dataesr/scientific-paragraphs-categorization huggingface dataset:
Information on daily dosage and medication type (researcher-defined classification depending on study aims, e. g. based on therapeutic use, mechanism of action, chemical molecule or pharmaceutical formulation) is optional. AdhereR is thus designed to be used after data extraction and preparation. For advice on these preliminary steps, we recommend referring to existing guidelines (e. g. ,  [4, 5] ) and database-specific documentation. We describe the time period investigated using two terms: follow-up window (FUW; the total period for which relevant medication events are recorded for included patients), and observation window (OW; the period within the follow-up window for which adherence is computed). For example, a 10-year FUW can be extracted from a EHD for patients with long-term treatment; within it, multiple adherence values can be computed for each patient for various OWs.
For demonstration and testing, AdhereR includes a hypothetical dataset of 1080 medication events involving 100 patients over a 2-year FUW. Five variables are available: patient unique identifiers (PATIENT_ID), event date (DATE; from 6 July 2030 to 3 September 2044), daily dosage (PERDAY; median 4, range 2-20 doses per day), medication type (CATEGORY; 50. 8% medA and 49. 2% medB), and duration (DURATION; median 50, range 20-150 days). The timing and characteristics of these events have been formulated to represent various adherence patterns that can illustrate the impact of different analysis choices on results. To facilitate the presentation, we will use two example patients; a subset of the dataset with all medication events related to these two patients is shown in Table  1 , and illustrates the file format required as input for AdhereR analyses.
Interactive visualization of individual medication histories For exploratory purposes we have implemented interactive plotting of medication histories and CMA estimates (simple, per-episode, or sliding-window) within RStudio (https://www. rstudio. com/). The user can select a patient (drop-down list of unique identifiers), a CMA (1 to 9), and change various parameters applicable to the FUW and OW, CMA, treatment episode, or sliding window. The effects of these choices on the CMA computation and values are visualized in real time (Fig  1 ). Depending on the complexity of the computation and the hardware, this might be more or less instantaneous (see software performance benchmarks in the following section).
Performance on large datasets and software and hardware requirements AdhereR was designed to work with very large datasets on available consumer-level hardware, and it is heavily optimized (the implementation is in pure R) without affecting the clarity of the code. If run on a multi-core/multi-processor machine or computer cluster, it gives the user the possibility to use (completely transparently) two parallel backends: multicore (preferred on Linux, BSD and MacOS but currently not available on Microsoft Windows) and snow (Simple Network of Workstations; available on all platforms), reducing dramatically the computation time. Another goal in designing AdhereR was to future-proof it as well as possible and to reduce the number of dependencies to a minimum. Finally, AdhereR aims to promote replicable and transparent science and is released under GPL v3 (https://www.
gnu. org/licenses/gpl-3. 0. en. html) encouraging use and extension in the spirit of open source. For more information on the implementation and performance, please consult the online package help, the vignette and the source code, freely available on CRAN (The Comprehensive R Archive Network; https://cran. r-project. org/) and in the package's GitHub repository (https://github. com/ ddediu/AdhereR). For example, Table  4  below gives the running times (single-threaded and two parallel multicore threads) for a database of 13922 unique patients and 112984 prescriptions of all CMAs described here on an Apple MacBook Air 11" (7, 1; early 2015) with 8Go RAM (DDR3 @ 1600MHz) and a Core i7-5650U (2 cores, 4 threads with hyperthreading @ 2. 20GHz, Turbo Boost to 3. 10GHz) CPU, using MacOS X El Capitan (10.
11. 6), R 3. 3. 1 (64 bits) and RStudio 1. 0. 44. Table  5  gives the same information for a very large database of 500,000 unique patients and 4,058,110 prescriptions (generated by repeatedly concatenating the database described above and uniquely renaming the participants) on a mid/high-range desktop computer with 16Go RAM and a Core i7-3770 CPU (4 cores, 8 threads with hyperthreading @ 3. 40GHz, Turbo Boost to 3. 90GHz), using OpenSuse 13. 2 (Linux kernel 3. 16. 7) and R 3. 3. 2 (64 bits).
