dataesr/scientific-paragraphs-categorization huggingface dataset:
This results in a variable number of participants who were presented with each advanced question (and also relies on the assumption that parents who were not presented with a given advanced item never engaged in the practice) (see Table  3 ). To ensure that this design feature did not affect our findings, we performed a second set of analyses on a subset of participants who were systematically presented with advanced numeracy items (data for this subset of participant are available on https://osf. io/vty3c/files/). To further limit the decrease in sample size associated with such an analysis, we only kept subjects who were presented with the two most frequently seen advanced items (i. e. , subtracting and multiplying double-digit numbers) and removed the other 3 advanced items from the calculation of the frequency of formal advanced home numeracy practices.
E. Bayraktar  is partially supported by the  National Science Foundation  and by the  Susan M. Smith chair. A. Cecchin  and  F. Delarue   acknowledge the financial support of French ANR  project  ANR-16-CE40-0015-01  on " Mean Field Games ". F. Delarue also thanks  Institut Universitaire de France and French ANR  projet  ANR-19-P3IA-0002  " 3IA Côte d'Azur -Nice -Interdisciplinary Institute for Artificial Intelligence ". A. Cohen  acknowledges the financial support of Research supported by the  National Science Foundation  ( DMS-2006305 ). -This is the final version of the paper. To appear in Journal de Mathématiques Pures et Appliquées.
We obtain the desired result by putting the above equalities together, concluding the proof of Proposition 2. 5. The numerical values of Ei(x), Li(x) and Fα(x) involve the use of standard numerical integration procedures available in every mathematical package. In this study, the software R is used (see R Core Team (  2014 )), combined with the package pracma and the function expint_Ei. Since fα(x) is strictly increasing with respect to x for x ∈ (0, 1) by Proposition 2. 2, Fα(x) is a convex function only. Figure  4  illustrates the possible shapes of Fα(x) for several values of α. From Figure  4 , we see that Fα(x) is convex. Various degrees of convexity are also observed. From Proposition 2. 4, the likelihood ratio order between the UPL and Po distributions implies that, for any x ∈ R, Kα(x) ≤ Fα(x), where Kα(x) denotes the cdf of the Po distribution, that is Kα(x) = x α+1 for x ∈ (0, 1), Kα(x) = 0 for x ≤ 0 and Kα(x) = 1 for x > 1.
1B ). As an exploratory analysis, we evaluated the association between antiviral or corticosteroid treatment and viral load decay. No significant association was found between antiviral treatment and time to viral clearance. Corticosteroid had no effects on the time to viral clearance in aged patients; however, younger patients treated with corticosteroids had a longer time to viral clearance as compared to young untreated patients (P = 0. 01; SI Appendix, Fig. S5 ; see Discussion). Alternative Models and Sensitivity Analyses. Alternative models of the immune response did not improve the data fitting, and parameter estimates were close to those obtained with the final model (SI Appendix, Tables  S5  and S6 ). In the subset of 76 individuals where antibody could be measured, the median time to seroconversion was 12 d (SI Appendix, Fig.
Materials and Methods Ethics. Hospitalized patients with a laboratory-confirmed SARS-CoV-2 infection were enrolled in the French COVID cohort (registered in clinicaltrials. gov NCT04262921)  (45). The study was conducted with the understanding and the consent of each participant or its surrogate. The study was sponsored by French National Institute of Health and Medical Research, and was supported by the Research & Action emerging infectious diseases (REACTing) consortium  (46)  and the French Ministry of Health. Ethics approval was given on February 5 by the French Ethics Committee CPP-Ile-de-France VI (ID RCB: 2020-A00256-33). Patients and Data Collection. We analyzed the results obtained from patients enrolled in the cohort between February 5 and April 1, 2020, hospitalized in 18 different hospitals and for whom nasopharyngeal swabs were available.
Data were collected from the French modified version of the open-access Case Report Form of the Clinical Characterization Protocol for Severe Emerging Infections of International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC)  (47). Data at admission time included demographic characteristics (age and gender), time since symptom onset, presence of various comorbidities, and clinical and biological markers. Survival data after hospital admission were collected up to 60 d postadmission. Patients lost to follow-up were censored at the last date of observation. Viral Load Assessment. Nasopharyngeal swabs were collected at admission and during patient's follow-up. In a subset of patients, viral load in the LRT was also available (SI Appendix, Table  S2 ). Viral load was determined by realtime semiquantitative RT-PCR, using either the Charité World Health Organization (WHO) protocol (testing E gene and RdRp) or the Pasteur institute assay (testing E gene and two other RdRp targets: IP2 and IP4)  (48, 49).
