dataesr/scientific-paragraphs-categorization huggingface dataset:
Choice of sample size of 30 meta-analyses per bias domain is justified in the Appendix. For each bias domain in turn, we fitted the bias model proposed by Welton et al to all meta-analyses in the ROBES database and obtained estimates (together with uncertainty) for the trial-specific biases within the 30 sampled meta-analyses. The binary outcome data mia r , mia n (representing number of events and total number of subjects) from each trial arm a of trial i within meta-analysis m were assumed to have a binomial likelihood,  and between-meta-analysis bias variation 2  (17). Treatment effects mi  are assumed random across trials within meta-analyses, with separate between- trial heterogeneity variances 2 m . The values of mi  and 2 m  were assumed to be unrelated across meta-analyses. ( ) ( ) ( ) ( ) ( ) 2 2 2 0 logit ~, ~, ~, mia mi mi mi mi mi mi m m mi m m p X Z Nd Nb b N b       = + + (1) Posterior mean values of the mi  were used as bias estimates, and viewed as model-based assessments for the extent of bias in particular trials.
These are shrinkage estimates of bias, based on borrowing information across the meta-analyses in the ROBES database. Next, for each bias domain in turn and within each sampled meta-analysis, we selected the pair of trials with the highest and lowest bias estimates, among the trials with a judgement of high or unclear risk of bias. Selected pairs of trials from each of the sampled meta-analyses formed our study data set in which empirical data-based and opinion-based approaches to assessing bias were compared. We recruited six assessors (AH, DC, RM, BCR, HW, IB) with expertise in clinical research methodology and evidence-based medicine, by personal invitation. For each trial pair, assessors were given information packs (see example in Appendix) and asked to complete them independently. In total, each trial pair was assessed three times, by three out of six assessors.
Data analysis We examined agreement in the trial pair rankings (ordering of trials A and B with respect to extent of bias) among the bias assessors, using unweighted kappa statistics and 95% confidence intervals. Analyses were performed for each bias domain separately and then for all bias domains combined, using rankings from all trial pairs in the study data set. We assessed agreement between the trial pair rankings produced by assessors and the ranking based on estimated biases from the bias model. We reported the proportion of trial pairs in which assessors chose one trial as more biased (rather than saying they were equally biased). Of the judgements in which one trial was believed to be more biased than the other, we calculated the proportion in which assessor opinion agreed with the model-based ranking of the trials.
Descriptive analyses Our data set consisted of 101 trial pairs in total because there was some overlap between the sets of 30 meta-analyses sampled for each of the four bias domains. Table  1  summarises the types of interventions and outcomes evaluated in the sampled meta-analyses. The majority (64%) of sampled meta-analyses corresponded to pharmacological vs. placebo/control comparisons, while 25% were non-pharmacological vs. control comparisons, and the remainder represented comparisons of two active treatments. Objective outcomes were evaluated in 36% of sampled meta-analyses overall, 16% evaluated semi-objective ("objectively ascertained but potentially influenced by judgement") outcomes and 46% evaluated subjective outcomes. The median number of trials included in the metaanalyses was 13 (inter-quartile range 9 to 24).
26 (95% CI: 0. 19 to 0. 32). The assessors specified a confidence level of 1 (not at all confident) to 5 (very confident) about their opinion. We summarize the confidence levels in Figure  1. Assessor confidence levels were comparable for sequence generation, allocation concealment and blinding. For each of these bias domains, the median confidence level across all trial pairs and all assessors was 3 (inter-quartile range (IQR): 2 to 4). Confidence levels tended to be lower for incomplete outcome data and for overall bias (median 2, IQR: 1 to 3 for each). Confidence levels were no higher when examined only in the bias domain for which the trial pair had been selected. For each bias domain, 30 trial pairs were ranked by each of three assessors, resulting in 90 assessor opinions. For sequence generation, 36 (40%) of the 90 assessor opinions ranked one trial as more biased than the other (Table  2 ).
