dataesr/scientific-paragraphs-categorization huggingface dataset:
Cholera surveillance system The Yemen Health Authorities with support from WHO set up an electronic integrated disease early warning system in 2013, which gradually increased from 98 reporting sites to 1982 by the end of 2016. 12 Upon confirmation of the first cholera case, public and private health facilities that were part of this countrywide cholera surveillance system collated suspected cases using a common line-list database (Excel 2010, Microsoft). Following the recommendations of the surveillance working group of the Global Task Force on Cholera Control,  13  a suspected case was any patient presenting with three or more liquid stools with or without vomiting in the past 24 h. A confirmed case was a suspected case with V cholerae O1 or O139 confirmed by culture. This database contains dates of disease onset and admission, age, sex, district of origin, dehydration severity (based on the WHO severity grading  5  ), laboratory results when available, and disease outcome (discharge or death).
Research in context Evidence before this study We did a PubMed search with dates between Jan 1, 1970, and March 14, 2018, for studies published in English using the search terms "cholera" [title] AND (outbreak OR rain OR (infection AND immunity)). We then manually selected relevant articles on the basis of the title and by reading the abstracts. Additionally, we obtained the history of cholera outbreaks in Yemen since 1971 from the Global Health Observatory data repository. The seventh cholera pandemic has caused a large burden of disease worldwide for more than 50 years, with the highest burden of disease in Africa, the Americas, and southern Asia. Explosive cholera outbreaks have been observed in the past 10 years years in several African countries and Haiti. Some of these outbreaks have been observed during rainy seasons and some studies have found positive associations between rainfall and cholera risk.
Rainfall data Because of the scarcity of functional ground weather stations in Yemen since the start of the war (appendix p 13), rainfall data were estimated indirectly from satellite imagery. High resolution (0•05°) gridded daily precipitation data were obtained for 2016-18 from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data. 15 Daily precipitation time series for each district were computed by a weighted mean of the grid cells, with weights proportional to the fraction of each grid cell contained within the district.
Data analysis We calculated weekly time series of suspected cholera cases, CFR (ratio of the numbers of suspected deaths over suspected cases), proportion of cases classed as severe (patients with fluid deficit assessed to be greater than 10% of their bodyweight 5 ), proportion of cases in children younger than 5 years, proportion of tested and positive rapid diagnostic tests, and proportion of tested and positive cultures. Exact binomial confidence intervals were calculated for all proportions. To describe the spatiotemporal dynamics, we divided the epidemic into three time periods: the first wave (Sept 28, 2016, to April 23, 2017), the increasing phase of the second wave (April 24 to July 2, 2017), and the decreasing phase of the second wave (July 3, 2017, to March 12, 2018; figure  1A ).
For each period, we calculated the attack rate ratio of each district as the ratio of the district to country-level attack rates. An attack rate ratio above 1 indicates that a district had a higher attack rate compared with the national average during that epidemic phase. We quantified the transmission rate of cholera through time by estimating the daily instantaneous reproduction number (R t ),  16  defined as the average number of secondary cases caused by an infected individual at time t. We used the daily suspected case counts and the serial interval distribution to estimate the posterior distribution of R t using the R package EpiEstim  16  (appendix pp 5-6). Consistent with previous estimates, we modelled the serial interval as a gamma distribution with a mean of 5 days and SD of 8 days.
27 These findings suggest that a large-scale campaign for hygiene education and public health information should be implemented during the 2018 Ramadan (May 15 to June 14), possibly involving Imams to deliver these messages. Our study is based on a well maintained line-list database of more than 1 million records that allowed us to estimate the time-varying reproduction number for each governorate, and to quantify the increased cholera risk following the rains in April, 2017. However, our analysis of the drivers of transmission in the second wave are based on a single cholera season and our estimates should be interpreted with caution. If cholera transmission in Yemen continues, this association should be reassessed, ideally using rainfall data measured by ground weather stations (currently not available in Yemen) rather than estimated from satellite imagery, which often underestimates precipitation events (appendix p 13).