Among them, only those with less than 10% of missing data and more than 10% of prevalence in our dataset were explored in our analyses. Thus, only age ≥65 y, male gender, chronic cardiac disease, chronic pulmonary disease, and obesity were evaluated in our analyses (Table  1 ). Factors Associated with the Viral Load at Admission. We aimed to evaluate the factors associated with the viral load at admission (defined as the first viral load available within 2 d of admission) in a linear regression model, according to the risk factors described above and the time since symptom onset. All factors associated with P ≤ 0. 1 in univariate analyses were tested in multivariate analyses. Backward elimination was used, and P < 0. 05 was considered statistically significant. In multivariate analyses, missing data for continuous comorbidities were imputed to the median population value.
lixoft. eu/). Goodness-of-fit was assessed by visual inspection of VPC (see more details in SI Appendix, Supplementary Information Text)  (59). Model building strategy for viral dynamics. We used only viral load data to construct the viral dynamic model following the strategy below: 1) The target cell-limited model (Eq. 2) was used to estimate the parameters. 2) The five models with immune effects (models B through F; SI Appendix and Eqs. 2 and 3) were tested. The model providing both the lowest BIC and the lowest residual errors was retained, provided that the SEs were not worsened and the goodness-of-fit graphics were adequate. 3) The impact of risk factors was then assessed (see above). Covariates were screened using empirical Bayes estimates (EBE) of individual parameters using nonparametric tests, and those with P < 0.
ACKNOWLEDGMENTS. We thank all investigators for the  French COVID cohort (see study groups in SI Appendix ). The study has received financial support from the  National Research Agency (ANR)  through the  ANR-Flash call  for  COVID-19  (Grant  ANR-20-COVI-0018 ) and the  Bill and Melinda Gates Foundation  under Grant Agreement  INV-017335. The French cohort was supported by the REACTing consortium and by a grant from the  French Ministry of Health  (Grant  PHRC 20-0424 ). The funders had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
In the central and bottom sections, the complete matrix (except the entries between MA and NE) is obtained using flight data for the first wave (from April 1st to May 31st) and the second (from September 1st to October 31st), respectively. we used for Europe  34. For NE, a large coupling is needed due to the tight connections between the two regions, in particular New York City with the neighbouring states and Massachusetts. As a second method, we used the flight data to estimate the number of travellers between different divisions, under the assumption that the k ij matrix entries are proportional to this set of data. To have a realistic matrix for k ij , we first take the mean number of flights from division i to division j during the period from April 1st to May 31st for the first wave, and from September 1st to October 31st for the second wave.
Then, we multiply the number of flights by an effective average number of passengers, and normalize it by 10 6 , following the definition of k ij 15. For the first wave, the optimal average number of passenger is found to be 10, while for the second wave we find an optimal value of 5. Note that these values do not correspond to the actual number of passengers in the flights: in fact, the values of the couplings k ij also take into account the probability of the passengers to carry the infection as compared to the average in the division of origin. When the value is low it might suggest that the sample of passengers in a flight is less infectious than average, as people with symptoms tend not to travel. Controls at airports may also contribute to this. The key information we extract from the flight data is the relative flux of infections among different divisions.
This conclusion is further corroborated in Ref. 37  for Europe. We learnt that the number of infected individuals in the current wave are not affected measurably by the vaccination campaign. However, it is foreseeable that it will impact specific compartments such as the overall number of deceased individuals. Our study included an immunization rate between 0. 64 to 2% of the total population each week. We also updated the results with the actual rates of vaccination in the different divisions, as of March 24, 2020. The results of our eRG model agree remarkably well with the new data from December 28, 2020, to March 17, 2021. To curb the current and the next waves, our results indisputably show that vaccinations alone are not enough and strict social distancing measures are required until sufficient immunity is achieved.
