dataesr/scientific-paragraphs-categorization huggingface dataset:
Each time step t is equal to one day. At each time step, infected individuals can contaminate their susceptible contacts, the probability of which is drawn from a normal distribution p = 0. 05 sd = 0. 02. Because there are differences in the average duration of contacts (see S3 Dataset and subsequent comments), we adjusted p for both Germany and Great Britain accordingly-p GER = p IT � 1. 125 and p GB = pIT � 1. 0208333-but kept the standard deviation constant. To check the sensitivity of our results to this baseline probability, we varied this value in additional robustness checks but no noteworthy difference was observed (see S6 Appendix). Exposed individuals cannot (yet) contaminate others. An implicit, underlying assumption in previous studies and in our own analyses is that contamination of susceptible individuals is equally probable once they have been in contact with an   1 , or  [25].
The simplicity of this SEIR model is helpful when comparing outcomes of various types of network. Only if we wanted to implement various policy scenarios aiming to hamper the propagation of the virus over the network would more complex models-including for example asymptomatic cases, or different behavioral properties between asymptomatic and symptomatic cases-be necessary. For each of the nine possible networks we generated 50 replications and simulated the diffusion of the virus over each of these networks 50 times. Each time we simulated the spread of the virus, in t = 0 five nodes aged between 26 and 59 years old were chosen at random and assumed to have become infected. We consider a simulation to be over when all nodes are either susceptible or recovered. Overall, for each network type we generated 2,500 data points, each corresponding to the final state of propagation of the virus.
We compared Italy with Germany and generated networks respecting the degree distribution and the age assortativity of the "empirical" networks of the previous section, but with varying levels of clustering. Unfortunately, Polymod data do not provide information about clustering among the participants' contacts, so we modeled a wide range of clustering values. Two scenarios could challenge our results. First, the effect of differences in the degree distribution could be compensated by higher levels of clustering. Specifically, higher clustering may compensate for the additional infections generated by more contacts. Second, the hampering effect of clustering on the diffusion of the virus could interact in a non-linear way with the effects of age assortativity. For instance, for some level of clustering the difference in age assortativity between the Italian and the German network may greatly slow the propagation of the virus in the Italian network, but have little effect in the German.
Amino acid differences V26A and H194Y common to both BF785 and Nig1657 were found at the spike N-terminal domain, which is responsible for binding to sialic acid and is believed to facilitate virus attachment and entry. Amino acid substitution S856Y in the S2 subunit was common to both BF785 and Nig1675, which may potentially affect the fusion efficiency. The role of individual amino acid substitutions or combinations on the observed phenotype deserves further study. It is important to note that amino acid substitutions in other parts of the genome may also contribute to the phenotypic differences observed. Alignment of the genomes of the viruses studied identifies a number of amino acid substitutions that are unique to the six African viruses studied. With reference to the EMC virus genome, these correspond to ORF1ab amino acid positions N581Y (nsp2), L654F (nsp2), K1066E (nsp3), L1585F (nsp3), I1911T (nsp3), S2000I (nsp3), A2333V (nsp3), F2481C (nsp3), I2639L (nsp3), I2676T (nsp3), and T5546M (nsp13) (Dataset S1).
Materials and Methods Virus. Viruses used in this study, virus abbreviations, host and country of origin, sampling date, clade designation, and GenBank accession numbers are listed in SI Appendix, Table  S1. MERS-CoV human/EMC/2012 (clade A) (EMC) was the prototype MERS-CoV, kindly provided by Ron Fouchier, Erasmus MC, Rotterdam, The Netherlands  (7). The specimen collection details and genetic characterization of the camel viruses from Burkina Faso (BF785) (clade C1. 1), Nigeria (Nig1657) (clade C1. 1), Morocco (MOR213) (clade C1. 1), Egypt (C270) (clade C1. 2), and Saudi Arabia (AH13) have been previously reported  (18, 29). The viruses from Kenya, camel/Kenya/CAC10200/2020 (clade C2) and Ethiopia, camel/Ethiopia/CAC9690/2020 (clade C2) were detected and isolated as part of this study. All the dromedary camel viruses used in this study were isolated in our laboratory in Vero cells (ATCC CCL-81) and were passaged, aliquoted, and stored at -80 °C until used.
