dataesr/scientific-paragraphs-categorization huggingface dataset:
This figure of 600 households was not arrived at from a calculation to determine the optimal number of households to be included, but was the result of a trade-off between the budgetary and logistic constraints of surveying up to three members in each household (ie, a potential maximum of 1800 individuals) in three successive waves of data collection (the first wave started on 27 July 2020). Taking into account the design of previous surveys conducted in the same area,  25  we assumed a response rate of 90% for the first wave of data collection and an attrition rate of 15% over the data collection period, resulting in an estimated 500 surveyed households by the end of the third wave (scheduled for mid-2021). In the planned analyses, potential selection bias will be assessed and reduced by using sampling weights computed as reciprocals of the probabilities of selection of each household.
Final weights will be calculated using an iterative process (ranking ratio estimation) involving sociodemographic data collected regularly by the Niakhar HDSS. Longitudinal phone survey in multi-adult households Data collection is expected to last at least until end-2021, unless the spread of COVID-19 at that time justifies extending data collection. The study started in March 2020 and it is funded until March 2022 (Inserm-ANRS, grant number ECTZ147735). Given that any application for funding for possible subsequent waves of data collection needs to occur well in advance, the decision about this issue has been postponed until mid-2021. For each wave, data are collected by surveying participants over their mobile phone. Participants' telephone numbers were recorded by community health workers (locally called Badjanou Gokh) prior to the first survey.
Phone interviews are conducted using computer-assisted telephone interviews (CATI) software. To achieve higher response rates, 15 calls (1 initial and 14 callbacks) are planned during the several weeks of data collection, at different times of the day and on different days, before discarding a non-responsive telephone number. While 12-15 calls and 6-10 calls are generally recommended for landline and mobile CATI surveys respectively,  26  we opted for a maximum of 15 calls given the frequency of poor telephone connections in the area. The COVID-19 sanitary context makes the implementation of the survey at each wave and the collection of data more complex than usual. Although this study protocol was the result of a close collaboration between Senegalese and French researchers, travelling restrictions prevented some of the latter from being physically present for the training of the CATI interviewers, for data collection preparatory meetings, for field meetings and for feedback sessions.
Open access The lack of the possibility to train interviewers up on CATI in Senegal because of the international travel restrictions meant that only experienced bilingual (French and Serer) interviewers already present in Senegal could administer the CATI surveys. As Serere is an oral only language, practice sessions to administer the questionnaires were performed by the interviewers in order to reach a consensus on the specific words to be used when performing the interviews in Serere. Consequently, the relevance and ease of understanding of each questionnaire item was assessed before the interviews took place in Serere. A total of seven interviewers collected data in the first wave. They were supervised by another senior interviewer whose specific role, in addition to supervision, was to share feedback on data collection with the two (Senegalese and French) research teams.
Data collection The first data collection wave began on 27 July 2020 and interviewing lasted 6 weeks. Furthermore, parallel data collection about local preventive strategies implemented in villages since July 2020 is about to be completed. Figure  3  summarises the major steps of data collection and presents the study sample to be followed in our longitudinal design. For the first wave, phone interviews took place with three different persons in each participating household as follows: the head of the household, his wife (for those who had more than one wife, the wife responsible for managing the household) and a relative from a city who had decided to come and live momentarily in the rural household because of the risk of COVID-19 and the fear of associated economic consequences. Specifically, heads of households had to decide which wife and which temporary visiting relative would be surveyed, and to provide their names and mobile phone numbers during the first phone interview.
When interviewing heads of households, their designated wives and visiting relatives on their mobile phones, the interviewers first presented the study and informed them about its longitudinal design, obtained their consent to participate and then interviewed them. As a way of thanking households for their participation, the community health workers provided them with a personal protection kit including hydroalcoholic gel and a face mask at the end of the first wave of data collection. As regards relatives temporarily living in the household, those individuals interviewed in the first wave of data collection will be surveyed in successive waves. New visiting relatives identified between two different waves of data collection will also be included in the study design by interviewing them in all data collection waves subsequent to their inclusion.
