dataesr/scientific-paragraphs-categorization huggingface dataset:
The decontamination efficacy for biological agents was expressed in terms of a log reduction using the equation (2): Log Reduction (LR) = log(CFU t /CFU spike ) (2) where CFU t is the mean number of viable organisms recovered from either each coupon or the foam or liquid solution used for a test, after decontamination. Test coupons in which no CFU was recovered, were assigned a CFU count of 1, resulting in a log CFU of zero. Standard deviations were calculated from the mean results of the replicated experiments. For each mean, the standard error of the mean (SEM) was calculated. Student t tests were performed using XLStat software (Addinsoft, Paris, France) to determine if two data sets of mean were significantly different from one another. A p-value<0. 05 was considered to be significant.
, 2017). Recently in our laboratory, the Xanthan foam described in this study was already successfully tested on different bacteria used in French standard NF T 72-194: Enterococcus hirae, Staphylococcus aureus, Salmonella typhimurium and Pseudomonas aeruginosa (data not shown). Moreover, the sporicidal capacity of this foam was checked on horizontal surface on Bacillus thuringiensis, Geobacillus stearothermophilus (biological indicator for biosafety level 3 decontamination) and Bacillus anthracis (biological weapon). The foam enabled to reduce more than 6 log of these spores in 30 min of contact time on horizontal coupons (data not shown). Therefore, in this paper we investigate the decontamination behavior of this new foam solution with coupons in a vertical position. Firstly, the formulation impact on the decontamination efficiency had been validated by experiments on liquid solution.
Techniques of testing 3. 1 Heat of hydration The heat flux due to the hydration reactions of the cement pastes was measured at 20 °C using a multi-channel isothermal microcalorimeter of TAM Air type interfaced by TA instruments software. After mixing, two samples of each paste were immediately taken from the mixture, placed in bottle, weighed and placed in the microcalorimeter with the reference samples. The heat release of the samples characterizing the reaction rate of the binder was measured automatically in order to quantify the influence of the type of the mineral admixture on the hydration and reactivity of cement at early age in the mixtures.
The MPPA (Appendix 3) was administered electronically, and responses from 86 students who attempted all survey questions were analyzed. Details about the deployment and this population are provided in Appendix 1. All research was supervised and approved by the UM Institutional Review Board for Human Subjects Research  (IRB #2008-12-19). We used Rasch analysis (eRm package  [23]  within the R statistical computing software) to model how the responses to the MPPA questions represented an ordered acquisition of knowledge and skills associated with the construct, marine primary production. The Rasch model  (24)  assumes that any interaction between assessment questions and responses is determined by the difficulty of the questions and the ability of the person responding. Results provide information about question difficulty and how well each question fits the model of step-wise progression in knowledge (i.
ACKNOWLEDGMENTS This project was conducted in fulfillment of requirements for the  University of Maine Master of Science in Teaching program  by  Ryan Weatherbee. Additional thanks to  William Ellis  for help coordinating interactions with the undergraduate student body in SMS. We appreciate helpful comments on earlier versions of the manuscript from  Natasha Spear  and  Molly Schauffler  and several anonymous reviewers. Both RW and SL contributed to the study design, analysis and interpretation of the data, and writing of this manuscript. Assistance in the form of a graduate stipend for RW was provided by the  University of Maine (UM) Research in STEM Education (RiSE) Center  and the  UM School of Marine Sciences (SMS). No potential conflicts to study participants exist. This study had no sponsors.
1D ). The 250 predominant uTR-Bs for Tfr and Tfh cells represent at least 20% of the total repertoire, compared with around 5% for Teff and Treg cells. Some uTR-Bs represent more than 1% of the total number of sequences in Tfr and Tfh cell samples, which is not observed in Treg and Teff cells. Finally, reconstruction of immunoscope profiles  (19)  from next-generation sequencing data highlight a Gaussian distribution profile for Teff and Treg cells, and numerous expansions in Tfol cell samples (SI Appendix, Fig. S2 ). Altogether, these results suggest that the global characteristics of the Tfh and Tfr cell repertoires are similar and that both subsets have a skewed diversity compared with non-Tfol cells. TRBVBJ Gene Usage and uTR-B Composition Are Different in the Four Cell Populations. A principal component analysis (PCA) of TRBVBJ combination frequencies (Fig.
