dataesr/scientific-paragraphs-categorization huggingface dataset:
We thank  Paolo Bianchini ,  Mark Norris , and  Dougal Mackey  for their thoughts and reflections. B. P. M. L. acknowledges funding through a  2012 Strasbourg IDEX (Initiative d'Excellence ) grant, awarded by the  University of Strasbourg. N. F. M. and B. P. M. L. gratefully acknowledge the  CNRS  for support through  PICS  project  PICS06183. H. -W. R. and E. F. S. acknowledge support by the  DFG  through the  SFB 881 (A3 ). E. F. B. and C. T. S. acknowledge support from  NSF  grant  AST 1008342. The Pan-STARRS1 Surveys have been made possible through contributions from the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , the  Johns Hopkins University, Durham University , the  University of Edinburgh, Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under grant No.
Ventilation Distribution Measurements Ventilation distribution was assessed by EIT (Figure  2 ). EIT uses the electrical conductivity of chest to generate cross sectional images of lung inferred from surface electrical measurements realized by a 16 electrodes belt placed to the skin. A few milliamperes current is applied across two electrodes; all other electrodes are used to measure resulting voltage. In biological tissue, conductivity varies between tissues depending on factors as air content. The changes in impedance are correlated to changes in air content. We used the Pulmovista® 500 tomograph (Drager medical) to perform EIT measurements. The electrodes belt was placed around the patients' chest between the 4th and 6th intercostal space. The Pulmovista® 500 tomograph measures impedance changes referring to an initial reference data set in real time.
Next, we examined mice in which YFP was knocked into the VEGFR3 locus to generate a VEGFR3 reporter  (Calvo et al. , 2011). Longitudinal sections of aortas from adult mice showed robust YFP expression in the inner cur vature of the aortic arch, with weaker expression throughout the rest of the aorta (Fig. 8  B and data not depicted). No fluor escence was observed in control mice lacking YFP expression. Interestingly, the inner curvature is a site where disturbed flow induces chronic inflammation in WT mice, characterized by the accumulation of fibronectin and VCAM1 within the intima, which primes the endothelium for development of atherosclero sis in hypercholesterolemia  (Davies et al. , 2013). To test the role for VEGFR3 in disturbed flow-induced inflammatory activa tion of the endothelium, we used an inducible VEGFR3 dele tion model.
Adult male Cdh5:Cre, Vegfr3 lox/lox , and WT control mice were treated with tamoxifen to induce VEGFR3 excision (Fig. 8 B ). After 3 wk, VEGFR3 deletion reduced staining for fibronectin (Fig. 8 D ) and VCAM1 (Fig. 8 E ) in the inner cur vature of the aortic arch compared with their WT counterparts. These data show that VEGFR3 contributes to shearinduced in flammatory signaling in vivo.
VEcad was previously shown to co immunoprecipitate with VEGFR2, and to modulate VEGFR2 downstream signaling in response to VEGF to decrease Erk and increase PI3K  (Carmeliet et al. , 1999). VEcad and VEGFR2 were also found to interact in flow signaling, where they co immunoprecipitate and where VEcad was required for ligand independent activation of VEGFR2 by flow  (Tzima et al. , 2005). These new data therefore show that the TMDs for VEcad and VEGFR2 mediate their interaction. The results also identify VEGFR3 as a novel component of the junctional complex. Like VEGFR2, VEGFR3 was activated by the onset of shear stress and signaled through PI3K and in tegrins in a VEcadTMD-dependent manner. Knockdown and rescue experiments showed that these VEGFRs are functionally redundant, as each can rescue loss of the other.
Shear stress alignment Cells were seeded on plastic-coated cell culture-treated slides with 10 µg/ml bovine fibronectin for 24 h, clamped into a 25 × 55 mm parallel plate flow chamber  (Frangos et al. , 1988) , and sheared at 12 dynes/cm 2 in complete media for 16 h. Cells were fixed in 3. 7% formaldehyde and stained with either anti-Flag (2368; Cell Signaling Technology) or anti-VEcad (C19; Santa Cruz Biotechnology, Inc. ), Alexa Fluor 647-phalloidin (Molecular Probes), and Hoechst (Molecular Probes). Slides were mounted in fluoromount-G (SouthernBiotech). Fluorescence microscopy was performed at room temperature with a microscope (80i; Nikon) equipped with 10× (NA 0. 45) and 20× (NA 0. 75) objective lenses and a CCD camera (Retiga 200R; QImaging). Images were collected throughout the length of the slide with NIS Elements software.