For computing densities, cell positions were determined using the built-in Find Maxima plugin in ImageJ (National Institutes of Health, Bethesda, MD, USA) through a custom made routine. Data analysis and plotting was performed in Matlab (Mathworks, Natick, MA, USA). For density profiles (Figure  1E ) and kymographs (Figure  1D ), the center of the colony was defined as the center of mass of all cells detected at all times. Cell positions were then turned into radial coordinates and cells were counted within concentric crown regions. Densities were calculated by dividing this count by the area of each crown. Density profiles such as the ones showed in Figure  1E  were treated to automatically extract the position, width and density of a ring in various experiments and at various time points.
Density profiles were first stripped of values lower than 500 cell/mm 2 in order to avoid asymmetric baselines behind and in front of the ring. Resulting profiles were then fitted in Matlab by a Gaussian function with a non-zero baseline. The non-zero baseline corresponds to density in the bulk, the maximum of the Gaussian gives ring position, its height added to the non-zero baseline gives the cell density in the ring and its width the width of the ring.
Cell tracking, diffusion coefficients, and aerotactic biases After retrieving cells' positions with optimized ImageJ macros based on Find Maxima, the individual trajectories were reconstructed with a squared-displacement minimization algorithm (http://site. physics. georgetown. edu/matlab/). Data were analysed using in-house Matlab programs. Timelaspse microscopy experiments devoted to cell tracking in the spot assay experiments was acquired at a high frame rate (1 frame every 15 s) (Figure  1 -figure supplements 4-5) due to the very high cell density in the ring region (up to 2000 cells/mm 2 , Figures  1E  and 3C ). For the microfluidic experiments, as cells were plated at a lower density (less than 200 cells/mm 2 ), 1 min time intervals was used to track cell trajectories (Figure  2C ). In order to highlight aerotactic biases, cells displacements over various time lags dt (dt up to 60 min) were projected in the radial direction for spot assays and in the gradient direction X for microfluidic experiments and eventually divided by dt to obtain velocity biases.
Fluorescence microscopy for oxygen measurements Fluorescence images of O 2 -sensing films (either for film calibration or for in situ oxygen measurements in the spot assay or in microfluidic devices) were taken with two inverted epifluorescence microscopes: (i) a TE2000-E inverted microscope (Nikon) equipped with motorized stage, a 4x Plan Fluor objective lens (Nikon), a X-Cite Series 120PC illumination lamp, a TRITC bandpass filter cube and a Zyla camera (Andor) (Figure  3Aii , Figure  2 -figure supplement 3), (ii) a IX83 inverted microscope (Olympus, Tokyo, Japan) equipped with a motorized stage, a UPlanSApo 4x objective lens (Olympus), a U-HGLGPS lamp (Olympus), a RFP bandpass filter and a Zyla camera (Andor). This second microscope was used for mosaic imaging, in order to scan the whole dimension of the three media channels (about 1 cm in length) thanks to the dedicated imaging software cellSens (Olympus) (Figure  2 -figure supplement 1).
In the 0. 5-1. 5% region were we observe most of the interesting aerotactic behaviors with Dictyostelium cells, the precision on the oxygen concentration DC/C is less than 0. 3. For the purpose of this paper, we can conclude that aerotaxis and aerokinesis occurs undoubtedly between C=0% and C=2% (Figure  2 ). Numerical simulation of oxygen tension. Oxygen tension inside the device was computed using commercial finite element software (COMSOL Multiphysics 5. 5; COMSOL, Inc, Burlington, MA, USA). The gas flow in the individual channels were simulated by solving the Navier-Stokes equations coupled with mass continuity for an incompressible fluid: G ðu Á rÞu ¼ G Du À rp; G r Á u ¼ 0; where u is the velocity vector, p is the pressure, and G and G are the gas density and viscosity (taken as 1 kg/m 3 and 10 À5 Pa.
Potts models Potts model simulations were run using CompuCell3D  (Swat et al. , 2012)  with a mix of prebuilt modules and home-made Python steppables in particular to implement the modulation of aerotactic strength by local oxygen levels. Most parameters were fitted to experimental measurements and both time and length scales were also adapted to achieve quantitative simulations. In all simulations, we used Compucell's Volume module which applies to all cells a Hamiltonian of the form: H volume ¼ l v V À V cell ð Þ where V is the volume of a cell and V cell a target volume set to 2 pixels. This already set the length scale of our simulations to 1pixel = 10mm. l v was set to 800. These values were adapted to reproduce the cell speeds observed in the microfluidic experiments. To achieve this relationship, we also decided to fix that one step of the simulation (Monte Carlo Step) was meant to represent 0.
