dataesr/scientific-paragraphs-categorization huggingface dataset:
However, in these two cohorts, recruitment was performed in the general population, lacking a complete and precise clinical and biological healthy status. Very few well-characterized healthy volunteers (HVs) groups have been studied by such approaches: 15 HVs  [5] , 100 HVs from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study  [6] , 22 HVs  [7]  and 54 HVs  [2]. However, this latest study was the starting point of the human metabolome project, an online database referring the published metabolites quantitative data, whatever the method of quantification  [8]. Before drawing conclusion about disease-related metabolic signatures, well-conducted standardized metabolomic studies with adequate sample sizes are needed to obtain reliable reference values of the human metabolome.
Subjects and ethical statement We included healthy volunteers (HVs) who participated in the VARIETE study, a populationbased cross-sectional study designed to establish reference values for insulin-like growth factor 1 (IGF-1) in the general population (ClinicalTrials. gov Identifier: NCT01831648)  [9]. Subjects were recruited by the clinical research units of 10 French university hospitals and enrolled between January 2011 and February 2012. To be included in the study, subjects aged between 18 and 89 had to be considered healthy, based on their medical history, clinical examination (including nutritional status and gonadal/sexual status), routine laboratory tests after an overnight fast (plasma sodium, potassium, calcium, phosphate, creatinine, glycemia, total blood cholesterol (TBC), liver enzymes, TSH, complete blood count, albuminemia, prothrombin time, and HIV, HBV and HCV serology), and BMI (between 19 and 28 kg/m 2 ).
Targeted identification and quantification The Biocrates AbsoluteIDQ p180 kit allows the identification and measurement of more than 180 metabolites, including amino acids, biogenic amines, acylcarnitines, lysophosphatidylcholines, phosphatidylcholines, sphingomyelins and the sum of hexoses (one resultant metabolite). Amino acids and biogenic amines were analyzed by liquid chromatography (LC) coupled to tandem mass spectrometry, while other metabolites were analyzed using flow injection analysis (FIA) coupled to tandem mass spectrometry. Identification and quantification were performed based on internal standards and multiple reactions monitoring (MRM) detection. After a pre-processing step (peak integration and concentration determination from calibration curves) with Multiquant software (AB Sciex, Darmstadt, Germany), data were uploaded into Biocrates MetIDQ software (included in the kit).
Data management and analysis Values below the lower limit of quantification (LLOQ) (or below the limit of detection (LOD) for FIA metabolites) were reported as ND (not detected). Values above the upper limit of quantification (ULOQ) were reported as ">" the actual ULOQ. Thus, reported concentrations were within the quantification range validated for each metabolite. Fourteen p180 kits were necessary to analyze the whole cohort. Data normalization to correct for between-kit variation was based on the median value of Biocrates QC Level 1 on each kit (4 replicates / kit). The LLOQ and ULOQ were normalized accordingly.
Statistical analysis Multivariate analyses-principal components analysis (PCA), orthogonal projection to latent structures (OPLS) analysis and OPLS-discriminant analysis (OPLS-DA)-were performed with SIMCA-P v13 software (Umetrics, Umeå, Sweden). Non-numerical data (values below the LLOQ or above the ULOQ) were treated as missing values. Metabolites with more than 80% of missing values were excluded from multivariate models. PCA, an unsupervised descriptive tool, was employed to provide an overview of the data. In order to confirm the patterns observed with PCA and to identify pattern-related metabolites, supervised models were constructed to relate metabolic profiles to biological and physiological factors, using OPLS for continuous responses (age and cholesterol) and OPLS-DA for categorical response (gender).
The quality and significance of the supervised models was assessed using the predicted variation in Y (Q 2 Y) calculated from 7-fold cross-validation, and the CV-ANOVA p-value calculated by Simca software (Analysis of variance of cross-validated predictive residuals). Interpretation of the models was based on S-plots. An S-plot is a scatter plot of metabolites, allowing the influence of each metabolite on the model to be visualized. It consists of plotting the modeled correlation (P corr ) versus the modeled covariance (p) between the metabolite concentrations and the sample scores on the predictive component. When unit variance scaling is applied to the data matrix to give equal importance to all metabolites whatever their range of concentration (which was the case here), the S-plot is a diagonal line instead of being S-shaped.
