dataesr/scientific-paragraphs-categorization huggingface dataset:
With R 0. hom being proportional to the mean degree and R 0. het being a function of R 0. hom and the coefficient of variation of the degree, we were able to project ratios of R 0. het and R 0. hom for various reported and recorded contact data from our study. When both short and long contacts are included in the analysis, the basic reproduction number R 0. het resulting from the diary data would only be 76 % of that coming from the sensor data. The opposite is true if only contacts >15 min are included in the analysis; then R 0. het from the reported data is 236 % of the corresponding value from the recorded data. vs. the strength measured by sensors. In addition, Table  3  shows median strength in each of the comparison categories. More than one third of participants have diarycomputed strengths compatible with sensor data, and diary data overestimate sensor data for more than half.
Under-and overrecording with sensors The SocioPatterns infrastructure used here required stationary receivers to store contact data (unlike, e. g. , TelosB motes  [16] , which store contact data directly on the sensors). As a consequence, only contacts that took place in an area covered by at least one receiver were recorded. The positioning of receivers is often -and was in our study -constrained by health-and-safety requirements (e. g. , no exposed wires someone could trip over), the availability of power sockets, the arrangement and orientation of walls, etc. Blind spots in the study area due to these constraints might have been present. Furthermore, we only covered the rooms, lecture theatres, and open spaces used for the conference, but we did not cover the canteen or outdoor spaces (see Additional file 13: Figure  S1 ).
Acceptability When interpreting participants' attitudes and assessments we have to bear in mind the specific setting the study took place in, i. e. , a semi-public space where aspects of privacy, social control and familiarity with other people might be different both from completely public or private spaces. Privacy regarding both reporting contacts in a diary and being recorded with a digital device worried only a minority of respondents in our setting. The same applies for being tagged with the visible ID. Consistent with this, only a very small minority of participants would like to be able to turn off the sensor. In all, respondents had a very relaxed attitude towards reporting and being reported in our specific setting. Apart from aspects of social control (that are already in place at a conference setting and cause contact measurements not to be an additional control), it might be the case that our participantsmostly German epidemiologistsknow and trust the national data protection laws.
Acknowledgements The authors thank  Dietrich Rothenbacher  (conference host),  Nicole Kroll  (both  University Ulm) , the staff at Dezernat V, Abt. V-1,  University Ulm , the executive board of the  German Society for Epidemiology (DGEpi)  e. V. for supporting the study, the participants for taking part,  Fredrik Schäfer  ( Helmholtz-Centre for Infection Research ) for entering data,  Pius Krütli  as well as  David Preradovic  (both  ETH Zürich ) for supporting the study team logistically.
Funding TS and PJW thank the  UK National Institute for Health Research Health Protection Research Unit (NIHR HPRU ) in Modelling Methodology at  Imperial College London  in partnership with  Public Health England (PHE)  for funding (grant  HPRU-2012-10080 ). PJW also thanks the  UK Medical Research Council for Centre funding  (grant  MR/K010174/1 ). The present work is partially supported by the  French ANR  project  HarMS-flu  ( ANR-12-MONU-0018 ) to AB, by the  EU FET  project  Multiplex 317532  to AB and CC, by the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program , managed by the  French National Research Agency (ANR)  to AB, by the  Lagrange Project of the ISI Foundation  funded by the  CRT Foundation  to CC, and by the  S3  project funded by the  Fondazione Compagnia di San Paolo  to CC.
ACKNOWLEDGMENTS We thank  L. Nicolao  and  N. Prokofiev  for useful discussions. We also acknowledge partial financial support from  CNPq (Brazil) , as well as the  European Commission  via  ERC-St Grant ColdSIM  (No. 307688 ),  EOARD, FWF-ANR  grant " BLUESHIELD ",  EU  via "RYSQ" and " COHERENCE", and UdS via Labex NIE and IdEX, Initial Training Network COHERENCE  and computing time at the  HPC-UdS.