It is of note that there were also more singletons represented at high copy numbers. Similar observations can be made from Tfr CDR3 networks (Fig. 5B , Bottom), with even fewer and smaller clusters, as well as many nodes with high copy numbers within clusters and in singletons. Similar results for Tfol cell clustering patterns were seen after immunization with INS (SI Appendix, Fig. S5 ). Inferring Subset-and Treatment-Specific TCR Motifs. As we could hardly detect specific immune responses with the previous methods, we next applied a TCR neighbor enrichment test (TCRNET) to detect events of convergent recombination and the presence of highly similar TCR sequences that are unlikely to be encountered by chance. Briefly, we measured the number of V(D)J recombination events in a given sample and compared it with the number expected from a metadataset that is a pool of all TCR sequences from all samples; this detects "core" uTR-Bs enriched in highly similar neighbor sequences and around which uTR-B sequence clusters are built (Fig.
Materials and Methods Mice, Immunization, and Cell Sorting. Mice, immunization, and cell sorting are described by Ritvo et al. (15)  and in SI Appendix. All experiments have been performed in agreement with current European legislation on animal care, housing, and scientific experimentation (agreement number A751315). All procedures were approved by the local animal ethics committee from Centre d'Experimentation Fonctionnelle of Sorbonne Université. TCR Deep Sequencing and Data Processing. RNA from sorted cells was extracted using an RNAqueous kit (Ambion) and processed by iRepertoire following that company's protocol (25) (SI Appendix). Raw data were processed using MiXCR (26) as described in SI Appendix. Data Analysis. Statistical comparisons and multivariate analyses (PCA and hierarchical clustering) were performed using R software, version 3.
05), *P < 0. 05, **P < 0. 01, and ***P < 0. 001]. Network Analysis and Visualization. The most abundant 1,000 CDR3 amino acid sequences were obtained from each pooled cell subset from nonimmunized and OVA-immunized mice. Each CDR3 amino acid sequence represented a node. Nodes were connected if a Levenshtein distance of 1 (one amino acid insertion/substitution/deletion) existed. A cluster was defined as a set with a minimum of two nodes and one edge. Data analysis was performed using Python programming language (https://www. python. org/; version 3. 6; Python Software Foundation). We used the following packages: Pandas  (27)  for data preparation, NetworkX  (28)  to create network objects (gml files) and to obtain node properties (i. e. , degree, clustering coefficient, number of clusters, number of edges, number of shared clusters and edges), StringDist (https://pypi.
org/project/StringDist/) to calculate Levenshtein distances, and seaborn (https://seaborn. pydata. org/) to generate figures. All network figures were made using Cytoscape (www. cytoscape. org/) (29). This approach was based on work performed by Madi et al. (20). Inferring TCR Sequence Clusters and Motifs Using the TCRNET. We infer TCR uTR-Bs that have an unexpectedly high degree of similar V(D)J rearrangements (neighbors) by comparing the observed number of neighbors in a given sample with the number of neighbors expected from the complete dataset. The neighbor count of a given TCR uTR-B d was computed by counting all nucleotide rearrangements that have the same V and J segments and differ from the uTR-B by no more than one amino acid substitution in the CDR3 region. We also computed neighbor count in the control (pooled) dataset D, as well as the total number of rearrangements having the same V, J and CDR3 length (L) in a given sample n, as well as in the control dataset N VJL.
From these values, we computed the enrichment P value using a binomial distribution p = P binom (d j D/N VJL , n VJL ). We apply a conservative threshold of P < 10 -8 to select enriched TCR uTR-Bs, roughly equal to 0. 05 divided by the total number of uTR-Bs (10 6 ) in the entire dataset. Data and Materials Availability. The raw sequences presented in this study have been submitted in the National Center for Biotechnology Information Sequence Read Archive (accession nos. SAMN09843511-SAMN09843539).
ACKNOWLEDGMENTS. We thank  B. Gouritin  and  F. Brimaud  for help in cell sorting and  G. Churlaud  for help in experiments. We thank  V. Kuchroo  for the mice provided. This work has been supported by  LabEx Transimmunom  (Grant  ANR-11-IDEX-0004-02 ),  European Research Council-Advanced Grant "Treg in pathology or disease" (TRiPoD)  ( 322856 ), and  Recherche Hospitalo-Universitaire  Grant "towards low-dose iL2 market approval for autoimmune diseases" ( RHU-iMAP ;  ANR 16-RHUS-0001 ). P. -G. R. is an " Ecole de l'Inserm Liliane Bettencourt " doctoral fellow and is sponsored by Servier. M. S. is supported by a  Russian Science Foundation  Grant ( 17-15-01495 ).