The authors would like to thank Pascal and Laurence Fragner for generation of VEGFR-GFP expression vectors, Dr. Kasey Baker for assistance performing preliminary mouse experiments, Dr. Yongli Zhang for technical advice, and Dr. Bruno Larrivee for assisting in adenovirus experiments. The Yale W. M. Keck facilities provided oligo synthesis, DNA sequencing, and proteomic services. The Yale Pathology Tissue Microarray facility provided support in tissue preparation. This work was funded by National Institutes of Health grant 5R01HL075092 to M. A. Schwartz and by fellowships from National Institutes of Health (5T32HL007950) and the American Heart Association (13POST16720007) to B. G. Coon.
Next, R1 files were reversecomplemented and merged with R2 files. The six libraries were standardized to the sequencing effort of the smallest library to avoid biases in comparative analyses introduced by the sampling depth. After quality filtering and merging, each of the six independent libraries were equalized to 48 million reads, as in the smallest library, by random subsampling using the python script "subsampler. py" (David Eccles, unpubl. ). The software Rockhopper  (Tjaden 2015)  was next used to align and annotate the reads on the genome of S. avermitilis. Raw sequences were deposited to the Sequence Read Archive of the National Center for Biotechnology Information under the BioProject PRJNA288961.
Statistical analysis Due to the small number of independent observations (three replicates) and the non-normal distribution of the data, comparisons of spore viability, cell-specific H 2 oxidation rate, and biomass yield in wild-type and hhySL -mutant strains of S. avermitilis were done by computing the Kruskal-Wallis analysis of variance on ranks using the Tukey post hoc statistical test implemented in SigmaPlot 12. 3 (Systat Software Inc. , San Jose, CA, USA). Comparative transcriptomic analysis of gene expression profiles was performed using the software R (R Core Development Team, 2008) using the package NOISeqBIO  (Tarazona et al. 2011). Briefly, expression levels in the raw gene expression table were standardized as a function of gene length using the algorithm RPKM  (Mortazavi et al.
2008)  and genes represented by a minimum threshold of 1 read per million were eliminated from the dataset. The resulting standardized gene expression table was used to compute the parameters of NOISeqBIO statistics. Two parameters were computed for each individual gene, namely the log-ratio of the average expression values and the average difference values for the two experimental conditions. Both parameters were accompanied with their standard errors to account for biological variability and then combined to derive differential expression Z i scores  (Tarazona et al. 2011). Statistical significance of differential gene expression was assessed by computing the probability of each Z i score to differ from the random distribution of Z 0 scores. This distribution of Z 0 scores was determined by 1000 permutations each consisting to randomly contrast sample labels before computing gene differential expression parameters.
The HAH is highlighted in a yellow box to show its potential role for ATP generation in the spores of S. avermitilis. (B) The heat map shows the expression level of the genes presented in (A), where red and green represent lower and higher expression level in spores upon eH 2 exposure, respectively. The name of the gene is followed by the expression ratio (eH 2 /aH 2 ) and the annotation. The asterisk represent the P values of the differential expression computed with NOISeqBIO where "*" stands for P ≥ 0. 95 and "**" stands for P ≥ 0. 99. The whole list of differentially expressed genes is provided in Data S1. affinity of the RNA polymerase for some promoters (including the ribosomal) in favor of those that encode stress response genes. In the case of Streptomyces, the alarmone informs a state of famine, which induces cell differentiation accompanied by the production of secondary metabolites  (Kang et al.
