dataesr/scientific-paragraphs-categorization huggingface dataset:
Mesh cleaning We remodeled the original meshes and got some clean meshes as shown in Fig. 12. Specifically, we removed all the teeth from the meshes. For the partial shapes, we used its symmetric shape to complete the missing parts. Also the surfaces are smoothed to remove the noises. Note that the two heads of the shape "El Sidron" are still missing, since we do not have any information about this part from the original shape itself. Since the missing parts are completed, we do not need landmarks to guide the map computation. We simply apply the same method as used in the original dataset but without any landmarks. Fig. 13  shows the computed maps on the 10 remodeled meshes. Again, the regions with the same color correspond to each other according to our compouted maps. Similarly, Fig. 14  and 15 visualize the overall similarity of the remodelled shapes using the metrics Rigid Alignment Measure and Shape Distortion Measure respectively.
5 days in week 20 to 2. 8 days in week 26 (Fig. 1b  and Extended Data Fig. 1 ). Accounting for this delay (Methods and Extended Data Fig. 2 ), we estimated that 14,061 (13,972-14,156)  virologically confirmed symptomatic cases had an onset of symptoms in the study period, showing a decreasing trend over time (2,493 in week 20, 1,647 in week 26). The test positivity rate decreased in the first weeks and stabilized at around 1. 2% (mean over weeks 24-26). A digital participatory system was additionally considered for COVID-19 syndromic surveillance in the general population  20  , including those who did not consult a doctor. Called COVIDnet. fr, it was adapted from the platform GrippeNet. fr (which is dedicated to the surveillance of influenza-like illnesses  4  ) to respond to the COVID-19 health crisis in early 2020.
COVID-19 pandemic trajectories and detection rates We used stochastic discrete age-stratified epidemic models  2, 14  based on demography, age profile  21  and social contact data  15  of the 12 regions of mainland France to account for age-specific contact activity and role in Estimates are based on the imputation of individuals without symptoms who tested positive at the time of testing into asymptomatic or presymptomatic; imputation of missing data on clinical status at the time of testing into asymptomatic, presymptomatic or symptomatic; and imputation of the date of onset of symptoms for presymptomatic and symptomatic cases (Methods). Imputations were performed n = 100 times. Uncertainties (black bars) correspond to the 95% confidence intervals. Test positivity rates were computed for cases with complete information.
Data for weeks 20-26 were consolidated in week 30. b, Breakdown of virologically confirmed cases with symptoms and complete information in the SI-DEP database by week of testing according to the declared onset of symptoms (left y axis; n = 5,514). The estimated time from onset to testing is also shown (right y axis; median and 95% confidence interval obtained from n = 100 imputations of the onset date). c, Weekly incidence of suspected cases of COVID-19 (median (dashed line), 95% confidence interval (shaded area) and 3-week moving average (solid line)), and percentage of individuals seeking healthcare (median and 95% confidence interval), estimated from the participatory surveillance system, COVIDnet. fr (average weekly n = 7,481). d, The number of suspected cases of COVID-19 in the participatory cohort who sought healthcare, and among those individuals, the number of individuals who received a prescription and performed a virological test when given the prescription.
First, we compared our model projections of the percentage of the population infected with the results of three independent seroprevalence studies performed after the first wave in France  7, 25, 26  (Methods). Modelling results are in agreement with serological estimates at the national and regional level (Fig. 3e  and Extended Data Fig. 6 ). Second, we compared the projected incidence of symptomatic cases of COVID-19 in week 26 (6. 69 (5. 84-7. 37) cases per 100,000) with the value obtained from the number of virologically confirmed cases (2. 55 (2. 48-2. 61) cases per 100,000) and two estimates based on COVIDnet. fr data (Fig. 3g ). The first estimate applies the measured test positivity rate to the incidence of self-reported suspected cases of COVID-19 (estimate 1, which yielded 8. 6 (95% confidence interval, 6.
