dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Authors thank  S. Taylor ,  S. A. Saldanha , and  M. Totrov  for helpful discussions and  E. Raush  for technical support. A. Bancet  is acknowledged for optimization of syntheses and characterization of compounds. All molecular images were prepared using ICM (http://www. molsoft. com/icm/). R. A. 's work was supported by  NIH/NIGMS   R01  grant  GM074832. IK was partially supported by  NIH   R01  grants  AI118985  and  GM117424. C. C. 's work was supported in part by  Institut National de la Santé et de la Recherche Médicale (Inserm) ,  Commissariat à l' énergie Atomique à l'Energie Atomique et aux Energies Alternatives (CEA) , the  Ligue Nationale contre le Cancer , the  Institut National du Cancer  (grant No. 57 ), and the  Agence Nationale de la Recherche  ( PCV08_324733 ) and  Université Grenoble-Alpes (UGA).
B. B. , T. L. and M. L. B. 's work was partially funded by the  Région Rhône-Alpes  (grant " ARC 1 Santé "  12-008707-01 ) and the  Institut National du Cancer (INCa)  Grant No. 2011-097 ). B. B. thanks the  Ministère de l'Education Nationale de la Recherche et de Technologie (MENRT)  and the  Ecole Doctorale Interdisciplinaire Sciences-Santé (EDISS)  for a PhD fellowship. C. C. thanks platforms supported by GRAL, financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ).
Lead-Electrocardiogram (ECG) A 15 lead-ECG was continuously recorded and stored on a CardioSoft ECG system (Version 6. 7. 3, GE Healthcare, Freiburg, Germany). Due to the species anatomy, we placed the conventional precordial leads V1-V6 one intercostal space above that used in current clinical electrocardiography  (Noriega et al. , 2013). The three additional posterior chest leads V7-V9 were placed after lead V6 at regular interelectrode spaces. The ECG was recorded at baseline, after induction of LBBB, and continuously during the coronary occlusion sequences. In each ECG recording, we measured the following parameters using electronic calipers (Cardio-Calipers software, Iconico©) and imaging magnification: (1) QRS complex duration; (2) amplitude of the R wave and S wave; and (3) ST segment displacement at the J point.
Data Analysis Quantitative data were expressed as the mean ± standard error of the mean (SEM). The changes in the ECG variables from baseline to LBBB induction were evaluated by the analysis of variance (ANOVA) with Bonferroni correction for post hoc comparisons. A p value < 0. 05 was considered significant. All analysis were performed using SPSS v. 22. 0 (IBM SPSS Inc. , Chicago, IL, United States).
Study design and data collection We conducted a retrospective study using the registry of the Trauma System of the Northern French Alps (TRENAU). Briefly, this trauma system gathers data from thirteen hospitals located in the Northern French Alps and consists of one level-I trauma center, two level-II trauma centers and ten level-III trauma centers  [14]. A registry has been implemented since 2009 and has been collected data prospectively using the Utstein-style template  [16]. The Regional Institutional Ethics Committee approved the implementation of the TRENAU registry (Comité d'Ethique des Centres d'Investigation Clinique de l'inter-région Rhône-Alpes-Auvergne, IRB number 5891) and, given its observational nature, waived the requirements for written informed consent from each patient. Consecutive adult patients recorded in the TRENAU registry from January 2009 to December 2014 were included if they died following a severe traumatic injury in the prehospital setting or within their stay at hospital.
Patients who died before the arrival of prehospital emergency physicians were not included in the TRENAU registry and, thus, were not considered for this study. The following data were collected for each patient: age, sex, mechanism, first recorded vital signs, the mechanism, Glasgow coma scale (GCS), age and arterial pressure (MGAP) score  [9]  in the pre-hospital setting, vital signs on admission, the need for emergency surgery or embolization, the injury severity score (ISS) and the Trauma revised injury severity score (TRISS)  [8]. The Injury Severity Score (ISS) is an anatomical scoring system that provides an overall score for patients with multiple injuries. Each injury is assigned an Abbreviated Injury Scale (AIS) score and is allocated to one of six body regions (Head, Face, Chest, Abdomen, Extremities (including Pelvis), External).
