dataesr/scientific-paragraphs-categorization huggingface dataset:
Existing approaches to characterising repositories (eg, re3data) should be explored for suitability in the clinical research domain and perhaps adapted or extended. Finally, how should the repositories, whatever their size, be assessed for compliance with standards of good practice, how can that assessment process be financially supported, and how can the results of the assessment be transmitted back to data generators and users? the need for empirical research The number of empirical studies about data sharing is, so far, relatively small. A limited amount of data is available, dealing with individual aspects of data sharing (eg, surveys about attitudes and experiences,  81 82  statistics about data requests and shared data,  83 84  and studies of the costs of data preparation  85  ). Given that the principles and recommendations in our document (and similar reports) are largely consensus-based, further evidence gathering should be a priority.
This is the case in Europe, with the introduction of the new General Data Protection and Clinical Trials Regulations, but also (for instance) in Japan, where the Personal Information Protection Act, Clinical Research Act and Next-Generation Medical Base Act were established in March and April 2017. These acts describe how to handle IPD for data sharing and deal with, among other things, informed consent regulations (K Takenouchi and D Nakatani, personal communication, 2017). We have to develop mechanisms to monitor and interpret the changing legislative and regulatory frameworks, and design systems around them appropriately. We believe that the international task force has constructed a comprehensive framework of policies and procedures for data sharing in clinical trials. The next steps will be to disseminate the principles and recommendations in this framework, engaging different communities and countries, liaising with other major initiatives in the field at regional and global levels, and discuss how the various components of the data sharing infrastructure we need can be funded and implemented.
JD-M attended all consensus meetings, was responsible for alignment of the work with the H2020-CORBEL project and approved the final manuscript. funding This project has received funding from the European Union's Horizon 2020 research and innovation programme (CORBEL, under grant agreement n° 654248). Disclaimer The views expressed by MD in this article are the personal views of the author and may not be understood or quoted as being made on behalf of or reflecting the position of the European Medicines Agency or one of its committees or working parties.
Data and coding standards should be built into any trial's data design prospectively, from the beginning of the trial. It is very difficult to try and apply standards and data definitions after a trial database has been designed and the data collected, or to try and change data structures unless a trial has been designed from the beginning with those data structures in mind (eg, it is much easier to map data to Clinical Data Interchange Standards Consortium -Study Data Tabulation Mod- el (CDISC SDTM), the tabular data format used by the Food and Drug Administration (US) (FDA), if it has been collected using Clinical Data Interchange Standards Consortium -Clinical Data Acquisition Standards Harmonisation (CDISC CDASH) data items). Legacy data conversion can be done when there is value in combining data from prior trials, but it is resource-intensive and may compromise data in- tegrity.
The time and costs required for retrospective 'standardisation' would put such an exercise beyond the resources of many non-commercial units. Instead, it is important that standards are designed from the start, with decisions made about the coding and oth- er systems to be used made as part of the trial design process. 23. Among the various data standards available, those from CDISC should be considered as offering the best starting point currently available for defin- ing and coding data and metadata in a consistent way. Ohmann C, et al. BMJ Open 2017;7:e018647. doi:10. 1136/bmjopen-2017-018647.
To do so we simulated 10 000 datasets  (Bolker 2008)  according to the best fitted model, then estimated the slope of the population density effect by fitting the model that generated the data, and then extracted the corresponding p-values. The probability for the null hypothesis to be rejected knowing it is true (i. e. b error) is the proportion of p-values greater than the nominal a  0. 05. We performed this power analysis by increasing the number of repetitions of transects within a year from 1 to 6 to test the effect of the number of repetitions on the statistical power to detect a change in abundance. Comparing how AI-p and AI-v related to population density was not straightforward because the two abundance indices were measured on different density ranges (Supplementary material Appendix 1 Table  A1 ).
66  0. 53 (SD) in the morning versus 0. 63  0. 59 in the evening; b  0. 091  0. 047, z-value  1. 940, p  0. 052) and in the evening for AI-v (0. 54  0. 30 in the morning versus 0. 63  0. 37 in the evening; b  0. 135  0. 074, z-value  1. 833, p  0. 067). As expected for a reliable abundance index, AI-p and AI-v were linearly (on the log-scale) and positively related We performed all analyses with the software R ver. 3. 0. 3 (< www. r-project. org >) and using the 'MASS' and 'ggplot2' statistical packages.