Sociodemographic characteristics As the study's framework provides for collected data to be matched with existing data in the Niakhar HDSS  23  database, only individual sociodemographic data needs to be collected during the telephone interviews, including marital status, educational level, number of children, the latter's ages and type of schooling (public or religious school), as well as the respondent's level of access to the internet. With regard to employment at the time of the survey, participants are invited to answer open-ended questions whose responses are consequently recoded into nine different standard categories (Farmers, Craftsmen, Workers, Employees, Intermediate professions, Managers and higher intellectual professions, Students, Pensioners, Not engaged in active employment).
Risk perceptions Collecting data on the perceived risks of COVID-19 is of crucial importance in understanding individuals' related attitudes and behaviours. In line with previous survey studies, the assessment of risk perceptions in the present study involves collecting information on introspective judgements. 28 29 After asking surveyed individuals whether they have heard about COVID-19 pandemic before being surveyed, an assessment is performed using a scale from 0 (not at all) to 10 (extremely) to measure how worried they are about getting the disease, and how contagious and severe they perceive it to be. 30 In addition, perceived mortality of COVID-19 is assessed based on a question about what the participant believes with be the number of deaths out of every 100 people with COVID-19. To provide greater insight into participants' COVID-19 risk perceptions, all the questions mentioned above are asked again for malaria, a well-known and common viral infection in SSA countries which also starts with flu-like symptoms.
Attitudes Given the unprecedented context of COVID-19 pandemic, assessing individual attitudes to the disease necessitates using ad-hoc questions. Considering the restrictive measures adopted in Senegal from March 2020 onward, attitudes to curfews in general, whether implemented in rural or urban areas are assessed in the present study. With regard to health issues, surveyed individuals are invited to report how worried they are about buying counterfeit drugs, their attitudes to vaccination in general and to (unavailable at the time of the first wave of data collection) the anti-COVID-19 vaccine. In addition, participants are asked whether they would consider having anti-COVID-19 vaccine for themselves and for their children if it were free of charge (fourpoint Likert-type scale from 'certainly yes' (=1) to 'definitely not' (=4) in all cases).
Open access Preventive behaviours It has been recently suggested that locally implemented COVID-19 preventive strategies could potentially explain, at least in part, the as yet slow spread of COVID-19 in SSA. 8 9  The present study collected data which could shed some more light on this issue. In our ongoing longitudinal study, preventive behaviours cover individual protection measures by the study's participants and collective prevention strategies implemented by administrative, religious or medical authorities in the area covered by the survey. With regard to the former, the study's questionnaires include items measuring how much the COVID-19 pandemic has led to changes in everyday life in the participating households, in changes in journeys to and from the local market or the closest city and in changes in the way relatives from cities are welcomed, especially in terms of possible quarantine on their arrival.
ETHICS AND DISSEMINATION COVID-19 is currently (as of February 2021) spreading relatively slowly in SSA although a sharp increase in its spread cannot be excluded. Given the current context, the present ongoing longitudinal study protocol aims to provide data on the attitudinal, behavioural and economic consequences of the disease in a rural area in Senegal at a time when very few data are available. Since rural areas may likely be seen as safe refuges, our study collects data from heads of rural households, their wives (in charge of managing the household) and relatives who leave cities to temporarily live in these households, specifically because of the COVID-19 pandemic. The study protocol was approved by the Senegalese National Ethical Committee for Research in Health (131/MSAS/ CNERS/Sec) and received authorisation from the Senegalese Ministry of Health (619/MSAS/DPRS/DR) and the French Commission on Information Technology and Liberties (CNIL 2220771).
