dataesr/scientific-paragraphs-categorization huggingface dataset:
In this respect, ChIP-Seq on mES cells revealed that the transcription factor Thap11 binds with its transcriptional co-regulator Hcf-1 to the sequence motif ACTACANNT CCCA (TBS) (Figure  4A )  (25) , suggesting a possible overlap of ZNF143 and Thap11 DNA-binding events. We compared the available ChIP-Seq data of Thap11 and its co-factor Hcf-1 (25) obtained from mES cells with our ZNF143 ChIP-Seq data obtained from the same cells. We found that 84. 7% of all Thap11 and 75. 8% of all Hcf-1-binding events located in promoters overlap the ZNF143-binding events located at +/À2 kb of a TSS (3385 peaks) (Figure  4B ). An individual motif occurrence search on the 651 peaks, common to the Thap11-and ZNF143-binding events (Figure  4B ), identified an SBS2 motif in the whole set of coordinates. Furthermore, a similar search performed on Hcf-1 peaks without reported Thap11 binding, showed that 51% (Figure  4B ) of them overlap with ZNF143 and contain also an SBS2 motif.
On the other hand, the negatively regulated genes (upregulated after ZNF143 knockdown) were only slightly enriched in genes involved in transcription processes (Supplementary Figure  S14B , blue bars). As we found THAP11 and ICN1 associated with ZNF143-binding motifs, we next analysed by RT-qPCR the effect of THAP11 and ICN1 knockdown on 51 ZNF143 target genes. After THAP11 knockdown in HeLa-S3 cells using specific siRNA (Figure  6D ), 28 of the 51 genes were significantly upregulated and 4 of the 51 downregulated, in agreement with the major repressor function of the factor. After knockdown of ICN1 (Figure  6A , lanes 7-10), by g-secretase inhibitor (GSI) treatment on HPB-ALL cells, 6 of the 51 genes were significantly upregulated and 20 of the 51 downregulated (Figure  6C ). Combining our expression data on this subset of genes, we observed that both SBS1-or SBS2containing genes are generally positively regulated by ZNF143 and ICN1, but, in contrast, genes carrying only SBS2 sites are significantly upregulated by THAP11 knockdown (Figure  6D ).
However, the authors of this study identified only an SBS2-type site as being bound by ZNF143. In contrast, our work performed on seven different human and mouse cell types clearly distinguishes the two motifs SBS1 and SBS2 as bound by ZNF143 and subjected to differential binding by two other unrelated transcription factors. As for ZNF143, our ChIP-Seq data on ICN1 showed higher tag enrichment for peaks associated to the SBS2 than to the SBS1 sites. This is consistent with our hypothesis that promoters containing either the SBS1 or SBS2 site are differently regulated. The gene expression analysis after GSI treatment in HPB-ALL cells confirmed the major activation function of ICN1 binding on both SBS1 and SBS2 sites. We can infer from our results that ZNF143, THAP11 and ICN1/RBPJ are not only involved in a competitive binding on overlapping sites but are also involved in the transcriptional activity of adjacent genes.
As shown for several transcription factors  (50) , it emerged from our data that the very ancient family of MLT1J retrotransposons could be at the origin of the SBS2 sites. Furthermore, a correlation exists between KRAB zincfinger gene expansions and retro-transposition waves  (43). We thus speculate that ZNF143 originated from an ancient KRAB zinc-finger transcription factor family targeting at the time still active MLT1J retro-transposons. In the course of evolution, these events could have caused expansion of the repertoire of ZNF143, THAP11 and ICN1 regulated genes by spreading out the SBS2 sites in promoter regions.
ACKNOWLEDGEMENTS The authors are grateful to  Prof. B. Kempkes  for the gift of the RBPJ construct,  Dr. V. Penard-Lacronique  for supplying the HPB-ALL cells,  Dr. P. Assmann  for mouse fibroblasts,  Dr. I Davidson  and  S. Urban  for the mouse embryonic stem cells,  Dr. S. Pffefer  for supplied material and  Dr. A. Krol  for critical reading of the manuscript. They also thank  B. Jost ,  S. Le Gras  and  C. Keime  from the sequencing platform of  IGBMC (Illkirch, France );  S. Baudrey  and  A. Schweigert  for valuable technical assistance. They are thankful for the contribution of  Y. N. Anno ,  O. Lecompte  and  O. Poch  to the initial phase of the project. They are also grateful to  E. Westhof  for his interest in their study. The overall study was conceived, designed and coordinated by P.