Main sources of metabolome variation in healthy volunteers Principal components analysis was performed on the dataset containing the concentrations of the detected metabolites for each VARIETE plasma sample. On a PCA score plot, each dot  represents a healthy volunteer (or more generally a sample), and dots can be colored using clinical metadata. Interestingly, the dot distribution was very homogeneous. Although HVs were enrolled in 10 French university hospitals in a one year period, cohesion in dots distribution indicated a weak inter-center variation thanks to the numerous efforts made to strictly standardize pre-analytical conditions of plasma collection. The PCA score plot corresponding to the first two principal components showed that PC1 and 2 explained respectively 26% and 9% of the dataset total variance.
Possible relationships between the location of the samples in the scores plots and, biological and physiological metadata were investigated visually. Although the dot distribution was very homogenous, some perceptible trends could be observed. The first source of variation in the metabolic profile-held by the PC1 -seemed related to total blood cholesterol (TBC) (Fig  1A ). Among 133 HVs with TBC over 6. 2 mmol/L, 95% were located on the right side of the PCA (PC1 > 0) and 5% on the left side (PC1 < 0). Coloring the same dots according to male (blue) or female (red) status revealed that the second source of variation in the metabolic profile-held by the PC2 -seemed related to gender (Fig  1B ), with 75% of females located in the upper part of the plot (PC2 > 0) and 74% of males located in the lower part of the plot (PC2 < 0).
These trends in extremes are found to be homogenous across the central belt of India (field significant at 95% confidence level, see "Methods"). Identifying homogeneous regions offers clearer pathways for exploring predictions and projections of these events and their trends since predicting individual events is a  Fig. 2  Evolution of moisture transport leading to widespread extreme precipitation events. Composite evolution of moisture transport (kg m -1 s -1 , vectors) and a, b specific humidity (g kg -1 , colors), c moisture advection (10 -6 g kg -1 s -1 , colors) and d moisture convergence (10 -5 g kg -1 s -1 , colors), leading to widespread extreme precipitation events (more than ten grid cells with daily rainfall ≥ 150 mm) over central India (on 0-day). Daily data for the period 1982-2015 is used for the composite analysis, based on NCEP (a, c, d) and ERA-Interim reanalysis (b).
Widespread extreme rain events and historical floods. We analyzed some of the historical floods (International Disaster Data Base) which resulted in the largest loss in terms of human lives and property-and which coincided with the occurrence of some of the strongest widespread extreme events. These individual events (Supplementary Fig. 3 ) show identical evolution as in the composites (Figs. 2  and 5 ). For example, the central Indian floods in 1989 and 2000, Mumbai floods in 2005, South Asian floods in 2007, etc. were a result of intense convective precipitation spread over a period of 3 days, tracing their moisture source to the northwesterly flow from the Arabian Sea (Supplementary Fig. 3 ). In fact, a handful of studies on the Mumbai floods in 2005 indicate that the extreme rain event was linked to an active moisture flow from the Arabian Sea, consistent with our results  61.
Earlier studies have suggested increasing heterogeneity within the rigid central Indian box  5, 24  , while we find that there is homogeneity along the central belt where the moist westerlies show increased variability. Dynamic recycling model. The DRM uses the Lagrangian solution for the equation of conservation of the total column water vapor and quantifies the relative contributions from different sources to the atmospheric moisture over a given sink region  56, 57, 73. Daily averages of evaporation, total column water and precipitation from the ERA-interim reanalysis data set are used as input to the DRM. In the current study, the source regions are identified as Arabian Sea (50°-75°E, 5°-30°N ), Bay of Bengal (80°-100°E, 5°-30°N), equatorial Indian Ocean (50°-100°E, 10°S -5°N), and central Indian subcontinent (73°-83°E, 16°-28°N).
Code availability. The DRM is developed based on Martinez and Dominguez  56  and may be obtained from the original authors through personal requests. The Matlab code used to post process the output, extract the moisture fraction and calculate the contribution during extreme precipitation days have been archived by the authors and are available on request from the author S. G. , subimal@civil. iitb. ac. in. Data availability. The rainfall data used in the study for the period 1950-2015 is obtained from the IMD (http://www. imd. gov. in/pages/advertisements_view. php? ff=20170320_advt_34). The depression tracks and frequency data is based on the "Cyclone eAtlas-IMD" published by IMD (http://www. rmcchennaieatlas. tn. nic. in). The daily SST for the period 1982-2015 is the OISST, available from the National Oceanic and Atmospheric Administration (NOAA) (https://www.