Acknowledgements We would like to thank the interviewers involved in the study, that is,  Modou DIOME ,  Cheikh DIOUF ,  Ndèye Selbé DIOUF ,  Moussa GUEYE ,  Fadiène NDIAYE ,  Mayé NDOUR ,  Coumba Sandiane SENE ,  Natalie TINE. Finally , our thanks to  Jude Sweeney  ( Milan, Italy ) for the English revision and editing of our manuscript. Collaborators Members of the  CO3ELSER Study Group are Elhadji Ba ;  Assane Ndiaye ;  Cheikh Sokhna  (UMR VITROME,  IRD-Université Aix Marseille, AP-HM, SSA, IHU-Méditerranée Infection, Marseille, France, and Campus International, IRD-UCAD de l'IRD, Dakar, Sénégal) Cyril Berenger ,  Gwenaelle Maradan  ( ORS PACA, Southeastern Health Regional Observatory, 13006, Marseille, France ),  Valerie Seror ,  Sebastien Cortaredona ,  Patrick Peretti-Watel  ( Aix Marseille Univ, IRD, AP-HM, SSA, VITROME, Marseille, France, IHU-Méditerranée Infection, Marseille, France ),  Olivier L'Haridon  ( Faculty of Economics, University of Rennes 1, Univ Rennes, CNRS, CREM UMR 6211, Rennes, France ),  Jocelyn Raude  ( EHESP School of Public Health, Rennes, France ).
They are called maximum likelihood estimates (MLEs). From the ideal mathematical point of view, they are the solutions the following system of equations: @ℓ(λ, z)/@λ = 0 and @ℓ(λ, z)/ @z = 0, with @'ðl; zÞ @l ¼ X n i¼1 p 2 @'ðl; zÞ @z ¼ X n i¼1 @gðx i ; zÞ @z 1 gðx i ; zÞ þ p 2 X n i¼1 @gðx i ; zÞ @z ð2l À 1Þ sin p 2 Gðx i ; zÞ h i þ p 2 lGðx i ; zÞ cos p 2 Gðx i ; zÞ h i l p 2 Gðx i ; zÞ sin p 2 Gðx i ; zÞ h i þ ð1 À lÞ cos p 2 Gðx i ; zÞ h i : In most of the cases, the analytical expressions for l and ẑ seem not possible. However, for given baseline cdf and λ, they can be approximated numerically by iterative techniques. Common routines are the optim function of the R software or PROC NLMIXED of the SAS (Statistical Analysis System) software. Also, one can determine the standard errors (SEs) of the MLEs which follow from the inverse of the observed information matrix.
Funding: This work is supported by an  ANR   TRANSCAN  ( ANR-18-CE35-0009 ), the  MAVA Foundation , a  CNRS International Associated Laboratory Grant , the  National Institute of Cancer (INCa , http://www. e-cancer. fr),the  SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553 , as well as by the  European Union   Horizon 2020 Research and Innovation program  under the  Marie Skłodowska-Curie  grant agreement No  765492.
change is social influence from others: Humans are social cooperators, who construe behaviour change as a collective problem  (Dezecache, Frith, & Deroy, 2020)  and align more closely with those they are closely bonded to  (Haun & Over, 2015). We collected data from 114 countries (n = 6,674) to investigate whether social influence is associated with adherence to COVID-19 guidelines. This study is unique in its consideration of both personal and social factors influencing people's adherence to the social distancing rules. The few existing peer-reviewed studies on social predictors of COVID-19 adherence have found that people engage in more preventive measures when they have higher social responsibility and trust  (Oosterhoff, Palmer, Wilson, & Shook, 2020)  and when they consider adherence to the rules as a norm endorsed by others  (Borgonovi & Andrieu, 2020; Lin et al.
Addressing this theoretical gap with a rich data set, this paper examines how social influence affects adherence at three scales: one's close circle, one's country, and the entire world. Specifically, we compared people's own adherence and approval of the rules to how much they believed others around them adhered and approved. We posit that across the three social scales, the degree of closeness with others will determine how much perceived adherence and approval of others impacts on one's own adherence. We argue that while feelings of vulnerability to the disease would influence people's adherence to the rules, in this context of social norm change, the strongest predictor of adherence would be the perceived adherence of one's close circle. Examinations of large, cross-cultural databases reveal a distinct pattern observed in humans' bond formation  (Hill & Dunbar, 2003).
