, amplified by PCR and subjected to Illumina sequencing at the IB2C highthroughput sequencing platform (Gif-sur-Yvette, France). Sequence reads were identified and demultiplexed using the standard Illumina software suite and adaptor sequences were removed by Cutadapt (version 1. 2. 1 to 1. 12). Reads >10 nt were aligned to the human reference genome (hg19) using the BWA (version 0. 7. 4) software with default parameters.
Only uniquely mapping reads (186. 0 × 10 6 for TF1-GFP and 143. 3 × 10 6 for TF1-BCRABL-1M ) were used in downstream analyses. To compute MRT profiles, tag densities in 100 kb windows were normalized and denoized as in  (34, 35)  using modified thresholds for the two EdU-based datasets (TF1-GFP and TF1-BCRABL-1M). At each locus, the distribution of replication times was obtained by normalizing the denoized tag densities of each S-phase fractions by their sum. MRT values were estimated as the mean of these distributions.
1 × 10 6 and 104. 9 × 10 6 paired-end RNA-seq reads for TLSE19 and IB118, respectively. For K562, HeLaS3 and IMR90, we used RNA-seq data from the ENCODE project. We selected two biological replicate paired-end sequence datasets from whole cell PolyA+ RNA per cell line. Read files in fastq format were downloaded from the European Nucleotide Archive https://www. ebi. ac. uk/ena under accession numbers SRR315336 and SRR315337 for K562 and accession numbers SRR315330 and SRR315331 for HeLaS3. For IMR90, fastq files for experiment 'En-codeCshlLongRnaSeqImr90CellPap' were downloaded from UCSC http://hgdownload. cse. ucsc. edu/goldenPath/ hg19/encodeDCC/wgEncodeCshlLongRnaSeq/; these data correspond to accession numbers SRR534301 and SRR534302. Gene transcriptional levels for the 12 cell lines were estimated using the same computational pipeline based on the TopHat suite of softwares  (36).
Tophat (version 2. 1. 1) and bowtie2 (version 2. 2. 9) were used to align RNA-seq reads to the human genome (hg19). RNA abundance were computed using Cufflinks (version 2. 2. 2). We fed the reference transcript annotation provided by Illumina iGenomes (ftp://igenome: G3nom3s4u@ussd-ftp. illumina. com/Homo sapiens/ UCSC/hg19/Homo sapiens UCSC hg19. tar. gz), to tophat (-G option) and cufflinks (-g option) and only considered RNA abundance estimates for genes present in the reference annotation. For each cell line, we thus obtained for the same set of 24 371 genes of size > 300 bp the estimated mRNA level expressed in FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) and a 95% confidence interval on the FPKM ([FPKM lo , FPKM hi ]). We considered a gene to be expressed when FPKM ≥ 1 and we filtered out cases where FPKM hi /FPKM lo > 2 (at most 118 genes were filtered out in TF1-BCRABL-6M).
Computational analyses of RFD, RNA-seq and MRT profiles All analyses were restricted to the 22 autosomes to avoid artefacts due to the XX or XY karyotypes of the studied cell lines. Computation and figures were implemented in python (version 2. 7. 13) using numpy (version 1. 11. 3), scipy (version 0. 18. 3) and matplotlib (version 2. 0. 0) scientific computing modules. RFD profile Pearson correlation C RFD between a pair of cell lines was computed using non-overlapping 10 kb windows with >100 OK-seq reads in both cell lines. RNA-seq correlation C RNA-seq was computed between log 10 (FPKM) of genes expressed in both cell lines. MRT correlation C MRT was computed between non-overlapping 100 kb windows with a valid MRT estimate. Each set of pair-wise correlation distances (D = 1 -C) was used to hierarchically cluster cell lines using minimum distance as linkage criterion (single linkage clustering).
To further control for false positives and reproducibility, the second investigator blindly annotated multiple RFD profile pairs including Step 1 changes and biological replicates. Zero changes were called between biological replicates, but 791 Step 1 changes were blindly called that included 78% of the 476 changes originally called. The two datasets showed almost identical percentages of new, enhanced, weakened and silenced IZs and similar distributions of MRT, RNA-seq FPKM and GC content, although the second dataset contained a higher proportion of enhanced or weakened IZs close to active genes. Overall, these results indicate very few if any false positive calls and little impact of the investigator on the global properties of the called IZs.
DISCUSSION We have generated novel RFD, gene expression and MRT datasets and have explored several approaches to compare the replication and transcription programs of twelve cancer and non-cancer cell types. A global, unbiased correlation approach revealed that the RFD and MRT profiles clustered in three separate groups corresponding to lymphoid, myeloid and adherent cells. Similar results were obtained by RNA-seq, except that HeLa clustered with myeloid instead of adherent cells. Therefore, cancer-associated changes in replication do not blur their developmental origin signature, although changes in gene expression may sometimes do so. It is notable that the two LMSs were more correlated to each other by RNA-seq than by RFD, which suggests that their cell of origin may be different and that the selection for a tumour phenotype may have resulted in stronger convergence of their transcription than their replication program.