, 2009). To verify this in our system, we seeded cells on the same 12-kPa gels. We then tracked the cells for 8 h and assessed their directional persistence as the ratio between their effective traveled distance (radial distance from the starting point) and their total traveled distance (sum of the total path). As previously shown, siZO1 and S168A cells were significantly less directional than siCT (Figure  1, F-H ). Thus, in agreement of previous work  (Tuomi et al. , 2009) , our data confirmed that ZO-1 localizes to the lamellipodia of monolayer edge cells, and depleting ZO-1 or preventing its association with α5 affects cell migration.
Statistical analysis All independent datasets were first checked for normality using the d'Agostino-Pearson K2 normality test. One-way or two-way (for time-lapse experiments) analysis of variance was performed for more than two comparisons. For one-to-one comparisons, we used a two-sided t test. For multiple comparisons, we used a Dunnett modified t test. If data sets were not normal, we used a Kruskal-Wallis test. All error bars shown are SEM.
ACKNOWLEDGMENTS We thank all of the members of P. R-C. 's laboratory and  X. Trepat ,  J. Alcaraz ,  D. Navajas , and  J. Ivaska  for technical assistance and discussions. We thank  A. Lahiguera-Belenguer ,  C. Ureña ,  N. Castro ,  A. Kosmalska ,  M. Uroz ,  P. Jiménez ,  A. Labernadie ,  R. Sunyer ,  V. Conte , and  D. Zalvidea  for discussions. This work was supported by the  Spanish Ministry for Economy and Competitiveness  ( BFU2016-79916-P  and  BFU2014-52586-REDT ), a  Career Integration Grant  within the  Seventh European Community Framework Programme  ( PCIG10-GA-2011-303848 ), the  European Commission  (Grant Agreement  SEP-210342844 ), the  Generalitat de Catalunya Fundació "La Caixa ," and  Fundació la Marató de TV3  (project  20133330 ). V. G. -T. , A. E. -A. , R. O. , and C. P.
Filtered reads were mapped with TopHat v2. 0. 9 software to use the UCSC mm10 release reference on new junctions and known annotations. RSeQC v2. 3. 3, PicardTools v1. 92 and SamTools v. 1. 18 software were used to verify biological quality control and summarization. HTSeq v. 0. 5. 4p3 was used to prepare the count data. R/Bioconductor edgeR software v. 3. 3. 6 was used to carry out data normalization and differential expression analyses. Genes that achieved 10 counts in at least two replicates were kept, whereas very low expressed genes were filtered out. The filtered data were normalized by the library size and differentially expressed genes were estimated using negative binomial general model statistics. To identify differentially expressed transcripts, we defined a criterion of a 2-fold and greater difference plus a significant (p < 0.
05) false discovery rate (FDR). Statistics: t-test with un-equal variance. The R/Bioconductor MFuzz package v. 2. 18. 0 was used for fuzzy clustering of the differentially expressed genes over time in both HS and FT compared to NI controls. Pathway analysis was performed using MetaCore (Thomson Reuters), as described previously  (Noristani et al. , 2015b (Noristani et al. , , 2016)). Accession number for RNA-Seq data: Gene Expression Omnibus (GE0) GSE96055.
225 × 225 µm 2 images were taken adjacent to the lesion center for each animal and the co-localization coefficient was measured between the two channels. In total we assessed co-localization within a 6750 × 6750 µm 2 surface area adjacent to the lesion site. Laser intensity and detector sensitivity settings were kept constant for all acquisition of all images in a given experiment. 5-7 animals were used per time-point. Colocalization analysis was performed using the Carl Zeiss LSM-710-NLO software. Morphometric bright field photographs were obtained and analyzed using NanoZoomer RS slide scanner (NanoZoomer Digital Pathology System and NDP view software, Hamamatsu, Japan).
Relative Optical Density To determine SCI-induced changes in BRCA1 expression in Microcebus murinus, the mean optical density (OD) was measured at different distances 3 months after spinal cord (HS) using ImageJ (National Institutes of Health, USA), as described previously  (Noristani et al. , 2010). OD is a sensitive and reliable method to measure expression level of a given signal and to detect changes caused by different experimental conditions. Following peroxidase immunostaining, spinal cord sections were scanned using Nanozoomer RS slide scanner that uses constant light intensity and exposure time to obtain peroxidase-labeled photographs (NanoZoomer Digital Pathology System and NDP view software, Hamamatsu, Japan). Digital images were exported with RGB color profiles, using identical exposure settings for all sections.
