dataesr/scientific-paragraphs-categorization huggingface dataset:
Models As toxicity data was collected at the end of each cycle of treatment, whose duration may vary across trials, the treatment cycle, as defined per protocol, was used as time unit, irrespective of its duration in days. The dose for the patient i, D i , was standardized by the MTD, i. e. the ratio between the planned dose and the MTD of this trial, or the maximal allocated dose during this trial when the MTD was not reached (� 25% of trials). We used mixed effect multivariate continuation ratio logit model  [18]  with correlated random effects to jointly assess the relationship between the J types of graded toxicity (J ¼ 5) denoted Y and the dose D and cycle C considered as continuous variables. The probability that the patient i presents a toxicity of type j (j ¼ 1; ⋯; 5) of grade higher than k (k ¼ 0; 1; 2) at the cycle c (c ¼ 1; ⋯; 6), was: with β jk ¼ P k l¼0 β jl the intercept for the grade k, γ jk ¼ P k l¼0 γ jl and ζ jk ¼ P k l¼0 ζ jl the parameters associated to the dose and the cycle respectively, and α ij eMVNð0; ΣÞ a patient specific random effect distributed according to a centered multivariate normal distribution with J � J covariance matrix Σ.
Discussion The richness of this large database highlighted some characteristics of the dose-response relationships for different types of toxicities. Our results suggest that the PO assumption may hold in most cases, but this statement cannot be generalized, specifically for digestive and general disorder toxicities. The cycle effect also depends of the type of toxicity as it ranged from no cycle effect for digestive and hematological toxicities, to a moderate effect attenuated for grade 2 or higher of the other types of toxicity. Those results may have application both at the design and at the analysis level. Assuming proportional odds for the dose effect enables to incorporate the occurrence of intermediate grades of toxicity in the estimate of the risk of toxicity and hence to increase its precision.
Acknowledgement The authors are indebted to all members of the EORTC-led DLT-TARGETT group, in particular  Eliza Rizzo  et  Jennifer Wong  who prepared the data and all the contributors: the  NCI , the  NCI-C , the  EORTC, Cancer research UK ,  Pfizer ,  Roch  and  Sanofi , and all the patients. We thank very much the  Cancer Therapy Evaluation Program  from the  National Cancer Institute  ( Dr Percy Ivy ,  NCI ) for providing full access to data from several phase I trials sponsored by the  NCI.
SARS-CoV-2 S-I53-50NP vaccination induces potent humoral responses in cynomolgus macaques The high binding and neutralization titers in mice and rabbits supported subsequent assessment of the immunogenicity of SARS-CoV-2 S-I53-50NPs in cynomolgus macaques, an animal model that is immunologically closer to humans. Six cynomolgus macaques were immunized with 50 mg SARS-CoV-2 S-I53-50NPs formulated in monophospholipid A (MPLA) liposomes by the intramuscular route at weeks 0, 4, and 10 (Figure  4A ). The MPLA liposome adjuvant was chosen because it is used in several human clinical trials (e. g. , NCT03816137, NCT03961438, and NCT04046978) and is a component of the widely used AS01 adjuvant, which was safe and effective in several phase 3 clinical trials  (Lal et al. , 2018; Agnandji et al. , 2012).
Animals and study designs Cynomolgus macaques were randomly assigned in two experimental groups. The vaccinated group (n = 6) received 50 ug of SARS-CoV-2 S-I53-50NP adjuvanted with 500 mg of MPLA liposomes (Polymun Scientific, Klosterneuburg, Austria) diluted in PBS at weeks 0, 4 and 10, while control animals (n = 4) received no vaccination. Vaccinated animals were sampled in blood, nasal swabs and saliva at weeks 0, 2, 4, 6, 8, 10 and 12. At week 12, all animals were exposed to a total dose of 10 6 pfu of SARS-CoV-2 virus (hCoV-19/ France/ lDF0372/2020 strain; GISAID EpiCoV platform under accession number EPI_ISL_406596) via the combination of intranasal and intra-tracheal routes (0,25 mL in each nostril and 4,5 mL in the trachea, i. e. , a total of 5 mL; day 0), using atropine (0. 04 mg/kg) for pre-medication and ketamine (5 mg/kg) with medetomidine (0.