FUNDING Ligue Nationale Contre le Cancer (Comité de Paris) ;  Agence Nationale de la Recherche  [ ANR 2010 BLAN 161501 ];  Association pour la Recherche sur le Cancer ; the  Fondation pour la Recherche Médicale  [FRM DEI201512344404];  Cancérop ôle Ile-de-France  and the  INCa  [ERABL,  2009-2-INV-03  and PL-BIO];  programme 'Investissements d'Avenir ' launched by the  French Government  and implemented by the  ANR  [ ANR-10-IDEX-0001-02 PSL*Research University ]; France Génomique national infrastructure, funded as part of the  'Investissements d'Avenir' program  managed by the  ANR  [ ANR-10-INBS-09 ]; X. W. was supported by a  Ph-D fellowship  from the  Chinese Research Council ; B. A. acknowledges support from  Science and Technology Commission of Shanghai Municipality  [15520711500];  Joint Research Institute for Science and Society (JoRISS).
We present the LiveFly toolbox for quantitative analysis of transcription dynamics in live Drosophila embryos. The toolbox allows users to process two-color 3D confocal movies acquired using nucleilabeling and the fluorescent RNA-tagging system described in the previous chapter and export the nuclei's position as a function of time, their lineages and the intensity traces of the active loci. The toolbox, which is tailored for the context of Drosophila early development, is semi-automatic, and requires minimal user intervention. It also includes a tool to combine data from multiple movies and visualize several features of the intensity traces and the expression pattern.
The processing is semi-automatic: it requires user input only at the beginning of the process, but allows users to intervene for manual corrections if needed. It also includes a Visualizer to combine the data from multiple movies and visualize several features of the intensity traces and the pattern. The toolbox has been used to study the hunchback patterning system  [9, 10] , reducing processing time for each movie from days to hours. The LiveFly toolbox has three modules as shown in Fig. 1 , forming a pipeline for movie processing and analysis. We present the functions and guide the reader through each module in the following sections.
1 System requirements 1-MATLAB 2013 or above, with the Image Processing Toolbox 2-The LiveFly toolbox: available for download from: https://github. com/huytran216/LiveFly_toolbox (see Note 1) 3-MATLAB toolbox for Bioformat: version 5. 2. 1 or higher, available for download from: https://www. openmicroscopy. org/bio-formats/downloads/. The Bioformat toolbox (zipped) is to be extracted to the Tool folder of the LiveFly's main folder.
Spot detection The spot detection module extracts the fluorescent spot location and intensity from the 3D green channel. Spots are first identified using the threshold method (see below) and then fitted with a 2D Gaussian landscape, from which the spot intensity is extracted. This module will also combine the spot information and the nuclei segmentation result to create the movie's complete output file, which is used in subsequent analyses. 2-a Run the script. An input dialog will appear asking you to either enter the debug mode to determine th or proceed with the spot detection. Select "Yes" to enter the debug mode.
Initial analysis 1-Click on Load movies to load data from all the movies. This step will read the movie output files, and extract information about each nucleus, including its position, nuclear cycle, lineage, and the intensity traces of active transcription loci. The exceptions specified in the correction file are taken into account. The program will extract several features of the traces, such as the total spot intensity (ΣI), the mean spot intensity (μI), time of first spot appearance (tinit), the total spot appearance duration (tactive), etc. See <Menu>Help>Show feature description> for the feature list and description.
3-Whether the movie begins or ends in the middle of a nuclear interphase (with leftcensored, rightcensored variables). These incomplete nuclear cycles will be ignored. Note 16. The data file is saved in the MATLAB *. mat format. This file can directly be loaded into the MATLAB workspace instead of using the Visualizer. Note 17. This step will retain the nuclei that are detected within a certain time range. It is helpful to remove nuclei that move in and out of the captured image region. Figure Captions.
in with examples of data. To access the visualizer, run Data analysis/ LiveFly_VISUALIZER. m 3. 4. 1 Managing the dataset 1-Create a new dataset by selecting Menu>File>Create dataset. 2-Select Add… to add a new movie to the dataset. A dialog will appear inquiring for the movie correction file. 3-Edit the movie list with Remove/Move Up/Move Down buttons. 4-Select the nuclear cycle of interest in each movie by editing the tick boxes. 5-save/load the dataset any time using Menu>File (see Note16).
Acknowledgements This work was supported by a  Marie Curie MCCIG  grant No. 303561  (AMW),  PSL IDEX REFLEX  ( ND, AMW, MC ),  ANR-11-LABX-0044 DEEP Labex  ( ND ),  ANR-11-BSV2-0024  Axomorph (ND and AMW) and  PSL   ANR-10-IDEX-0001-02. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The EncoMPASS online database (http://encompass. ninds. nih. gov) collects, organizes, and presents information about membrane proteins of known structure, emphasizing their structural similarities as well as their quaternary and internal symmetries. Unlike, e. g. SCOP, the EncoMPASS database does not aim for a strict classification of membrane proteins, but instead is organized as a protein chain-centric network of sequence and structural homologues. The online server for the EncoMPASS database provides tools for comparing the structural features of its entries, making it a useful resource for homology modeling and active site identification studies. The database can also be used for inferring functionality, which for membrane proteins often involves symmetry-related mechanisms. To this end, the online database also provides a comprehensive description of both the quaternary and internal symmetries in known membrane protein structures, with a particular focus on their orientation relative to the membrane.