Statistical Analysis Paired t-tests were used to compare differences in BRCA1 intensity at similar distance between above and below the lesion site in Microcebus murinus. Significance was defined as p ≤ 0. 05. The data were analyzed using GraphPad Prism 5. 0 (GraphPad Software, Inc, CA, USA). All data are shown as the mean ± standard error of the mean (SEM).
Relationship between average expression and log 2 of the fold-change in microglia after direct comparisons between HS and FT groups at 72 (C), 1 week (D), and 2 weeks (E). Horizontal blue lines indicate the cut off criterion used to define differentially expressed genes with a FC >2 (logFC comprised between -1 and 1) and a significant false discovery rate (FDR) values (p < 0. 05). Red points indicate significantly deregulated genes following injury. Table  S1  | Database of differential expression comparison of activated microglia RNA-Seq data relative to non-injured control microglia at 72 h, 1 and 2 weeks after hemisection and full transection injuries. Table  S2  | Pathway analysis of differentially expressed genes in activated microglia after hemisection and full transection injuries. Table  S3  | Database of the expression level of cellular markers (microglia, neuronal, astrocyte and oligodendrocyte).
ACKNOWLEDGMENTS We thank  M. Rabano  and  M. dM Vivanco  for technical assistance in flow cytometry samples collection. Transcriptomic experiments were done at iGE3Genomics Platform,  University of Geneva Switzerland ; we thank in particular  M. Docquier  and  C. Delucinge  for their assistance in transcriptomic analyses. Histological analysis was done at Montpellier Rio Imaging platform and we thank  H. Boukhaddaoui  for his assistance in image analysis. We thank  Gareth Thomas  for comments on the manuscript.
FUNDING This work was supported by the  Spanish Government ,  Plan Nacional  de I+D+I 2008-2011 and  ISCIII-Subdirección General de Evaluación y Fomento de la investigación  ( PI10/00709 ) [to FP] and the Government of the  Basque Country grant  ( Proyectos de Investigacion Sanitaria and Fondo Comun de Cooperacion Aquitania-Euskadi)  [to FP], the  French Government ,  ANR-FNS grant ,  GliALS  (N •  ANR-14-CE36-0009-01 ) [to FP], the patient organizations " Demain Debout Aquitaine " [to YG and HN] and [to FP and HN].
Underfocus was 8-9 mm and the magnification was chosen to give an object pixel size between 4. 3 A ˚and 7. 3 A ˚. The total electron dose per tilt series was 120-150 e -/A ˚2. Tilt series were aligned to gold fiducial markers and tomograms were reconstructed by back-projection with the IMOD software package  (Kremer et al. , 1996). A final filtering step applying non-linear anisotropic diffusion  (Frangakis and Hegerl, 2001)  was performed to increase contrast. Tomograms were manually segmented with the program AMIRA (FEI).
Statistical analysis The quantitative data were reported as the medians and range. Normally distributed quantitative data were analyzed with Student's t test, and the Mann-Whitney test was used otherwise. The qualitative data were reported as the number of patients (percentage of patients) and were compared using the Pearson's 2 test or the Fisher's exact test, as appropriate. As worse results were not expected after training, the tests were one-sided, and the level of statistical significance was set at P < 0. 05. As this study was the first of its kind, data was lacking to formulate formal power calculations to determine the number of included patients to assess compliance for ERP: a convenience sample was therefore chosen. The analysis was performed using the Statistical Package for the Social Sciences software (SPSS, version 20, Chicago, IL, USA).
Results All residents of the department were enrolled: they were 5, ranging from PGY 4 to 7. They had been working in the department for one month at the beginning of the study. All had performed over 50 laparoscopic procedures, but none had performed any laparoscopic colorectal surgery (LCS) as primary operator. Twenty consecutive patients were included as planned. Preoperative data of pre-training and post-training patients are reported in Table  1. Demographic data and clinical presentations were comparable between groups, and preoperative management did not differ after the training. Most patients presented with cancer (17; 85%), located in the right colon in 8 cases, left colon in 2 cases, rectum in 7 cases (2 high-, 3 mid-, and 2 low-rectum). The other 3 patients presented with inflammatory bowel disease in 2 cases (one Crohn's disease, one ulcerative colitis) and unresectable polyp in one case.