Generation of MERS-CoV Spike Virus Pseuotyped Lentiviruses and Pseudotype Assays for Virus Entry. We used the method described by us previously to generate lentivirus pseudotypes expressing a mammalian codon-optimized EMC/2012 spike (GenBank: AFS88936. 1) pcDNA3. 1(+) (31). Multi site-directed mutagenesis (Agilent) was used to introduce the substitutions to mimic the S protein of BF785 (GenBank: AVN89344. 1) (V26A, A89S, H194Y, T424I, S856Y, and A1158S) and Nig1657 (GenBank: AVN89387. 1) (V26A, H167Y, H194Y, L495F, L588F, S856Y, A1158L, and L1200F). Sanger sequencing was performed to verify the mutated sites. HIV/MERS spike pseudoparticles containing S protein of EMC/2012, BF785, and Nig1657 were generated as reported previously  (31). pNL Luc E-R-vector encoding the luciferase reporter gene and pcDNA-S plasmids (EMC/2012, BF785, and Nig1657) or an empty vector were cotransfected in 293T cells using TransIT-LT1 transfection reagent (Mirus).
For immunofluorescence staining, the Calu-3 cells were infected with the recombinant MERS-CoVs at MOI of 5. The cells were fixed with 4% paraformaldehyde (PFA) (Biotium) at 16 h postinfection, permeabilized with Zhou et al. PNAS | 7 of 8 Phenotypic and genetic characterization of MERS coronaviruses from Africa to understand their zoonotic potential https://doi. org/10. 1073/pnas. 2103984118 0. 3% Triton X-100 (Sigma-Aldrich), and immunolabeled using primary polyclonal antibody against MERS-CoV nucleoprotein (Abbiotec) and secondary Alexa Fluor 488-conjugated goat anti-rabbit antibody (Thermo Fisher). Nuclei were stained by DAPI (Thermo Fisher). Images were acquired by a Zeiss LSM710 inverted confocal microscope (Carl Zeiss) with a 10× objective. The percentage of infected cells was analyzed using the cell scoring function on the Metamorph software (Molecular Devices).
Cell-Cell Fusion Assay. A total of 1. 25 × 10 5 Huh-7 cells were first seeded on coverslips in 24-well plates (TPP Techno Plastic Products) for 24 h. Plasmids encoded with the spike of EMC/2012, BF785, or an empty vector were transfected in the cells using TransIT-LT1 transfection reagent (Mirus)  (25). At 20 h posttransfection, the cells were fixed with 4% PFA, permeabilized with 0. 3% Triton X-100, and immunolabeled using primary polyclonal antibody against MERS-CoV spike protein (SinoBiological) and secondary Alexa Fluor 488-conjugated goat anti-rabbit antibody (Thermo Fisher). Nuclei were stained using DAPI. Images were acquired by a Zeiss LSM710 inverted confocal microscope with a 20× objective. Cell scoring function in Metamorph software was used to quantify syncytia formation by calculating nuclei/ syncytium.
ACKNOWLEDGMENTS. We acknowledge  Dr. Jean Millet  for advice on the MERS-CoV spike fusion assays and  Dr. Eric H. Y. Lau  for advice on statistical analysis. We acknowledge the technical assistance of  Dr. B. Getachew ,  Mr. K. Adamu ,  Mrs. H. Mohammed  ( National Veterinary Institute, Ethiopia ),  Drs. O. Njagi ,  G. Njogu ,  L. Wangui ,  J. Ngoci , and  W. Mbaya  ( Central Veterinary Laboratory, Kenya ),  Dr. T. Nyariki  (National MERS-CoV Coordinator,  Food and Agriculture Organization [FAO], Kenya) , and  Drs. Gijs  van 't Klooster,  Nega Tewolde , and  G. Gari  ( FAO Emergency Centre for Transboundary Animal Diseases [ECTAD] Program, Ethiopia ). We acknowledge the support of  Yilma Makonnen ,  Emma Garder ,  Ryan Aguanno ,  Cristian De Battisti , and  Agneleque Angot  ( FAO of the United Nations ).
Results: The proposed strategy is applied to a dataset of patients with myocardial infarction (TRACE data frame). The effects of 5 covariates (age, sex, diabetes, ventricular fibrillation, and clinical heart failure) on the hazard of death are analyzed using multiplicative and additive hazards regression models. The proposed strategy is shown to fit the data optimally. Conclusions: Survival analysis is improved by using multiplicative and additive hazards regression models together, but specific steps must be followed to fit the data optimally. By providing different measures of the same effect, our proposed strategy allows for better interpretation of the data.