Acknowledgement D. Dutykh  acknowledges the support from  French Agence Nationale de la Recherche , project  MathOcean  (Grant  ANR-08-BLAN-0301-01 ) along with the support from  ERC  under the research project  ERC-2011-AdG 290562-MULTIWAVE. Also D. Dutykh  would like to acknowledge the support from the  University of Picardie Jules Verne  during his.
Methods Study population. Our analysis relied on contact data collected on 29 patients and 46 HCW, including 27 nurses and 11 physicians at Hôpital Edouard Herriot in Lyon, France  12. During 5 consecutive days, including nights, each patient wore an electronic sensor (radio frequency identification) which recorded face-to-face interactions with all other persons in the ward. With this technology, individuals wear the devices on their chest so that signals exchange between devices is only possible in a radius of 1-1. 5 meters and when they are facing each other, as the human body acts as a shield at the radio frequency used. The system detects and records closerange encounters during which a communicable disease infection could be transmitted, for example, by contact  12. The protocol was approved by the French national bodies responsible for ethics and privacy, the "Commission Nationale de l'Informatique et des Libertés" (CNIL, http://www.
We observed a plateau of emergence probability after a transmission probability threshold of 0. 03 (Fig. 2 ). This result suggests that regular increments of SIC cannot be expected when transmission probability increases, which was also reported recently from contact data obtained in various settings by Leventhal et al. 17  for infectious diseases other than EVD. Some study limitations should be addressed. The observed contact matrices came from an acute geriatric unit and certainly differ from other medical or intensive care wards. We assumed that effectiveness of control measures was 100% as soon as an index case was detected, which may be the ideal scenario but might differed from the actual situation. Then, our estimations could be underestimated. Although we tested contact matrices, which represent well interactions between patients, nurses and physicians, we ignored the heterogeneity of individual behaviours.
Penalized selection criteria like AIC or BIC are among the most popular methods for variable selection. Their theoretical properties have been studied intensively and are well understood, but making use of them in case of high-dimensional data is difficult due to the non-convex optimization problem induced by L0 penalties. An elegant solution to this problem is provided by the multi-step adaptive lasso, where iteratively weighted lasso problems are solved, whose weights are updated in such a way that the procedure converges towards selection with L0 penalties. In this paper we introduce an adaptive ridge procedure (AR) which mimics the adaptive lasso, but is based on weighted Ridge problems. After introducing AR its theoretical properties are studied in the particular case of orthogonal linear regression.
5, 1. 5) provided a third limiting model which added one false positive to the second model. Interestingly the model obtained with the second starting point which was doing best in terms of misclassification had the largest BIC criterion (141. 03), while the other two models had almost identical BIC criterion (140. 06 and 140. 07). In general our experience with simulations shows that although the limit of the AR procedure depends on the starting point, the different solutions obtained will have very similar values of the selection criterion that one attempts to approximate. In fact the instability of solutions does not come as a surprise bearing in mind that variable selection based on information criteria is well known to suffer from instabilities with respect to small changes within the data [? ].
Count data were simulated from Poisson regression models of size k = 10 and k = 25, respectively. The covariates X i,j and the non-zero coefficients β j were independently drawn from normal random variables according to X i,j ∼ N (0, 0. 1 2 ) and β j ∼ N (0, 1. 5 2 ). For the first scenario with p < n AR and stepwise selection give almost identical results, which is quantified by the extremely small mean squared error (MSE) of the difference between obtained BIC values. This illustrates on the one hand that the Newton-Raphson AR procedure (9) works really Example of a full regularization path for L0 adaptive Ridge Poisson regression with n = 300, p = 50, and β * = 0 except for the first k = 10 coordinates. The covariates Xi,j and non-zero coefficients βj are independently drawn from normal random variables according to Xi,j ∼ N (0, 0.
Like for Poisson regression we compared AR logistic regression to a standard stepwise selection procedure based on BIC. We present simulation results for a scenario which is similar to the first scenario for Poisson regression, with p = 50, n = 300 and k = 10. Again both the covariates X i,j and the non-zero coefficients β j were independently drawn from normal random variables according to X i,j ∼ N (0, 0. 1 2 ) and β j ∼ N (0, 3. 5 2 ). Figure  9  illustrates that for logistic regression the relationship λ = log(n)/4 no longer gives the best results, but that the slightly smaller penalty of imsart-aap ver. 2012/04/10 file: ARR_ArXiVe. tex date: May 11, 2015 λ = log(n)/5 performs better for this scenario. No more improvement of MSE was observed by further decreasing the penalty λ (data not shown).