Published methods for bias adjustment in meta-analysis suggest making use of either empirical databased evidence on biases or opinion on biases  (3, 13, (15) (16) (17) , but no previous comparison has been made between data-based distributions and assessors' opinions on bias. Access to a large collection of meta-analyses for which review authors have reported risk of bias judgements and supporting information has enabled us to carry out a comparative study. We note that the empirical data-based distributions for bias were themselves informed indirectly by opinion, since they were derived from a hierarchical model fitted to trial data within meta-analyses, in which judgement about each trial's risk of bias was used as a covariate. The model-based rankings rely on the appropriateness of the assumed model for the data and also on the risk of bias judgements reported by Cochrane reviewers.
When assessors are asked to provide opinions on biases affecting studies in a single meta-analysis, the number of studies assessed would typically be much smaller. We observed less agreement among assessors for incomplete outcome data than for the other bias domains. This may be related to the greater complexity of the bias in this domain, which depends on several factors, including the amount and distribution of missing data across intervention groups, the likely difference in outcome between missing and non-missing participants, and how the problem has been addressed in reported analyses  (24). We aimed to assess agreement among assessors pragmatically, so we did not attempt to increase inter-observer agreement before carrying out the elicitation exercise. In this work, opinions about biases were based on summary information about trials, informed primarily by the study characteristics and risk of bias tables reported by Cochrane reviewers and supplemented by additional information extracted from the trial reports by the research team.
It is desirable to incorporate these judgements about suspected biases into the statistical analyses performed and interpretation of the review findings  (10, 11). The Cochrane database could in time provide extensive evidence on the degree of bias associated with combinations of risk of bias judgements for different domains. In a separate paper, we have explored methods for quantifying bias by using empirical distributions for the bias affecting trials with a specific set of risk of bias judgements, in combination with expert opinion  (27). However, our finding in this paper that the majority of trial pairs were ranked as equally biased suggests that incorporating opinion on bias may not reduce uncertainty much, compared with using empirical distributions alone. We found moderate agreement between opinion and data-based evidence in the rankings of trial pairs by bias severity, in pairs where assessors ranked one trial as more biased.
Acknowledgements This project was funded by the  UK Medical Research Council (MRC)  grant ( MR/K014587/1 ). RMT and KMR were supported by the  MRC  programme grant ( U105260558 ). RMT was also supported by the  MRC  grant  MC_UU_12023/21  and KMR also by the  MRC  grant  MC_UU_00002/5. HEJ was supported by a  MRC career development award in biostatistics  ( MR/M014533/1 ). JPTH is an  NIHR Senior Investigator  ( NF-SI-0617-10145 ), a member of the  MRC Integrative Epidemiology Unit at the University of Bristol , and is supported by the  NIHR Applied Research Collaboration West  ( ARC West ) at  University Hospitals Bristol NHS Foundation Trust  and the  NIHR Bristol Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust  and the  University of Bristol. JS was supported by the  MRC  fellowship ( G0701659/1 ) and the  National Institute for Health Research (NIHR) ARC West at University Hospitals Bristol NHS Foundation Trust.
Acknowledgement The authors thank an anonymous referee for careful reading of the manuscript and suggestions that improved the text. The authors also thank  Jean-René Chazottes  for numerous helpful discussions and for reading various drafts of this paper. A month-long stay of RK at the École Polytechnique in Paris during which most of the work was carried out was supported by  Le Centre National de la Recherche Scientifique(CNRS). RK was partly supported by the  Deutsche Forschungsgemeinschaft (DFG)  via  RTG 2131 High-dimensional Phenomena in Probability -Fluctuations and Discontinuity.
ACKNOWLEDGEMENTS The authors are grateful to  Benoît Perthame  and  Nicolas Vauchelet  for suggesting this problem and delightful discussions. This work was completed while T. D. was a visitor at Laboratoire Jacques-Louis Lions, whose kind hospitality he appreciates. T. D. recognises the support of the  Polish National Agency for Academic Exchange (NAWA)  and  National Science Center (Poland) , grant no  2018/30/M/ST1/00423. M. S. fondly acknowledges the support of the  Fondation Sciences Mathématiques de Paris (FSMP)  for the postdoctoral fellowship.
