dataesr/scientific-paragraphs-categorization huggingface dataset:
Higgin's I 2 was conducted to assess the heterogeneity of the studies  [28]. If a subgroup consisted of sufficient RCTs, Egger's test was performed and funnel plot was visually assessed to determine the risk of publication bias. Symmetric statistics and trim-and-fill methods were conducted using R version 3. 4. 1 (meta package) following the Cochrane recommendations  [29]. Furthermore, metaanalyses were conducted for the following adverse events and clinical outcomes: mortality, bradycardia, tachycardia, hypotension, hypertension, circulatory insufficiency, ICU length of stay, and time to extubation or duration of MV. If both time to extubation and duration of MV were presented, we used time to extubation data for the metaanalyses. In case no means and standard deviations (SDs) were reported (for ICU length of stay, time to extubation or duration of MV), we used medians, interquartile ranges (IQRs), or ranges to estimate these attributes, forcing us to make several assumptions (Additional file 1: Tables  S1-S3 )  [30, 31].
Incidence of delirium Overall incidence of delirium in the dexmedetomidine group was significantly lower compared to placebo (RR 0. 52; 95% CI 0. 39-0. 70; I 2 = 37%; Fig. 2 ), compared to standard sedatives (RR 0. 63; 95% CI 0. 46-0. 86; I 2 = 69%; Fig. 3 ), as well as compared to opioids (RR 0. 61; 95% CI 0. 44-0. 83; I 2 = 0%; Fig. 4 ). When only RCTs with a low risk of bias were included, we retrieved a RR of 0. 66 (95% CI 0. 43-1. 03; five data sets included) for the placebo comparison and a RR of 0. 64 (0. 41-0. 98; two data sets included) for the standard sedative comparison (Table  3 ). It is important to mention that the two data sets from the standard sedatives comparison were presented in the same article  [38]. In the opioids group, there was no RCT without risk of bias. Visual inspection of the funnel plot from the analysis of standard sedatives reveals some deviation from the funnel shape (Additional file 1: Figure  S1 ), indicating the presence of publication bias (Egger's test: p = 0.
Statistical significance was set at P , 0. 05 (two-sided). All data were analysed using InfoStat Software v. 2017 (Nacional University of Cordoba, Argentina) for Windows, as described previously  (Di Rienzo et al. 2017). Differences in sperm motility indicators were estimated using a model that included the effects of generation and time after activation as permanent effects. All analyses were performed using Bayesian methodology. The posterior median of the difference between generations (D), the highest posterior density region at 95% (HPD 95% ) and the probability (P) of the difference being positive when D. 0 or negative when D , 0 were calculated. Bounded uniform priors were used for all effects. Residuals were normally distributed a priori with mean of 0 and a variance of s2e. The priors for the variances were also bounded uniformly.
PCA and subpopulations The PCA was performed to reduce the dimensionality of the present study, which included nine CASA-Mot kinematic variables (VCL, VSL, VAP, LIN, STR, WOB, ALH, BCF and area) for anadromous males and farmed parr males for each time after activation (5 and 35 s). Two PCs with a smaller number of variables were selected and explained 76% of the total variance of all data (Table  1 ). PC1, designated 'velocity', was positively related to the velocity parameters (VCL, VSL, VAP) and BCF, whereas PC2, designated 'linearity', was positively related to progressivity parameters (LIN, STR) and negatively related to ALH and area. These two PCs were used to identify three well-defined subpopulations (SP1, SP2 and SP3; Fig. 3 ), characterised by median values of the nine CASA-Mot parameters (Table  2 ).
Unless indicated otherwise, data are given as the median (interquartile range). Within rows, values with different superscript letters differ significantly (P , 0. 05). FS1 offspring were produced by crossing male and female WS fish; FS2 offspring were produced by crossing WS males and FS1 females. SP1, slow non-linear subpopulation; SP2, fast and non-linear subpopulation; SP3, fast and linear subpopulation; VCL, curvilinear velocity; VSL, straight line velocity; VAP, average path velocity; LIN, linearity; STR, straightness;.