Discussion and conclusions AdhereR is, to our knowledge, the first open-source implementation that allows a flexible and comprehensive investigation of EHD-based adherence to medications on consumer-grade hardware and software. It estimates persistence by identifying treatment episodes for each patient and calculating the start date, end date, and duration of each episode. Adherence (implementation) is estimated under three conditions: per observation window (assuming persistence), within each treatment episode (accounting for persistence), and for consecutive sliding windows (with or without overlaps). Interactive and publication-ready plotting is included for visualization of medication events, as well as persistence and adherence estimates. These allow in-depth exploration of longitudinal medication use patterns and visual comparison of the impact of different methods of calculation on persistence and adherence values.
g. carry-over only for the same medication, new treatment episode if medication changes). The current version is sufficiently flexible to offer concrete solutions for various clinical scenarios, and we would welcome feedback and questions on its application or development for less common scenarios. Developing a strong science on adherence to medications requires sustained improvement of methodological rigor, transparency, and replicability. Open-source software has been a catalyzer of this progress in many scientific fields. We hope that AdhereR will support this process adherence research and invite the research community to contribute to its improvement and further development.
Acknowledgements The expert workshop was financially supported by European  FP7 programme ANTIGONE (ANTIcipating the Global Onset of Novel Epidemics , project number  278976 ). MR's research is partly supported by  NIAID / NIH  contract  HHSN272201400008C. SK's research is partly supported by grants of the  German Research Foundation (DFG) :  KN1097/ 3-1  and  KN1097/4-1. AEM is supported by a  Biotechnology and Biological Sciences Research Council (BBSRC)  grant  BB/M014088/1. VJM is supported by the  Division of Intramural Research of the NIAID/NIH. Marcel A Mu  ¨ller is supported by the  Zoonoses Anticipation and Preparedness Initiative  ( ZAPI  project;  IMI Grant Agreement n8   115760  granted to  Christian Drosten ).
e. of the zero set of f in R 3 ). The crucial breakthrough in this direction was achieved in the diploma thesis by Stephan Endrass (1992), supervised by  Barth. In this thesis, the program surf was developed (on a Cadmus workstation), which was able to produce impressive images of algebraic surfaces from their equations. When Barth had constructed his now famous sextic surface  [13]  (see Section 2. 4), it was a great moment for him to actually see the surface after he had found it by theoretical means. The program surf was extended and transferred to other systems during the 90s in Barth's group in Erlangen. Since 2000, further development has been done by the algebraic geometry group in Mainz, and the program is still in constant use nowadays via the GUI program surfer (https://imaginary.
Acknowledgements The authors are very grateful to  Patricia Gonçalves  and  Claudio Landim  for useful discussions and suggestions and specially to  Milton Jara  for an illuminating discussion on the derivation of Proposition 4. 4. This work has been supported by the  Brazilian-French Network in Mathematics  and the project  EDNHS ANR-14-CE25-0011  of the  French National Research Agency (ANR)  and the project  LSD   ANR-15-CE40-0020-01 LSD  of the  French National Research Agency (ANR).
IV. NUMERICAL RESULTS In the following experiments, the modes are always processed in the natural order in SeLRAP and SeMP, for simplicity. The reported computing times were measured in Matlab R2013a running on a Intel Xeon ES-2630v2 2. 60 GHz with 32 GB RAM 1866 MHz. For conciseness, the notation i = (I 1 ,. , I N ) specifies the tensor dimensions in each scenario.
Data analysis. For each condition, we performed 500 simulations reaching the expected final status. At the end of each simulation, we quantified clonal diversity with Simpson index. This measure, which was previously used in other models addressing of tumor heterogeneity  19, 26  , accounts for both clonal richness and equitability, while being bounded. For p i being the frequency of clone i in the tumor, Simpson index was computed as: ∑ = - H p 1 ( 3 ) i i 2 We obtained similar patterns when measuring clonal diversity with Shannon index (Supporting information, Fig. S1 ). We also recorded the number of generations (tumor age) necessary to reach N max cells and the tumor size after t max simulations for simulations during a given time. In order to have a common measure of growth kinetics for both types of simulations, mean tumor growth rate was then computed for each simulation as: = Tumor growth rate Tumor size Tumor age (4) These measures were not normally distributed (assessed by a Shapiro-Wilk test), and did not follow homoscedasticity (assessed by a Brown-Forsythe test).