Then, the estimation of the TCP-G model parameters is investigated by the maximum likelihood method, with an emphasis on the one defined with the Weibull distribution as baseline. To evaluate the performance of the obtained estimates, two sampling schemes are considered, namely the simple random sampling and the ranked set sampling. As expected, nice numerical results are obtained for both. Then, two practical data sets are employed to show the modelling ability of the TCP-G family. More precisely, with the consideration of the Weibull distribution as baseline, we show that the TCP-G family generates very competitive models compared with other widely known general families, such as the Kumaraswamy-G and beta-G families with however one more parameter. The rest of the paper is organized as follows.
The deep analysis of two famous data sets shows all the potential of the new family, with fair and favorable comparison to well-established models in the same setting. From the perspective of this work, one can apply the TCP-G family in a regression model framework (creating new possible distributions on the error term). Also, one can investigate some natural (and not too complicated) extensions of the TCP-G family as those defined by where α > 0, λ ∈ (0, 1] and G(x; ξ) denotes the cdf of a univariate continuous distributions with parameter vector denoted by ξ. These extensions needs further investigations; there is no guarantee as to their superior efficiency over the former TCP-G family is provided at this stage, opening new work chapters for the future.
In 2015, females of An. minimus were collected near the houses along forest fringe using Centres for Disease Control and Prevention miniature light traps (BioQuip, USA: model 2836BQ) with a timer rotator (Collection Bottle Rotator model 1512. 25, John W. Hock, USA), baited with CO 2 from dry ice in a bucket. The traps were set up in 30 households in each of the four villages. Mosquitoes were collected for five nights per month from 18:00 to 6:00. Monthly An. minimus group collection in 2015 from four villages was detailed in Table  S1  and the climatic data of temperature, humidity and rainfall was recorded in Table  S2. The season was categorized into summer (March-May), rainy (June-October) and dry cool (November-February) seasons based on rainfall, relative humidity and temperature (Table  1 ).
Geometric morphometric data The left wings of the 89 (67 + 22) selected specimens were removed and mounted onto a slide using Hoyer medium  (Schauff, 2001)  for imaging and landmark digitization. The wing images were taken using the NES-Elements D 3. 2 software and a manual microscope (Nikon Eclipse 6600, Japan) connected with a digital camera (Nikon Digital Sight DS-Ri1, Japan). Sixteen type I landmarks  (Bookstein, 1991)  were used (Fig. 3 ) according to previous descriptions  (Sumruayphol et al. , 2016). The scale unit of each wing image was 1 mm.
Statistical analysis The CLIC package version 99 was used for statistical analysis (https://xyom-clic. eu/the-clic-package/). Wing metric data were collected as x and y coordinates and transformed using generalized Procrustes analysis (i. e. , they were corrected for size to focus on shape and for position and orientation to remove non-biological changes). The resulting shape variables were called partial warps (PW); the final shape variables were represented by relative warps (RW), which are the principal components of the PW  (Rohlf & Slice, 1990). The global size of the wing was computed as centroid size  (Bookstein, 1991) , and the units of centroid size, originally pixels, were converted to millimetres  (Ruangsittichai et al. , 2011). Size and shape variables were analysed separately, and their possible relationship, or allometry, was measured by linear regression techniques.
, 1999; Harbach, 2019). Currently, multiple molecular markers from both ribosomal genes and mitochondrial DNA are available to distinguish An. minimus from An. harrisoni  (Harbach et al. , 2007). Our molecular analyses confirmed the validity of the diagnostic character described by  Rattanarithikul et al. (2006)  (i. e. , a humeral pale spot located at the costa of the wing). However, this small tuft of white scales is labile and can easily be lost in damaged specimens, making it difficult or impossible to recognize the species. Here, we selected two samples of unquestionably confirmed specimens of An. minimus and An. harrisoni and used them as reference data to evaluate the diagnostic power of the geometric technique approach. The morphometric method applies after a wing preparation procedure involving slide mounting and imaging preparation.