Then 0. 07 pmols of library were loaded onto an R9. 5 flow cell containing at least 800 active pores and run for 48 hr, on a MinION sequencer (ONT) and sequenced by the Minknow ONT software (v1. 7. 10 or v1. 7. 14). Base calling was carried out using Albacore (from ONT, v1. 2. 4 or 2. 0. 2) with default settings yielding 2. 46x10 6 reads (22. 9 Gb) (Data S1) that were trimmed using Porechop 0. 2. 3  [27]  with default options. Trimmed reads (Nanofilt v. 1. 1. 3 [2] ) with a quality score Q > 9 and longer than 500 bp (1. 34x10 6 reads; 17 Gb) were combined and further self-corrected using canu v. 1. 6 [29]  with default options. Genome assembly was then performed based on the corrected long reads using SMARTdenovo v. 1. 0. 0 run with default settings. A last polishing step with Pilon v1. 22  [30]  was carried out using paired-end (PE) Illumina sequencing reads (ca.
The cluster generation process was performed on cBot (Illumina) using the Paired-End Clustering kit (Illumina). Each individual library was further paired-end sequenced on a HiSeq 2500 or 2000 (Illumina) using the Sequence by Synthesis technique (providing 2x125 or 2x100 bp reads, respectively) with base calling achieved by the RTA software (Illumina). After removal of sequencing adapters, reads were mapped onto the HaxR assembly using default options of the mem program from the bwa 0. 7. 12 software package  [33]. Read alignments with a mapping quality Phred-score < 20 and PCR duplicates were further removed using the view (option -q 20) and rmdup programs from the samtools 1. 3. 1 software  [34] , respectively. Read coverage at each contig position for each individual sequences was then computed jointly using the default options of the samtools 1.
5). We further fitted the r distribution of the 492 remaining contigs (398 Mb in total) as a Gaussian mixture model with two classes of unknown means and the same unknown variance. The latter parameters were estimated using an Expectation-Maximization algorithm as e3 Current Biology 28, 1-7. e1-e7, October 22, 2018 of the latter sequence to the HaxR assembly (i. e. , Red-nSpot allele) using nucmer from the package Mummer  [41]  allowed the scaffolding of the five neighboring HaxR contigs represented in Figure  1C.
Larval RNAi We synthesized double-stranded RNAs (dsRNA) with T7 polymerase as described previously  [16]. DNA fragments for the transcription were amplified by PCR using primers containing T7 polymerase promoter sequences at their 5 0 ends (see Data S1for primer sequences). We used cDNA from Black-4Spots or Red-nSpots forms. Sense and antisense transcripts were simultaneously synthesized using RiboMax express RNAi System (Promega), annealed, treated with RQ1 DNase (Promega), and precipitated with ethanol. The quality of dsRNA was examined by agarose gel electrophoresis, and the concentration was roughly measured by spectrophotometer ND-1000 (NanoDrop Technologies), and 2 mg/mL in nuclease free water were used for injection. Larvae were anesthetized just before pupation on a CO 2 pad, and 400-600 nL of dsRNA was injected into hemolymph using Nanoject (Drummond Scientific).
RNA samples were reverse transcribed using First Strand cDNA Synthesis Kit (New England BioLabs). We omitted DNase I treatment, because all pairs of forward and reverse primer for qPCR were designed in different exons of each gene, which are separated by long introns. Furthermore, for accurate comparison, we confirmed the absence of nucleotide substitutions in the primer sequences in the different color pattern forms. qPCR and data analysis were performed on StepOne Real-Time PCR System (Applied Biosystems) with Power SYBR Green Master Mix (ThermoFisher). The data was normalized using eukaryotic initiation factor 4A (eIF4A) and 5A (eIF5A), and statistical significance of expression differences was established using two-tailed-t test. All primer sets and each R 2 value of standard curves are listed in Data S1.