The third sample (n = 329) was derived from the SAHL-D validation study. The participants from the SAHL-D validation study were healthy adults between the ages of 20-85 years old and were recruited by a test panel of the Netherlands Institute for Health Service Research (NIVEL)  [11]. The fourth sample (n = 112) included patients with chronic musculoskeletal pain (ages 17-76 years old)  [20]  and was recruited in an interdisciplinary outpatient rehabilitation center (Heliomare Wijk aan Zee) in the Netherlands. All data were collected between 2013 and 2015. Patients unable to understand the Dutch written language were excluded in all four samples. Educational levels were classified into low, middle and high following the International Standard Classification of Education (ISCED); low (level 0-2: early childhood; primary education, lower secondary education); intermediate (level 3-5: upper secondary, post secondary, short cycle tertiary) and high (level 6-8: bachelor, master, doctoral)  [21].
Data analysis Rasch analyses were performed in WINSTEPS  [24]. Descriptive statistics were performed for sample characteristics, such as age, gender and educational level using SPSS version 24. The psychometric analysis consisted of two steps. First, we examined the psychometric properties of the 33-item SAHL-D. Second, the scale was shortened by selecting the best performing items. Although the scale gives four response options among which only one is correct, most of the data available (samples 1, 3 and 4) had been recorded as binary variables (correct versus incorrect response); we therefore used the Rasch model for binary data to perform these analyses  [14]. For the long form, a distractor analysis was performed in one sample where all response options were recorded (sample 2). Step 1.
To determine optimal cut-off scores for the short SAHL-D, we calculated receiver operating characteristic (ROC) curves using SPSS Inc. Version 24. 0  [32]. To calculate the ROC curves, we used adequate prose literacy as the reference standard, which was assessed in the original SAHL-D validation study  [11]. Prose literacy was assessed by a subset of items from a reading comprehension test widely used for ninth graders in Dutch pre-university secondary education (total 16 items) (n = 222). The prose literacy test and the cut-off values (scores ≤6 reflect inadequate prose literacy; scores ≥7 reflect adequate prose literacy) are described in more detail elsewhere  [11].
Transcriptome profiles reveal that E6*I deregulates cellular targets independently of E6. To assess cellular targets potentially deregulated by E6*I, the whole transcriptome of stable cell lines -U-E6All (clones #1, #6 and #7), U-E6*I (clones #1, #6 and #14) and U-CTL (clones #1, #3 and #par) -were analyzed by RNA sequencing. First, unsupervised hierarchical clustering analysis based on RNA sequencing datasets reveals 2 clusters. One of these clusters contains the 3 clones of U-E6All cells (Fig. 4A  and S6 ). Venn diagram representation of deregulated genes in U-E6All and U-E6*I compared with U-CTL show that 419 transcripts were significantly deregulated (FDR < 0. 05) in U-E6All (Fig. 4B ), of which 264 were upregulated and 155 were down-regulated (Fig. 4C  and Table  S1 ). The expression of 53 transcripts were deregulated in U-E6*I cell lines (Fig.
5A ). Based on protein-coding transcripts deregulated in U-E6All cells, a protein interaction network was constructed using the STRING database (Fig. 5B ). Many of the deregulated transcripts identified in U-E6All cells have been previously identified in HPV-infected cells and tumor samples such as transcripts encoding ribosomal subunits, SYCP2, NUP210 and DICER1  [29] [30] [31] [32] [33]. Gene ontology and KEGG pathway analysis of U-E6*I cell lines revealed 6 gene classes deregulated by ectopic expression of E6*I: Extracellular Matrix Organization (GO:0030198), Response to decreased oxygen levels (GO: 0036293), MAPK signaling pathway (KEGG: hsa04010), Focal adhesion (KEGG: hsa04510), Cytokine-cytokine receptor interaction (KEGG: hsa04060) and Pathway in cancer (KEGG: hsa05200) (Fig. 6A ).
Congruent with a previous study using U-2 OS cells transiently (B) Protein-protein interaction network, corresponding to the protein-encoding genes deregulated in U-E6All cell lines, generated using STRING database  75. Line thickness indicates the strength of data support. expressing E6 isoforms or only E6*I  24  , we report that stable expression of E6*I increases ROS production but no change is observed in cells co-expressing E6. Finally, radio sensitivity assays showed that U-E6All cells are more sensitive to radiation than both E6*I and control cells. These latter results could be partially explained by the TP53 wild-type status of our cellular model given the fact that E6 was shown to disrupt the p53-mediated cellular responses to DNA damage  41. Altogether, these data suggest that E6 isoforms have a specific impact on cellular phenotype and that the impact of E6*I is modulated when co-expressed with E6.