It is used in two particular designs: decision modeling-based CEA and trial-based CEA. The major difference between both approaches is that in the case of trial-based CEA, data are gathered at the patients level in particular studies and it may lead to overlearning from the study, which may lead to mistakes in interpretation when the results are inferred for populations. In contrast, decision modeling-based CEA are based on easily generalizable data. The cost-effectiveness analysis is used to measure the incremental cost-effectiveness ratio (ICER) and the incremental net benefit (INB). The ICER is defined as the ratio: ICER = E(C 1 ) -E(C 0 ) E(T 1 ) -E(T 0 ) where C 1 is the cost of the tested therapeutic, C 0 is the cost of the control group which is usually measured in term of a given monetary unit, T 1 is the effectiveness of the tested therapeutic which is usually measured in term of survival life years, T 0 is the effectiveness of the control group and, E(•) is the expectation function.
Inference on the copula models There exists a bunch of parametric copula families, but for the purpose of these simulations, we limited our selection to the most well-known ones: Gaussian, Student, Clayton, Gumbel, Frank and Joe copulas. However, with a real dataset, the reader may test any consistent parametric copula using the proposed way. During simulations, for each iteration, we collected the information about the selected family using the information criterion. At Tables  3  and 4 , one can see these results for each DGP. We indicated, in bold, the copula that was selected the most in the 500 iterations and claimed that it was the copula to be retained for the mentioned DGP. When the selection of the copula was only in-between those used to simulate the data (as on Table  3 ), it was obvious that the chosen copula family is the one used for the generation process.
Example: acupuncture for chronic headache in primary care data We used the acupuncture for chronic headache in primary care data from Vickers et al. [35] [36] [37]  containing The data collection focuses on the measure of effectiveness in terms of QALY gained and the cumulative cost associated in UK pounds (£). Patients themselves reported unit costs associated with non-prescription drugs and private healthcare visits. The cost of the study intervention was estimated from the standard cost for a NHS professional multiplied by the contact time with the patient. Thus, patients in the treated arm had a mean time of 4. 2 hours with study acupuncturist. No imputation for missing data was done if the three questionnaires were not complete and consequently for which QALY cannot be measured. Therefore, in the acupuncture arm, there was 136 participants and in the control arm, 119.
Impact of an artificially created censoring on the acupuncture example To challenge our methodology via a censored dataset, we decided to create an artificial censoring on QALY variable (since that costs are assumed to always be observed). This censoring variable followed an exponential distribution where λ = 0. 30. Hence, the QALY variable has been censored on approximately 30% of data. Using the same methodology than for the acupuncture original dataset, we obtained the information shown on Table  6. One notes that we did not report the costs information on this table since that it does not change from the information on Table  5  (costs is not a censored variable). Using these information, one obtains an estimated ICER such that ÎCER = 10879. 89£/ unit of effectiveness, with a confidence interval which is: Hence, with a certain level of censoring, the estimation of ICER loses accuracy, and the confidence interval gets larger.
Finally, at step 5, one determines the INB and the ICER using joint cdf. In case of subgroups cohorts analysis, one reiterate the procedure from step 1 to 5 to get two supplementary copulas and then, the joint cdf of costs and QALY for the crossed-arms covariance terms. The methodology presented here can easily be implemented on computational software. Indeed, on R, using the packages copula  [38]  and CDVine  [39] , one can easily apply the whole process to a dataset, either censored or not. On SAS, the use of a PROC COPULA will be enough to fit a certain copula on a whole dataset.
Acknowledgements The authors would like to thank  P. Baseilhac ,  N. Crampé ,  N. Guay ,  N. MacKay  and  J. Ohayon  for discussions and their interest in this work. The authors also thank the anonymous referees for their valuable remarks and suggestions. V. R. acknowledges the  UK EPSRC  for the Postdoctoral Fellowship under grant  EP/K031805/1. S. B. is supported by a public grant as part of the " Investissement d'avenir " project, reference  ANR-11-LABX-0056-LMH ,  LabEx LMH.
August 4, 2016. Supported by grant  ERCCZ LL-1201  and  CE-ITI P202/12/G061 , and by the  European Associated Laboratory "Structures in Combinatorics" (LEA STRUCO). Supported by grant  ERCCZ LL-1201  and by the  European Associated Laboratory "Structures in Combinatorics" (LEA STRUCO) , and partially supported by  ANR  project  Stint  under reference  ANR-13-BS02-0007.