Trauma centres and registry This multicentre observational study used the data collected prospectively from a trauma registry (Trauma-base®, traumabase. eu) shared between the six trauma centres of the Paris area in France. These six centres progressively joined the registry between January 2011 and June 2015. Since then, data collection is exhaustive and covers the whole administrative area around Paris, Ile-de-France. The Traumabase® obtained approval from the Advisory Committee for Information Processing in Health Research (CCTIRS, 11. 305bis) and from the National Commission on Informatics and Liberties (CNIL, 911461) and meets the requirements of the local and national ethics committee (Comité de Protection des Personnes, Paris VI). The structure of the database integrates algorithms for consistency and coherence.
Data monitoring is performed by a central administrator. Sociodemographic, clinical, biological and therapeutic data (from the prehospital phase to discharge from the intensive care unit) are systematically collected for all trauma patients. A description of the Emergency Medical System (EMS) and the Trauma System and the characteristics of the Ile-de-France can be found in Hamada et al. [21]. In France, a 24/7 available dispatching physician located in a centralized call centre decides which emergency vector, either a paramedic-staffed ambulance or a physician-staffed mobile intensive care unit, is to be deployed on the basis of the trauma bystanders' call. Patients brought to dedicated trauma rooms in trauma centres are suspected to be major trauma by the pre-hospital team and are necessarily transported by a physician-staffed ambulance.
Potential pre-hospital predictors Thirteen predictors of SH were selected or computed using exclusively pre-hospital data from management on scene and during transport. These criteria were selected based on their clinical significance and ease of use in the pre-hospital time-critical setting: age, sex, minimal systolic, diastolic and mean arterial blood pressure (SBP, DBP and MBP), maximal heart rate (HR), minimal oxygen saturation (SpO 2 ), minimal Glasgow Coma Scale (GCS), clinically unstable pelvis, early on-scene point-ofcare haemoglobin concentration  [31] , tracheal intubation and vasopressor administration. The maximal Shock Index was calculated according to the formula: SI = maximal HR / minimal SBP  [32].
Model calibration was graphically checked using a calibration plot representing the agreement between risks of SH predicted by the model and observed proportions in the validation cohort. Discrimination was explored using the AUC ROC. A bootstrap methodology was used to compute the confidence interval (CI) of the AUCs (R package "pROC" V1. 10 [40] ). The AUC of Red Flag was compared to the AUC of the MGAP score. All tests that were two-sided.
An appropriate number of activations, however, maintains team readiness and training. The clinical consequences of this alert will have to be assessed (times, process, outcomes, etc. ). Our study obviously has some limitations. The first is its retrospective design, as it usually precludes the ad hoc choice of the data studied. It might have been interesting to investigate the contribution of other criteria which were not collected in our study: pre-hospital ultrasonography, described as a bleeding characterization criterion in the ABC score  [15] , or pre-hospital blood lactate dosage described as a predictor of trauma severity  [47]. However, this is actually a strength of our study, as it allowed the analysis of pre-hospital data that are routinely collected in practice by the EMS which reinforces the interest in Red Flag as a pragmatic, easy-to-use tool.
Acknowledgements The authors are grateful to  Alexander Fordyce  for his English editing. They also thank  Samuel Degoul  for his statistical input on using the R software. *The Traumabase ® Group:  Arie Attias, MD  ( Department of Anaesthesiology and Critical Care, Hôpital Henri Mondor, APHP, Créteil, France );  Sylvain Ausset, MD  ( Anaesthesiology and Critical Care, Hôpital Interarmées Percy, Clamart, France );  Mathieu Boutonnet, MD  ( Anaesthesiology and Critical Care, Hôpital Interarmées Percy, Clamart, France );  Gilles Dhonneur, MD, PhD  ( Université Paris Est and Department of Anaesthesiology and Critical Care, Hôpital Henri Mondor, APHP, Créteil, France );  Jacques Duranteau, MD, PhD  ( Université Paris Sud and Department of Anaesthesiology and Critical Care, Hôpital Bicêtre, Groupement Hôpitaux Universitaires Paris Sud, AP-HP, Kremlin Bicêtre, France );  Olivier Langeron, MD, PhD  ( Sorbonne Universités ,  UPMC Univ Paris 06 and Department of Anaesthesiology and Critical Care, Groupe Hospitalier Pitié-Salpêtrière Charles Foix, AP-HP, Paris, France) ;  Catherine Paugam-Burtz, MD, PhD  ( Université Denis Diderot and Beaujon University Hospital, Hôpitaux Universitaires Paris Nord-Val-De-Seine, Clichy, AP-HP, France );  Romain Pirracchio, MD, PhD  ( Université Paris Descartes and Department of Anaesthesiology and Critical Care, Hôpital Européen Georges Pompidou, APHP, Paris, France );  Bruno Riou, MD, PhD  ( Sorbonne Université ,  UMRS 1166 ,  IHU ICAN and Department of Emergency Medicine, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix, AP-HP, Paris, France );  Guillaume de St Maurice, MD  ( Anaesthesiology and Critical Care, Hôpital Interarmées Percy, Clamart, France );  Bernard Vigué, MD  ( Department of Anaesthesiology and Critical Care, Hôpital Bicêtre, Groupement Hôpitaux Universitaires Paris Sud, AP-HP, Kremlin Bicêtre, France ).