In this Letter we present the result of a search for pair production of top squarks with RPV decays of the lightest sparticle, using multilepton events and bottom-tagged (b-tagged) jets. The dataset used here corresponds to an integrated luminosity of 19. 5 fb -1 , recorded in 2012 with the CMS detector at the LHC in proton-proton collisions at a center-of-mass energy of 8 TeV. The coordinate system in CMS is right-handed, with the origin at the nominal interaction point. Pseudorapidity is given by η ≡ln[tan(θ/2)], where the polar angle θ is defined with respect to the counterclockwise beam direction. The azimuthal angle φ is measured relative to the direction to the center of the LHC ring. The CMS detector  [22]  has cylindrical symmetry around the pp beam axis with tracking and muon detectors covering the pseudorapidity range |η| < 2.
Signal samples  [11]  are generated with MADGRAPH and PYTHIA and passed through the CMS fast-simulation package  [34]. Next-to-leading-and next-to-leading-log cross sections and their uncertainties for the SUSY signal processes are from the LHC SUSY cross sections working group  [35] [36] [37] [38] [39]. Multilepton signals have two main sources of backgrounds, the first arising from processes that produce genuine multilepton events. The most significant examples are WZ and ZZ production, but rare processes such as tt W ± and tt Z also contribute. We assess the contribution from these processes using samples simulated by MADGRAPH. Samples simulating WZ and ZZ have been validated in control regions in data. For the rarer background processes, we rely solely on simulation. The second source originates from objects that are misclassified as prompt, isolated leptons, but are actually hadrons, leptons from a hadron decay, etc.
Another source of background leptons is internal conversions, where a virtual photon decays to a dilepton pair. These conversions produce muons almost as often as electrons, and have been discussed in detail elsewhere  [20]. We measure the conversion factors of photons to light leptons in a control region (low E miss T and low hadronic activity). The ratio of the number of + -± candidates to the number of + -γ candidates in the Z boson decays defines the conversion factor, which is 2. 1% ± 1. 0% (0. 5% ± 0. 3%) for electrons (muons). A systematic uncertainty of 4. 4% in the normalization of the simulated samples accounts for imperfect knowledge of the integrated luminosity of the data sample  [40]. Signal cross sections have uncertainties from 15% to 51% in for stop masses between 250 GeV and 1.
For this, we crossed a dataset on microparasite diversity (agents of rodent-borne diseases) in murid rodents in SEA, a major clade of reservoirs of zoonotic diseases  (15) , with an original geo-referenced dataset in order to detect association between landscape features, where rodents were trapped, and the total extant of microparasite species richness harbored by rodent species. More precisely, we used the land covers of seven sites in Thailand, Cambodia, and Lao PDR  (28)  and literature data on the diversity of microbial agents circulating in rodents of these countries  (29)  to identify pathogenic habitats in this tropical area.
It was also designed to have an estimation of the rodent density using the number of catches by night trap. Rodents were identified on the basis of their morphology or using species primer specific and/or barcoding assignment. Complete data for animals used as reference for barcoding assignment are available on the 'Barcoding Tool/RodentSEA' section of the CERoPath project web site: http://www. ceropath. org/. Overall, 2,427 murine rodents trapped in the seven study sites were integrated into a Geographic Information System (GIS) based on the geographic coordinates of their sampling site. They represent a total of 30 species, but include 12 species with less than 10 specimens.
Environmental indices For each site, recent 2007Á2008 high spatial resolution SPOT satellite images were acquired. When possible, cloud-free scenes (i. e. from the dry season) were chosen. The scenes had a pixel size of 2. 5 m in panchromatic mode and 10 m in multispectral mode. SPOT-Digital Elevation Model (DEM) with a spatial resolution of 20 m together with the SRTM (Shuttle Radar Topography Mission, http://srtm. usgs. gov/) DEM (90 m resolution) was also acquired. These DEMs allow several calculations to describe the topography, including the slope and the delineation of watersheds. For each site the SPOT scene was classified into different land-cover types using an object-based approach (eCognition Developer † commercial software). Each scene was first segmented into objects which were then classified using a supervised process based on three types of object properties: intrinsic characteristics (reflectance values, slope values, shape and texture), topologic characteristics (relations to neighboring objects) and contextual characteristics (semantic relationships between objects).