Study design 4 Specify the study design (case-control, cohort); whether data were collected under a controlled trial setting; whether data were collected retrospectively or prospectively. Report power and sample size considerations. Patient population 5 Specify the source(s) of the patients and, if multiple sources, whether they are pooled or treated as separate cohorts; define inclusion/exclusion criteria; report whether comorbidities and medications were assessed by self-report or medical records; define methods/system used for tumor staging; describe the larger patient population from which the study sample was drawn; define how major changes in treatment protocol were handled in the analysis. Radiation exposure 6 Specify details of radiation treatment parameters including: organ(s)-at-risk, dose-time fractionation; dose rate, target volume selection (e.
64 × 10 -11 ). More recently, a GWAS meta-analysis was performed using data from four cohorts of men treated for prostate cancer for whom toxicity was measured at 2-year post-RT  (26). Two SNPs were identified in this study that met genome-wide significance. One was rs17599026, which resides on chromosome 5q31. 2 and associated with urinary frequency and characterized by an OR of 3. 1 (95% confidence interval 2. 1-4. 7, p = 4. 2 × 10 -8 ). rs7720298, which is situated on chromosome 5p15. 2, was associated with decreased urine stream with an OR of 2. 7 (95% CI 1. 9-3. 9, p-value = 3. 2 × 10 -8 ). This SNP is located in an intronic region downstream of DNAH5 exon 30. Using a candidate gene approach, a study of more than 5,000 patients who underwent RT for either prostate or breast cancer reported an association between overall toxicity and rs1801516 in the ATM gene with ORs of 1.
FUNDiNG This project has received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no  601826  (REQUITE). This study was also supported by the  SIRIC Montpellier Cancer (Grant INCa -DGOS-Inserm 6045 ), grants, and contracts to BSR from the  United States National Institutes of Health  ( 1R01CA134444  and  HHSN261201500043C ), the  American Cancer Society  ( RSGT-05-200-01-CCE ), the  United States Department of Defense  ( PC074201  and  PC140371 ),  K07CA187546  from the  United States National Institutes of Health (SLK) ,  Associazione Italiana Ricerca sul Cancro  ( AIRC-IG16087 ), grants to AV from  Instituto de Salud Carlos III  ( FIS PI13/02030  and  PI16/00046 ), and  Fondo Europeo de Desarrollo Regional  ( FEDER 2007 ( FEDER -2013) ).
Data sources. Records of notified AIOs from January 2008 to December 2015 were provided by the Department of Animal Health of Vietnam. Records included dates of AIO detection (or date of reception of samples at the laboratory), and names of provinces and districts where AIOs were detected. Recorded AIOs were detected through passive surveillance which is based on the spontaneous report of AIO suspicions by poultry holders to local veterinary authorities. According to the Vietnamese government's regulation, suspicions of AIOs were based on the observation of mortality or morbidity in domestic birds: death of 5% of a flock within 2 days or less, or mortality associated with some specific symptoms such as cyanosis or head swelling. AIOs were then confirmed through RT-PCR (Reverse transcription polymerase chain reaction) or virus isolation  39.
Monthly meteorological data records from January 2008 to December 2015 were supplied by the Vietnamese Institute of Meteorology, Hydrology and Environment. These records came from 63 weather stations encompassing all provinces of Vietnam. Recorded variables were: average relative humidity (%), average absolute humidity (g/m 3 ), cumulative rainfall (mm), total number of hours of sunshine and average temperature (°C). Data about poultry consumption in Vietnam in 2004 and 2012 were supplied by the General Statistics Office of Vietnam. These data were collected through the Vietnamese Household Living Standard Survey (VHLSS) survey  40. The 2004 VHLSS recruited 9189 households covering all provinces of the country, of which 9180 provided data on their poultry meat consumption. The 2012 VHLSS survey recruited 9399 households, all of which reported their poultry meat consumption.
The optimal number of clusters satisfying these conditions was searched by successively trying 3 to 6 clusters. The clustering process was performed with the "HiClimR" R package  42. Correlation between time variation of AI incidence and ecological variables. Correlation between monthly counts of AIOs and monthly averages of 5 climatic variables (average temperature, absolute humidity, relative humidity, sunshine duration, rainfall) in each of the identified climatic regions was assessed using wavelet coherence analysis  44, 45  , a tool particularly well adapted to deal with non-stationary time series such as epidemiological ones. Coherence between wavelet transforms of time series was calculated for each month of the study period and for different wavelet periods (varying from 1 to 25 months) using the "biwavelet" R package  46.