Acknowledgments We thank all the health-care workers and staff of the cholera surveillance system in Yemen, who collected this data amid challenging circumstances. We also thank  Ghassan Abou Chaar ,  John Edmunds ,  Flavio Finger ,  Antonio Gasparrini ,  Charles Gaudry ,  Adam Kucharski ,  Dominique Legros ,  David Olson , and  Sherin Varkey  for helpful discussions during the analysis of the data and the writing of the manuscript.
Acknowledgements CT acknowledges stimulating discussions and suggestions from  Philippe Bougerol ,  Alain Comtet ,  Aurélien Grabsch ,  Jean-Marc Luck ,  Nicolas Pavloff ,  Yves Tourigny  and  Denis Ullmo. We thank  David Saykin  for sharing his results and writing Appendix E. We thank the  PCMI Summer School 2017 , where some of this work was performed. The research at King's College London was supported by  EPSRC  grant  EP/N009436/1  The many faces of random characteristic polynomials. This research was also supported by  ANR  grant  ANR-17-CE30-0027-01  RaMaTraF. We are grateful to an anonymous referee for numerous useful remarks.
Duration of Zika Viremia in Serum To the Editor-Zika virus emerged in French Guiana in December 2015 shortly after its first occurrence on the American continent  [1]. In this context of emergence, molecular diagnostic approach is essential to confirm first human cases and to monitor the outbreak. Nevertheless, its use must be based on a robust diagnostic algorithm built from data of Zika virus RNA persistence in body fluids with minimal bias. Here, we prospectively assessed Zika virus RNA persistence by real-time reverse transcription polymerase chain reaction in the serum of 38 infectedpatients at restricted time intervals from onset of symptoms until virus clearance  [2]. Detection threshold was 1. 8 log 10 copies/mL. No censored data and no intermittent virus detection were observed. Patients were included based on Zika virus RNA detection in their serum or urine no longer after 5 days after symptom onset.
A single qubit may be represented on the Bloch sphere or similarly on the 3-sphere S 3. Our goal is to dress this correspondence by converting the language of universal quantum computing (UQC) to that of 3-manifolds. A magic state and the Pauli group acting on it define a model of UQC as a positive operator-valued measure (POVM) that one recognizes to be a 3-manifold M 3. More precisely, the d-dimensional POVMs defined from subgroups of finite index of the modular group P SL(2, Z) correspond to d-fold M 3 -coverings over the trefoil knot. In this paper, one also investigates quantum information on a few 'universal' knots and links such as the figure-of-eight knot, the Whitehead link and Borromean rings, making use of the catalog of platonic manifolds available on the software SnapPy. Further connections between POVMs based UQC and M 3 's obtained from Dehn fillings are explored.
According to  [29] , the knot K and the fundamental group G = π 1 (S 3 \ K) are universal if every closed and oriented 3-manifold M 3 is homeomorphic to a quotient H/G of the hyperbolic 3-space H by a subgroup H of finite index d of G. The figure-of-eight knot and the Whitehead link are universal. The catalog of the finite index subgroups of their fundamental group G and of the corresponding 3-manifolds defined from the d-fold coverings  [30]  can easily been established up to degree 8, using the software SnapPy  [27]. In paper  [15]  of the first author, it has been found that minimal ddimensional IC-POVMs (sometimes called MICs) may be built from finite index subgroups of the modular group Γ = P SL(2, Z). To such an IC (or MIC) is associated a subgroup of index d of Γ, a fundamental domain in the Poincaré upper-half plane and a signature in terms of genus, elliptic points and cusps as summarized in [15, Fig.
Both authors acknowledge the support of the  PROCOPE-DAAD  project  RanConGraph  (ref. 57134837). The second author was partially supported by the  FP7-PEOPLE-2013-CIG  project  CountGraph  (ref. 630749), the  Spanish MICINN  projects  MTM2014-54745-P  and  MTM2014-56350-P , the  DFG  within the  Research Training Group Methods for Discrete Structures  (ref. GRK1408 ), and the  Berlin Mathematical School.
One idea is to take as expansion forms the forms defining the SU(3) structure themselves; it works well when they satisfy some simple differential conditions relating them to each other. For type II this means  [4]  taking a real two-form J and a complex three-form Ω, related to each other in a so-called nearly Kähler structure (namely, a manifold whose cone has G 2 holonomy). For M-theory  [5] , the SU(3) structure requires also a one-form η, and the differential conditions define a Sasaki-Einstein manifold (a manifold whose cone is a Calabi-Yau manifold). The family of metrics is very simple in this case, corresponding to a change of the overall volume. Any strategy to obtain compactifications where finer data on the internal manifold are probed needs to include a prescription for the appropriate additional forms on which to expand the SU(3) structure forms.