Transgenerational phenotypic consequences of prenatal exposure to vinclozolin in males Oral intake is the most common way of VCZ exposure in human. To mimic this mechanism of exposure in mouse, pregnant females were exposed to VCZ by oral intake (in the drinking water) during the entire duration of pregnancy with two different doses: a low dose (1mg/kg bw/d) (VD1) and a high dose (100mg/kg bw/d) (VD2). The offspring obtained from these exposed females were denominated F1 animals. F2 animals were obtained from the mating of F1 males with unexposed females, and F3 animals from the mating of F2 males with unexposed females (S1 Fig). We first performed a phenotypic analysis of adult male mice in all three generations (F1 to F3). We found no differences in body weight, testis weight, and other morphometric markers between VCZ exposed and control animals (data not shown).
Methylation analysis RRBS was performed on 100ng of genomic DNA prepared from PGCs or spermatozoa as described  [62]. Final RRBS libraries were amplified with 17 cycles of PCR and subjected to paired-end sequencing (2x75bp) on an Illumina HiSeq2000 apparatus by Integragen SA. Sequencing reads were cleaned with Trim Galore and aligned to the mouse mm10 genome with BSMAP, allowing 4 mismatches. We only retained reads with a unique best hit in the genome. Methylation percent values were calculated with BSMAP as the ratio of the number of Cs over the total number of Cs and Ts. For all data analysis, we filtered CpGs to have a minimum sequencing depth of 8x. RRBS was performed on pools of PGCs purified from 30 testes. For sperm, we performed RRBS in triplicates on sperm pooled from three exposed animals, as well as five control pools from unexposed animals.
To find differences in DNA methylation, we used the methylkit R package to search for changes in methylation greater than 20% in 400bp tiles containing at least 3 CpGs. All data processing and representation were performed with the R software (http://www. r-project. org). The RRBS data is available at the NCBI Gene Expression Omnibus database under accession number GSE65784 For gene-specific analysis of DNA methylation, we performed bisulfite conversion of genomic DNA with the Epitect kit (Qiagen). Subsequently, PCR amplifications were performed at regions of interest followed by cloning, as described  [63]. We used the following primers for bisulfite PCR: Lin28a GGGTTTTTTGTATTTTAAGGTGTGTT and AAAATCCCCATCTCCAAATATATATATA TAA; Lin28b TTTTATGAGATTAGAGAAGTTTGTGTGTAG and TCCAAAAATAAAAA CTTTCCCTTTAAAC.
Background The SafeBoosC phase II randomised clinical trial recently demonstrated the benefits of a combination of cerebral regional tissue oxygen saturation (rStO 2 ) by near-infrared spectroscopy (NIRS) and a treatment guideline to reduce the oxygen imbalance in extremely preterm infants. Aims To analyse rStO 2 -alarm-related clinical decisions and their heterogeneity in the NIRS experimental group (NIRS monitoring visible) and their impact on rStO 2 and SpO 2. Methods Continuous data from NIRS devices and the alarms (area under the curve of the rStO 2 out of range had accumulated 0. 2%h during 10 min), clinical data at discrete time points and interventions prompted by the alarms were recorded. Results Sixty-seven infants had data that fulfilled the requirements for this analysis. 1107 alarm episodes were analysed.
Software analysing procedure and study workflow In the experimental group the alarm was activated when the area under the curve (AUC) of the rStO 2 out of normal range, defined as rStO 2 between 55% and 85%, had accumulated 0. 2%h during the past 10 min. 1  The units are expressed as %h because the AUC is calculated from time (x-axis) and rStO 2 % (y-axis). Software was specifically developed to calculate the alarm. The software received data from the NIRS device and calculated the AUC and generated the alarm. The software also showed trends in rStO 2 and provided a recording system for interventions related to the treatment guideline as well as a dataset ( physiological and biochemical variables and ventilator settings). The data were manually entered into this software programme by the clinical staff.
