dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA-Seq data analysis We have generated two RNA-Seq data sets using two cell lines, Huh7 and HepG2. The RNA-Seq data of Huh7 include six controls and six treated samples. The HepG2 dataset contains two controls and two treated samples. The alignment and quality control of RNA-Seq data followed the pipeline developed by the NCI's Genomic Data Commons (GDC, https://gdc. cancer. gov/). The alignment was performed using a two-pass method with STAR2  [17]. The quality assessment was carried out by FASTQC (https://www. bioinformatics. babraham. ac. uk/projects/ fastqc/) on the pre-alignment, and RNA-SeQC  [18]  and Picard tools (http://broadinstitute. github. io/picard/) on the post-alignment. Read Counts were performed by Summarized Experiment of DESeq2  [19]. The count data were normalized by betweenLaneNormalization function of EDASeq  [20]  to normalize the data using upper-quartile (UQ) normalization and then estimate the factors of unwanted variation using empirical control genes, e.
Funding sources Research reported in this publication was supported by the National Cancer Institute's Cancer Centre Support Grant award number P30CA012197 issued to the Wake Forest Baptist Comprehensive Cancer Centre (BP) and by funds from the Charles L. Spurr Professorship Fund (BP). DWG is supported by R01 AA016852 and P50 AA026117. Funders had no role in study design, data collection, data analysis, interpretation, or writing of this report.
Acknowledgements The authors wish to acknowledge the support of the  Wake Forest Baptist Comprehensive Cancer Centre Cancer Genomics, Proteomics ,  Flow Cytometry ,  Cellular Imaging ,  Biostatistics , and  Bioinformatics Shared Resource , supported by the  National Cancer Institute's Cancer Centre Support Grant  award number  P30CA012197  and  Wake Forest Comparative Pathology Laboratory. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Cancer Institute. Funders had no role in study design, data collection, data analysis, interpretation, or writing of this report.
Statistical analysis SPSS Statistics 24 (SPSS, Chicago, IL, USA) was used to analyze the data. To assess the normality of the data sets, the Shapiro-Wilk test was used. The one-way analysis of variance test and independent samples Ttest were used for normally distributed data. The Kruskal-Wallis test and Mann-Whitney U test were used for non-normally distributed data. Differences with a p-value < 0. 05 were considered significant.
Acknowledgments This research was funded by  Agency for Innovation by Science and Technology, University Ghent Special Research Fund/Concerted Research Actions  ( 01G02215 ), and  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement [648124]). R. Xiong is a postdoctoral fellow of the  Research Foundation-Flanders (FWO-Vlaanderen)  ( 1500418N ).
Patients In 2006-2008, 1,198 outpatients with cirrhosis and ascites were included in 3 multicentre randomised controlled trials conducted to examine the efficacy of satavaptan in treating ascites in patients with cirrhosis (www. clinicaltrials. gov registration numbers NCT00358878, NCT00359437 and NCT00366795). 22 More than 100 hospitals in more than 20 countries included patients in this study. The responsible local and national Ethics Committees and IRB's for each participating site approved the study protocols, patient information and consent forms prior to starting the study as required by Good Clinical Practice and national laws. We refer to the approval by the Barcelona clinical research ethics committee (Barcelona Comités de Ética en Investigación Clínica de referencia, CEIC-Ref), which was used as a reference committee for other sites.
Data collection Baseline data were collected at inclusion. During the follow-up patients were seen every 4 weeks in their specialised hepatology departments. Blood biochemistry, urine dipstick analysis, use of medical drugs, and development of infections were recorded in detail at each of these visits and on the date of the final drug safety assessment. The protocols for the trials did not specify diagnostic criteria for infections, so all infections in our study rely on the diagnostic criteria used in the participating hepatology departments. Infections were recorded and classified according to the ICD-10 system, a standard WHO system for classifying diagnoses, and were categorised according to infection agent and site. An infection could be classified as bacterial, viral, or fungal based on clinical criteria; the trial protocols did not require confirmation by culture or other means.
Development of Data Collection Sheet and Questionnaire The data collection sheet and questionnaire were based on different publications' findings, and included four parts. The first part included the socio-demographic characteristics of patients as well as questions about patients' body mass index, the reason of being admitted to the hospital and time of admission, imaging techniques and time, and drug exposure at home. The second part included patients' symptoms on admission and time of symptoms. The third part addressed all potential risk factors of stroke. The last part was related to patients' laboratory data upon admission to the hospital. The data collection sheet was written in English language and information collected were based on patients' medical records at the hospitals. The questionnaire was written in English and administered in Arabic and was based on patients' answers and medical records at the hospitals (The questionnaire is attached as a supplementary material).