2-11. 5) cases per 100,000); the second additionally assumes that only 55% would be confirmed as a suspected case by a physician and prescribed a test (according to COVIDnet. fr data; estimate 2, which yielded 4. 7 (3. 4-6. 3) cases per 100,000). Our projections are in line with plausible estimates from COVIDnet. fr, and suggest that, on average, at least 80% of suspected cases should be tested to reach the predicted incidence. Sensitivity analysis showed that the findings were robust to elements of the contact matrices that could not be informed by empirical data (Supplementary Figs. 8, 9 ). Furthermore, a model selection analysis showed that changes in contact patterns over time due to restrictions and the activities of individuals of different age classes after lockdown (for example, partial attendance at school and remote working) are needed to accurately capture the transmission dynamics (Supplementary Table  2  and Supplementary Fig.
Virological surveillance data The centralized database SI-DEP for virological surveillance 3 collects all tests performed in France for any reason. In the period under study, guidelines recommended individuals to consult a general practitioner at the first sign of COVID-19-like symptoms and to obtain a prescription for a virological test (a prescription was compulsory to access the test)  8. In addition, routine testing was performed for patients admitted to the hospital with any diagnosis, healthcare personnel and individuals at other facilities (for example, in some care homes for older people or long-term healthcare facilities). Data include detailed information for the individuals tested in France, including (1) the date of the test; (2) the result of the test (positive or negative); (3) location (region); (4) the absence or presence of symptoms at the time of testing; (5) self-declared delay between onset and test in presence of symptoms.
The delay is provided with the following breakdown: onset date occurring 0-1 day before date of test, 2-4 days before, 5-7 days before, 8-15 days before, or more than 15 days before. For some tests, information on points (4) and (  5 ) is missing. The SI-DEP database provided complete information for 23,210 (66%) out of 35,264 laboratory-confirmed cases of COVID-19 tested between week 20 (11-17 May) and week 30 (19-26 July), with an increasing trend of complete information over time (from 49% in week 20 to 76% in week 30) (Extended Data Fig. 1 ). Among confirmed cases with complete information, 12,716 (55%) showed no symptoms at the time of testing (Extended Data Fig. 1 ). The study referred to the period from week 20 to week 26. Data up to week 30 were used to consolidate the data in the study period accounting for the delays.
Imputation of asymptomatic versus presymptomatic cases, onset date and missing information Individuals who tested positive on a given date were recorded in the SI-DEP database as: cases with symptoms at the time of testing, with a self-declared delay from onset of symptoms; cases without symptoms at the time of testing; or cases with no information on presence or absence of symptoms at the time of testing. These three subsets of cases were analysed to account for the presence of presymptomatic individuals among those with no symptoms at the time of testing, imputation of missing data and the estimation of dates of infection or symptom onset. For laboratory-confirmed cases of COVID-19 who had symptoms at the time of testing, we estimated their date of onset using the information on the date of test and the time interval of onset-to-test delay, which was self-declared by the patients (Fig.
1b ). In the time period between weeks 20 and 30, 20% of cases had an onset-to-test delay of ≤1 day, 63% had a delay of ≤4 days, 83% had a delay of ≤7 days and 88% had a delay of ≤15 days (Extended Data Fig. 1 ). We fitted a Gamma distribution to the onset-to-test delay data with a maximum likelihood approach, using three different periods of time (May, June and July), to account for changes in the distribution of self-declared delays over time (that is, longer delays at the beginning of the study period, shorter delays at its end) (Extended Data Fig. 2 ). The estimated average delay in May, June and July was 12. 9 (95% confidence interval, 7. 0-16. 1), 5. 1 (3. 7-6. 3) and 2. 7 (2. 0-3. 1) days, respectively. July data were used to consolidate data corresponding to infections with onset in June and tested with delay.
Imputation was done by sampling from a binomial distribution and repeated 100 times. Data on contact tracing could not be used to inform data on infection or symptom onset, because of national regulatory framework on privacy preventing the matching of the two databases (virological tests and contact tracing). Given the low sensitivity of PCR tests in the early phase of the incubation period, we considered that imputed presymptomatic cases belonged to the prodromic phase. Onset date for presymptomatic cases was estimated by sampling from an exponential distribution with a mean of 1. 5 days, corresponding to the duration of the prodromic phase in our model (Supplementary Table  1 ). For imputed asymptomatic, we assumed the same delay from infection to testing as in cases with symptoms. Given the structure of our compartmental model and to match the definition of the time used for symptomatic individuals (week of onset), we considered a delay in the detection of asymptomatic individuals starting from the end of the prodromic phase (corresponding to the symptom onset time for symptomatic infections) to the date of testing.