A dedicated treatment guideline was available to the clinical staff listing possible interventions to normalise an out of range rStO 2. 6 The interventions were focused on respiratory and circulatory support and are described in detail elsewhere. 6 The treatment guideline was in accordance with the current national clinical practices. If an alarm prompted a clinical intervention this was reported in the software from a drop down menu listing various interventions. If a different intervention to that listed in the treatment guidelines was implemented, it was documented as 'Other'. If after careful evaluation, the attending staff judged that there was no reason to believe that the infant was compromised, then the decision was 'No action'. 6  dataset was recorded every 6 h according to the protocol and included heart rate, peripheral oxygen saturation (SpO 2 ), mean arterial blood pressure, mean airway pressure, inspired oxygen fraction, and pH, blood gases, lactate, base excess, haemoglobin and glycaemia if available.
These data were also requested at the start of the alarm and 30 min later (first review); in case the alarm was not resolved (AUC still above 0. 2%h in the last 10 min) within the first 30 min a second review dataset was also requested 30 min after the first review. The bedside software programme displayed the quality of the NIRS values provided by the device; this prevented missing values and false readings that could lead to inappropriate interventions. The various cerebral oximeters used in the study were comparable in terms of absolute value, repeatability and sensitivity to changes in oxygenation on the adult, human forearm. 11 12 ocal staff was trained as appropriate to supervise all participants during the intervention. To minimise skin irritation related to the device it was recommended to move the NIRS sensor to a different location at the skull as often as every 4 h to avoid damage from heat and/or pressure.
Data analysis Each site downloaded data to the data management centre after completion of the participant's intervention period. Using a Matlab (MathWorks, Natick, Massachusetts, USA) script created for this purpose, NIRS files, event files (decisions and sensor reposition information) and dataset were preprocessed. Data were reviewed manually to check data consistency. Sensor reposition that occurred within the 10 min period following a previous sensor site reposition was considered a single event. In addition, NIRS sensor repositioning occurring within the 10 min period that followed an alarm in which the decision taken was 'No action' was analysed separately; this was considered as an intervention that was termed 'Alarm-prompted reposition'. In these cases the alarm apparently was judged to be the consequence of poor sensor-skin contact yielding to nonreliable warning.
Statistics The data were analysed using the statistical software SAS V. 9. 2 (Cary, North Carolina, USA). The quantitative data are given as means (SD or 95% CI) or median (IQR) and the qualitative data as counts and percentages. Mixed-model analysis was used to adjust the effect of repeated measures per centre or total population. To avoid the effect of variations in study duration among infants results are expressed as 'per time unit'. In addition to rStO 2, the peripheral SpO 2 was chosen as the physiological variable to evaluate the impact of warnings and eventual clinical decisions on the infants' status. The peripheral SpO 2 value was calculated as the averaged mean value entered at the time of the alarm and at 30 and 60 min of warning. The rStO 2 value was derived from the averaged mean of all samples obtained during 1 min at the time datasets were entered.
RESULTS Among the 86 infants included in the SafeBoosC trial in the NIRS experimental group, 1 67 (78%) had data that fulfilled the requirements for this analysis. The remaining 19 infants were excluded due to inappropriate use of the study software. Infants were recruited in eight different centres, with a variable number of infants in the experimental group per centre (ranging from 2 to 18). The mean duration of NIRS recordings during the intervention period was 70. 24 (SD 9. 22) hours without differences between centres. The clinical characteristics of the study population are detailed in table  1. Periodical repositioning per protocol of the NIRS sensor, as suggested in the protocol, varied according to centre ( p<0. 001), with a median of 0. 18 (IQR 0. 19) repositions per hour (ranges 0.
The time needed for the alarm to resolve varied according to type of decision taken (p<0. 001) (figure  2 ). We explored the impact of the decision taken on SpO 2. The averaged mean SpO 2 at start of the alarm warning was 90. 0% (SD 9. 6), while it was 92. 4% (SD 6. 8) at the first (30 min) and 92. 9% (SD 6. 7) at the second (60 min) review. However, the profile of SpO 2 and rStO 2 changes showed differences according to the decisions taken in response to alarm (figure  3 ). Higher FiO 2 was associated with lower rStO 2 ( p=0. 057) and  SpO 2 ( p<0. 001). Table  3  shows rStO 2 and SpO 2 according to the level of supplemental oxygen at the time of dataset.