Data Collection and Definition of Certain Comorbidities All variables were collected based on information present in patients' files/physician's diagnosis/patient's answers. In the score generation study, all data elements of the data collection sheet were collected retrospectively from patients' files at the hospitals. In the score validation study, all data elements of the questionnaire were collected concurrently after patients' diagnosis at the hospitals. Patients or proxies were interviewed for data collection (A proxy is someone who lives with the patient and knows everything about the patient medical history and medication. A proxy is only interviewed when a stroke patient is in coma or a stroke patient has stroke-induced language disturbances). The socioeconomic and demographic factors and stroke.
Summary of primary results Two hundred and fifty stroke cases were admitted to the interesting hospitals between January 1 st , 2012 and December 31 st , 2014. Among those, 48 stroke cases were not diagnosed with stroke by CT scan or MRI and therefore were excluded. Overall, 732 patients were included in the score generation study with 202 cases diagnosed with stroke and 530 stroke-free controls. The mean age of stroke was 68±13 years. Males constituted 53. 5% of stroke patients. Among stroke patients, 25. 7% had TIA, 59. 4% had ischemic stroke, and 14. 9% had hemorrhagic stroke. The in-hospital stroke mortality rate was 7. 4%, with hemorrhagic rate higher than ischemic rate, 20% vs 7. 5% respectively. Due to missing data in regards to patients' address (43. 7% of the sample), this sample mainly represented Lebanese patients living in Beirut (46.
Validation of DS-Stroke Around 650 hospitalized patients were included in the score validation study for DS-Stroke validation. Data were collected through a standardized questionnaire for 205 stroke patients and 445 stroke-free patients admitted to five Lebanese tertiary hospitals between January 1 st , 2015 and December 31 st , 2016. Among included participants, 1. 3% of controls had a history of stroke compared to 34. 6% of cases. Among stroke cases, ischemic stroke constituted the majority of cases (69. 8%) followed by hemorrhagic stroke (15. 1%) and transient ischemic attack (15. 1%). Twelve stroke patients were excluded from the study due to lack of imaging or clinical information. Moreover, 75. 1% of included stroke cases had a CT scan, 16. 1% had an MRI, and 8. 8% had both, CI and MRI.
Comparison between DS-Stroke and ABCD2 The ABCD2 score is calculated as follows: 1 point for Age ≥ 60 years, 1 point for BP ≥ 140/90 mmHg, Clinical features of the TIA: 2 points for unilateral weakness/1 point for speech disturbance without weakness/0 point for other symptoms, Duration of symptoms: 2 points for ≥ 60 minutes/1point for 10-59 minutes/0 point for < 10 minutes, and 1 point for a history of diabetes. The ABCD2 factors were collected for patients from the questionnaire and data available for the score validation study. In the score validation study, 87 patients arrived at the hospital with dizziness, nausea, and unbalance/spinning symptoms and were considered for the comparison between DS-Stroke and ABCD2. An ABCD2 score of ≤ 3 points was considered as low-risk of stroke and an ABCD2 score of ≥ 4 points was considered as intermediate-to high-risk of stroke.
ACKNOWLEDGEMENTS This work has been supported by the  PHC   CEDRE 2016  project  N°30108XK  and the  Lebanese National Council for Scientific Research (CNRS-L). The authors also gratefully thank the  Research Council of Saint-Joseph University of Beirut, Lebanon, the laboratory LAPLACE , the  Agence Universitaire de la Francophonie (AUF) , the  Institut Français of the French Embassy in Lebanon , and the  Institut Supérieur des Sciences Appliquées et Economiques (ISSAE-CNAM Liban)  for their financial contribution.
For each energy point, the incident photon beam intensity was monitored with ionization chambers. The photon beam energy was calibrated using the K-edge energies of Cr and Mn as references. The energy resolution and the energy calibration of the spectrometer were determined from the elastically scattered peaks in the photon energy domain of interest. For the Cr K transitions, the experimental resolution (FWHM) was 0. 47 eV and for the K h  1,3 lines 0. 49 eV. For the K 1,3 diagram and K 2,5 VtC transitions, the data were collected in two successive scans with an acquisition time of 1 s per point, whereas for the K h  1,3 hypersatellite transition six scans with a collection time of 5 s per point were performed.