In addition, we confirm the upregulation of SYCP2, Nup210 and DICER1 transcripts, which were previously shown to be increased in HPV16-positive clinical samples from cervical  29  and head and neck cancers, and HPV-negative cell lines expressing E6 and E7 proteins  30, 33. Comparative RNA sequencing analysis also revealed that E6*I expression was sufficient to modulate the cellular transcriptome. KEGG pathway and GO enrichment annotations showed that E6*I-deregulated transcripts were assigned to extracellular matrix organization, response to decreased oxygen levels, mitogen activated protein (B) Protein-protein interaction network, corresponding to the protein-encoding genes deregulated in U-E6*I cell lines, generated using STRING database  75. Line thickness indicates the strength of data support.
Reads alignment. Raw reads quality was assessed using the fastQC software v0. 11. 3 (Babraham Institute, UK). Reads from each sample were then aligned to the reference human genome (GRCh38, release 87) using TopHat v2. 1. 1 (John Hopkins University, USA)  71  with a minimum mapping quality score of 30. For each sample, an average of 45 million reads were uniquely mapped. Differential expression analysis of cellular transcripts deregulated by E6 and/or E6*I. Cufflinks (Trapnell Laboratory, University of Washington, USA) was used for transcripts assembly and to assess transcripts abundance, which was normalized between all 9 samples using the fragments per kilobase per million mapped reads (FPKM) method to correct transcript length and library size bias  72. Differentially expressed transcripts between control condition U-CTL and U-E6All or U-E6*I conditions were finally identified using Cuffdiff (Trapnell Laboratory).
Transcripts with a false discovery rate (FDR) < 0,05 were considered significantly deregulated. No fold change cutoff was applied to the data. Nevertheless, more than 95% of transcripts with FDR < 0,05 had a fold change >2. Accession numbers. RNA sequencing data in the form of BAM files have been submitted to SRA@ncbi. nih. gov under the BioProject PRJNA421890 and BioSamples SAMN08159315 (U-CTL #par); SAMN08159316 (U-CTL #1); SAMN08159317 (U-CTL #3); SAMN08159318 (U-E6All #1); SAMN08159319 (U-E6All #6); SAMN08159320 (U-E6All #7); SAMN08159321(U-E6*I #1); SAMN08159323 (U-E6*I #6); SAMN08159324 (U-E6*I #14).
Acknowledgements We thank  Muriel Masson  for kindly providing the pXJ40-E6All vector,  Julie Frejaville  and  Anne Peigney  for technical assistance,  Gilles Travé  for helpful advice and discussions and  Nathalie Dourdin  for carefully reading the manuscript. We also wish to thanks  Paul F. Lambert  for kindly providing W12 cell clones. K. M. and P. P. had fellowships from the  French  ministry of research and technology. J. P. had fellowships from the  Conseil Régional de Franche-Comté. This work was supported by  Cancéropôle Est (CE) ,  Ligue Contre le Cancer (Comité du Doubs and CCIR-GE) ,  Université de Franche-Comté (UFC)  and  Région Franche-Comté.
This provides good discrimination of electrons and muons entering the detector station from the top, a feature which is important to identify muon-poor air showers induced by photons, not only with the FD but also with the SD. Moreover, the detector stations also provide a large cross section for inclined and up-going air showers, a feature that is of key-importance for the detection of neutrino-induced air showers. Both of these aspects will be discussed in more detail below. The modular structure of the surface detector array and fluorescence telescopes allowed the data taking to start in 2004 in a partial configuration. In 2008 the detector was completed and since then data have been taken, practically without interruption. Once installed, the local stations work practically without external intervention.
The selection of DGL showers is just made requiring the reconstructed zenith angle to be in the 58. 5 • -76. 5 • range allowing for uncertainties. It is obtained fitting a plane to the positions of the triggered stations and the start time of the signals. For the selection of DGH showers, the reconstructed θ must exceed 75 • , the apparent average speed of the signal at the ground must be smaller than 1. 044c 4  , its spread must be smaller than 0. 08c, and the eccentricity must exceed a value of 3. The ES (DG) neutrino identification has been optimized using extensive shower simulations for different energies, arrival directions, decay (interaction) depths, and impact parameters, with standard software such as AIRES  [28]  and CORSIKA  [29]  and including the SD response of the individual detector stations using specific software  [15, 30]  based on GEANT4  [31].