Parasite diversity We compiled surveys of microparasites investigated in rodents trapped in SEA and published in the literature  (29). The data comprise a total of 20,272 rodents from 13 species of murine rodents that have been investigated for a total of 10 important microparasites' taxa (Table  2 ). This microparasite dataset helped at obtaining the overall microparasite diversity of rodents. Therefore, it does not concern the same rodent individuals as those trapped by the CERoPath project, which has only investigated a few microparasites, which are incorporated in this dataset  (30, 31)  (but see www. ceropath. org). The microparasites were viruses, bacteria and protozoans. Viruses were all zoonotic and included hantaviruses, Lymphocytic choriomeningitis virus (family Arenaviridae, genus Arenavirus), Rabies virus, and Hepatitis E virus.
Statistical analysis We performed a principal components analysis (PCA) on individual number of rodent species trapped in each the four types of habitat (i. e. low resolution) to illustrate their distributions using the package 'ade4' in R software (R Development Core Team, 2010). In order to better estimate the preferred habitat of rodent species, we performed tree regression analysis (TRA)  (32, 33)  on the minimal distances between each individual rodent and each land-cover habitat type using package tree in the R software (R Development Core Team, 2010). We performed generalized linear models (GLM) to identify the likely variables that may explain the microbial diversity of rodents using the R software (R Development Core Team, 2010). We performed a multiple regression with microparasite species richness as the dependent variable and environmental indices as the independent variables: distance to forest, distance to steep agriculture land, distance to flat agriculture land, distance to built-up zones, distance to water, and slope (which refers to each individual rodent trap).
High microparasite diversity in flat agriculture areas Most of studies related to rodent-borne diseases to date focused on one pathogen, notably hantaviruses or arenaviruses, trying to link habitat characteristics, structure of rodent communities or season to prevalence of infections in rodents or to human outbreaks. The main limit of such studies is that they ignore the great diversity of pathogens circulating in rodents, particularly in the tropical areas where microparasite diversity is higher compared to the temperate zones  (38). Our comparative analysis using two important datasets (i. e. 1,275 of 2,070 rodents for the computation of environmental indices and more than 20,000 rodents for the estimation of microbial diversity) is one of the few that questions the environmental determinants of parasite diversity in tropical area.
Acknowledgements This study was funded by the  French ANR Biodiversity  grant  ANR 07 BDIV 012 ,  CERoPath  project  'Community Ecology of Rodents and Their Pathogens in a Changing Environment ' (www. ceropath. org),and by the  French ANR CP&ES  grant  ANR 11 CPEL 002 ,  BiodivHealthSEA (Local impacts and perceptions of global changes: Biodiversity, health and zoonoses in Southeast Asia ) (www. biodivhealthsea. org). SPOT images and SPOT DEMs were obtained with financial support of the  ISIS (Incitation al'utilisation scientifique d'images SPOT) program  of  CNES  ( Centre National d'Etudes Spatiales ).
However, often they are not sufficiently expert to be able to effectively exploit the power of different tools and databases or to perform the comparison of the results of more than one experiment (which usually requires some scripting). This remains true even though there are a number of publicly available servers that allow functional enrichment analysis given a list of genes, the most used of which are DAVID (6,7), g:Profiler  (8, 9) , Gorilla  (10) , High-Throughput GoMiner  (11) , Babelomics  (12)  and GeneCodis 3  (13). All these tools require the user to provide lists of genes, which implies that the identification of genes the transcripts of which are up-and downregulated needs to be performed separately and in advance. Furthermore, should the user wish to see how the results change when a different P-value or fold-change threshold is applied to identify differentially expressed transcripts, the list has to be rebuilt, resubmitted to the server and the results compared.