Seasonal variation in poultry consumption. Here we aimed to assess whether the daily consumption of poultry meat by Vietnamese households changes during the Lunar New Year celebration period and to quantify this change. The VHLSS data collected in 2004 and 2012 were used to estimate the quantities of poultry meat consumed by households during and outside celebration periods. The VHLSS questionnaires include two separate sections on food consumption; one concerning the quantity of food consumed by interviewed households during annual celebrations, and another dedicated to food consumed during the remainder of the year. However, it does not indicate when these celebrations occur and their duration. Therefore, dates of annual celebration periods potentially involving the use of poultry meat were provided by two experts in poultry production from the National University of Agriculture of Hanoi.
Acknowledgements A. D. , M. P. and D. U. P. received funding from the  RiskSur  project, funded by the  European Union Seventh Framework Programme  (FP7/2007-2013 grant n°  310806 ): http://www. fp7-risksur. eu/. G. F. and D. U. P. currently receive funding from the  BALZAC  research programme 'Behavioural adaptations in live poultry trading and farming systems and zoonoses control in Bangladesh' (ref:  BB/L018993/1 ). This is one of 11 programmes funded under ZELS, a joint research initiative  between Biotechnology and Biological Sciences Research Council (BBSRC) ,  Defence Science and Technology Laboratory  (DSTL),  Department for International Development (DFID) ,  Economic and Social Research Council (ESRC), Medical Research Council (MRC)  and  Natural Environment Research Council (NERC ): http://www.
A. Elliptic flow fluctuations We start with v 2 [Fig. 1-(a) ]. Equation (  3 ) holds approximately up to 20 -30% centrality, and gradually breaks down as the centrality percentile increases. The difference between ε 2 {4}/ε 2 {2} and v 2 {4}/v 2 {2} can be attributed to a cubic response term, proportional to (ε 2 ) 3  [39]. Once this nonlinear hydrodynamic response is taken into account, agreement with ATLAS data is excellent all the way up to 70% centrality. As we shall explain below, a similar nonlinear hydrodynamic response is also needed for other models of initial conditions in order to match experimental data. Between 0% and 20% centrality, Eq. (  3 ) holds to a good approximation. Therefore, in this centrality window, the ratio ε 2 {4}/ε 2 {2} provided by initial condition models can be tested directly against experimental data for v 2 {4}/v 2 {2}.
The fact that the power distribution can be a poor approximation for large systems such as Pb+Pb collisions, even if the anisotropy is solely due to fluctuations, has already been pointed out in Ref. [52]. Even though precise figures depend on the particular model used, we   [47]. Full circles: ALICE data  [41]. Shaded band: ATLAS data  [24]. ALICE and CMS data are not shown in panel (b) for the sake of readability, but are compatible with ATLAS data. Remaining symbols correspond to values of ε3{4}/ε3{2} from several models of initial conditions. Open symbols: T R ENTo, with p = -1 (triangles), p = 0 (circles), p = 1 (squares). Full crosses: IP-Glasma  [18]. Full diamonds: Monte Carlo rcBK  [16]. predict on the basis of our Monte Carlo calculations, and of Eq. (  3 ), that v 3 {6}/v 3 {4} should lie between 0.
(3) always holds. Event-by-event hydrodynamic simulations confirm that v 2 and v 3 scale linearly with the corresponding initial anisotropies, ε 2 and ε 3  [56]. We first select a model of initial conditions by requiring that it reproduces the first nontrivial ratio of cumulants, v 2 {4}/v 2 {2}, which has been measured by the CMS Collaboration  [54] , as a function of centrality percentile. As in the previous section, we employ the T R ENTo model. However, the sets of parameters that give a reasonable description of Pb+Pb data fail to describe p+Pb data. Specifically, the values p = -1 and p = 0, which provide a good description of experimental data in Fig. 2 , yield a negative ε 2 {4} 4 in p+Pb collisions (i. e. , an undefined ε 2 {4}), and values of ε 2 which are much smaller than needed in order to explain the magnitude of the measured v 2.