Previous data from Auger as well as data from TA, AGASA, Yakutsk, and Haverah Park are included for comparison. The lines and shaded regions give the predictions for top-down models and GZK photon fluxes, respectively, assuming different parameters (references can be found in  [72] ). with background photons via inverse-Compton scattering, resulting in an electromagnetic cascade that ends at GeVTeV energies. Comparing these expected diffuse GeV-TeV fluxes measured with instruments sensitive in this energy range provides another important cross-link to γ-astronomy (see e. g. [64] ). The search for UHE photon primaries is based on the different development and particle content of electromagnetic and hadronic air showers. The induced electromagnetic cascades develop slower than hadronic ones so that the shower maximum X max is reached closer to the ground.
Acknowledgments The successful installation, commissioning, and operation of the  Pierre Auger Observatory  would not have been possible without the strong commitment and effort from the technical and administrative staff in Malargüe. We are very grateful to the following agencies and organizations for financial support:  Argentina -Comisión Nacional de Energía Atómica ;  Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT) ;  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) ;  Gobierno de la Provincia de Mendoza ;  Municipalidad de Malargüe ;  NDM Holdings  and  Valle Las Leñas ; in gratitude for their continuing cooperation over land access; Australia -the  Australian Research Council; Brazil -Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ;  Financiadora de Estudos e Projetos (FINEP) ;  Fundacão de Amparo à Pesquisa do Estado de Rio de Janeiro (FAPERJ) ;  São Paulo Research Foundation (FAPESP)  Grants No.
2010/07359-6  and No. 1999/05404-3 ;  Ministério da Ciência, Tecnologia, Inovacões e Comunicacões (MCTIC) ;  Czech Republic -Grant  No. MSMT CR LTT18004 ,  LO1305 ,  LM2015038  and  CZ. 02. 1. 01/0. 0/0. 0/16013/0001402 ;  France -Centre de Calcul IN2P3/CNRS ;  Centre National de la Recherche Scientifique (CNRS) ;  Conseil Régional Ile-de-France ;  Département Physique Nucléaire et Corpusculaire  ( PNC-IN2P3/CNRS );  Département Sciences de l'Univers (SDU-INSU/CNRS) ;  Institut Lagrange de Paris (ILP)  Grant No. LABEX ANR-10-LABX-63  within the  Investissements d'Avenir Programme  Grant No. ANR-11-IDEX-0004-02 ;  Germany -Bundesministerium für Bildung und Forschung (BMBF) ;  Deutsche Forschungsgemeinschaft (DFG) ;.
Docking studies Docking simulations were performed in the allosteric site of GK protein with Glide 5. 8 module of Schrödinger Suite 2012 extra precision mode  [56] [57] [58]. The X-ray crystallographic information of GK protein with the allosteric activator was obtained from RCSB protein data bank. After studying a numbers of entries, the best entry (PDB code: 3IMX) was selected based on resolution and was used as the docking model. The 2D structures for the designed ligands were drawn in MarvinSketch and transformed to 3D with LigPrep 2. 5 with OPLS2005 force field  [59]. An analogous docking method was used for the molecular docking of the synthesized derivatives as described in detail in earlier publications using Glide and the ligand poses with most favorable docking score (Glide score and Glide energy) were selected  [24, 25].
Molecular docking studies Lead optimization of the synthesized derivatives was done via calculation of drug-likeness properties (log P, mol. wt. , hydrogen bond acceptors (HBA), and hydrogen bond donors (HBD) and all the synthesized derivatives showed appreciable drug-like properties as ascertained using Lipinski's rule of 5 (Table  2 ). The docking studies were performed using Glide in the allosteric site of GK protein (PDB entry: 3IMX) and validated by docking of 3IMX ligand in the allosteric site. The designed GK activators were docked in the allosteric binding site comprising of Arg63, Tyr215, Met210, Tyr214, Val452 and Val455 residues. Glide score and Glide energy of the synthesized derivatives are presented in Table  2. All the synthesized 3,5-disubstituted benzamide derivatives showed significant binding in the allosteric site as determined by analysing the H-bond and hydrophobic interactions of the selected best docked poses.
The thiazol-2-yl group attached to benzamide nucleus of compounds 6c, 6e and 6h protruded in the hydrophobic pocket showing the interactions with Val455, Ala456, and Lys459 of the R13 helix, as well as Pro66 of connecting region I and Ile159 of the large domain, the phenyl ring was found to pack between Tyr214, Met210 and Val455 whereas the substituted aryl group of sulphonamide oriented into the hydrophobic pocket comprising residues Leu451, Val 455, Tyr215, and Trp99. Docked pose showing H-bond interactions of the selected compounds 5b, 5f, 5i, 6c, 6e and 6h in the allosteric binding site of GK protein is presented in Fig. 3. The selected 3,5-disubstituted benzamide displayed similar H-bonding interactions with amino acid residues in the allosteric binding site of GK protein as that of the cocrystallized ligand (PDB ID: 3IMX).