Using these recursive formulas one can hence perform one update step of (29) in O(n). Alternatively, one can use dynamic programming to find the best solution of (  27 ) with at most k max ≥ 1 segments in O(k max × n 2 ). Such a strategy is for example explained in ? ] and implemented in the Segmentor3IsBack R package  [? ]. In order to validate the adaptive Ridge approach in the context of least squares segmentation we will compare its performance with the exact approach in a small simulation study. We consider a simple Gaussian design with n = 500 consecutive measurements and three breakpoints at positions 100, 250 and 375. Based on a Gaussian model 200 data sets were generated with mean values -0. 3, 0. 7, 1. 5, 0. 5 in the four different segments, and a common standard deviation of σ 2 = 1.
Data sources and data collection In the enhanced surveillance zones and outbreak sites, the MoH teams collected data at health centers level using the same standardized data collection forms, which included sex, age, location, date of symptoms, culture results but also clinical information such as watery diarrhea, rice water stool, vomiting, dehydration. We identified all deaths among patients admitted to a cholera treatment facility. We did not include deaths occurring in the community or after treatment center discharge. In parallel, the MoH continued to register the overall number of suspected cases in their routine surveillance system using line lists with a limited number of variables (date of onset, district, age and sex). We used district-level population estimates for 2011 or 2012 that corresponded to the geographic area under surveillance.
For point 2, we conducted a literature search and identified few studies that reported culture sensitivity relative to another gold standard, as culture itself has been the gold standard for years. Consequently the study by Alam et al. was used as an approximation, recognizing that the included data may not be definitive. For calculation of case fatality ratios (CFR), we included in the denominator patients admitted to a cholera treatment center with cholera symptoms and as the numerator all deaths that were identified at the treatment center Comparisons between groups were performed using Pearson's chi-square test. Graphs were produced with R open-access software. Statistical analyses were performed using STATA software (version 12. 1, College Station, Texas 77845 USA).
Discussion In the Africhol surveillance zones, we found an overall annual corrected incidence of confirmed cholera presenting to a treatment facility of 0. 3 cases per 10,000 population, which increased to 20 cases per 10,000 during large epidemics. Strong spatial and temporal clustering occurred, with most cases from surveillance zones in Conakry, Guinea and Goma, DRC. Within our study many suspected cases were not cholera confirmed by culture. Furthermore the CRF measured at clinic level remained low in our surveillance sites. From the surveillance data collected in our sites, we were able to identify three epidemiological patterns of cholera: confirmed cases throughout the year such as Goma (DRC); sporadic cases plus additional outbreaks at irregular intervals such as in Lome (Togo), Mbale (Uganda), and Conakry (Guinea); and history of recurrent cholera epidemics but no cases during the surveillance period, such as Beira (Mozambique) or Abidjan (Cote d'Ivoire).
Acknowledgments We acknowledge the tremendous daily effort of the physicians, clinical officers, nurses, laboratory technicians and community health workers at each site. We thank all the patients and families for the time they spent responding to our surveillance questionnaires. We thank the ministries of health in the Africhol member countries and the member institutions of the Africhol consortium. Finally, we acknowledge the substantial contributions of the non-author members of the  Africhol coordination team (Leonard Heyerdahl ,  Souleymane Sakande ,  Themba Mhlanga ,  Johara Nadri ,  Lan Ngo ,  Richard Wood  and  Alexandre Blake ).
Acknowledgements. A. A. Thanks Robin Pemantle  for discussions on urns. A. A. and E. S. thank the  CIRM  for a friendly atmosphere during their stay as part of a research in pairs program. This work has been carried out thanks to the support of  A * MIDEX  grant ( ANR-11-IDEX-0001-02 ) funded by the  French Government "Investissements d'Avenir" program. E. S. thanks  Université Paris-Est, Créteil. Finally, we thank two anonymous referees for their careful reviewing.