Acknowledgements We would like to thank  A. Belavin ,  G. Bossard ,  D. Cassani ,  M. Graña ,  A. Grassi ,  A. Kashani-Poor ,  D. Morrison ,  C. Strickland-Constable ,  D. Waldram  and  A. Zaffaroni  for interesting discussions; we especially thank  A. Dimca ,  C. Sabbah  and  J. Stoppa  for invaluable help with some mathematical aspects of this work. S. K. and A. T. were supported in part by  INFN  and by the  European Research Council  under the European Union's  Seventh Framework Program  (FP/2007-2013) - ERC  Grant Agreement n. 307286 (XD-STRING). The work of S. K. was further supported in part by the  John Templeton Foundation  Grant  48222 , by the  KU Leuven   C1  grant  ZKD1118 C16/16/005 , by the  Belgian Federal Science Policy Office  through the  Inter-University Attraction Pole   P7/37 , and by the  COST Action MP1210 The String Theory Universe.
This was just chosen here to keep the presentation simple. At the domain boundaries, the function Ω has to be C 1 in x. Together with the PI equation, this guarantees that the solution will be smooth on the whole considered line x ∈ R. Since equation (  5 ) is singular for s = 0, i. e. , for z → ∞, no condition needs to be given there (the numerical approach will automatically produce the regular solution at this point). Remark 1. All roots appearing in this paper are to be understood as being defined on a twosheeted Riemann surface. The sign of the roots will be fixed as usual at some point which is not a branch point. By analytic continuation as for instance in  [14] , sheets on the Riemann surface can be numerically established independently of the definition of the root in Matlab.
Polynomial interpolation and τ -method Since each of the above three domains have been mapped to the interval [-1, 1], the task is now to approximate a smooth function on this interval. A standard approach is to choose a suitable discretisation of the independent variable l, see for instance  [8, 29, 31] , and to use polynomial interpolation on the collocation points l j , j = 0,. , N c. This leads to an approximation of the derivatives in terms of so-called differentiation matrices obtained by differentiating the interpolation polynomials. In  [17] , a collocation method with cubic splines distributed as bvp4 with Matlab was applied. In  [18] , a Chebyshev collocation method on Chebyshev collocation points l j = cos(jπ/N c ), j = 0,. , N c was used. The latter is related to an expansion of the solution in terms of Chebyshev polynomials T n (l) = cos(n arccos(l)), n = 0, 1,.
Acknowledgements. We want to thank  Samuel Boissière ,  David Chataur ,  Brendan Hassett ,  Giovanni Mongardi ,  Marc Nieper-Wißkirchen  and  Ulrike Rieß  for useful discussions. We also thank  Samuel Boissière ,  Daniel Huybrechts  and  Marc Nieper-Wißkirchen  for hospitality. GM is supported by  Fapesp grant   2014/05733-9. SK was partially supported by a  DAAD grant.
The five covariates analyzed were temperature and absolute humidity, mobility flows, population size, and proportion of children within the region. Our study is based on a dataset provided by GROG (Groupes Regionaux d'Observation de la Grippe) an influenza surveillance network in France. The advantage of this network is that it combines clinical case definitions with identification of the virus. This is an important validation process because influenza can be clinically confounded with other co-circulating respiratory viruses. Our analysis has the same modeling basis as  Eggo, Cauchemez & Ferguson (2010)  and  Gog et al. (2014). We put particular emphasis on the idea that the impact of a factor can be viewed at different scales that should be disentangled. For the studied covariates, we used permutation tests that overcome the problem of non-adjustment of the dynamic epidemic models (because not all factors that affect epidemic onset variability can be modeled).