Acknowledgements -We are especially grateful to  G. Van Laere  for having managed the Chizé roe deer population during the three last decades. We are also grateful to the  Service Départemental 79  of the  Office National de la Chasse et de la Faune Sauvage  and to all volunteers for their participation to abundance indices. We thank  Météo-France  for providing us with weather data. We acknowledge  M. Hewison  and  F. Klein  for useful comments on analyses and previous drafts of this manuscript, and  A. Smet  for editing the English language. Funding -C. Bonenfant and  J. -M. Gaillard  are supported by research grants from  CNRS.
Macitentan reverses the sunitinib-induced aerobic to anaerobic switch Label-free pan-analysis of protein expression in mouse hearts collected from the three groups (vehicle, sunitinib and sunitinib+macitentan) is shown in Table  2. Data analysis using Ingenuity® and Pathway Studio® softwares showed that sunitinib induced an impairment in carbohydrate and fatty acid oxidative metabolisms and in the TCA cycle. Mitochondrial homeostasis was also deregulated, as shown by reduced expression in both the inner and outer mitochondrial membrane complexes, in mitochondrial transporters and in mitochondrial translation factors. In line with the effect of sunitinib on myocardial flux dysfunction. Several patterns of protein clusters were drastically different in the sunitinib and vehicle groups (Figure  7C ).
CT was acquired in a PET-CT scanner (nanoScan PET-CT; Mediso, Hungary) using the following acquisition parameters: semi-circular mode, 39kV tension, 720 projections full scan, 300ms per projection, binning 1:4. Then, PET acquisition was started and, 30 seconds later, 10MBq of 2'-deoxy-2'-[ 18 F]fluoro-D-glucose (FDG; Advanced Applied Applications, France) in 0. 2mL saline was injected via the catheter. The first scan was a dynamic acquisition of 30. 5min and was followed by a gated cardiac scan of 30min duration. PET data were collected in list mode and binned using a 5ns time window, with a 400-600keV energy window and a 1:5 coincidence mode. Data were reconstructed using the Tera-Tomo reconstruction engine (3D-OSEM based manufactured customized algorithm) with expectation maximization iterations, scatter and attenuation correction.
The first scan was reconstructed starting 10s before FDG injection with the following time sequence: 26 x 5s; 6 x 30s; 5 x 120s; 3 x 300s and 3 x 600s. The second scan reconstructed in a single time frame from 45 to 60min post-injection. FDG accumulation was quantified as mean Standard Uptake Value (SUV, ratio of the radioactivity concentration in myocardium on the whole body concentration of the injected radioactivity) between 45 and 60min post-injection in 3D volumes-of-interest (VOI) delineated semi-automatically by iso-contours at 45% threshold of maximal value in the myocardium on PET/CT fusion slices using the PMOD software package (PMOD Technologies Ltd, Zürich, Switzerland). Metabolic flux was quantified using compartmental modeling tool of PMOD software using the same VOI as above and a VOI semi-automatically delineated on the vena cava for the arterial input function as previously described  (65).
Cardiac Output (CO) and the percentage of fractional shortering (FS) were then calculated using the VevoLab Software (Visualsonics). Mitral flow deceleration time was calculated from mitral flow velocities using Pulsed Wave Doppler (PW Doppler). Ascending aorta diameter (Ao) and Left Atrium diameter (LA) were measured using M-mode in parasternal long axis. Aortic velocity tracking integral (AoVTI) was measured using PW Doppler, in suprasternal view allowing measurement of mean aortic velocity and peak aortic velocity.
Assessment of microvascular density Cardiac microvessels were stained using Isolectine B4 Griffonia Simplicifolia-FITC (Sigma Aldrich). Nuclei were counter stained with DAPI. Microvessels and nuclei were counted in 4 fields at a magnification of x200 in 2 independent sections from each heart using Matlab® based software. Microvessel density was normalized to the number of nuclei.
Assessment of cardiac fibrosis Frozen sections were incubated with Picrosirius red (VWR) 0. 1% in picric acid (Sigma) in a Leica ST5020 automatic stainer during 30min and dehydrated in ethanol and xylene. Whole sections were observed at a magnification of x20 using a Nanozoomer HT 2. 0 (Hamamatsu) and fibrosis was quantified using Matlab® based software.