We assigned this date by sampling the delay from the monthly gamma distribution. Imputation of the dates was repeated 100 times. For laboratory-confirmed cases of COVID-19 with no information on symptoms at the time of testing, missing data were imputed by sampling from a multinomial distribution with probabilities equal to the rate of occurrence of the outcomes (asymptomatic, presymptomatic or symptomatic with five possible time intervals for the onset-to-test delay) reported for cases with complete information and assuming the imputation of cases without symptoms into asymptomatic and presymptomatic, as described above. Imputation was performed by region and by week and repeated 100 times. Presymptomatic and symptomatic individuals were aggregated together by onset date (Fig. 1a ) to estimate the rate of detection of symptomatic cases.
Participatory surveillance data and analysis COVIDnet. fr is a participatory online system for the surveillance of COVID-19, available at https://www. covidnet. fr/. It was adapted from GrippeNet. fr  4  to respond to the COVID-19 health crisis in March 2020. GrippeNet. fr is a participatory system for the surveillance of influenza-like illnesses available in France since 2011 through a collaboration between Inserm, Sorbonne Université and Santé publique France, supplementing sentinel surveillance  4, 37. The system is based on a dedicated website to conduct syndromic surveillance through self-reported symptoms volunteered by participants resident in France. Data are collected on a weekly basis; participants also provide detailed profile information at enrolment  38. In addition to tracking the incidence of influenza-like illnesses 4,37 , GrippeNet.
Two independent estimates obtained from COVIDnet. fr cohort data for the incidence of symptomatic cases in week 26 are shown in Fig. 3. These estimates were computed as follows. Estimate 1 = (COVIDnet. fr estimated incidence of suspected cases in week 26) × (test positivity rate from SI-DEP in week 26); estimate 2 = (COVIDnet. fr estimated incidence of suspected cases in week 26) × (estimated proportion screened and confirmed as a suspected case of COVID-19 by a physician, and prescribed a test; estimates from COVIDnet. fr) × (test positivity rate from SI-DEP in week 26). The two estimates were used to validate model projections and identify the specific surveillance mechanisms that needed improvement. reduction of 30% in contacts for older individuals in the exit phase, informed by survey data.
We used simulations of the stochastic model to predict values for all quantities of interest (500 simulations each time). We fitted the model to the daily count of hospitalizations H obs (d) on day d throughout the period and the number of people testing positive by week of onset, split according to disease status (symptomatic or asymptomatic), denoted Test s,obs (w) and Test a,obs (w) in week w of the exit phase. We used hospital admission data up to week 27 (29 June-5 July) to account for the average delay from infection to hospitalization. Data in week 27 were consolidated by waiting for one additional week to account for updates and missing data (week 28, 6-12 July 2020). We assumed a Poisson distribution for hospitalizations and a binomial distribution for the number of people getting the test, therefore the likelihood function is We reduced the required computations with an optimization procedure in two steps, first maximizing the likelihood function in the pre-lockdown and lockdown phase to estimate the first four parameters, and then maximizing the likelihood in the exit phase by fixing the first four parameters that describe the epidemic trajectory before the exit phase to their maximum likelihood estimators (MLEs).
This second step was further simplified through an iterative procedure, and we show through simulations that the simplified optimization procedure is consistent and well-defined. The parameter space was explored using NOMAD software  55. Fisher's information matrix was estimated at the MLE value to obtain the corresponding confidence intervals. Simulations were then parameterized with 500 parameter sets obtained from the joint distribution of transmission parameters at MLE (one stochastic simulation for each parameter set). A Bayesian estimate of the posterior parameter distribution using Markov chain Monte Carlo (MCMC) would also have been an alternative to maximum likelihood and confidence interval estimation. In this case, however, MCMC would have considerably slowed down parameter exploration, with negligible added value to the fitting procedure.