Two of the aforementioned servers, Babelomics and GeneCodis 3, give the possibility of performing the analysis in parallel on two different lists of genes, for example, upregulated and downregulated ones or deregulated in different experiments, but the burden of comparing the results is still left to the user. The aforementioned considerations prompted us to provide experimentalists with a more user-friendly tool for analyzing their data from a functional point of view. The FIDEA tool was developed through a number of iterations with our collaborating experimental groups, and we believe that the resulting system is sufficiently easy to use and at the same time complete and flexible to be useful in the functional analysis of differential expression experiments.
IMPLEMENTATION The FIDEA core consists in a Perl code and a set of Perl modules. The Perl modules are used to process the input, convert the gene IDs, perform the functional annotation and create the textual output files. The Benjamini and Yekutieli FDR is performed by the Statistics::Multtest Perl package. The R language and the ggplot2 package are used to create publication-quality PDF output images. The server front-end is implemented in standard HTML markup language using the Javascript programming language and AJAX technologies using the jQuery library. CanvasXpress is used for displaying the interactive images. The server runs under the Linux (Debian) operating system on a machine with 4# Intel Xeon E7-4820 2. 00 GHz processors and 80 GB random access memory. The associations between functional annotations and gene ID are stored in a local MySQL database (see Supplementary Figure  S1  for a detailed scheme of the DB).
Because of the large number of independent databases, the identifiers are, in most cases, redundant. FIDEA maps all functional annotations to the ENSEMBL gene IDs  (21)  and converts all supported gene IDs (Entrez, UCSC, gene symbol and Refseq) to ENSEMBL gene IDs. Organism-specific IDs can also be used, namely, Flybase ID, Gene and Annotation Symbol for D. melanogaster, Gene Name, Zfin Gene ID for D. rerio and SGD Systematic Name, Primary SGD and Gene Name for S. cerevisiae. Regardless of this internal conversion, the final results are given using the original gene ID provided by the user. For all three ontologies included in Gene Ontology (Biological Process, Molecular Function and Cellular Component) FIDEA applies the 'true path rule'  (22) : any gene associated with a given GO term is always associated with the ancestors of that term leading back to one step before the ontology root.
ACKNOWLEDGEMENTS The authors are grateful to the groups of  Prof. Gulino ,  Prof. Bozzoni  and  Prof. Levrero  who helped debugging and improving the server and to  Paolo Marcatili  and all the other members of the  Biocomputing Unit  for useful discussions and for testing the server. They are indebted to the developer of  CanvasXpress ,  Isaac Neuhaus , for his responsiveness and kindness.
Dataset The total integrated luminosity of the dataset analysed is 3. 9 fb -1. A multijet trigger, in which one of the jets is required to be identified as a hadronically-decaying τ lepton, was designed to record pp → tt → τ h + jets events. It consists of two consecutively applied filters, referred to as jet and τ filters. The jet filter requires the presence of four central jets reconstructed in the calorimeter (|η| < 2. 5, p T > 40 GeV), referred to as calorimeter jets. The τ filter requires the presence of one isolated particle-flow  [16]  τ candidate (|η| < 2. 5, p T > 40 GeV, at least one track with p T > 5 GeV), matched to one of four trigger jets. Due to the increasing rate of the recorded events with the rising instantaneous luminosity, the thresholds on the jets and τ lepton were raised to p T > 45 GeV during the later part of the data taking period.
The corrections are propagated to the E miss T measurement and to the trigger-efficiency measurement. The energy of the particles that are not clustered into jets is varied by ±10%, leading to an additional uncertainty in the E miss T. The uncertainty due to applying b-tagging data-to-simulation scale factors for b, c and lightflavoured jets to the simulated events is estimated by shifting the value of the applied scale factors by the uncertainty corresponding to one standard deviation  [22]. The uncertainty in the reweighting method applied to the multijet data sample is estimated to be 5%. The uncertainty in the luminosity measurement is estimated to be 2. 2%  [23]. Table  2  summarizes the uncertainties entering the cross section measurement, split into systematic and statistical ones. The statistical uncertainty includes the D NN fit uncertainty, the statistical uncertainty of the trigger turn-ons, as well as the uncertainty due to the limited size of the simulated samples.
Acknowledgements We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MEYS (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES (Croatia) ;  RPF (Cyprus) ;  MoER ,  SF0690030s09  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NKTH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  CINVESTAV ,  CONACYT ,.