SUMMARY AND CONCLUDING REMARKS In this work, we have investigated the energy, shape and line width of the K h  1,3 hypersatellite X-ray line of metallic Cr. For comparison, the K 1,3 diagram line and the K 2,5 VtC lines were also measured. The experiment was carried out at the SSRL using the seven-crystal Johann-type hard X-ray spectrometer. The high-energy resolution and high-collection efficiency of the X-ray spectrometer combined with a photon beam intensity of about 10 13 photons/s allowed to measure the weak K h  1,3 hypersatellite with high precision. To the best of our knowledge, the K h  1,3 hypersatellite transition of Cr was measured for the first time. The energies, energy shifts, line shapes, and line widths obtained in this work for the K 1,3 diagram line and K 2,5 VtC transitions were found to be in good agreement with the experimental and theoretical data available in the literature.
ACKNOWLEDGEMENTS The financial support of the  Swiss National Science Foundation  (Grant No. 200020-146739 ) is acknowledged by F. Z. , J. H. , and  J. -Cl. D. M. G. acknowledges the support of the FCT, under the Contract No. SFRH/BPD/92455/2013  and the project  PTDC/FIS-AQM/31969/2017 , "Ultra-high-accuracy X-ray spectroscopy of transition metal oxides and rare earths. "This work was also partly supported by the research center grants nos. UID/FIS/04559/2013  to  LIBPhys-UNL , and  UID/MULTI/04046/2013  to BioISI, from the  FCT/MCTES/PIDDAC. Parts of this work have been carried out in the framework of the SPARC collaboration. We.
The secondary antibodies used were Alexa Fluor 546 goat anti-rabbit, Alexa Fluor 647 goat antimouse, Alexa Fluor 488 goat anti-chicken and Alexa Fluor 488 donkey anti-goat (all 1:750, Molecular Probes). Image acquisition was performed with Zeiss LSM510, LSM710, and Nikon A1 laser scanning confocal microscopes, and on a Zeiss AxioImager M2 upright widefield fluorescence/differential interference contrast microscope with charge-coupled device camera using a ×5 objective. Confocal images were analyzed using ImageJ and the Zeiss LSM Image Browser software. For anatomical characterization and quantification of eYFP-expressing neurons in the VTA, DR and MnR using different transgenic mouse lines, 50 µm coronal sections of the brain area of interest were collected (VTA, bregma: -2. 92 to -3. 88 mm; DR, bregma: -4.
Transsynaptic rabies virus tracing. We used a rabies virus-based genetic mapping strategy  68  to label presynaptic inputs onto designated starter cell populations, and quantified input cell data using a customized, semi-automated whole-brain mapping MATLAB script  52. Specifically, DAT-Cre, PITX3-Cre, SERT-Cre and ePET-Cre mice were injected with AAV-FLEX-TVA-mCherry (i. e. , a cellular receptor for subgroup A avian leukosis viruses) and AAV-FLEX-RG (i. e. , rabies virus glycoprotein; 800 nl, 1:1) into the DR and three weeks later, 300 nl RV-EnvA-ΔG-GFP (i. e. , pseudotyped, glycoprotein-deficient, GFP-expressing rabies virus) was injected into the same region (see "Stereotaxic Injections" for coordinates). Seven days after injection, mice were perfused with 4% paraformaldehyde (PFA) in PBS.
For input mapping, 75 µm sections of the whole brain were prepared and scanned using a Zeiss Axio Scan Z1 microscope. Individual slices were aligned using customized Matlab scripts. GFP-positive pixels were identified on the basis of a pixel-intensity threshold in the green channel. False-positive pixels (artifacts) were manually identified and removed. Positive pixels were assigned to different brain areas based on "The Mouse Brain in Stereotaxic Coordinates"  53  (Fig. 4a ). The "anterior cortex" included orbitofrontal, prelimbic, and infralimbic cortices; "olfactory nuclei" included the accessory olfactory bulb and the anterior olfactory nucleus; and, "other cortex" and "other thal" included all cortical or thalamic areas, respectively, that were not otherwise described. The olfactory bulb and cerebellum were not analyzed, and the injection site (DR) along with nearby regions where true starter cells (TVA-positive, RG-positive, RV-positive) could not be distinguished from pseudostarter cells (TVA-positive, RG-negative, RV-positive) were excluded from analysis (VTA, CLi, RLi).