Analyses were performed on residual sera obtained from clinical laboratories, and estimates were weighted to account for the lack of representativeness. Estimates by EpiCoV 26 (Enquête Eṕideḿiologie et Conditions de vie lieés à la Covid-19) used ELISA-S tests and further validated these with a seroneutralizing antibody test at higher specificity (see ref. 26  for more details). This was the only seroprevalence survey that was conducted in a representative sample of the population. For this reason, we used it as the reference study. For all studies, we report in Fig. 3e  and Extended Data Fig. 6  the estimates 14 days before the last blood collection to account for the time needed to mount a detectable presence of antibodies. For the EpiCoV survey, we used the last date at which samples were sent back to the laboratory.
We used EpiCov as the reference study as it was the only one study that was conducted on a representative sample of the population. Second, there are limitations to the dataset of hospital admissions used to calibrate the models: the database infrastructure for data collection became operational in mid-March and was filled in retrospectively. Notification biases would inevitably alter the inference of parameters in the pre-lockdown phase. This may have differed region by region; however, we have no way to control for this potential bias; possible errors would have affected regions with small-size epidemics more than others. In support of this hypothesis, we note that a similar but independent mathematical model fitted to regional hospitalization data  24  in the first wave predicted small epidemics in Pays de la Loire and Brittany, similarly to our model.
Information provided by SI-DEP database and imputation of missing data. a, Breakdown of laboratory-confirmed cases of COVID-19 (n = 35,264) according to information available. Missing data refer to cases for which information regarding absence or presence of symptoms at the time of testing and self-declared onset-to-test delay were not provided. b, Breakdown of laboratory-confirmed cases of COVID-19 with complete information (n = 23,210) according to absence or presence of symptoms on the date of testing and onset-to-test delay. c, Estimated number of asymptomatic, presymptomatic and symptomatic confirmed cases by week of testing, after imputation of missing data about presence or absence of symptoms. Darker and lighter colours indicate cases with complete information and missing data, respectively.
Acknowledgements This study was partially supported by  ANR  projects  DATAREDUX  ( ANR-19-CE46-0008-03 ) and  EVALCOVID-19  ( ANR-20-706 COVI-0007 );  EU   H2020 grants MOOD  ( H2020-874850 ) and  RECOVER  ( H2020-101003589 ); and REACTing COVID-19 modelling and surveillance grants. We thank  P. Crepey ,  C. Pelat ,  E. Chatignoux ,  J. Paireau  and  D. Levy-Bruhl  for discussions, and all participants of the COVIDnet. frsystem.
Data availability Data used for model parameterization (contact matrices  15  , school attendance  16  , presence at workplaces  17  and avoidance of physical contacts  18  ) are available at the cited sources. Hospitalization data used for model calibration are made available with the code. Virological data 3 are available at the cited source; data on onset-to-test delay are made available with the code. COVIDnet. fr individual data cannot be shared owing to restrictions imposed by the French national data protection authorities. Requests for custom access to aggregated and post-processed data can be made to the GrippeNet. fr/ COVIDnet. fr Scientific Committee (https://covidnet. fr/fr/covidnet/ confidentialite-et-securite-des-donnees/) through the submission of a scientific proposal describing the aims, methods, data format requested and team proposing the project.
Extended Data Fig. 1 | 1 Information provided by SI-DEP database and imputation of missing data. a, Breakdown of laboratory-confirmed cases of COVID-19 (n = 35,264) according to information available. Missing data refer to cases for which information regarding absence or presence of symptoms at the time of testing and self-declared onset-to-test delay were not provided. b, Breakdown of laboratory-confirmed cases of COVID-19 with complete information (n = 23,210) according to absence or presence of symptoms on the date of testing and onset-to-test delay. c, Estimated number of asymptomatic, presymptomatic and symptomatic confirmed cases by week of testing, after imputation of missing data about presence or absence of symptoms. Darker and lighter colours indicate cases with complete information and missing data, respectively.