Hydrodynamic diameter distribution, polydispersity, and zeta potential (ζ) of the PFC nanoparticles were determined by dynamic light scattering (DLS) with a Malvern Nano ZS Zetasizer (Malvern Instruments Ltd, Malvern, UK). All determinations were made in multiples of five consecutive measurements. Measurements of ζ were reproducible to within ±4 mV of the mean value given by 16 determinations of 10 data accumulations. Atomic force microscopy measurements (AFM, Bruker Dimension Icon) were executed in standard tapping mode. UV/Vis absorption spectra were performed on a calibrated spectrophotometer (Perkin-Elmer Lambda 35) at room temperature. The dissolved oxygen (O2) and the release rate of O 2 in pure water and perfluorocarbon (20% v/v) solution were measured with a digital multi parameter meter (Multi 3430 WTW, Germany) coupled to an O 2 sensor (CellOx 325, WTW, Germany).
Flow cytometry assay Normal epithelium cell BEAS-2B were grown in the presence of 9 mM M-PFC NPs for 24 h. Trypsinized cells were harvested and washed in cold PBS(3×), then isolated by centrifugation (1273 g for 5 min). Single cell suspensions were added to pre-chilled 75% ethanol and fixed at -20°C overnight. The cells were rinsed with phosphate buffer, resuspended, then post-fixed with Cell Cycle Assay KIT (Biyuntian Company, Shanghai, China). Flow cytometry was performed using a FACScan (Becton Dickinson, San Jose, CA) equipped with 488 nm and 633 nm lasers. Samples were gated to exclude debris and clumps, and electronic compensation was used to remove residual spectral overlap. The experiment was repeated in triplicate and data were analyzed using MultiCycle software WinCycle 32 (Phoenix Flow Systems, Inc.
Acknowledgments This work was supported by the  National Basic Research Program of China  ( 2015CB931800 ),  National Natural Science Foundation of China  ( 81627901 ,  31210103913 ) and the  Key Laboratory of Molecular Imaging Foundation  ( College of Heilongjiang Province ). LNW expressed appreciation to the support of  National Natural Science Foundation of China  ( 81771903 ). Natural Science Foundation of Heilongjiang Province of China  ( LC2016034 ,  LC2013C26 ), the  Youth Science WU LIANDE Foundation of Harbin Medical University  ( WLD-QN1404 ,  WLD-QN1119 ),  Heilongjiang Postdoctoral Funds for Scientific Research Initiation  (No. LBH-Q15090 ), the  Fourth Hospital of Harbin Medical University Fund for Distinguished Young Scholars  ( HYDSYJQ201601 ). This research was further supported in whole or part by grants from the  CA154737 (GML) ,  CA199092 (GML) HL122471  (GML), HL112518 (GML), HL113392 (GML) and an HHSN26820140042C (GML), Additional support for the MR scanners was provided by the  Foundation for Barnes Jewish Hospital.
For the present analysis, we focused on PE survivors with available echocardiographic data at 6 months and over long-term follow-up (2 years or longer). Complete recovery of echocardiographic parameters between baseline and the 6-month visit was defined as normalisation of all echocardiographic parameters of RV dysfunction as documented by the investigators in the PEITHO case report form (Table  1 ). The New York Heart Association (NYHA) functional classification was used to provide an estimate of residual (or new) functional limitation during physical activity or at rest, and was assessed by the investigator team during a visit of the patient to the participating centre. The functional class prior to the acute PE event was not determined. The findings were collected in ad hoc-developed case report forms.