Acknowledgments We would like to thank  José Figueroa-O'Farrill ,  Mariana Graña ,  George Papadopoulos , and especially  Dan Waldram  for helpful discussions. A. C. has been supported by the  Laboratoire d'Excellence CARMIN. C. S-C. has been supported by the  ANR  grant  12-BF05-003-002  and a grant from the  Foundational Questions Institute (FQXi) Fund , a donor advised fund of the  Silicon Valley Community Foundation  on the basis of proposal FQXi- RFP3-1321  (this grant was administered by  Theiss Research). C. S-C. thanks the  CERN-CKC TH Institute on Duality Symmetries in String and M-Theories  for hospitality. A. C. and C. S-C. also thank the  Program on the Mathematics of String Theory at Institut Henri Poincaré, Paris  for hospitality during the completion of this work.
The mass balance observations over the glacier consists of annual records for the period 2002-2014, the dataset corresponds to the negated mass balance because we are interested in extremely large losses of volume in the glacier (see Fig. 10 ). In order to study a threshold model for this data, estimations for the tail index γ are computed using Hill, t-Hill (as previously defined) and Zipf's estimator (see  Kratz and Resnick (1996) ) which is a form of least square estimator  (Schultze and Steinebach 1996)  of the tail index. It follows the fact that if we suspect that X 1,n ≤. ≤ X n,n are the order statistics from a Pareto family, then the plot of -log 1 - i n + 1 , log X i,n , 1 ≤ i ≤ n , should be roughly linear with intercept 0 and slope γ. The Zipf's estimator is computed for a given k n , as γzipf (k n ) = slope -log 1 - i k n + 1 , log X n-k n +i,n , 1 ≤ i ≤ k n.
We show through a simulation study how the joint analysis of data from phase I and phase II studies enhances the power of pharmacogenetic tests in pharmacokinetic (PK) studies. PK profiles were simulated under different designs along with 176 genetic markers. The null scenarios assumed no genetic effect, while under the alternative scenarios, drug clearance was associated with six genetic markers randomly sampled in each simulated dataset. We compared penalized regression Lasso and stepwise procedures to detect the associations between empirical Bayes estimates of clearance, estimated by nonlinear mixed effects models, and genetic variants. Combining data from phase I and phase II studies, even if sparse, increases the power to identify the associations between genetics and PK due to the larger sample size.
11 All subjects were genotyped at baseline using a DNA microarray developed by IRIS of 176 SNPs known for being involved in the PK of drugs. These 176 polymorphisms were matched to a reference Hapmap panel (Hapmap 3, release 2) for a Caucasian population  12  and we used the Hapgen2 software  13  to simulate genetic variants retaining their frequencies and the correlations between polymorphisms found in the human genome (see details in Supplementary Material, Supplementary Figures  S1-S3 ). PK profiles were simulated with a two-compartment model with dose-dependent double absorption (Supplementary Figure  S4 ), with the parameters in Table  1 , under two conditions: 1) no gene effect (H 0 ); 2) gene effect on clearance CL (H 1 ). Under H 1 , six SNPs were drawn randomly without physiological assumptions or prior knowledge, and assumed to explain in total 30% of the IIV on CL through the following additive genetic model on the logtransformed CL: log CL sim i ð Þ5log l CL ð Þ1 X 6 k51 b k 3SNP ik 1g i CL (1) where CL sim i is the simulated individual clearance, l CL the typical clearance, b k the effect size associated to the variant allele of SNP ik , and g i CL the interindividual random effect for clearance of subject i.
Causal SNPs were different from one dataset to another. Assuming an additive genetic model, genotypes take values 0, 1, or 2, reflecting the number of mutated alleles. We chose this model to simplify the simulations but dominant or recessive genetic models could be easily simulated by changing genotype values. b k was computed as a function of the coefficient of genetic component (R GC k , the percentage of the interindividual variability in CL explained by the SNP) and the minor allele fraction (p k ), as follows: b k 5 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi R GC k 3x 2 CL 2p k 12p k ð Þ2R GC k 32p k 12p k ð Þ s (2) where x 2 CL is the variance of interindividual random effects on CL due to nongenetic sources.