ACKNOWLEDGMENTS We thank  G. Beydon  for constructing and screening the BAC library; L. Saun e and  M. Galan  for assistance in MiSeq sequencing;  E. Lombaert ,  I. Goryacheva ,  R. Koch ,  J. Lundgren ,  O. Nedved , and  C. E. Thomas  for help in sampling H. axyridis populations;  A. Tayeh  for assistance in lab-rearing of H. axyridis; and  A. Bretaudeau  for making the H. axyridis genome assemblies publicly accessible. We are grateful to  N. Gompel ,  N. Rode ,  Ruth Hufbauer , and  R. Vitalis  for comments on the manuscript. A. E. , B. F. , J. F. , and M. G. acknowledge financial support from the national funding agency  ANR (France)  through the  European Union program ERA-Net BiodivERsA  (project  EXOTIC ) and the  GenoToul bioinformatics platform Toulouse Midi-Pyrenees  for providing computing resources.
B. P. and J. Y. acknowledge financial support from the  CNRS , the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  grant agreement no. 615789 , and the  France-BioImaging  infrastructure supported by the  Agence Nationale de la Recherche  ( ANR-10-INSB-04-01 ,  call ''Investissements d'Avenir' '), as well as the support of the  NIG  supercomputer at  ROIS National Institute of Genetics (Japan). C. L. P. , C. D. , and M. M. acknowledge financial support from the  France G enomique National  infrastructure, funded as part of '' Investissement d'Avenir'' program  managed by  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ).
genouest. org/sp/ harmonia_axyridis HaxB4 whole genome assembly This paper http://bipaa. genouest. org/sp/ harmonia_axyridis Pool-Seq data (14 pools) This paper SRA: PRJNA474099 RNA-Seq data [26] SRA: PRJEB13023 BAC library This paper https://cnrgv. toulouse. inra. fr/ Library/Asian-ladybird Experimental Models: Organisms/Strains Black-2Spots Gard, France N/A Black-4Spots Gard, France N/A Black-nSpots Ol. Nedved's Czech Republic N/A Red-nSpots Gard, France N/A d CONTACT FOR REAGENT AND RESOURCE SHARING (Continued on next page) e1 Current Biology 28, 1-7. e1-e7, October 22, 2018.
Treating pain and opioid use disorder represents a clinical challenge. While most studies that have assessed opioid analgesic use in opioid substitution treatment (OST) patients primarily address opioid analgesic misuse (1, 2), only few studies focused on OST patients assessed the prescription of analgesic opioids for chronic pain. We sought to compare the prevalence of analgesic opioid prescription (AOP) in two groups of chronic non-cancer pain (CNCP) patients: OST patients vs. the general population. This was a population-based cross-sectional study based on the French national healthcare claims database SNIIRAM (Système National d'Informations Inter-Régimes de l'Assurance Maladie) covering over 66 million people (98. 8% of the French population). Overall, 67,173 participants ≥15 years old undergoing continuous OST in 2015 ("OST patients" group) were included and age-and gender-matched by means of a 1:1 ratio with 67,173 patients without OST ("control" group).
Data The SNIIRAM database contains: (1) demographic data: gender, date of birth, information on the complementary private insurance coverage indicating low-income status; (2) presence of chronic disease, represented by a list of 30 long-term diseases (Affection Longue Durée [ALD]) with their associated ICD-10 (International Classification of Disease, version 10) codes; 3) medications, recorded as dispensed preparation packs including ATC (Anatomical Therapeutic Classification) codes, with pack descriptions including number of tablets, number of packs, dates of prescription and dispensation, as well as prescriber's specialty; (4) date and duration of hospital admissions, including both primary, related, and secondary diagnoses, with ICD-10 codes. We were authorized to use the SNIIRAM database by the French national commission on information technologies and liberties (the French national data protection agency CNIL).