Western blotting Twenty micrograms of each heart lysate were loaded onto a 10% SDS-PAGE gels (mini-protean TGX gels, BioRad) and transferred to nitrocellulose membranes. The membranes were blocked and immunoblotted with the following primary antibodies: GLUT1 (1:1000, ab652, Abcam), PGC1α (1:1000, ab54481, Abcam) and hexokinase II (1:500, bs-3993R, Interchim). Membranes were then incubated with horseradish peroxidase (HRP)-conjugated anti-rabbit secondary antibodies (1:10000, 474-1506, KPL). Chemiluminescence detection was performed using the ECL kit (Clarity Western ECl substrate; BioRad). Quantitation of immunoblots was done on digitalized images using ImageJ software. The intensity of immunoreactive bands was normalized by the loading control (Cyclophilin B, 1:1000, ab16045, Abcam).
The mass spectrometer acquired data throughout the elution process and operated in a data-dependent scheme with full MS scans acquired with the Orbitrap, followed by up to 10 MS/MS HCD fragmentations in the Q-Exactive (Thermo Fisher) on the most abundant ions detected. Settings were essentially as in  (69)  with slight modifications: the recurrent loop of the 10 most intense nLC-eluting peptides were HCD-fragmented between each full scan (data dependent mode). Resolution was set to 70,000 for full scans at AGC target 1,10e6 within 60ms MIIT. The MS scans spanned from 350 to 1500m/z. Precursor selection window was set at 2Th, and MS/MS scan resolution was set at 17,500 with AGC target 1,10e5 within 60ms MIIT. HCD Normalized Collision Energy (NCE) was set at 27%. Dynamic exclusion was set to 30s duration.
Spectra were recorded in profile mode. The mass spectrometry data were analyzed using Maxquant version 1,5,2,8  (70). The database used was a concatenation of human sequences from the Uniprot-Swissprot database (Uniprot, release 2015-02) and a list of contaminant sequences from Maxquant. The enzyme specificity was trypsin. The precursor mass tolerance was set to 4. 5ppm and the fragment mass tolerance to 20ppm for Q-Exactive data. Carbamidomethylation of cysteins was set as constant modification and acetylation of protein N-terminus and oxidation of methionines were set as variable modification. Second peptide search was allowed and minimal length of peptides was set at 7 amino acids. False discovery rate (FDR) was kept below 1% on both peptides and proteins. Label-free protein quantification (LFQ) was done using both unique and razor peptides.
At least 2 such peptides were required for LFQ. The "match between runs" (MBR) option was allowed with a match time 0. 7 min window and an alignment time window of 20min. For analysis, LFQ results from MaxQuant were imported into the Perseus software (version 1. 5. 1. 6). Reverse and contaminant proteins were excluded from analysis. Contaminating proteins from culture medium, essentially coming from added serum, such as immunoglobulins or transferrin were also removed from the protein list. Protein copy numbers per cell were then calculated using the "Protein ruler" plugin of Perseus by standardization on total histone MS signal as described  (71).
Modeling with Ingenuity® and Pathway Studio® We used ingenuity pathway analysis (Ingenuity Systems, Redwood City, CA) and Pathway Studio (https://www. pathwaystudio. com/) to study pathways deregulated by sunitinib as already described  (72). For both, data from proteomic were classified according to the fold change and p-value compared to reference group (i. e: vehicle) using a paired t. test and only significant deregulated proteins were enter in the software. Briefly, these software create hypothetical networks (including several proteins) to highlight networks that are significantly (p <0. 05) different compared to a reference group (i. e: vehicle group or sunitinib + macitentan group). Those networks were assigned to a biological function(s), pathway(s) and/or disease(s) using the Ingenuity Pathways Knowledge Base, Pathway Studio database and the scientific literature.
Statistical analysis Statistical analysis and data comparison for proteomics data were done using either Perseus or Excel software to classify the data using the two tailed Student t. test. In modeling software, p-value cutoff was 0. 05 calculated with right-tailed Fisher Exact Test (Ingenuity®) and with Mann-Whitney U-test (Pathway Studio®). Data are expressed as mean ± SEM. Analyses were performed with Graphpad Prism (7. 00). Unpaired and paired t tests were used to compare two data sets, one-way ANOVA was used to compare three data sets, and two-way ANOVA was used to compare three groups followed across time.