Cells were visualized with a 40x waterimmersion objective on an upright fluorescent microscope (BX51WI; Olympus) equipped with infrared-differential interference contrast video microscopy and epifluorescence (Olympus). Patch pipettes (3. 8-4. 4 MΩ) were pulled from borosilicate glass (G150TF-4; Warner Instruments) and filled with internal solution, which consisted of (in mM) 117 CsCH 3 SO 3 , 20 HEPES, 0. 4 EGTA, 2. 8 NaCl, 5 TEA, 4 MgATP, 0. 3 NaGTP, 5 QX314 and 0. 1% neurobiotin, pH 7. 35 (270-285 mOsm). Electrophysiological recordings were made using a MultiClamp700B amplifier and acquired using a Digidata 1550 digitizer, sampled at 10 kHz, and filtered at 2 kHz. All data acquisition was performed using pCLAMP software (Molecular Devices). Channelrhodopsin-2 (ChR2) was stimulated by flashing 473 nm light through the light path of the microscope using an ultrahigh-powered light-emitting diode (LED) powered by an LED driver (Prizmatix) under computer control.
A dual lamp house adaptor (Olympus) was used to switch between fluorescence lamp and LED light source. The light intensity of the LED was not changed during the experiments and the whole slice was illuminated (5 mW/mm 2 ). Light-evoked excitatory postsynaptic currents (EPSCs) were obtained every 10 s with one pulse of 473 nm light (5 ms) with neurons voltage clamped at -70 mV. Series resistance (15-25 MΩ) and input resistance were monitored online. For pharmacological experiments, we recorded baseline responses for 3 min and bath applied 10 μM CNQX (Tocris) for 5-10 min to block AMPA/kainate receptor mediated currents. Data were analyzed offline using IgorPro Software (Wavemetrics). Light-evoked EPSC amplitudes were calculated by averaging responses from 10 sweeps and then measuring the peak amplitude in a 50 ms window after the light pulse.
Cells that did not show a peak in this window that exceeded the baseline noise (10 pA) were classified as non-responders. Statistics. Data from electrophysiology, mouse line characterization, and anatomical tracing experiments that met assumptions of equal variance were analyzed using two-tailed t-tests, one-way, and two-way ANOVAs using GraphPad Prism 6 (Graphpad Software). For data that did not meet the equal variance assumption (Spearman's test) we applied a Box-Cox transformation with lambda values determined using RStudio, and then performed statistical analysis on transformed values as described above. For data that failed to meet the equal variance assumption even after a Box-Cox transformation, we compared group means directly using heteroscedastic t-tests. Wherever multiple comparisons were made, the false discovery rate was held at the 0.
Acknowledgements We are grateful to  Seyedeh Atiyeh Afjei  for technical assistance, to  Boris D. Heifets  for sharing reagents and for comments on the manuscript, and to  Robert C. Malenka  for sharing equipment. We thank the entire  Lammel laboratory  for helpful discussion, the  UNC  vector core for AAV viruses and the  CNR Biological Imaging Facility  at the  University of California, Berkeley. S. L. is a  John P. Stock Faculty Fellow and Rita Allen Scholar. This work was supported by grants from  NIDA  ( R01-DA042889  (S. L. ),  R00-DA041445 (K. T. B) ),  NIMH  ( R01MH112721 (S. L. ) ), the  Shurl and Kay Curci Foundation  (S. L. ), the  Rita Allen Foundation  (S. L. ), the  Wayne and Gladys Valley Foundation  (S. L. ), a  NARSAD Young Investigator Award  ( 23543 ; S. L. ), and a  Brain Research Foundation  grant ( BRFSG-2015-7 ; S.
Mass Spectrometry-Based Proteomic Analyses IPs eluate proteins were stacked in the top of a SDS-PAGE gel to be able to treat the whole sample in a single band, and in-gel digested using modified trypsin (Promega, sequencing grade) as previously described  (Alfieri et al. , 2017). Resulting peptides were analyzed by online nanoLC-MS/MS (UltiMate 3000 and LTQ-Orbitrap Velos Pro, Thermo Scientific). For this, peptides were sampled on a 300 µm × 5 mm PepMap C18 precolumn and separated on a 75 µm × 250 mm C18 column (PepMap, Thermo Scientific). MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Peptides and proteins were identified and quantified using MaxQuant, version 1. 5. 8. 3  (Tyanova et al. , 2016). Spectra were searched against the Uniprot database (Mus musculus taxonomy, May 2017 version) and the frequently observed contaminants database embedded in MaxQuant.