In this direction, a few pieces of work using R134a, R410A, and air-water have been proposed  [39] [40] [41] [42] [43]. These works evaluate the probability of a flow regime under some operating conditions and flow quality based on a sequence of experimental images. However, probabilistic flow pattern maps based on extensive experimental and synthetic databanks aiming to assess transition zones have not been developed yet. Accordingly, the present work aims to develop a probabilistic-based approach to identify the more likely flow regime under some operating conditions. To this end, experimental records from an extensive literature review and synthetic data from OLGA v. 7. 3. 3 simulations were implemented. Oil and Gas Simulator-Schlumberger (OLGA) was selected because is one of the commonly implemented simulators in the Oil and Gas industry and its good agreement with experimental results  [32, 33].
This paper is organized as follows: Section 2 contains a review of flow pattern maps and their classification. Section 3 describes the experimental and synthetic datasets. Section 4 presents the flow pattern probabilistic-based approach. Section 5 summarizes the basic findings and presents suggestions for future work. Besides, the paper counts with three appendices associated with the procedure developed to estimate the posterior probability (Appendix A. 1 in Supplementary Material), the probabilistic flow pattern maps with several inclinations (Appendix A. 2 in Supplementary Material), and the pattern maps for liquidliquid flow following a parallel approach (Appendix A. 3 in Supplementary Material).
Experimental and synthetic datasets 3. 1 Experimental dataset The experimental dataset has 7702 records distributed among 22 authors as shown in Table  3. These authors performed measurements for different liquid-gas combinations (covering refrigerants to viscous oils), superficial velocities, pipe lengths, and diameters. However, the water-air system is the most frequently used combination with about 62% of all the gathered records. Table  3  also presents pipes orientation during the experiments, which include vertical, horizontal, and inclined pipes with upward and downward directions. As expected, a larger number of data points is reported for horizontal cases and a lower amount of records for vertical downward pipes (À90°).
Synthetic dataset The steady-state synthetic records were generated with OLGA Multiphase Toolkit v. 7. 3. 3 obtaining 59 574 records with the main parameters depicted in Table  4. The effect of pipe inclination on the flow pattern was evaluated by considering 5°steps from À90°to 90°. The remaining required parameters, except for the gas and liquid superficial velocities, were randomly selected based on the ranges obtained in the experimental database. This procedure aims to perform a correct overlap between the experimental and synthetic data by setting the gas velocity from 1e-02 to 40 m/s and the liquid velocity from 1e-03 to 68 m/s. These ranges were chosen based on the available experimental data. Transitions between neighboring flow patterns were represented by a mesh rather than a boundary line.
Experimental data processing A filtering process was used to select the experimental records within an acceptable error in the void fraction prediction. This acceptable error is determined based on a tolerable difference between experimental and synthetic predictions of the void fraction. However, 4407 experimental records did not report this parameter, and a direct classification is not possible; therefore, a supervised learning approach was used to classify those records as acceptable/non-acceptable for the construction of the flow pattern map. For this purpose, experimental records already classified as acceptable or non-acceptable were used as a training set. For the classification process, two types of supervised classifiers were considered: (i) Support Vector Machines (SVM) and (ii) K-Nearest Neighbors (KNN).
5. After the supervised classification, a total of 5806 over 7702 acceptable records were obtained, which corresponds now on as the experimental dataset for the flow pattern map construction. Slug flow; 4. Bubble flow; 5. Two-phase oil/water; 6. Single phase gas; 7. Single phase oil; and 8. Single phase water. For this work, it was not contemplated the flow regimes 5, 6, 7, and 8, whereas the flow regimes 0 and 1 were coupled as one flow regime (Stratified). For the sake of simplicity, the flow regimes are denoted in this work as 0 (Segregated Flow) -SG; 2 (Annular Flow) -A; 3 (Intermittent Flow) -IT; and 4 (Bubble Flow) -BF. The OLGA Multiphase Kit assumes that for vertical or near vertical (±75°or more) there is not segregated flow, but only annular flow. This assumption is linked to the gravitational forces that impede to observe this phase for these inclinations (as in Fig.