Authentic virus neutralization assay Serum samples were tested for their neutralization activity against SARS-CoV-2 (German isolate; GISAID ID EPI_ISL 406862; European Virus Archive Global #026V-03883) as described previously  (Okba et al. , 2020). In short, serum samples were serially diluted in 50 mL Opti-MEM I (GIBCO), supplemented with GlutaMAX (GIBCO) and penicillin (100 U/mL). Serum dilutions were mixed with 400 plaque-forming units of virus to a total of 100 mL. This mixture was incubated at 37 C for 1 h. After incubation, the mixtures were put on Vero-E6 cells (ATCC CRL-1586) and incubated for 1 h. Cells were then washed, Opti-MEM I supplemented with GlutaMAX (GIBCO) was added, and incubated for 8 h. Cells were fixed with 4% formaldehyde in PBS and stained with polyclonal rabbit anti-SARS-CoV Ab (Sino Biological) and secondary Alexa488 conjugated goat-anti-rabbit Ab (Invitrogen).
Viruses and cells For the cynomolgus macaque challenge study, SARS-CoV-2 virus (hCoV-19/France/ lDF0372/2020 strain; GISAID EpiCoV platform under accession number EPI_ISL_406596) was isolated by the National Reference Center for Respiratory Viruses (Institut Pasteur, Paris, France)  (Lescure et al. , 2020). Briefly, Vero E6 cells were cultured in DMEM + 5% FBS. PCR-positive sample from patient 0371 was diluted 1:2 with DMEM with antibiotics but without FBS and supplemented with 1ug/ml trypsin-TPCK (Sigma-Aldrich), added to the cells and incubated one hour at 37 C. The inoculum was then replaced with DMEM medium containing antibiotics and 1mg/ml trypsin-TPCK at 37 C. Three days later, clear cytopathogenic effects were observed and RT-qPCR was performed to detect presence of virus. Culture supernatant was harvested, aliquoted and stored at À80 C.
Viral sequencing 30 RNA samples from nasal swabs and bronchoalveolar lavage at day 3 and 5 were selected for sequencing. cDNA and multiplex PCR reactions were prepared following the ARTIC SARS-CoV-2 sequencing protocol v2  (Tyson et al. , 2020). The inoculum was also sequenced. V3 primer scheme (https://github. com/artic-network/primer-schemes/tree/master/nCoV-2019/V3) was used to perform the multiplex PCR for SARS-CoV-2. All samples were run for 35 cycles in the two multiplex PCRs. Pooled and cleaned         S3.
ACKNOWLEDGMENTS We thank  B. Delache ,  S. Langlois ,  J. Demilly ,  N. Dhooge ,  P. Le Calvez ,  M. Potier ,  F. Relouzat ,  J. M. Robert ,  T. Prot , and  C. Dodan  for the non-human primate experiments;  L. Bossevot ,  M. Leonec ,  L. Moenne-Loccoz , and  J. Morin  for the qRT-PCR and ELISpot assays and preparation of reagents;  B. Fert  for her help with the CT scans;  M. Barendji ,  J. Dinh , and  E. Guyon  for the non-human primate sample processing;  S. Keyser  for the transports organization; N. Dimant and  B. Targat  for their help with the experimental studies in the context of COVID-19-induced constraints;  F. Ducancel  and  Y. Gorin  for their help with the logistics and safety management; and  I. Mangeot  for her help with resource management. Ramos B cells were obtained from  Drs.
R. W. S. and M. J. v. G. are recipients of support from the  University of Amsterdam Proof of Concept  fund (contract 200421) as managed by  Innovation Exchange Amsterdam (IXA). The  Infectious Disease Models and Innovative Therapies (IDMIT)  research infrastructure is supported by the  Programme Investissements d'Avenir , managed by the  National Research Agency (ANR)  under reference  ANR-11-INBS-0008. The  Fondation Bettencourt Schueller  and the  Region Ile-de-France  contributed to the implementation of IDMIT's facilities and imaging technologies. The non-human primate study received financial support from  REACTing , the  ANR  ( AM-CoV-Path ), and the  European Infrastructure   TRANSVAC2  ( 730964 ). The funders had no role in study design, data collection, data analysis, data interpretation, or data reporting.
Page 7, " A is the cross-sectional area" has revised to " x A is the cross-sectional area".     2 1 2 1 , 1, 2, , , 1, 2, , 2 1 2 1 i j i j x H i n t S j n n n           have been used to discrete the variable   , x t to   , i j x t. " has revised to "The variable   , x t is discretized by the reasonable match points     2 1 2 1 , 1, 2, , , 1, 2, , 2 1 2 1 i j i j x H i n t S j n n n           based on the collection method. " 22. Page 14, line 177, "is determinate" has revised to "can be obtained". 23. Page 14, line 178, "using Matlab platform and least square method" has been revised to "using the numerical algorithm in MATLAB platform. The fractional-order equation is transformed to algebraic equations. The least square method is used to calculate the solution of the algebraic equations.