Detection of immunoreactive bands was performed using the enhanced chemiluminescence (ECL) detection kit (Pierce Biotechnology, Rockford, IL). The protein levels that corresponded to the immunoreactive bands were quantified using the ImagePC image analysis software (Scion Corp. , Frederick, MD). Immunoprecipitation Assay-Cells were lysed with RIPA buffer, as previously described  (24). Three mg of total protein were incubated with primary antibody for 16 h at 4 °C under continuous agitation. Protein A-and protein G-agarose beads (Merck Millipore) were added, samples were further incubated for 2 h at 4 °C, and beads with bound proteins were collected by centrifugation and washed twice with ice-cold PBS. Immuno-precipitated proteins were resuspended in 50 l of SDS loading buffer and analyzed by Western blot.
, Beverly, MA) and incubated with the sections overnight at 4 °C. Following incubation with the antibodies, sections were washed three times with TBST and incubated for 1 h at room temperature with the corresponding Alexa fluorescent secondary antibodies diluted in blocking solution (1 g/ml). Sections were finally washed three times, 5 min each, with TBST and mounted with DAPIcontaining Vectashield mounting medium (Vector Labs, Burlingame, CA). Images were captured with a Nikon 80i Upright Microscope equipped with a Nikon Digital Sight DS-Fi1 color camera, using the Metamorph image acquisition software. All images were captured and processed using identical settings. Co-localization analysis was performed using Intensity Correlation Analysis plug-in within WCIF ImageJ software  (30). Results are expressed with the Mander's overlap coefficient (ranges between 1 and 0, with 1 being high and 0 being low co-localization) and Pearson's correlation coefficient (value close to 1 indicates reliable co-localization).
After SDS-PAGE, proteins were visualized by silver staining. Relevant bands were excised and treated for in-gel digestion as described  (31). Briefly, the silver was destained using Farmer's reagent and trypsin (porcine, modified, sequence grade, Promega, Madison, WI) was introduced to the dried gel pieces. After overnight tryptic digestion, the peptides were bound to a C18 ZipTip and after washing, they were eluted with acetonitrile containing the matrix (alfa-cyano 4-hydroxycinnamic acid) directly onto the target plate. The mass list was generated by MALDI-TOF mass spectrometry on an Ultraflex TOF/TOF from Bruker Daltonics. The search for identity was performed using the search engine ProFound by scanning the current version of the NCBI nr sequence database. The spectrum was internally calibrated using autolytic tryptic peptides, and the error was set at Ϯ0.
PTN significantly induced PI3K activation in CHO cells overexpressing wild-type ␤ 3 or ␤ 3 Y785F, whereas it had no effect in cells overexpressing ␤ 3 Y773F (Fig. 3C ). Cell Surface NCL Directly Interacts with ␣ v ␤ 3 -To study whether NCL directly associates with ␣ v ␤ 3 , immunoprecipitation assays were performed in HUVEC lysates. NCL was found to interact with ␣ v ␤ 3 by mass spectrometry (Table  1 ) and Western blot (Fig. 4A ) analysis. In the former, among the proteins co-immunoprecipitated with ␣ v ␤ 3 , NCL (accession number AAA59954) was found to be a binding partner with a minimum sequence coverage of 19%. This relatively low sequence coverage can be explained by the particular structure of NCL, which is very rich in acidic (24. 9%) and basic (16. 3%) residues in highly basic regions, e.
Flow Cytometry-BMDMs were harvested after 7 days of culture and then incubated on ice for 30 min with the indicated antibodies, washed, and processed using an LSR II flow cytom-eter (BD Biosciences). The antibodies used were R-phycoerythrin-conjugated anti-CD11b (BioLegend) and FITC-F4/ 80 (eBioscience). Stained cells were subsequently washed and analyzed using an LSR II flow cytometer (BD Biosciences). Data were analyzed using FlowJo software (version 8; Tree Star, Inc. ). Immunofluorescence-Cells grown on glass coverslips were washed with PBS, fixed in 2% paraformaldehyde solution for 15 min at room temperature, and permeabilized with 0. 2% Triton X-100 in PBS for 5 min at room temperature. Immunofluorescence analysis was performed using anti LC3B antibody (Cell Signaling) and Alexa Fluor 594-conjugated secondary antibody (Invitrogen).