Acknowledgements We would like to acknowledge data collection performed by the staff of the  Department of Public Health in Tha Song Yang District ,  Tak Province ,  Thailand and all of the staff in Department of Medical Entomology, Faculty of Tropical Medicine, Mahidol University. This study was supported by grants ( U19AI089672  and  D43TW006571 ) from the  National Institutes of Health, USA. The authors declare that they have no conflict of interests.
Discussion The present study offers a thorough analysis of the COVID-19 severity classification proposed by the WHO. We used data describing laboratory-confirmed patients at hospital admission to help clinicians in early recognition of pejorative evolution of COVID-19-related pneumonia. The WHO classification did show good prognostic power for severity prediction throughout our cohort, confirmed by both conventional statistics and Bayesian network modeling. Moreover, this study proposes a new and original severity predictive analysis through Bayesian network modeling. With the herein designed model, WHO severity classification could be strengthened by adding five other variables to help predict Critical Illness in SARS-COV-2 induced pneumonia patients to propose our COVID-19 EPI-SCORE. As far as we know, this is the first risk score developed using Bayesian Network Analysis based on a European cohort.
For example, the Bayesian association model with Critical Illness as Target Node showed a very strong association between age and comorbidities; thus, these two variables could not be both represented in the final score. Age and comorbidity burden have indeed already been demonstrated to be predictive of severity and mortality  [17]  and used through different score propositions like the ones mentioned above. Cardinal symptoms were previously described as fever, cough, and dyspnea  [1]. Patients presenting with cough and dyspnea in this study were indeed more severely ill. Anosmia and dysgeusia were noted in only 6. 4% of all patients included in our study. This represents a marked reduction compared to the data published already in the literature  [18, 19]. The low frequency of anosmia and dysgeusia is probably due to the fact that these symptoms are very difficult to objectify, in particular in patients with fever, cough and dyspnea, which were the most prevalent symptoms at admission.
7. 1). The Calibrant Delivery System was used for continuous recalibration between every 5 injections. Each chromatography run was approximately ten minutes long. All compounds were analyzed using Multiple Reaction Monitoring (MRM) mode with two transitions per compound. The most sensitive, first MRM transition was used for quantitation while the second MRM transition was used for qualitative identification. The detected MRM transitions with corresponding compound dependent parameters are given in Table  2. The acquired MRM data were processed using PeakView ® software (version 2. 2) and quantified with the MultiQuant ™ software (version 3. 0).
The specificity and efficacy of all primer pairs, which are listed in Table  3 , were tested before use. qPCR amplification was carried out for 40 cycles with an annealing temperature of 60 °C in a CFX96 thermocycler (BioRad). Ct data were obtained using the Bio-Rad CFX Manager software. Gene expression levels of the targets were calculated relative to the housekeeping gene PPIA (Cyclophilin A).
Proteins were subsequently electrotransferred onto nitrocellulose membranes (GE Healthcare, Little Chalfont, UK). Membranes were incubated for 90 min at RT in Tris-buffered saline 0. 1% Tween 20 (TBST; 20 mM Tris-HCl pH 8. 0, 500 mM NaCl, 0. 1% Tween 20; Bio-Rad) supplemented with 5% skimmed milk. After this blocking step, membranes were incubated with the primary antibody mouse anti-P-gp (ref. C219; GeneTex, Ivine, CA, USA) for 2 h at RT or the primary antibody mouse anti-BCRP (ref. ; Dako) for 1 h at RT. Following another rinsing step, the HRP was assayed using an enhanced chemiluminescence kit (GE Healthcare) and revealed by the Western blot Imaging system Azure c600 (Azure Biosystems, Dublin, Ireland). The bands' optical densities were measured using the TotalLab TL 100 1D Gel Analysis software (Nonlinear Dynamics, Newcastle, UK).
Statistical analyses For permeability and transcriptional expression measurements, results were expressed as mean ± SEM. For R123 accumulation assays, results were expressed as percentage ± SEM. Each experiment was performed in triplicate. For analysis involving two groups, an unpaired t-test was used. For analysis of more than two groups, a 1-way ANOVA was used, followed by a Bonferroni's multiple comparison test. All data were analyzed by the GraphPad Prism 5. 0 software (GraphPad Software, San Diego, CA, USA). The threshold for statistical significance was set to p < 0. 05.