Acknowledgments MG, GP, FC, DB, AG, RR and EB defined the study protocol. RR, AP, FC, DB and GP cooperated in the practical execution of the study and data gathering. GP drafted a first version of the manuscript, which was then revised and integrated by MG, EB, AG, CEG and SC. CO was responsible of serologies and NPSs execution. EB, PB and GM analyzed the data. All authors approved the final version of the manuscript. The study was funded thanks to the non-conditioning economical support from  CISOM (Corpo Italiano di Soccorso dell'Ordine di Malta) ,  Banca Mediolanum ,  Fondazione Rava ,  Mylan Italia and FC Internazionale Milano.
Acknowledgements: The author is very grateful to  Danielle Dhouailly  ,  Professor Emeritus  of  Biology at Grenoble-Alpes University , and  Stefan Nonchev , Professor at  Grenoble-Alpes University and Institute of Advanced Biosciences , for their careful proof-reading and quite helpful comments and suggestions. He also gratefully acknowledges  Jacques Demongeot, Professor at Grenoble-Alpes University and Faculty of Medicine , for his quite sensible and far-seeing advices.
Background: Data generated by phase I trials is richer than the classical binary DLT measured at the first cycle used as primary endpoints. Several works developed designs for more informative endpoints, e. g. ordinal toxicity grades and/or longitudinal data which relied however on strong assumptions, in particular the proportional odds (PO) assumption. Methods: We evaluated this PO assumption for the dose and cycle on a large database of individual patient data from 54 phase I clinical trials of molecularly targeted agents. The PO model is a specific case of the continuation ratio logit model (CRLM) with null parameters. We compared the PO and CRLM models using the widely applicable information criterion (WAIC). We considered a longitudinal multivariate ordinal toxicity outcome (cutaneous, digestive, hematological, general disorders, and other toxicities).
Introduction In oncology, dose finding phase I clinical trials aim at determining the maximum tolerated dose (MTD) as the dose presenting an acceptable rate of severe toxicity during the first cycle of treatment, also called dose limiting toxicity (DLT). Groups of patients are enrolled at increasing dose levels. A given patient is assigned to a dose that is administered in repeated cycles until treatment failure; intra-patient dose escalation is usually not allowed. At each cycle of treatment, adverse events of various types (digestive, hematological, cutaneous, general disorders, etc. ) are measured on a graded scale that ranges from 0 (absence of toxicity) to 4 (severe life threatening toxicity). One of the main limitations of dose finding trials is the limited amount of information extracted from the primary outcome  [1, 2]  that results from (i) the composite nature of the outcome 'worst observed toxicity', (ii) the dichotomization of this graded outcome in presence or absence of severe toxicity (DLT), and (iii) the use of data collected at cycle 1 only although more than 50% of the first severe toxicity occur after cycle 1  [3, 4].
However, sample sizes for dose finding trials being typically small; to account for multiple toxicity grades, a natural simplification of the cumulative logits is the proportional odds (PO) model that assumes that the effect of covariates (here the dose level and the cycle) is similar on the various cumulative logits. This assumption reduces the number of parameters to estimate and allows using intermediate grades of toxicity to refine the estimate of the risk of severe toxicity  [17]. Before we implement such an assumption in prospective clinical trials, we explored the PO assumption on real data. The effects of dose and cycle on various types of toxicity were modeled in a large database of individual data of patients treated in 54 phase I clinical trials of molecularly targeted agents provided to the DLT-TARGETT group, a European Organization for Research and Treatment of Cancer (EORT-C)-led initiative.
Study design Full toxicity data of 54 completed phase 1 studies evaluating molecularly targeted agents (MTAs) was provided by four academic institutions (Cancer Research UK (United Kingdom), EORTC, National Cancer Institute-Canada and National Cancer Institute) and three pharmaceutical companies (Pfizer, Roche and Sanofi). The MTAs were administered as single agent to adult patients with solid tumors. All patients who received at least one cycle of treatment were included in the analysis. The reader may refer to the publication by Postel-Vinay et al. [4]  for complete details about the data collection and the study design.