Acknowledgements All authors critically reviewed the revised versions of the paper and approved the final draft for submission. All the authors had access to the data and contributed substantially to the submitted report. There were no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years and no other relationships or activities that could appear to have influenced the submitted work. Contributors JR, SH-S, AP, TA, FvB, OC, ED, MF, CG, PL, AMP, GP, MW and GG conceived and designed the study. JR designed the boxes for blinding the device displays and designed and maintained the NIRS data collection software. JR, SH-S, AP, TA, FvB, OC, ED, MF, CG, PL, GP, AMP, MCB, FC, PLO, LS, MY and GG assisted with acquisition of the data.
, 2011; Hacker et al. , 2011) , and AROME-EPS  (Bouttier et al. , 2012; Nuissier et al. , 2012; Vié et al. , 2011) , which is used in this paper. Modern ensembles usually include perturbations to the initial conditions (to represent uncertainties on atmospheric analyses) and to the surface conditions (to represent uncertainties on the analysis and modelling of surface variables). The purpose of this work is to objectively quantify the benefits of a high resolution ensemble, and to understand the relative importance of initial and surface perturbations. A key difficulty of high-resolution ensemble verification is the need to use large forecast samples and dense observation datasets, because the models used are typically restricted to a small geographical area: this limits the number of independent weather events that can be sampled on any given day.
The feasible length of ensemble prediction experiments is limited by their high computational cost, and dense observation datasets can be difficult to collect routinely. These constraints hamper research on state-of-the-art ensembles by making it difficult to extract robust information from probabilistic scores  (Candille and Talagrand, 2005). In the impact experiment considered here, the aim is to decide whether modifications to an ensemble prediction system significantly alter its performance or not. We pursue this objective over a period and area where an exceptionally dense set of observations is available: the Special Observing Period 1 of the HyMeX field experiment, called HyMeX-SOP1 below. The HyMeX-SOP1 is described in  Ducrocq et al. (2014). There is substantial geographical overlap between the AROME-EPS ensemble system studied here, and the HyMeX-SOP1 area of interest.
Much observation data collected for HyMeX-SOP1 is relevant for the AROME-EPS verification, and most of it is not available for use during normal operations because of institutional and commercial data policies. As documented below, the HyMeX-SOP1 specific data is primarily located around the Mediterranean sea. It is relatively rich in observations of high precipitation events. The design of convection-permitting ensembles differs from global ensembles because of the higher resolution and the small geographical extent of the used models. Following the principles of ensemble prediction, the aim is to run forecasts where perturbations have been introduced to represent the major sources of uncertainty in the numerical simulation process. Four types of perturbations are known to be important: the representation of initial condition errors (e.
The HyMeX-SOP1 verification data As documented in  Ducrocq et al. (2014) , the HyMeX-SOP1 period lasted from 5 Sept to 5 Nov 2012. Its focus was the study of heavy precipitation events over the Northwestern Mediterranean region, including northeastern Spain, southeastern France and northern Italy including the Alpine range. In this study, we deal with the region covered by the AROME-EPS system, which covers France and most of the HyMeX-SOP1 area. There hardly was any exceptional rain event during the SOP1 proper, but our experiments spans a slightly longer period that includes a dozen heavy Mediterranean rain cases, with daily rainfall accumulations exceeding 100mm. The AROME-EPS system also samples some oceanic and continental weather since its domain includes parts of England and Germany.
The AROME-EPS verification and model domains used are represented in Figure  1. The HyMeX-SOP1 period is an opportunity to access specially collected observations that would not be available at other times. The HyMeX-SOP1 database contains about three times more such data than routinely available to a national weather centre like Météo-France, through improvements in both geographical coverage and observation frequency. Routinely collected data includes dense national networks, and sparser data from WMO networks and reports provided by neighbouring countries through bilateral agreement. In the area of interest, abundant low-level observations were released by participating institutes during HyMeX-SOP1. both geographical coverage and observation frequency. In this study, three-hourly model output will be verified against observations of two metre temperature (T2m), relative humidity (RH2m, converted from dewpoint as necessary), ten metre wind speed (ff10m), and precipitation over three-hour periods (rr3).