These metabolic properties were then analyzed in the BBTB ECs and revealed that even if a slight increase in the transcriptional expression was measured for P-gp with the DIPG-013 and DIPG-014 cell lines, neither increase in the protein level nor in the activity of P-gp/ BCRP were observed compared to the control model. Thus, contrary to what is usually suspected and evocated, our result revealed an absence of a "super BBB" phenotype induced by the tumoral DIPG environment itself  [34]. This result can be correlated with a recent study using an in vivo xenograft rat model of DIPG demonstrating an absence of modulation of efflux pumps  [33]. However, only one study analyzed the expression of efflux pumps in the DIPG vasculature using histological analysis of pediatric DIPG brain tissue. Our results are also in accordance with these histological data revealing the expression of the efflux pumps P-gp, BCPR and MRP1 at the vasculature of DIPG patients in the vicinity of the tumor rather than in the tumor cells themselves.
Acknowledgements We wish to thank the  SMART  platform for the mass spectrometry analysis. We thank  Marie-Christine Boucau  for her help with western blot analysis. We are grateful to the "Société française de lutte contre les cancers et les leucémies de l'enfant  et de l'adolescent" (SFCE) , the  Association "l'étoile de Martin " and the  Association "Cassandra contre la leucémie"  for their support.
Funding This study is granted by the ' Conseil régional du Nord-Pas-de-Calais' (Fellowship  to  C. Deligne ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The mass spectrometer of the  Spectrométrie de Masse de l' Artois (SMART)  core facilities used in this study was funded by the  European Regional Development Fund (ERDF) , the  Conseil Régional Hauts de France and Artois University. This work was granted by the  "Société Française de lutte contre les Cancers et les leucémies de l'Enfant et de l'adolescent" (SFCE) , the  Association "l'étoile de Martin" and the Association "Cassandra contre la leucémie ".
Acknowledgments The authors warmfully thank  Hugo Touchette  for reading an early version of the manuscript as well as the first preprint, and providing useful comments; as well as the referees, whose suggestions helped us making more precise various aspects of this work. The authors are grateful to  Ofer Zeitouni  for an interesting discussion about scalings in large deviations theory, as well as to  Jianfeng Lu  for pointing out the work [15]. We also thank  Julien Reygner  for general discussions on large deviations. The PhD of Grégoire Ferré was supported by the  Labex Bézout   ANR-10-LABX-58-01. The work of  Gabriel Stoltz  was funded in part by the  Agence Nationale de la Recherche , under grant  ANR-14-CE23-0012  (COSMOS), and by the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement number  614492.
A standardised investigation form collecting sociodemographical information, clinical details and history of exposure (history of travel to or residence in Wuhan, China or contact with a confirmed case) is completed for each possible case at regional level, in collaboration between the clinicians, the ARS and SpFrance. Data are entered into the secure web-based application Voozanoo (Epiconcept, Paris). Possible cases have to be hospitalised, isolated and cared for in one of the 38 French referral hospitals designated by the Ministry of Health, according to the guidelines for the management of patients with Middle East respiratory syndrome (MERS)  [3]. For each possible case, respiratory samples from the upper respiratory tract (nasopharyngeal swabs or aspirates) and when possible from the lower respiratory tract (bronchoalveolar lavage fluid, when indicated, or induced sputum) are collected and sent to one of the laboratories accredited to perform SARS-CoV-2-specific real-time RT-PCR.
Therefore, she may have acquired the infection from her husband, although this cannot be proved. The active surveillance of close contacts of confirmed COVID-19 cases and the implementation of control measures, including home quarantine for those evaluated at moderate/high risk of exposure, decrease the risk of human-to-human transmission originating from imported cases and subsequently delay propagation of the virus in the general population. This allows our healthcare system to prepare for any further spread of the epidemic. Besides, the epidemiological and clinical data collected about the confirmed cases and their contacts will increase our knowledge of COVID-19. The rapid and collaborative management of the first imported COVID-19 cases in France highlights the fact that the French healthcare system is adequately prepared to respond to such emerging diseases threats.