R GC k was respectively equal to 1, 2, 3, 5, 7 and 12% for the six causal variants  14  to mimic a multifactorial genetic effect. Then under H 0 , x 2 CL 50:06 (as in Table  1 ), while under H 1 30% of the variance is explained by the genetics so that x 2 CL 50:04 (example on the magnitude of simulated effect sizes is available in Supplementary Table  S1 ). The simulated datasets were then fitted with the base model without genetic covariates. Individual clearance estimates (EBE CL i ) were estimated and all associations with the 176 simulated polymorphisms were tested assuming a linear relation without reestimating model parameters, as in a GAM analysis. 10  compared two association methods to detect gene effects. Lasso 15 is a multivariate penalized regression which simultaneously estimates effect size coefficients and selects variants by setting a large number of coefficients to 0.
In both approaches, we control the Family Wise Error Rate (FWER), representing the percentage of datasets where at least one variant is selected under H 0 , by correcting the nominal a by the number of tests performed ( Sid ak correction) corresponding to the number of polymorphic SNPs considered N t (Figure  1 ). The FWER was set to 20% (with a prediction interval for 200 datasets equal to [14. 5-25. 5]) for an exploratory analysis. The prediction interval determined when to adjust a to control the FWER under H 0.
Evaluation For each analysis scenario, 200 datasets were simulated under H 0 and H 1. The ability of the designs to estimate the population and individual parameters under H 0 was first evaluated through estimation bias and g-shrinkage (see details in Supplementary Material, Supplementary Figures  S5-S6 ). Under H 1 we evaluated the performance of each scenario in terms of true and false positive counts (TP and FP) and rates (TPR, the proportion of TP detected among the causal variants; and FPR, the proportion of FP detected among all potential false associations) for parameter CL, as well as the probability to detect genetic variants. Assuming that SNPs located on genes coded for metabolism enzymes and transporters affect mostly the drug distribu-tion and elimination, we also applied association tests on Q, the intercompartmental clearance, and V2, the peripheral volume, separately.
Shrinkage Two g-shrinkage estimates were computed using a metric proposed by Bertrand et al. 4  based on estimated variances, with respect to the estimate of x2 in the dataset; one over the ĝi from phase I subjects and one over the ĝi from phase II subjects (Figure  3 ). The g-shrinkage for phase I   subjects was low (median 5 23%) thanks to the large number of observations per subject. A large range of gshrinkage estimates for phase II data was observed across analysis scenarios, but was below 50% in scenario SPI/ II 3s. 96h. Loss of signal RD signal was always negative for the six SNPs, indicating that part of the signal was lost during the estimation step (Figure  4 , top). This loss was smaller in the scenario with phase I data alone (SPI) than in scenarios combining phase I and phase II data.
Acknowledgments. Adrien Tessier  received funding from  Institut de Recherches Internationales Servier. The authors thank  Laurent Ripoll  and  Bernard Walther  from  Institut de Recherches Internationales Servier  for their advice in pharmacogenetics. The authors also thank  Herv e Le Nagard  for the use of the computer cluster services hosted on the  ''Centre de Biomod elisation UMR1137. '' This work received the  Lewis Sheiner Student Award  from the  Population Approach Group in Europe (PAGE)  committee and was presented as oral communication in the Lewis Sheiner Student Session at the 24th annual PAGE meeting: Tessier, A. et al. Modelling pharmacogenetic data in population studies during drug development. PAGE Abstract #3333 <http://www. page-meeting. org/?abstract5 3333> (2015).
Based on the a priori hypothesis put forward in Gagnon et al. [37] , the current study explores the potential of integrating sea surface temperature (SST) conditions to serve as a proxy for epidemic risk several months before the onset of a DF outbreak. In addition, we push our analysis further by also investigating the use of large-scale atmospheric circulation and regional climate patterns as more optimal indicators for predicting outbreaks. Using a long-term epidemiological surveillance dataset, this study explores the possibility of using a predictive model to assist public health authorities in implementing timely, appropriate and efficient prevention and mitigation strategies.