The outcome included a confirmed diagnosis of CTEPH or PPEI at long-term follow-up. The diagnostic workup for Table  1  Definition of echocardiographic recovery at 6 months and of post-PE impairment at long-term follow-up CTEPH, chronic thromboembolic pulmonary hypertension; LVEDD, left ventricular end diastolic dimension; NYHA, New York Heart Association; PE, pulmonary embolism; RVEDD, right ventricular end diastolic dimension; RV, right ventricular; sPAP, systolic pulmonary artery pressure * The definition of echocardiographic probability of pulmonary hypertension followed the criteria recommended by current European guidelines for standardising the follow-up assessment of patients with (chronic) pulmonary hypertension, but some of the parameters and cut-off values were adapted to correspond to the data collected in the case report forms of the PEITHO trial Recovery of echocardiographic parameters between baseline and 6 months Echocardiographic parameters a) sPAP > 35 mmHg (vs ≤ 35 mmHg) or tricuspid systolic velocity > 2.
The AMS operates continuously on the ISS and is monitored and controlled continuously from the ground. A detailed description of the instrument is found in Ref. [30]. Monte Carlo simulated events were produced using a dedicated program developed by the collaboration based on the GEANT-4. 10. 1 package  [31]. The program simulates electromagnetic and hadronic interactions of particles in the material of the AMS and generates detector responses. The Monte Carlo event samples have sufficient statistics such that they do not contribute to the errors. Data analysis. -The data analysis follows the procedure used for our measurement of the time-averaged electron and positron fluxes  [32]  with improved low-energy effective acceptance  [33]. The fluxes of cosmic-ray positrons and electrons for Bartels rotation i in the energy bin E of width ΔE are given by Φ e AE ;i ðEÞ ¼ N e AE ;i ðEÞ A eff;i ðEÞT i ðEÞϵ trig ðEÞΔE ; ð1Þ where N e þ ;i and N e -;i are the numbers of positrons and electrons, respectively, A eff;i is the effective acceptance, and T i is the exposure time in the given time bin.
The time stability of the energy scale is monitored using the ratio E=p of the energy measured in the ECAL E to the momentum measured in the tracker p. The momentum scale is monitored by measurements of the proton mass using the velocity measured by the RICH and the momentum measured by the tracker. The energy scale is found to be stable at the level of 0. 2% for all Bartels rotations and all energies. This produces a negligible uncertainty on the fluxes and R e. Most importantly, several independent analyses were performed on the same data sample. The results of those analyses are consistent with the results presented in this Letter. Results. -The results on the time-dependent primary cosmic-ray electron flux, positron flux, and their ratio R e are provided in the Supplemental Material  [34]  as functions of energy at the top of the AMS.
Research supported by  São Paulo Research Foundation (FAPESP)  Grants No. 2014/19149-7 , No. 2015/50378-5 , and No. 2016/ 10222-9 , Brazil; CAS,  NSFC ,  MOST , the provincial governments of  Shandong, Jiangsu, Guangdong , and the  China Scholarship Council , China; Action  H2020 MSCA-IF-2015  under Grant No. 707543 -MAtISSE ,  European Union ; the  Finnish Funding Agency for Innovation (Tekes)  Grants No. 40361/01  and No. 40518/03  and the  Academy of Finland  Grant No. 258963 , Finland;  CNRS/ IN2P3 ,  CNES, Enigmass , and the  ANR, France ;  Pascale Ehrenfreund ,  DLR  under Grant No. 50OO1403  and  JARA-HPC  under Project No. JARA0052 ,  Germany ;  INFN  and  ASI  under ASI-INFN Agreements No. 2013-002-R. 0  and No. 2014-037-R. 0 ,Italy; CHEP and  NRF  under Grants No. NRF-2009-0080142  and No.
NRF-2012-010226  at  Kyungpook National University  and No. NRF-2013-004883  at  Ewha Womans University ,  Korea ; the  Consejo Nacional de Ciencia y Tecnología  and  UNAM, Mexico; FCT  under Grant No. PTDC/FIS/122567/2010 , Portugal; CIEMAT,  IAC ,  CDTI , and  SEIDI-MINECO  under Grants No. ESP2015-71662-C2-(1-P/2-P ), No. SEV-2015-0548 , No. MDM-2015-0509 , and No. RyC-2013-14660, Spain; the  Swiss National Science Foundation (SNSF) , federal and cantonal authorities, Switzerland;  Academia Sinica  and the  Ministry of Science and Technology (MOST)  under Grants No. 103-2112-M-006-018-MY3 , No. 105-2112-M-001-003 , and No. CDA-105-M06 , former Presidents of  Academia Sinica Yuan-Tseh Lee and Chi-Huey Wong and former Ministers of MOST Maw-Kuen Wu  and  Luo-Chuan Lee , Taiwan; the  Turkish Atomic Energy Authority  under Grant No.