(  18 ) can be transformed into the following form: Φ n (x)U φ n (0) = 0, Φ n (0)U φ n (t) = 0, Φ n (H)U φ n (t) = 0 D x Φ n (0)U φ n (t) = 0, D x Φ n (H)U φ n (t) = 0 (49) The variable (x, t) is discretized by the reasonable match points x i = 2i-1 2(n+1) H, i = 1, 2, • • • , n, t j = 2j-1 2(n+1) S, j = 1, 2, • • • , n , based on the collection 175 method. Eq. (  48 ) is transformed into a set of algebraic equations. The coefficient y ij (i = 0, 1, 2, • • • n; j = 0, 1, 2, • • • n) can be obtained by using the numerical algorithm in MATLAB platform. The fractional-order equation is transformed to algebraic equations. The least square method is used to calculate the solution of the algebraic equations. The numerical 180 solution of the fractional derivative equation can be obtained. The proposed algorithm can be summarized as follows: Algorithm: Numerical solution for the fractional governing equation Input: α, q α , q 0 , q 1 , ρ, A x , I, f (x, t) Output: 1.
Function approximation y(x, t) ≈ Φ n (x)U φ n (t) 2. Derive calculation of integer and fractional operator matrices 3. Substitute the operator matrix into the initial equation 4. The initial equation is transformed into an algebraic equation 5. Let x i = 2i-1 2(n+1) H, i = 1, 2, • • • , n, t j = 2j-1 2(n+1) S, j = 1, 2, • • • , n 6. Solve algebraic equations with MATLAB mathematical software 7. Obtain the solution of the initial equation y (x, t).
Mat. 1 for a more detailed contextualisation). Two main data sets were considered for the present study. The official data from the National Health Commission of the People's Republic of China  [9]  were used to study the original outbreak at China's scale (from 21 January to 10 April 2020). The data from the Johns Hudson University [10] were used to monitor the outbreaks at the province's scale in China and at the country's scale for other countries. Their description and pre-processing are given with further details in Suppl. Mat. 3 (Fig. S1 ). The time series from three original variables were considered: (1) the daily cumulated number C Σ (t) of confirmed cases, (2) the daily number s(t) of severe cases currently under intensive care and (3) the daily cumulated number D Σ (t) of deaths; from which the derivatives (required for the modelling approach are used in the present study) were deduced, corresponding to the daily number of new cases C 1 (t), the daily additional severe cases s 1 (t) and daily number of new deaths D 1 (t) (see Eq.
g. , higher order moments, ergodicity, and asymptotic behaviors, would be of interest and need further research. For example, what are the asymptotic behaviors of the proposed models when → ∞. In addition to the simple comparisons in Section 5, in-depth stochastic comparisons of the two modeling frameworks as in Mercier and Castro 7 would be another interesting research topic. (ii) Statistical inference. The complexity of the two modeling frameworks poses challenges for statistical inference. Parameter estimation of the degradation path adjustment model is relatively simple, since the degradation increments between any two successive PM actions follow a multivariate normal distribution (see Property 1 and related discussions). The loglikelihood function can then be formulated using dataset of degradation increments, and various methods, like Bayesian Markov chain Monte Carlo, can be employed to solve the problem.
ACKNOWLEDGMENTS This work was supported by the  PHC PROCORE -France/Hong Kong Joint Research Scheme  (grant numbers  42636VE ,  F-CityU103/18 ), the  Hong Kong Research Grants Council  under a  Theme-based Research Scheme  (grant number  T32-101/15-R ) and a  General Research Fund  (grant number  CityU 11203519 ), and the  National Natural Science Foundation of China  (grant number  71971181 ). The work was done while the first author was visiting the  Univ. Grenoble Alpes , while at  City University of Hong Kong.