Measures Several socio-demographic information were retrieved from this database: year of birth, gender, low-income status, and date of death. OST patients were selected through specific ATC codes: N07BC01, NO7BC51 for HDB and N07BC02 for methadone. Several comorbidities were recorded: human immunodeficiency virus (HIV) infection (ICD-10 code "B24"), hepatitis C virus (HCV) infection (ICD-10 codes "B182" and "B171"), and hepatitis B virus (HBV) infection (ICD-10 codes "B180, " "B181, " and "B16"). Alcohol use disorder was identified either by a specific ICD-10 code ("F10") or at least one dispensation of acamprosate, disulfiram, or naltrexone (N07BB ATC code). Concomitant prescriptions of opioid analgesics were identified through specific ATC codes N02AA01 and N02AA51 (morphine), N02AB03 (fentanyl), N02AA05 and N02AA55 (oxycodone), N02AX02, and N02AX52 (tramadol), N02AA59 and N02AA79 (codeine), N02AA08, and N02AA58 (dihydrocodeine), N02AA02 (opium).
Concomitant prescription of non-opioid analgesics included nefopam (ATC code N02BG06), paracetamol (N02BE01), non-steroidal anti-inflammatory drugs (M01A), and triptans (N02CC). ATC codes for psychotropic drugs were also recorded: N06A (antidepressants), N05A, except N05AN (antipsychotics), N05AN, N03AG01, and N03AG02 (mood stabilizers), N05B (anxiolytics), and N05C (hypnotics). The total quantity of analgesic opioids that were consumed was also calculated, each opioid converted to an oral morphine equivalent (OME) dose  (39). Patients' characteristics were presented as mean ± standard deviation [min-max] or as median [interquartile range] for continuous data and as the number of patients and associated percentages for categorical parameters. Comparisons of patient characteristics between the "CNCP OST patients" group and "the CNCP control" group were performed using the chi-squared test or the Fisher's test procedure test for categorical variables and t-test for quantitative variables, when appropriate.
To determine the influence of various factors associated with AOP in CNCP patients, a univariate logistic regression model was performed. The associated p-values were computed with their corresponding odds ratios (ORs) and their 95% confidence intervals (95% CI). To study the risk factors associated with AOP, a multivariate logistic regression analysis was performed. All clinically-relevant variables or other variables associated with p < 0. 25 in univariate analysis were included in the model. The corresponding adjusted ORs were calculated with their 95% CIs. A second univariate and multivariate analysis was then conducted to determine the factors associated with AOP in the subgroup of CNCP OST patients. SAS software for Windows Version 9. 3 (SAS Institute, North Carolina, USA) was used for all statistical analyses.
STRENGTHS AND LIMITATIONS This study represents an original approach in that it is the first to focus on OST patients whose CNCP was treated by analgesic opioids. Furthermore, we used the largest French continuous nationwide claims database (covering 99% of the French population), enabling an exhaustive identification of the OST population in France. Moreover, this is the first European study that provides valuable data about AOPs in OST patients, as France accounts for almost one quarter of all opioid-maintained patients in Europe. In addition, our findings contribute to current knowledge with specific data on prescribed analgesic opioids in CNCP buprenorphine-maintained patients, given that the majority of previous studies were carried out in methadonemaintained patients. Interestingly, unlike others countries, HDB is the most frequent prescribed OST in France, while MTD is the most common treatment elsewhere in Europe and the rest of the world.
Nevertheless, several limitations inherent to claims database are present. First, our data does not include detailed clinical information on for example, severity of addiction, other risks behaviors, or other detailed related to mental disorders that could prevent opioid prescription by doctors. Importantly, characteristics of chronic pain are lacking and particularly no information on pain intensity was available. The main result of our study shows that OST patients were less frequently prescribed analgesic opioids, compared to a general chronic pain population, a result which may actually simply reflect more severe pain in the latter group. However, this seems unlikely since the literature widely reports higher prevalence of pain in patients with opioid use disorders (31-41 vs. 12-30% in the general population)  (15, 16, 24, 27, 82).