Motivating example In this section, our proposed strategy for optimal fitting of multiplicative and additive hazards regression models is applied to a motivating example: the TRACE data frame provided in the timereg R package. This data frame is a subset of a dataset extracted from a study of 4259 patients with myocardial infarction who were admitted to a hospital in Denmark in 1977-1988 and were followed until death or censoring  [20]. The 1878 patients included in the TRACE data frame had a mean age of 67. 0 years (sd: 11. 4). Overall, 52. 29% of patients had clinical heart failure, 69. 54% of patients were women, 10. 01% of patients had diabetes, and 7. 24% had ventricular fibrillation. Median survival was 6. 52 years [6. 09; 7. 25]. The TRACE data frame is interesting for our purposes because it contains covariates with well-known time-varying effects on the hazard of death (ventricular fibrillation) as well as covariates with constant effects on the hazard of death (diabetes and sex)  [21].
As regards multiplicative hazards regression models, our strategy relies not only on classical diagnostic tools (the Schoenfeld and martingale residuals) to check the proportional hazards assumption and the assumption of log-linearity, but also on pseudo-observations and Arjas plots to assess goodness-of-fit. Other approaches for fitting multiplicative hazards regression model have been proposed. Thus, Sasieni and Winnett  [16]  have introduced a new kind of residuals, the martingale difference residuals, which is used to check both the proportional hazards assumption and the assumption of log-linearity. The limitation of this approach is that it does not require a precise definition of the functional form, even for very large datasets. By contrast, in our strategy, pseudoobservations are used to graphically represent not only the functional form of the continuous covariate but also its variation over time.
Acknowledgements. We would like to thank:  Sergei Starchenko  and  Kobi Peterzil  for sharing with us the idea of using pairs as an approach to study spaces of definable types;  Martin Hils  for interesting discussions around the subject;  Ludomir Newelski  for sharing with us Example 5. 2. 11; and  Elliot Kaplan  for pointing us out a proof of Theorem 6. 0. 3 for CODF. P. Cubides Kovacsics was partially supported by the  ERC  project  TOSSIBERG  (Grant Agreement  637027 ),  ERC  project  MOTMELSUM  (Grant agreement  615722 ) and individual research grant Archimedische und nicht-archimedische Stratifizierungen höherer Ordnung, funded by the  DFG. J. Ye  was partially supported by  NSF  research grant  DMS1500671.
An infectious disease surveillance is considered as a public health method that detects important risks and it is used to inform the planning of public health interventions or further investigations. At the outset of COVID-19 outbreak, the Ministry of Health (MSP) applied a surveillance system to collect information on all people with COVID-19 throughout the country according to the guidelines published by the World Health Organization (WHO) on January 30, 2020. The Emergency Committee-WHO declared that the 2019-nCoV is a PHEIC (Public Health Emergency of International Concern) so the MSP introduce in the national surveillance thorough the investigation chart "EPI 1". Data on all COVID-19 cases were obtained from all 24 Ecuadorian provinces. COVID-19 cases were identi ed by reverse transcriptase-polymerase chain reaction (RT-PCR)  (16, 17).
Data on COVID-19 -Ecuador (MSP) and literature search The COVID-19 dataset used in this study was downloaded from the web page: https://public. tableau. com/pro le/direcci. n. nacional. de. vigilancia. epidemiol. gica. msp#!/vizhome/COVID19ecu_MSP_DNVE/COVID-19MSP, accessed on march 13, 2021; which is a data maintained by the organization: MSP-Dirección Nacional de Vigilancia Epidemiológica and updated daily. The dataset contents country level data on con rmed cases, deaths, and testings. The data from sociodemographic indicators was downloaded from the Instituto Nacional de Estadisticas y Censos" (INEC) from the last national survey done on 2010. We conducted a systematic literature research of online databases, the terms and relative varinas were as follows: COVID-19, 2019-nCoV; fatality rate.
Data extraction and quality assessment -Epidemiological rates Data extraction and evaluation of literature quality were managed independently by two investigators (F. K and J. X). Microsoft Excel database was used to saved available information, including demographic data. The case fatality rate of COVID-19 was calculated as the number of total deaths due to COVID-19 divided by the number of total con rmed COVID-19 cases of year 2020, accessed on march 13 2021, multiplied by 100. It was calculated in this study as it might re ect the severity of COVID-19 and it is an indirect measure of the e ciency of healthcare response  (19). The morbidity rate of COVID-19 was calculated as the number of con rmed COVID-19 cases till December 31, 2020 divided by the total population multiplied by 100. 000.