Demographics Participants provided their age, gender ('man', 'woman', 'non-binary', 'prefer not to say'), highest completed education ('No schooling completed', 'Primary education (age: 5-10)', 'Secondary education (age: 11-17)', 'University undergraduate degree/professional equivalent', 'Postgraduate degree'), current student and employment status, and whether they were studying/working from home ('yes', 'sometimes', 'no'). Participants were also asked of their country of residence at the time of answering, which was used to obtain the stringency of lockdown measures in that country using the OxCGRT database  (Hale et al. , 2020).
Results The role of social adherence, social approval, and personal approval in self-adherence We examined our hypotheses regarding the role of social influence on adherence in two models: the social adherence model and the social approval model. These models assessed whether self-adherence to distancing (adherence self ) could be predicted by the perceived adherence or perceived approval of others at three social scales: close circle (recent contacts whom participants said they would turn to in hardship), country (fellow citizens), and world (humankind). Both models included people's own approval of the distancing rules as a predictor, demographic variables and stringency of local COVID-19 measures as covariates, and participants' country of residence as a random effect. Participants who indicated not having contacted anyone in their close circle in the past week (n = 1,199) had missing data for the adherence and approval variables of close circle and were therefore excluded from these analyses.
Acknowledgements We thank the following people for their assistance with the translation of the survey items and distribution in different countries:  Mustak Ibn Ayub ,  Bahador Bahrami , C ßa grı  Mert Bakırcı ,  Sofia Bonicalzi ,  Jewel Crasta ,  Harsimar Kaur ,  € Ozg€  un K€ oksal,  Louis Longin ,  Iyad A Naim ,  Parisa Navidi ,  Victoria € Ostgren ,  Mahmuda Shaoun ,  Fredy Santiago Monge Rodriguez , and  Jimena Zapata. We thank  Carl Brickbauer  for help with illustrations, and  Emma Cohen ,  Bahador Bahrami ,  Jonathan Jong ,  Michael Buhrmester , and the two anonymous reviewers for providing feedback on earlier versions of this manuscript. MEZ is supported by the  Wellcome Trust  grant number  204702. JS.
For the dose-regimen PD model, we considered nonlinear mixed-effects models, and for the PD toxicity model, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. We evaluated the operating characteristics of the DRtox through simulation studies under various scenarios. The results showed that our method outperforms traditional model-based designs demonstrating a higher percentage of correctly selecting the MTD-regimen. Moreover, the inclusion of PK/PD information in the DRtox helped provide more precise estimates for the entire dose-regimen toxicity curve; therefore the DRtox may recommend alternative untested regimens for expansion cohorts. The DRtox should be applied at the end of the dose-escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia (NCT03594955) once all toxicity and PK/PD data are collected.
Motivation This work was motivated by the ongoing first-in-human dose-escalation study of SAR440234 administered as a single agent to patients with relapsed or refractory acute myeloid leukemia, high-risk myelodysplastic syndrome or B-cell acute lymphoblastic leukemia (NCT03594955)  [33]. SAR440234 is a novel bispecific T-cell engager antibody that activates and redirects cytotoxic T lymphocytes (CTLs) to enhance the CTL-mediated elimination of CD123-expressing tumor cells. CTL activation induces the release of inflammatory cytokines, which can potentially cause cytokine release syndrome (CRS). CRS is a systemic inflammatory response and among the most commonly observed toxicities of T-cell engaging bispecific antibodies, such as blinatumomab, which is a bispecific anti-CD19/CD3 antibody  [20].
Sensitivity analysis We also evaluated the DRtox under different prior distributions and after increasing the variability in toxicity. The results of these analyses are shown in Web Appendix D. To evaluate the impact of the prior distributions, we compared the main results with those obtained with a stronger prior distribution measured with an approximate ESS of 9, which is high for a trial including 30 patients. As the prior distributions are based on Scenario 1, stronger prior information increased the performance in the scenarios in which the dose-regimen toxicity relationship is similar to that in Scenario 1 (Scenarios 1, 3, 5 and 6), but the performance was decreased in Scenario 4. Therefore, defining prior distributions using reliable data from previous studies can increase the performance, but attention should be paid to the quality and quantity of the information used to avoid decreasing the performance.