Epidemiological data Epidemiological data comes from the GROG network, a French surveillance network made up of voluntary general practitioners (GPs) and pediatricians. Sentinels record acute respiratory infections (ARIs) weekly and randomly send nasal samples for antigenic confirmation (or rejection) of influenza infection (see Supplemental Information 2 for more detail). Influenza incidence of clinical cases is then estimated as: I influenza ðtÞ ¼ I ARI ðtÞ Â T þ ðtÞ where I ARI (t) is the incidence of ARI cases and T + is the proportion of influenza-positive samples among ARI individuals. Details about the calculation of I ARI (t) and T + are given in Supplemental Information 2. Epidemiological data are available from the epidemic years of 2006-2013 for all regions of metropolitan France (Fig.
Mobility data Flows of people generate contacts (including infectious ones) between populations from different regions. They can therefore promote influenza spread between connected regions and represent an important risk factor for regional epidemic onsets. The National Institute of Statistics and Economic Studies (INSEE) provided mobility data in France. Place of residence and workplace are reported for employed individuals, while residence and school location are reported for students. We defined mobility flows as being journeys between home and work or school (Fig. 1 ). Note that these data are not representative of all possible journeys (e. g. , vacations, weekends). Flows were only measured between regions and not at the lower scale (so, for example, travels from city 1 of region A to city 2 of region B and travels from city 3 of region A to city 4 of region B are considered to be equivalent in our analysis).
Demographic data Favorable demographic characteristics of regions can also influence the spread of influenza and, hence, epidemic onset. We considered two demographic metrics (evaluated using INSEE data). The first metric is (the logarithm of the) population size, i. e. , the number of individuals living in a given region, because contacts between individuals can be stronger in more populated regions, increasing the spread of the virus. We preferred considering population size instead of population density, as populations are not homogeneously distributed within regions (population density can be low due to large unpopulated areas despite cities aggregating many individuals). The second metric is the proportion of children from 0 to 19 years old, this age-class being the most affected by influenza and often suspected to be a major source of influenza transmission  (Wallinga, Teunis & Kretzschmar, 2006; White, Archer & Pagano, 2014).
Full-size  DOI: 10. 7717/peerj. 4440/fig-  2  This linear mixed model was performed with the R software (R Core Team, 2016) using the "lme4" package, using the following command line: lmerðEpidOnset $ ð1j RegionÞ þ 1j Year ð Þ; data ¼ FluOnsetDataÞ where FluOnsetData is the analyzed data set. Here the epidemic onset date was taken as a response variable (variable EpidOnset of the data set). Region and Year are the variables of the data set providing, for each observed epidemic, the associated Region and Year indexes (considered as qualitative variables), respectively. A similar analysis was performed using demographic variables as variable responses, using the following command lines: lmer PopSize $ 1jRegion ð Þþ 1jYear ð Þ; data ¼ FluOnsetData ð Þ lmer PropChild $ 1jRegion ð Þþ 1jYear ð Þ; data ¼ FluOnsetData ð Þ where PopSize and PropChild stand for the population size and proportion of children variables, respectively.
That is why the likelihood (L) of a region R and an epidemic year Y is defined as: L ¼ e À P W À1 i¼0 R;Y ;i ð Þ Á 1 À e À R;Y ;W ð Þ The global likelihood (L g ) is defined as the product of the regional likelihoods for each epidemic year, given by: L g ¼ Y R;Y e À P W À1 i¼0 R;Y ;i ð Þ Á 1 À e À R;Y ;W ð Þ Model parameters were inferred using maximum likelihood estimation. Models and permutation tests were implemented in Matlab. It should be noted that, due to an insufficient covering during some weeks in some regions, influenza incidence could not be estimated for these points. Because the statistical procedure requires incidence values to calculate the terms associated with mobility flows, we replaced missing incidence values by zeros in the program. Among the 107 observed regions/years, five did not show any epidemic.
Including these data points in the analysis is feasible (under its current form, the Matlab code integrates this possibility). However, including them altered the results of the analysis in a way that we think is counterproductive (see Supplemental Information 3 for more details), so we preferred to exclude them from the analysis. From a biological point of view, this choice is reasonable because it is likely that these regions/years present specific characteristics (e. g. , an important proportion of immune individuals) meaning that, despite an important flow of virus entry, they could not enter into the epidemic state. This case scenario was not integrated in the model, which assumes that, provided a sufficient flow of virus entry, any region could enter into the epidemic state during any season.