In a previous publication  [15]  presented the effect of microstructural heterogeneities (SDAS, pore size, oxides …) on the fatigue behavior of these materials in terms of fatigue failure mechanisms, the size effect and the stress gradient effect as well as the scatter in the fatigue strength. As the main objective of this paper is to analyse the 3D spatial distribution of pores that exists in these alloys, the following section shows the data concerning the micro-porosity obtained from CT X-Ray tomography scans and the volume-rendering analyses done using the AVIZO software of samples extracted from the studied alloys. In these analyses, only defect sizes larger than 18 μm 3 are taken into account. This decision was made due to experimental observations indicating that defects smaller than this value are harmless with regard to fatigue.
Porosity In order to characterize the defect size and spatial distributions in these alloys, Computed-Tomography (CT) analyses were undertaken. The CT scans were done by the MATEIS laboratory at INSA Lyon with a resolution of 8 μm/voxel. The AVIZO software was then used in order to analyse the raw data. An inspection volume of 363 mm 3 was used. Note that the CT scan resolution can influence the precision of the defect size measurements, in particularly for small defects. Fig. 9  illustrates the defect populations within two notched specimens. For more detail on the use of the notched specimen see  [15]. It can be clearly seen that Alloy B has considerably larger pores when compared to Alloy A. This is to be expected as Alloy B was obtained by the lost foam casting process. From these two scans, only the scan corresponding to BVN1-05 is used in the Point Process analyses.
In addition, further measurements using the X-ray microtomography technique were conducted by (V-D. Le  [20] ) at the PLA-CAMAT (attached to the University of Bordeaux and CNRS), in France on a X GE V/TOME/SX micro-tomograph. A spatial resolution of 5 μm × 5 μm × 5 μm was used with a 160 kV (max) X-ray source. This data was also reconstructed and analysed using the Avizo® software. In these measurements, 9 samples from Alloy B were scanned. Adding the sample BVN1-05, a total of 10 scanned samples for the alloy B are considered, which corresponds to a total scanned volume of 2. 2 × 10 3 mm 3. For Alloy A, 4 samples were scanned corresponding to a total volume of 0. 9 × 10 3 mm 3.
Randomly choose m = N/10 nearest neighbor distances. 2. Average these m distances. 3. Calculate the expected mean distance given by the Poisson process. 4. Calculate the corresponding Z m value (and calculate the P-value). 5. Repeat steps 1 to 4 'X' times. 6. Taking into account the average value of Z m (and the average value of P-value) and considering the significance reference values given in Table  1 , determine the degree of clustering. These steps have been implemented using Matlab. The results are presented below. Fig. 16  shows the results of the non-intensive sampling technique of    the NN-distances for Alloy A (in red) and Alloy B (in blue) and the NNdistances from a numerical Poisson process (in yellow) used as a reference. The results are summarized in Table  7. It can be concluded that there is no strong evidence suggesting that a clustered spatial pattern is observed in the studied alloys (see Table  8 ).
Generation of the spatial positions of defects In the following section, numerical processes are generated that have almost the same density as the natural processes with the homogeneous Poisson process. Firstly, a verification of the ability of the script that was developed to generate numerical Poisson processes is presented in Fig. 17    From these results, the presence of some clustering can be seen in the natural pattern, when compared to the numerical patterns corresponding to a homogenous Poisson process. Nevertheless, as previously estimated using the Clark-Evans test, the degree of cluttering is still less than the standard level. Therefore, the resulting process can be and will be used for future modelling work.
Highly oscillatory ordinary differential equations (ODEs) has a long history since they are ubiquitous to describe dynamical multi-scale physical phenomena in physics or chemistry. They can be obtained by appropriate spatial discretization of a partial differential equations or can directly describe the behavior of dynamical quantities. In addition to the standard difficulties coming their numerical resolution, highly oscillatory ODEs involve a stiffness (characterized by a parameter ε ∈ ]0, 1]) creating high oscillations in the solution. Hence, to capture these small scales (or high oscillations), conventional methods have to consider a time step smaller than ε leading to unacceptable computational cost. We present here HOODESolver. jl 1 , a general-purpose library written in Julia dedicated to the efficient resolution of highly oscillatory ODEs.