Acknowledgements We thank  R Fulcrand  for his expert help in microfabrication;  G Torch ,  C Zoude ,  G Simon  and  A Piednoir  for technical assistance. This study was supported by the  CNRS -Mission pour les Initiatives Transverses et Interdisciplinaires -« De  ´fi Modelisation du vivant -2019», by the  GDR ImaBio  ( AMI fellowship ) and by the  IFS LyC Collaborative Research  Project  2019  ( Tohoku University ). S Hirose was supported by the  STARMAJ Program  ( France-Japan: Research Internships for Master's Students, Universitede Lyon ) and  K Funamoto  by the  CNRS  (invited researcher position). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  639638  to  V Calvez ) and from  Agence Nationale de la Recherche  (grant  2019 ,  ADHeC project , No  ANR-19-CE45-0002-02  to JP Rieu).
Bats were captured in different ecological environments including 839 (83. 3%) in anthropized sites, i. e. , villages (14. 4%) or cities (68. 9%), and 168 in forest sites (16. 6%). The identification of the species was confirmed on 903 (90%) of the 1007 collected bats, 807 cytb and 96 12S sequences were obtained. The species identification of the remaining samples was extrapolated as described in the methods. For some insectivorous bats (Molossidae, Hipposideridae, Vespertilionidae, Nycteridae), identification was only possible at the genus level due to the lack of reference sequences in Genbank. Details on bat families, genera and species collected at each site are shown in Table  1. A total of 892 (88. 6%) samples were from frugivorous bats belonging to 11 genera and 115 (11. 4%) were from insectivorous bats, representing seven genera from five families.
The species identification of the remaining samples was extrapolated as described in the methods. For some insectivorous bats (Molossidae, Hipposideridae, Vespertilionidae, Nycteridae), identification was only possible at the genus level due to the lack of reference sequences in Genbank. Details on bat families, genera and species collected at each site are shown in Table  1. A total of 892 (88. 6%) samples were from frugivorous bats belonging to 11 genera and 115 (11. 4%) were from insectivorous bats, representing seven genera from five families. Overall, 48. 4% of the samples were from female bats and 51. 6% were from males, 97. 4% were from adult bats and 2. 6% were from juveniles. - - - - - - - - - 1 1 - - Family MOLOSSIDAE Chaerephon sp. -- - - - - - 3 - 3 14 14 - - 17 Scotophilus nux - - - - - - - - - 1.
Collection Sites Samples were collected in four provinces between February 2018 and October 2019 from free-ranging bats in various locations during ongoing EVD outbreaks and in areas where outbreaks have already been documented in DRC. Samples were collected as described previously  [40]. Briefly, bats were captured using mist nets or harp traps in roosting and foraging sites. Bats were released immediately after sampling for ethical and conservation purposes. Dried blood spots (DBS), rectal and oral swabs were collected and preserved as described previously  [40, 55]. Samples were stored in the field at ambient temperature then at -20 • C in the laboratory. Data on capture sites (GPS coordinates, ecological environment), capture method, morphology (body measurements, weight, color), sex, age class (adult, juvenile) and visual identification of species were recorded for each bat sampled.
Molecular Confirmation of Bat Species The species identification recorded in the field was confirmed at the molecular level on a subset of bats using DNA extracted from DBS as described in our previous studies  [40, 55]. Briefly, a fragment of approximately 800 base pairs (bp) of the mitochondrial cytochrome b (cytb) was amplified using primers adapted from Irwin et al. [59]. In addition, for certain species of bats, primers targeting the 12 S region of ribosomal RNA were used  [60]. PCR products were directly sequenced on an ABI 3500 sequencer (Applied Biosystems, Courtaboeuf, France). Sequences were submitted to NCBI for BLAST analysis to identify the most similar bat species. For sequences with no or low similarity (<97%) hits with species in Genbank, phylogenetic tree analysis with reference sequences was performed using maximum likelihood methods with RAxMLv8 [61] implemented in MegAlignPro version 17.