ACKNOWLEDGEMENTS We acknowledge the practitioners of the sentinel network Reseau des GROG and the labs involved in the surveillance. We thank  Isabelle Daviaud  from  Open Rome  who sorted the epidemiological data from the sentinel network Reseau des GROG and  Annick Auffray  from  Meteo-France  for her kind help with meteorological data. We thank  Robin Buckland  for proofing the English manuscript. This work was archived using the computing facilities of CC  LBBE/PRABI  and  CC IN2P3. It was performed within the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of the  University of Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
IntrODuCtIOn Randomised clinical trials (RCTs) and meta-analyses (MA) of RCTs based on systematic review provide the highest level of evidence for assessing intervention effectiveness. There are two types of MAs: aggregated data MA and individual patient data MAs (IPD-MAs). The first and more common type is based on aggregated data (ie, data from people enrolled in a trial have been statistically combined) extracted from public sources (eg, journal publications). IPD-MA is based on original data from each patient included in each RCT. ] [3]  IPD-MAs of RCTs have many advantages compared with aggregated data MAs. First, they limit selective reporting when they include unpublished RCTs, excluded patients and unpublished or misreported outcomes. 4 5 oreover, access by the MA coordinators to the IPD allows checking of data quality and integrity and correction and completion of the data in collaboration with investigators.
Data collection For every eligible RCT, we collected the following data from the available publications: number of randomised patients, date of first patient randomised, date of publication, countries involved, type of publication (eg, conference abstract), nature of funding, whether the RCT was multicentre or not and whether it was international or not. This information was extracted from RCT publications for IPD-unavailable RCTs and from IPD-MA publications for RCTs with IPD supplemented by the RCT publications and/or our archives if necessary. To assess the RoB in IPD-unavailable RCTs and compare it with the risk in IPD-available RCTs, we first selected RCTs reported in English as full-text articles  30  and excluded conference abstracts and unpublished RCTs because methodological information was insufficient most of the time.
31 32 Second, IPD-available RCT publication(s) were selected to match with IPD-unavailable RCT publication on MA to which they belonged and the period when the RCT was performed based on the date of randomisation of the first patient (before 1997/1997 and after). If more than two IPD-available RCTs by IPD-unavailable RCT were available in the strata defined by the two above criteria, we used the Excel ALEA function to randomly select publication of two IPD-available RCTs by IPD-unavailable RCT publication. The number of IPD-available RCT publications selected was limited because of time constraints. Two investigators blinded to whether IPD was available or not extracted data from the selected publications for both IPD-unavailable and the sample of IPD-available RCTs to evaluate RoB using the Cochrane tool.
rEsults Three hundred and forty-nine RCTs (73 018 patients included between 1965 and 2010) were eligible for at least one of the 18 MAs. IPD from 60 RCTs (5890 patients) were unavailable for our published IPD-MAs (figure  1 ): 32 (53%) because the data were lost by the investigators, 19 (32%) because the investigators could not be contacted, 2 (3%) because the investigators refused to share the RCT data and 7 (12% and 638 patients) for an unknown reason. Of the 289 IPD-available RCTs, 18 RCTs (ie, excluded IPD-available RCTs) were excluded from the published MAs after IPD checking due to major doubts on quality using tools different from the Cochrane RoB tool (figure  1 ). Reasons for their exclusions are presented in online supplementary etable 2. The main reason was suspicion of biased randomisation (10/18 (56%) excluded RCTs).
Last, our results are consistent from one MA to the other. The main limitation of this study is that we included only MAs with overall survival data, performed by our group, in oncology. But based on our collaboration with other groups performing IPD-MAs  10 15 16 48  and our participation in the development of the guidelines for IPD-MAs,  2 6  we think our IPD-MA methods correspond to those used by other teams. Then, our results would be applicable to  Open access other topics where overall survival is the main outcome, such as cardiovascular disease. Other studies are needed to confirm applicability, however. Another limitation is the lack of evaluation of the impact of the exclusion of 18 IPD available RCTs on treatment effect due to the loss of the data of 7 of them. But results similar to the IPD-unavailable RCTs, in particular those with high RoB, are expected since the main reason for exclusion was the high RoB for randomisation.