Acknowledgments In vivo imaging was performed at the  Life Imaging Facility of Paris Descartes University (Plateforme Imageries du Vivant -PIV ), supported by  France Life Imaging  (grant  ANR-11-INBS-0006 ) and  Infrastructures Biologie-Santé (IBISA). The authors are grateful to  Fanny Aprahamian  and  Mélanie Rouil  for help with histology, to  Corinne Lesaffre  of the  PARCC Histology Core Facility , to  Philippe Chafey ,  Morgane Le Gall  and  François Guillonneau  from the  3P5 proteomic facility of Paris Descartes University  and to  Professor Elie Mousseaux  for helpful discussions.
INTRODUCTION The first detections of binary black hole mergers were made by the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO)  (Aasi et al. 2015; Abbott et al. 2016a ) during its first observing run (O1) in 2015  (Abbott et al. 2016b,c,d). Following a commissioning break, LIGO undertook a second observing run (O2) from November 30, 2016 to August 25, 2017, with the Advanced Virgo detector  (Acernese et al. 2015)  joining the run on August 1, 2017. Two binary black hole mergers  (Abbott et al. 2017a,b)  and one binary neutron star merger  (Abbott et al. 2017c ) have been reported in O2 data. Here we describe GW170608, a binary black hole merger with likely the lowest mass of any so far observed by LIGO. GW170608 was first identified in data from the LIGO Livingston Observatory (LLO), which was in normal observing mode.
The LIGO Hanford Observatory (LHO) was operating stably with sensitivity typical for O2, but its data were not analyzed automatically as the detector was undergoing a routine angular control procedure (Section 2 and Appendix A). Matched-filter analysis of a segment of data around this time revealed a candidate with source parameters consistent between both LIGO detectors; further offline analyses of a longer period of data confirmed the presence of a gravitational wave (GW) signal from the coalescence of a binary black hole system, with high statistical significance (Section 3). The source's parameters were estimated via coherent Bayesian analysis  (Veitch et al. 2015; Abbott et al. 2016e). A degeneracy between the component masses m 1 , m 2 prevents precise determination of their individual values, but the chirp mass M = (m 1 m 2 ) 3/5 (m 1 + m 2 ) -1/5 is well measured and is the smallest so far observed for a merging black hole binary system, with the total mass M = m 1 + m 2 also likely the lowest so far observed (Section 4).
More details on LHO data are given in Appendix A. Similar procedures to those used in verifying previous GW detections  (Abbott et al. 2017b ) were followed and indicate that no disturbance registered by LIGO instrumental or environmental sensors  (Effler et al. 2015)  was strong enough to have caused the GW170608 signal. Calibration of the LIGO detectors is performed by inducing test-mass motion using photon pressure from modulated auxiliary lasers  (Karki et al. 2016; Abbott et al. 2017d; Cahillane et al. in preparation). The maximum 1-σ calibration uncertainties for strain data used in this analysis are 5% in amplitude and 3 • in phase over the frequency range 20-1024 Hz. The Advanced Virgo detector was, at the time of the event, in observation mode with a horizon distance for signals comparable to GW170608 of 60-70 Mpc.
SEARCH FOR BINARY MERGER SIGNALS 3. 1. Low latency identification of a candidate event GW170608 was first identified as a loud (SNR ∼9) event in LLO data, via visual inspection of singledetector events from a low-latency compact binary matched filter ('template') analysis  (Usman et al. 2016; Nitz et al. 2017b,a). Such events are displayed automatically to diagnose changes in detector operation and in populations of non-Gaussian transient noise artifacts (glitches)  (Abbott et al. 2016f ). Low-latency templated searches  (Cannon et al. 2015; Messick et al. 2017; Adams et al. 2016; Nitz et al. 2017b)  did not detect the event Normalized energy Figure  1. Time-frequency power maps of LIGO strain data at the time of GW170608. The characteristic upwardschirping morphology of a binary inspiral driven by GW emission is visible in both detectors, with a higher signal amplitude in LHO.