Trypsin was chosen as the enzyme and two missed cleavages were allowed. Peptide modifications allowed during the search were: carbamidomethylation (C, fixed), acetyl (Protein N-ter, variable) and oxidation (M, variable). Minimum peptide length was set to seven amino acids. Minimum number of peptides, razor + unique peptides and unique peptides were all set to 1. Maximum false discovery rates (FDR) -calculated by employing a reverse database strategy -were set to 0. 01 at peptide and protein levels. The matching between runs option was activated. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  (Vizcaino et al. , 2016)  with the dataset identifier PXD008778. Statistical analysis was performed using ProStaR  (Wieczorek et al. , 2017).
In this protocol, iBAQ values were used to be able to compare those results with those previously obtained on p140Cap interactome in synaptosome  (Alfieri et al. , 2017). Proteins identified in the reverse and contaminant databases, proteins only identified by site, proteins identified with only 1 peptide and proteins exhibiting less than 3 iBAQ values in one condition were discarded from the list. After log2 transformation, iBAQ values were normalized by median centering before missing value imputation (replacing missing values by the 2. 5 percentile value of each column); statistical testing was conducted using limma t-test. Differentially interacting proteins were sorted out using a log2 (fold change) cut-off of 1 and a FDR threshold on remaining p-values of 1% using the Benjamini-Hochberg method.
BIOINFORMATIC ANALYSES OF MS DATA Building the Protein-Protein Interaction Network and Clustering p140Cap networks were constructed from 373 proteins for the cancer interactome and from the previously published 351 proteins for the synaptic interactome. Protein-protein interactions were obtained by mining publicly available databases: BioGRID  (Chatr-Aryamontri et al. , 2015) , IntAct  (Kerrien et al. , 2012)  and DIP  (Salwinski et al. , 2004). The first two are gold standard PPI repositories, and were used together with DIP because they are defined to the same standardized format (i. e. , PSI-MI), which provides a way to filter for a set of "direct and physical" human interactions obtained in experiment -interactions not predicted or inferred. Our set of PPIs was constructed then, by selecting only those MI Ontology terms which are related to "direct and physical" interactions.
We also included interactions (in our set) from BioPLEX, since the BioPlex PPIs are already deposited in the Intact and BioGRID databases. The largest connected component of each network was split into a set of communities by use of five clustering algorithms. Those included the Modularity -maximization based algorithms: agglomerative random walk (wt)  (Pons and Latapy, 2006) , the coupled Potts/Simulated Annealing "SpinGlass"(sg)  (Reichardt and Bornholdt, 2006; Traag and Bruggeman, 2009) , and the divisive spectral based fine tuning (Spectral)  (Mclean et al. , 2016) , and Non-Modularity based algorithms, including information-theoretic based "InfoMAP" algorithm (infomap) and the Mixed-Membership Stochastic Blockmodel "SVI"  (Gopalan and Blei, 2013).
Estimating the Overlap Between Diseases Annotation We tested our gene-disease annotation (GDA) data on p140Cap synaptic and Cancer PPI networks, using a network-based approach to identify the location of disease modules, localized regions of connections between disease-related proteins, on the interactome  (Menche et al. , 2015). We investigated the overlap and separation of each disease-disease pair by measuring the mean shortest distance for each disease d, using the shortest distance between each GDA to its next nearest GDA neighbor. The overlap, or separation, of each disease-disease pair in the network could be quantified using: S AB = d AB - d AA -d BB 2 where d AA and d BB quantify the mean shortest distances within the respective diseases and d AB -the mean shortest distances between diseases.
Functional Characterization of the p140Cap-Containing Protein Complex in Breast Cancer To obtain a functional view of the p140Cap cancer interactome, we tested its enrichment against Gene Ontology (GO), KEGG and Reactome databases (Supplementary Table  S2 ). According to GO Ontology, the 373 p140Cap-interacting proteins were significantly enriched for a number of GO Cellular Compartment (CC) terms. In particular the enrichment in "Cell-substrate junction" (P = 4. 96E-39) and "Focal adhesion" (P = 8. 47E-39) terms (were P -p-value, adjusted by multiple testing) indicates that p140Cap protein complexes mediates cell communication, which is consistent with the previously described role for p140Cap in cell-matrix and cell-cell adhesion  (Di Stefano et al. , 2007; Damiano et al. , 2010). In addition, we found that the high significance in "Proteasome complex" (P = 1.