Statement of need The Julia package DifferentialEquations. jl (Rackauckas & Nie, 2017)  efficiently solves many ODE problems using recent and advanced numerical techniques. However the available algorithms do not easily solve this type of stiff problems, because they do not take into account the highly oscillatory character of the solution. Indeed, the solution presents oscillations whose period is proportional to ε. If ε is small, conventional methods struggle to solve such multi-scale phenomena since they require to use tiny time steps to capture high oscillations and become computationally very costly. On the one side, specific methods inspired by the averaging theory have been designed to deal with the regime ε 1. On the other side, when ε ∼ 1 the problem ceases to be stiff and a classical integrator gives accurate result in a reasonable time.
, 2017) ; its development has been motivated by these research needs and it has already been used in some papers  (Chartier, Lemou, et al. , 2020). HOODESolver. jl provides software implementations of several theoretical ideas contained in the recent literature around the so-called two-scale method. In particular, a very recent extension proposed in  (Chartier, Lemou, et al. , 2020)  enables to reach high order accuracy. The implementation focuses on a multistep method (namely Adams-Bashforth method) coupled with a spectral method for the discretization of the additional variable representing the fast scale. Hence, HOODESolver. jl provides an efficient way for researchers to solve a highly oscillatory ODE system, and as such it can be used by the scientific community: • researchers interested in solving highly oscillatory problems arising in their research field (electromagnetic waves, quantum mechanics, plasma physics, molecular dynamics,.
Related research and software The development of the HOODESolver. jl package was initially motivated by the need of efficient multiscale solvers for the charged particles dynamics in an external strong magnetic field. Indeed, due to the Lorentz force, charged particles undergo rapid circular motion around the magnetic field lines. This constitutes the basis of the magnetic confinement of a plasma in a chamber. Obviously, computing a highly oscillatory dynamics is a long-standing problem occurring in many relevant applications  (Engquist et al. , 2009; Hairer et al. , 2006). However, we are not aware of other software packages with similar purpose, excepting the very recent (py)oscode package  (Agocs, 2020)  which combines WKB techniques and standard integration methods to ensure a user-specified tolerance.
Data We considered 54 NUTS3 administrative regions (named départements in French) over 42 weeks (from March 23, 2020 to January 10, 2021). We did not consider all existing regions, but only the 54 for which the IPTCC can be computed. In order to evaluate the consequences of the climatic conditions on the pandemic, we used the weekly numbers of hospital admissions and deaths due to SARS-CoV-2 for the same 54 administrative regions considered. The data are official and publicly available at www. data. gouv. fr. The hospitalizations and deaths due to SARS-CoV-2 were consistently measured over the period and are thus more reliable than the series that report the number of infections, as the latter highly depend on the availability of tests and on the population's willingness to get tested. Daily data are available since March 19, 2020, but we chose to use the weekly frequency in order to avoid a seasonal effect induced by lower reporting during weekends.
The meteorological data came from the 63 Météo-France stations. These stations are homogeneously distributed over mainland France and they were chosen in order to better represent the diversity of the country's climate. The dataset provides a daily average for air temperature (measured in °C) and relative humidity (measured in %); these values were calculated as an average of the daily maximum and minimum values of temperature and relative humidity. With them, it is easy to compute the daily absolute humidity for each station, measured in g/m 3 , using the Clausius-Clapeyron equation  [8]. In order to analyze the potential relation between climate conditions and virus transmission, the IPTCC was created to characterize the potential for virus transmission according to climatic conditions. Following  [10] , the IPTCC is a function of absolute humidity (AH), relative humidity (RH), and temperature (T), and the formula can be written as follows: 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 = 100 * 𝑒𝑒 - 1 2 � (𝑇𝑇-7.
Acknowledgments Part of this research was done when the J. R. was visiting IRIF -Université Paris Denis Diderot in July 2018. J. R. thanks the hospitality of the institution during his research stay. V. R. was partially supported by grants  ANR 2010 BLAN 0204  (Magnum). V. R. and J. R. were supported by the  Project PHC   Procope ID-57134837  ' Analytic, probabilistic and geometric Methods for Random Constrained graphs '. J. R. was partially supported by the  FP7-PEOPLE-2013-CIG  project  CountGraph  (ref. 630749). Finally, M. N. and J. R. were supported by the  Spanish  project  MTM2017-82166-P  and by the  H2020-MSCA-RISE-2020  project  RandNET  (ref. 101007705 ).