This figure, and all others in this Letter, were produced from noise-subtracted data (Section 4). with high significance because LHO data were not analysed automatically. The morphology of the LLO event is consistent with a compact binary merger signal, as shown in Figure  1  (lower panel), but a noise origin could not be ruled out using LLO data alone. Consequently, LHO data were investigated and were determined to be stable at frequencies above 30 Hz (see Appendix A). A segment of LHO data around the event time was then searched with a filter starting frequency of 30 Hz, using templates approximating the waveforms from compact binary systems with component spins aligned with the orbital angular momentum  (Bohé et al. 2017; Pürrer 2016). The fraction of SNR expected to be lost due to imposing the 30 Hz cutoff, as compared to the lower starting frequencies typically used in O2 data (Dal Canton & Harry 2017), is ∼1% or less.
Offline search To establish the significance of this coincident event, a period between June 7, 2017 and June 9, 2017 was identified for analysis during which both LIGO interferometers were operating in the same configuration as at the event time. Times at which commissioning activities at LHO produced severe or broad-band disturbances in the strain data were excluded from the analysis. Standard offline data quality vetoes for known environmental or instrumental artifacts were also applied, resulting overall in 1. 2 days of coincident LHO-LLO data searched. Two matched filter pipelines identified GW170608, with a network SNR of 13. An candidate event is assigned a ranking statistic value, in each pipeline, that represents its relative likelihood of originating from a GW signal vs. from noise.
Single-primer genotyping Single-primer genotyping was performed by PCR with primer Chif1 (5 0 -CTAGGGCTGGTG GG-3 0 ). Thermocycling parameters were the following: 94˚C for 3for3 min; 30 cycles of 94˚C for 30 sec, 54˚C for 30 sec, and 72˚C for 1 min; and then a final elongation at 72˚C for 5 min. The PCR products and a 100 bp ladder (Bio-Rad, USA) were electrophoresed at 100 V for 90 min on a 1% agarose gel prepared in 1× TBE and visualized with ethidium bromide staining and ultraviolet (254 nm) using the Doc-Print gel documenting system (VilberLourmat, France). The particular subspecies was determined by the size of the obtained amplicons (Table  1 ). In silico MicrobesGenotyping database The MLVA data described in this report is included in S1 Draft whole genome sequencing and whole genome single nucleotide polymorphism (SNP) analysis Complete genomes and genome assemblies were downloaded via Genbank/NCBI.
Sequence Reads Archives (SRA) files were downloaded from the European Nucleotide Archive (ENA). SRA files quality was checked using FastQC. Reads with poor quality towards the ends were trimmed using Fastx_trimmer from the FASTX-Toolkit. Complete genomes and assemblies were converted into artificial 300 bp long reads using wgsim without introducing errors or mutations. Read files were then imported into BioNumerics version 7. 6. 2 to be mapped on the reference genome. SNPs were called using the BioNumerics SNP analysis pipeline with the "strict SNP clustering (closed SNP dataset)" option.
We first evaluated the validity of this assay by using the MLVA data associated with  [25]   Since 2008 SRCAMB acts as the Tularemia Reference Center and has assembled a collection of 173 F. tularensis live strains or DNA samples collected by Russian anti-plague and medical institutions (S1 Table ). The strains were genotyped using MLVA15. One hundred and twelve genotypes are resolved. The Simpson's diversity index was 0. 9894 (95% confidence interval 0. 9850-0. 9938). The number of detected alleles and Simpson's diversity index calculated at all used VNTR markers are indicated in Table  2. In agreement with previous reports, VNTR locus FtM3 is the most variable locus. The data was merged with MLVA15 data from Johansson et al. , to produce a minimum spanning tree. Fig  1  shows the resulting clustering, the color code reflects subspecies and additional subdivisions.
Using the MLVA data from the 144 corresponding strains, the Automated Selection of Typing Target Subsets (AuSeTTS) software  [29]  provides a list of nine VNTR loci sufficient to achieve the same discrimination as the 15 loci in the tested data set: Ft-M3, Ft-M5, Ft-M6, Ft-M7, Ft-M8, Ft-M12, Ft-M19, Ft-M20, Ft-M24. In keeping with previous similar MLVA assays, and in order to distinguish this set from other selections with an identical number of loci, we call this selection MLVA9 Obolensk. An even simpler MLVA6 assay was proposed by Gu ¨rcan et al. [30]. Four of the six loci proposed by these authors are included in the MLVA9 Obolensk panel (Ft-M3, Ft-M6, Ft-M20, Ft-M24), the last two (Ft-M21 and Ft-M22) with seven and six base-pair repeat units respectively, were not included in the present investigation.