INTRODUCTION Protein structure determination and prediction, active site detection, and protein sequence alignment techniques all exploit information about relationships between protein structures. Hence, over the last few decades online projects for collecting and organizing structural data have proliferated, fuelled by the quasi-exponential growth of experimentally determined protein structures. However, membrane proteins, which constitute 20-30% of any given genome and >50% of all FDA-approved drug targets, are noticeably underrepresented in such databases. For example, membrane proteins account for only ∼2% of the Protein Databank (PDB)  (1) , of which ∼800 structures represent unique proteins as of September 2018 (http://blanco. biomol. uci. edu/ mpstruc/). Membrane proteins also appear to pose a challenge to common strategies for relating protein structures such as those used by SCOP  (2)  and CATH (3), whose classifications for membrane proteins are inconsistent  (4).
Despite their importance, the few online resources that address structural symmetries provide limited information. For example, the SymD webserver  (13)  can analyze a protein structure file for symmetries, but the reported results do not describe the repeating elements and the method can detect only one symmetry per structure. In the PDB database, the only information about symmetry refers to that found between chains in a complex  (14) , and not internal symmetries. Importantly, no available resource relates the symmetry axis to the membrane orientation, which can provide an important clue to protein function.
D316 Nucleic Acids Research, 2019, Vol. 47, Database issue To address these issues, we created the Encyclopedia of Membrane Proteins Analyzed by Structure and Symmetry (EncoMPASS). Expanding on the offline, manuallycurated membrane protein structure classification database HOMEP  (15) , the EncoMPASS database is automated and uses accurate structure and sequence alignments to relate membrane protein structures. Instead of building a hierarchical classification in the style of e. g. SCOP, we constructed EncoMPASS around the structural and sequence similarities of all protein chains with similar transmembrane topologies. This results in networks of sequence and structural homologues for each protein chain. Uniquely, EncoMPASS also provides detailed information about both quaternary and internal structural symmetries, including the type of symmetry, the symmetry axes and their orientation with respect to the membrane, as well as a sequence alignment of the symmetry-related residues.
The database curates results of several symmetry detection approaches and allows the user to select the analysis that best fits their objectives. On the online version of the EncoMPASS database, the user is guided through the data by graphical and interactive interfaces, such as: 3dmol representations (16) of each membrane protein complex, each chain, and all symmetryrelated structural repeats; intuitive, interactive graphs that elucidate sequence and structure relationships among the database entries; and search bars that allow for either a straightforward or a more tailored exploration of the database. All data used to generate the displayed content are also available for download.
DATABASE CONTENT To maximize the accuracy of our structural analysis, the En-coMPASS database collects crystallographic structures of membrane proteins with resolution ≤3. 5 Å. EncoMPASS uses the manually-curated Orientations of Proteins in Membranes (OPM) database  (17)  as the primary source of structure coordinates that are reliably oriented relative to the predicted lipid bilayer. However, the information in the OPM coordinate files is occasionally incomplete or inconsistently formatted, or the biological assembly can be in disagreement with the one reported in the PDB. Indeed, predicting the correct biological assembly of membrane protein structures is a major challenge (bioRxiv: https://doi. org/10. 1101/391961), leading to discrepancies between the assignments in the PDB (1), PDBTM  (18)  and OPM databases.
To maximize the accuracy of the predicted biological assembly we follow the strategy implemented in PDBTM  (18). First, each structure is classified as either potentially problematic or plausibly correct. Then, for each member of the former set, we check whether a structure in the latter set can serve as a template for the biological assembly. If no template is found, we assume the biological assembly described in the PDBTM web server. Thus, the coordinate file published on EncoMPASS is a revised version of the coordinate file in the PDB database, with its biological assembly optimized where necessary, and with membrane boundaries estimated by the algorithm underlying the OPM database, PPM  (19). As of September 2018, EncoMPASS contains 2344 coordinate files, corresponding to 67% of all membrane protein entries present in the PDB; the majority of the excluded entries do not meet the resolution threshold.
To this end, we also report the per-residue C␣-C␣ distances between a given chain and all others aligned to it. Protein sequence similarity provides complementary information to structural similarity and can highlight, for example, proteins undergoing conformational changes during their function. Thus, independent of the structural alignment, the sequences of all the same pairs of topologicallyrelated proteins are aligned with MUSCLE  (23). For each protein complex and chain we also report the results from a comprehensive symmetry analysis. First, En-coMPASS curates the results of two standard algorithms for symmetry detection, CE-Symm 2. 2 (bioRxiv: https:// doi. org/10. 1101/297960) and SymD 1. 61  (24). In addition, the database includes results from a multi-step symmetry detection (MSSD) procedure (bioRxiv: https://doi.