Illustration of the records test on COVID-19 We applied the records of COVID-19 cases in Lebanon from the date of identification of the index case until the 13th of March 2020. The first case was announced on the 21st of February 2020. The collected data consist of the birth date and the notification date of each of the COVID-19 cases identified in Lebanon during this period. Therefore, X n : = (ΔT n ) -1 , where ΔT n is the nth waiting time between two successive notification (resp. birth) date. We list in Table  1  our collected data. As shown in Table  1 , the number of observed records is equal to 6 (N n = 6), the record values: R From the figures presented above, we see the trajectory of {X n } n≤10 with the successive observed records, drawn as red circles and the number of maximal COVID-19 records between 21 February 2020 and 13 March 2020.
n * ) = n ′ n ′ + n * (3) Consequently, the probabilities of observing a COVID-19 record in Lebanon for the next 5 days can be easily calculated from equation  (3). For example the number of days in the database, n ′ = 22 days (between 21 February 2020 and 13 March 2020), we deduce the probability of waiting time for a new record: 22/(22 + 5) ≈ 0. 82; confirming the rapid growth of COVID-19 in Lebanon over a short period. The fact that an event occurring for the first time becomes a frequently encountered phenomenon manifests itself by.
Data descriptions Data on containment activities were collected as part of an effort by the Punjab Information Technology Board (PITB) in Pakistan to improve accountability after the 2011 dengue outbreak in Pakistan  [21]. Health care workers were equipped with smartphones and recorded pictures, before and after performing a containment activity using a mobile application, as verification that the activity had been performed (see Fig A in S1 Appendix for screenshot of smartphone application). These pictures, labelled with the type of activity, global positioning system (GPS) coordinates and the time-stamp of the activity were automatically submitted to a centralized server (see S1 Appendix section "Smartphone Application" for further details on data collection). We received the containment activities data for the cities of Lahore and Rawalpindi from PITB.
Data on confirmed dengue incidence from both cities was also received from PITB. Details of confirmed dengue incidence from all public hospitals in Punjab province of Pakistan are collected in a centralized server. Confirmation of dengue cases in hospitals in both cities is performed using enzyme-linked immunosorbent assay (ELISA) kits which measure anti-DENV IgM or IgG antibodies in patients' serum  [22]. Further details on the systematic methods used by hospitals can be found in the S1 Appendix section "Patient Portal and Criteria for Patients". For our study we were provided access to a unique anonymous identifier for each dengue case along with the date of when they first experienced symptoms of dengue. Each patient's home address is precisely tagged with geo-coordinates using smartphones; transformed linked coordinates were also made available for the study (spatial analysis), and spatially aggregate data for the time series analysis.
To do so, we used a spatial dependence metric τ(d 1 , d 2 , t 1 , t 2 ) which quantifies the likelihood of observing cases in the distance window d 1 -d 2 and temporal window t 1 -t 2 compared to if the clustering of cases was independent in space and time: t i ðd 1 ; d 2 ; t 1 ; t 2 Þ ¼ PrðO i ðd 1 ; d 2 ; t s ðd 1 ; d 2 ; t 1 ; t 2 Þ ¼ ð P N i¼1 jO i ðd 1 ; d 2 ; t 1 ; t 2 Þjz i Þ � ð P N i¼1 jO i ð�; �; �; �Þjz i Þ ð P N i¼1 jO i ðd 1 ; d 2 ; �; �Þjz i Þ � ð P N i¼1 jO i ð�; �; t 1 ; t 2 Þjz i Þ ð2Þ where z i is 1 if the case is labelled s, N is the total number of cases in the dataset regardless of their label, and O i (�, �, �, �) is the set of all cases in the dataset. For our analysis, we used a fixed time window of 30 days: t 1 is selected as the day when the patient experienced first symptoms of dengue virus, and t 2 = t 1 + 30.
This time window was chosen to ensure that cases considered were from the same transmission chain, though we performed a sensitivity analysis using additional time windows (see Fig C in S1 Appendix for sensitivity analysis using additional time windows). Dependence could then be observed across variation in the distance window. Understanding this baseline likelihood then allows us to then assess changes in the likelihood with the application of containment activities, while controlling for the epidemiological context. Spatial signature of containment activities. To identify the impact of containment activities on the spatial dependence of dengue cases (the "spatial signature" of an activity) we first labelled all cases in the dataset as either a "containment" case, s = a, or a "non-containment" case, s = b, based on the presence of a containment activity in its spatio-temporal vicinity.
The model was optimized using maximum likelihood and we used the "SCAM" package in R to fit the model. Using the estimates of θ a , θ j , and θ p we identified the variation in R 0 (reproductive number of dengue) by variation in the amount of each containment activity. The R 0 is calculated using the following equation: R 0 i ðtÞ ¼ b i ðtÞ g ð10Þ where, γ is the recovery rate and is equal to 1 time step in our study, given the fact that infected patients are immediately admitted in the hospital and removed from the infected population  [24]. To assess the utility of containment data in modelling of dengue transmission, we trained additional variants of the TSIR model using only environmental parameters and population density. Additional details on initial conditions, and sensitivity analyses of parameters used in the study are discussed in the S1 Appendix subsection "Model Parameters, Initial Conditions and Fit".