Data and simulated samples The analysis is performed on data collected in 2016 with the CMS detector during protonproton collisions at the LHC at a center-of-mass energy of 13 TeV, corresponding to a total integrated luminosity of 35. 9 fb -1. Two signal models are simulated: the first considers a spin-1 HVT W boson decaying into a W and a Z boson for both A and B scenarios, and the second considers a spin-2 bulk graviton G decaying into two Z bosons. Both processes are generated at leading order (LO) with the MAD-GRAPH5 aMC@NLO v2. 2. 2  [19]  matrix element Monte Carlo (MC) generator for a range of different mass hypotheses for the resonances from 0. 6 to 4. 5 TeV. Signals are generated assuming the resonances have negligible width (0. 1% of their masses) compared to the experimental resolution (4-8% depending on their masses); this assumption is the so-called "narrow-width approximation".
Event selection Events are required to satisfy criteria at the HLT trigger level on either p miss T or the missing hadronic activity, H miss T , which is defined as the magnitude of the transverse component of the negative sum of the three-momenta of all the objects identified as jets at trigger level. To avoid inefficiencies due to the prescaling of the triggers during high-luminosity LHC operation, several triggers are used, variously requiring H miss T or p miss T > 90, 110, 120 GeV, or p miss T > 170 GeV, in order to have at least one nonprescaled trigger at any given time. The p miss T trigger efficiency has been measured with data events satisfying one or more singlemuon triggers. A W leptonic decay topology is selected (W → µν), since it ensures the presence of p miss T in the event, due to the neutrino.
One muon identified by offline algorithms is required: this not only guarantees that the sample does not overlap with the search region of the analysis (where events with muons are rejected), but also reduces the contamination from particles or jets misidentified as leptons at the trigger level. The additional condition of having at least one AK8 jet is applied, in order to select events with a topology similar to that of the considered search. The combination of p miss T triggers reaches a plateau in efficiency of 96% around p miss T > 200 GeV, which is chosen as the minimum p miss T threshold for the event selection. An independent efficiency measurement has been performed using a data set satisfying singleelectron triggers, and the discrepancy with the result based on the muon data set is taken as a systematic uncertainty, which amounts to 1%.
Summary A search has been made for heavy diboson resonances (WZ, ZZ) decaying into a pair of vector bosons, one of which is a Z boson decaying into νν and the other is a W or Z boson that decays into qq. The data were collected by the CMS detector from proton-proton collisions produced at the LHC at a center-of-mass energy of 13 TeV. In this analysis, the hadronically decaying W or Z boson is reconstructed as a large-cone jet. The invisible decay of the Z boson manifests itself as a large amount of missing transverse momentum recoiling against the jet. The trans- verse components of the VZ system momentum are used to define the transverse mass variable, where a search for a localized excess is performed. The expected background is described with a hybrid data/simulation approach that takes advantage of data sidebands to predict the background normalization and shape in the signal region.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT , and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR  and  RAEP (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI  and  FEDER (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Without treatment, all such places could become contamination exchange areas contributing to contamination propagation. This study measured the disinfectant efficiency of a new biological decontamination foam using hydrogen peroxide as the disinfectant. The work was carried out on both foam and liquid (no air bubbles) forms to determine the decontamination efficiency. In addition, the study gave data related to the negative or positive contribution of each chemical product contained in the solution. For both foam and liquid forms, the decontamination impact from the stabilizer agent was evaluated. Further tests were performed by using the liquid form containing only the hydrogen peroxide. Bacillus thuringiensis (Bt) spores, biological indicators for decontamination tests in the field of CBRN Defense, were used for all tests.
Data Processing Recovery rate and log 10 reduction (LR) were determined at last three times in duplicate for each preparation. Recovery data were calculated for each preparation with and without coupons following the equation (1): Mean % recovery = [Mean CFU pc /CFU spike ] × 100 (1) where mean CFU pc is the mean of CFU recovered from at last three duplicate positive controls (pc) for each preparation with or without coupons, and CFU spike is the number of CFU spiked (inoculated) on the coupon, or inside the liquid or foam. Decontamination efficacy for each treatment was calculated to measure the efficacy of the foam treatment in terms of the spore log 10 reduction (LR). This was calculated by dividing the number of viable spores extracted after the decontamination tests by the number of spores originally spiked.