2 (DNASTAR. Madison, WI, USA) in order to determine the genus. At least one sample per species and per sampling event was confirmed at the molecular level. Species identification was then extrapolated to the remaining samples by combining the molecular and morphological data records in the field. We also used morphological details on the forearm and weight measurements to distinguish Epomophorus sp. and Micropteropus pusillus, as previously documented  [55, 62].
5 • C/cycle and 72 • C for 1 min, 35 cycles of 92 • C for 20 s, 50 • C for 30 s and 72 • C for 1 min. After agarose gel electrophoresis, PCR products of the expected size were sequenced by Nanopore sequencing technology. Briefly, PCR products were barcoded and pooled using the Native Barcoding Kit (Oxford Nanopore Technologies, Oxford, UK). Sequencing libraries were generated from the barcoded products using the Genomic DNA Sequencing Kit EXP-NBD104-114/SQK-LSK109 (Oxford Nanopore Technologies) and were loaded onto an R9. 4 flow cell. Base-calling, adapter removal and demultiplexing were achieved using MinKnow software, and the obtained Fastq sequences were analyzed using Nanopipe pipeline (http://www. bioinformatics. uni-muenster. de, accessed on 30 March 2021), and compared to other Filovirus genus references.
Funding: This work was supported in part by grants from  Institut National de la Santé et de la Recherche Médicale (INSERM) /the  Ebola Task Force/REACTing ,  EBO-SURSY  project funded by the  European Union  ( FOOD/2016/379-660 ) and Christophe Mérieux Prize 2015 (to J. -J. M. T. ). P. M. K. was supported by fellowships from  IRD (ARTS, Allocation de Recherche pour une thèse au Sud). BIODIV-AFREID  project  (BiodivERsA ERA-Net COFUND  programme, 2018-2019 joint call for proposals (grant number  ANR-19-EBI3-0004 ). Institutional Review Board Statement: The study was conducted in compliance with the  Ministry of Health and Ministry of Environment  and the national ethics committee from the  DRC  ( ESP/CE/009/2016 ). Informed Consent Statement: Not applicable.
Our contribution In this study, we propose a forecasting platform that combines multiple data sources and statistical models to track flu activity in France at a spatial resolution that, to our knowledge, has not been explored before. Our forecasting platform produces accurate region-specific realtime and short-term flu activity forecasts for the twelve continental French regions. In our approach, we incorporated data sources such as Google data or Twitter microblogs, Electronic Health Record data, and weather that were not considered in the US study  [9]. In addition, the EHR Data used here came directly from a clinical data warehouse rather than cloud-based billing and EHR company which required integrating structured and unstructured clinical data. Additionally, historical synchronicities across regions are captured with a Network model.
Data sources Sentinelles network data. We obtained weekly ILI incidence rates (per 100,000 inhabitants) for the French regions (twelve) from the French Sentinelles network (websenti. u707. jussieu. fr/sentiweb). We retrieved these data in August 2018 from 05 January 2004 to 13 March 2017. We considered these data as the gold standard and as our task for our prediction models. Google data. We obtained the frequency per week of the 100 most correlated internet queries (if correlation � 0. 60) by French users from Google Correlate (https://www. google. com/trends/correlate). Because our prediction period spans 05 January 2015 to 20 February 2017, we utilized the ILI signal for each French region, from January 2004 to December 2014 to obtain the most highly correlated search terms using the tool Google Correlate.
In this way, we obtained different search terms for each individual region. The signals obtained correspond to queries performed by French users at the national level. We retrieved Google Correlate data in August 2018 for the period going from 05 January 2004 to 13 March 2017. Electronic health record data. We retrieved EHR data from the clinical data warehouse (CDW) of Rennes University Hospital (France). This CDW, called eHOP, integrates structured (laboratory test results, prescriptions, ICD-10 diagnoses) and unstructured (discharge letter, pathology reports, operative reports) patients' data. It includes data from 1. 2 million inpatients and outpatients and 45 million documents that correspond to 510 million structured elements. eHOP consists of a powerful search engine system that can identify patients with specific criteria by querying unstructured data with keywords, or structured data with querying codes based on terminologies.