Thus besides the specific findings regarding containment activity efficacy, this study also provides important lessons regarding modeling limitations of real-world (observational) data that can inform future intervention or containment activity deployment. First, we examined the relationship between deployed instances of each containment activity type and the spatial dependence of geo-located dengue cases in their proximity, in the cities of Rawalpindi and Lahore. This method allowed us to control for exogeneous heterogeneities that could create spatial or temporal clustering (e. g. , biases in where the activities were deployed are minimized as non-containment cases which are compared, are chosen in the immediate space and time vicinity of cases which had containment activities deployed next to them).
This is also the case for fish seeding. There is conflicting evidence regarding the effectiveness of fish seeding in the literature  [49, 50]. As well, based on deployment of this activity in our dataset, the time series method did not find evidence of a significant relationship between increase in fish seeding activity and reduction in R 0 in either city. In addition, no conclusion about fish seeding could be drawn from the spatial dependence method based on the number of activities performed in our dataset. Therefore, more cases which were adjacent to only fish seeding activities, or more fish seeding per adjacent case would be required in the real-world setting to robustly assess this containment activity. Among source reduction containment activities, we found an increase in tap fixing in Lahore to be associated with a statistically significant decrease in the reproductive number of dengue in Lahore.
Acknowledgements The authors gratefully acknowledge financial support from the  Leverhulme Trust  through a  leadership award , from the  European Research Council  through a consolidator grant, project  615897 , and from the  Royal Society  through a research professorship. We are also grateful to  Máté Gerencsér  for allowing us to use his ideas for the results of Appendix C and to  Ajay Chandra  for discussions on the application of the results of [CH16].
Population ('herd') immunity (whether via natural infection or vaccination) will be expected to modify transmission intensity within the urban cycle, but not in the sylvatic cycle where transmission intensity is driven by non-human primates and their data please contact William Perea -WHO (pereaw@who. int). Data from serological surveys identified in the literature are available on https:// github. com/kjean/YF_buden_model The GADM version 2. 0 spatial data used to create maps is Figs 1, 2, 4 and 5, S1-S4 and S6 Figs were download in 2016 from the GADM website: https://gadm. org/. The GADM data are freely available for academic use and other non-commercial use: https://gadm. org/license. html. The GADM version 2. 0 spatial data used to create maps is Figs 1, 2, 4 and 5, S1-S4 and S6 Figs were download in 2016 from the GADM website: https://gadm.
Model overview A complete description of the model is available in S1 Appendix. Briefly, both model variants include a generalized linear model (GLM) fitted to the presence or absence of any reported YF event between 1984 and 2013 at the first sub-national administrative level (province) (S1 Table ), using various environmental and demographic covariates together with a countrybased proxy for surveillance quality. We defined a reported YF event as either outbreak reports published by WHO or laboratory-confirmed cases reported in a YF surveillance database managed by WHO-AFRO, to which 21 countries contribute. The GLM provides estimates of the probability of any YF report across the endemic zone over the 30-year period considered. In a second step, we describe this probability of a report as dependent on the number of infections and on the unknown rate of under-reporting (which was assumed to vary between but not within countries).
Model validation We conducted an out-of-sample validation process using the data of three recent serological surveys from the Democratic Republic of Congo, South Sudan and Chad in 2015. To our knowledge, these serological surveys were the latest available in the endemic region at the time we conducted this research. Validation was conducted by comparing: i) transmission parameters estimated by fitting the age-seroprevalence profiles from those surveys to those predicted for the corresponding provinces in the YF burden model, ii) age-seroprevalence profiles directly observed in the surveys to those predicted by the YF burden model (S2 Appendix).
Discussion By combining a regression model fitted to the reported local presence of YF with serological data collected in 11 countries, this modelling study presents updated estimates of YF burden across 34 African countries in terms of number of infections, severe cases, deaths, and DALYs lost. We also estimated that PMVCs conducted since 2005 will prevent a total of 3 to 6 million deaths over the lifetime of vaccinees, depending on whether we disregard or maximize herd effects, respectively. As both sylvatic and urban transmission cycles (with none or maximal herd effects, respectively) do contribute to the disease burden, the real impact of vaccination  likely lies between these two extreme cases. Finally, we derived estimates of vaccine demand and impact of future PMVCs under a variety of targeted vaccination scenarios.