In practice, our methods should be applied at the end of the dose-escalation phase of the motivating trial once all PK/PD and toxicity data are collected. Our model can address missing data as follows; (1) Regarding missing doses in the dose-regimen and associated cytokine profiles, as we are using nonlinear PK/PD modeling, our method would take into account whether a patient misses one or more planned doses as the model considers the actual regimen received and not the planned regimen. (2) Regarding missing cytokine data, which is expected to be rare in this trial as the cytokine is carefully monitored by frequent sampling to detect its peak, individual cytokine peaks could be predicted from the population PK/PD model. However, it would be more common for PK/PD data to be below the limit of quantification, but these data are considered by the PK/PD model as censored data rather than missing data.
In the case toxicities occur at the beginning of the administrations, resulting in a high number of incomplete PD observations, we propose the use of the predicted PD given by the PK/PD model under the complete regimen planned. The hierarchical-DRtox added a constraint, i. e. , toxicity must occur at the maximum of the PD response. Errors in the PK/PD estimation may lead to an undefined hierarchical model. In our simulation study, we observed this latter issue in less than 2% of the trials. In the real world, this issue could indicate that the proposed PK/PD model is incorrect, and that another model should be considered. However, in our simulation study, we decided to run other simulated datasets to replace the upper 2% of the trials for all methods. One way to relax this constraint is to allow the patients' toxicity threshold to vary among administrations by adding a second latent variable, which could lead to complex models that are challenging to estimate.
Acknowledgements This work was partially funded by a grant from the  Association Nationale de la Recherche et de la Technologie , with  Sanofi-Aventis R&D , Convention industrielle de formation par la recherche number  2018/0530. The authors would like to thank  Raouf El-Cheikh ,  Laurent Nguyen  and  Christine Veyrat-Follet  for their time and explanations of PK/PD modeling and  Paula Fraenkel  for her thoughtful review of the manuscript.
Solvers and numerical methods The open-source SU2 software 1 suite  [25]  written in C++ and Python is the basis for this study. It is built specifically for the analysis of partial differential equations (PDEs) and PDE-constrained optimization problems on unstructured meshes with the state-of-the-art numerical methods. The solver is rapidly establishing itself within the opensource community as a prominent software for multi-physics analysis and design, including the simulation of chemically reactive and non-equilibrium flows  [20, 26]  with the development of SU2-NEMO (Non-Equilibrium MOdels solver). The approach adopted for numerical discretization is based on classical literature on the subject. A finite-volume edgebased formulation is used with the AUSM scheme  [27]  (advection upstream splitting method) together with MUSCL (monotonic upstream-centred scheme for conservation laws) and standard limiting procedures.
A dual time-stepping approach with a second-order backward-difference discretization of the time derivative terms is adopted to address unsteadiness. The closure of the governing equations for the system of interest is achieved by means of coupling with appropriate thermochemistry models (presented in Sect. 2. 1). In this regard, the implementation in the present work extended SU2-NEMO by linking to the Mutation++ library 2  [14]  that provides efficient algorithms for the computation of thermodynamic, transport (viscosity, thermal conductivity, and diffusion) and chemical kinetic gas properties. The library has been designed for robustness over a wide range of temperatures and its accuracy in dealing with multi-temperature models, with the following constraints in mind: (1) high fidelity of the physical models, ensuring that the laws of thermodynamics are satisfied and that results are validated against the existing experimental data, (2) low computational 1 https://github.
Validation of thermochemical non-equilibrium models Both the SU2 software and the Mutation++ library have been extensively validated  [14, 25]. To validate the additional numerical implementation that concerns the link between these two codes, the canonical problem of a zero-dimensional adiabatic heat bath was chosen. This type of test case allows to isolate the vibrational-to-translational energy transfer together with vibration-chemistry coupling from the CFD (computational fluid dynamics) simulation, with the purpose of validating the thermochemical relaxation process. The case of heat bath with a chemically reacting mixture of standard five-species air (N 2 , O 2 , N, O, and NO) in thermal non-equilibrium was tested. The initial number density is set to 10 25 m 3 /s with molar fractions N 2 : 0.