Data Analysis The sample size calculation was performed basing on the assumption that the main measurement (femoral tunnel position) is continuous and fairly normally distributed and that a 20% difference in the outcome measures is clinically relevant. With a significance level of 5%, at least 20 participants per group were needed to be included. Statistical analysis was performed using SPSS software (SPSS Inc. , Chicago, Illinois, United States). t-tests were performed to compare TT and AM techniques tunnel position in a/t and b/h measures. To account for the comparisons of the femoral tunnels, the significance level was set at p < 0. 001. Data were presented as mean value AE standard deviation.
Denoting ρ = ka c , one obtains:        φ 0 (ρ) = ρ, φ 1 (ρ) = ρ -tan -1 (ρ), φ 2 (ρ) = ρ -tan -1 3ρ 3-ρ 2 , (26) Figure  1  shows the channel radii calculated with Eq. (26) by using the phase shifts provided by the OPTMAN code with a set of optical model parameters recently established for the nuclear system 238 U+n  [13]. A good agreement is observed with the equivalent hardsphere radii up to 150 keV, which is a satifactory upper energy limit for the analysis of the Unresolved Resonance Range of the neutron cross sections. Equations (  18 ), (  19 ) and (  22 ) were implemented in the OPTMAN code for calculating the neutron strength functions and distant level parameters for l = 0, 1, 2. Results obtained for 238 U+n are reported in Fig. 2. They were obtained by using the same set of optical model parameters as used in Fig.
2000)  that there is a significant correlation between the breathing characteristics and the swimmer's skills plus the stroke characteristics. From this statement we wanted to validate an IMU devices mounted on the head of a swimmer to measure its breathing characteristics, and experiment on an instant feedback to correct those movements. The tested device is a Swimbot (Meudon, France), based on Newton 2 smart watch core (Ingenic, Beijing) including a 1. 2 GhZ M200 CPU running a custom Android 5. 1 with a 16 bits 9 axis IMU (MPU-9250 (InvenSense, San Jose, California) embedded, using the Android sensors fusion algorithm to provide a rotation quaternion at a sampling rate of 50 Hz. The other data provided are: accelerometer, magnetometer and gyroscope and two software sensors provided by Android: linear acceleration (without the gravity) and quaternion orientation.
Methods Measurements were validated both with manual video analysis (VIRB cameras, Garmin, Schaffhausen) and with a motion capture (MoCap) system: OptiTrack (NaturalPoint, Corvallis). Makers have been placed on the head of a mannequin and on the device. The OptiTrack has been calibrated with a mean tracking error of 8. 46e-4 mm on markers position and provides rotation quaternions of the head. The second monitoring device was two VIRB cameras and movement was reconstructed by tracking markers position with image processing software (Blender, Blender Foundation, Amsterdam). From both MoCap system and Swimbot we extracted quaternions then converted to Euler angles and from the video file, the position of two markers in the alignment of the face to get an orientation vector from which we deduced the roll: easy to use system for roll analysis and real time feedback.
This method allowed the constitutive production of viruses able to enter new cells but unable to integrate because of the presence of DTG. Recovery of viral replication under these conditions required the production of a virus resistant to DTG in the culture supernatant. After approximately 60 days of culture, we succeeded in selecting a virus resistant to DTG, as well as RAL and EVG. Surprisingly, we found no mutations in the IN gene but rather five located in the nef region, one mutation six nucleotides upstream of the 3= PPT and four other changes clustered in the 3= end of the 3= PPT, inside the G tract, resulting in GCAGT instead of GGGGGG. The analysis of Nef sequences available in the Los Alamos HIV-1 database, representing a total of 23,874 sequences, showed that the motif was highly conserved, with each G considered individually showing variability on the order of 0.
Purified RT-PCR products were directly sequenced for bulk genotyping by using a cycle sequencing reaction with the BigDye Terminator kit (Applied Biosystems). Furthermore, some of the purified RT-PCR products were cloned with the TOPO TA Cloning kit (Invitrogen) and 31 clones were reamplified by PCR for sequencing. All of the sequences obtained were analyzed with the CodonCode Aligner software.