dataesr/scientific-paragraphs-categorization huggingface dataset:
• Biological indicators were in favor of a COVID-19: lymphopenia, negative influenza PCR test or thrombocytopenia. For patients admitted after March 3 rd 2020, the head nurse reviewed all patient files and performed a first selection of potential COVID-19 cases based on respectively reason for consulting, nurse's clinical assessment, results of medical examinations, with a particular focus on imaging and biological tests. The selected patients were further evaluated by an emergency physician who established a list of probable or confirmed COVID-19 patients. This list was then revised by integrating data coming from the radiology service (scanner examinations) and the medical laboratory (biological tests). At the end of this process, 552 patients were retained as COVID-19 probable or confirmed cases, out of the 7,184 patients admitted to the emergency care service from December 29 th 2019 to May 18 th 2020.
Data analysis In the analysis of the population-based survey presented in this paper, we focused on the people living in the Haut-Rhin department and excluded the attendants to the POC gathering coming from other departments. Overall epidemic evolution was compared in the survey data and in the retrospective analysis of emergency care service medical records. For both data sources, we focused our work on the moment incidence crossed over the threshold defining the outbreak. We particularly looked at the delay between the beginning of the outbreak and the sanitary alert form local authorities on March 3 rd  [10]. The epidemic threshold was estimated first from the upper limit of the 95% confidence interval of the incidence moving mean on the last seven days. The December 15 th 2019 to January 15 th 2020 period was used as a reference to compute the seven days moving mean confidence interval.
Discussion The results presented in the previous section have been obtained from three independent sources: a population-based survey of the households living the Haut-Rhin department, a retrospective analysis of the Diaconat-Fonderie private not for profit hospital emergency care unit in Mulhouse and influenza PCR test data from the Diaconat-Fonderie biological laboratory. They all point toward an early circulation of the COVID-19 in the Haut-Rhin population, much earlier than the first official case identified on February 26 th 2020 in the Grand Est region [4] and the epidemic alert on March 3 rd  [22]. The converging evidences for an epidemic starting end of January 2020 can be summarized as follows: -the consistency of the epidemic threshold crossings for our three independent data sources -the speeds of increase of incidence approximately at the same time (between January 26 th and January 30 th ) among Haut-Rhin households (data not shown), -the continuous flow of patients with COVID-19 clinical signs visiting Diaconat-Fonderie emergency care services in that period -the significant increase of the number of negative PCR influenza tests performed by Diaconat-Fonderie laboratory -and the early occurrence of anosmia among Haut-Rhin inhabitants.
0%) of the 1,516 COVID-19 suspicious cases were tested for COVID-19: 34 tests were negative and 87 positive. Results of serological tests were not available when the datasets have been analyzed. Although Diaconat-Fonderie emergency care services documented probable COVID-19 cases in January, the only biological data used to reach our conclusions are the results of influenza PCR tests. As the hospital laboratory observed a significant increase of negative influenza PCR tests at the end of January 2020, we can hypothesize that the growing number of undiagnosed COVID-19 infected patients resulted in a growing number of negative influenza tests that went unnoticed. Also, due to privacy rules, information on patient localization is very limited, consisting only in the department where the household lives, excluding therefore a spatial chaining of cases.
To further document the virus history and propagation, collecting statistics on influenza PCR tests from other biological laboratories in Mulhouse and other cities in Alsace should help to document the epidemic onset. Of great interest are the data from the other emergency services in Mulhouse and particularly the public hospital which was in first line during the COVID-19 crisis. Imaging data from radiology units should also help trace the early cases that were unseen during the early days of 2020. Further analysis of the population-based survey should help understand if the early COVID-19 suspicious cases had common activities or social networks as households were also invited to document their social contacts. The role of comorbidities has to be explored. It is also necessary to better understand how the attendants to the POC gathering contributed to the nationwide epidemic spread which lead to a complete French lockdown after march 17 th , one month after the beginning of the POC gathering and seven weeks after the outbreak started being significant.
Acknowledgments: We acknowledge fruitful discussions with  Arnaud Fontanet  ( Institut Pasteur ) and  Michel Schmitt  (department of radiology, Albert Schweitzer hospital). We are very thankful to  Sandra Deplanche  ,  Sophie Monceau  and  Clermont-Auvergne University  ( UCA ) board for their very efficient support to address all ethical requirements. Research is funded by  CHU Clermont-Ferrand ,  CNRS  and  UCA.
The impact of arcs capacities on the total distance is provided by the gap indicator gap (LI, UL) in Table  4a ,b. This indicator, which is characteristic of the dataset used, can be interpreted as a deviation from a minimum distance boundary that can only be reached if there is no capacity constraint on the arcs (i. e. , gap = 0 means that the smallest minimum distance is reached). These distance gaps in Table  4a ,b are only for the full integration scenarios as the capacities of the arcs play no role (i. e. , gap = 0) when there is no integration (NI), whether they are pooling or not. However, the impact of the arcs capacities of the network is significant with the integrated distribution network (FI) as seen in Table  4.
(2018)  and are also restricted by data availability in terms of country and time series coverage. Equation (1) shows that in this framework, policies will not have a direct impact on human capital. Instead, they will amplify or attenuate the impact of the core drivers on human capital through interactions with the core drivers. 2 Other variables were considered as core drivers but were found to be either highly correlated with the three selected variables giving rise to multi-collinearity or had wrongly signed and/or imprecisely estimated coefficient estimates. These variables included per capita income as well as public and total spending on healthcare per capita (all in PPP terms). (1) where h is human capital per capita, S is public spending on education per capita for country i and time t, respectively.
It is not only difficult to measure the quality of teaching, but available measures for teacher quality including age, gender and qualification tend to work poorly in the aggregate regression analysis. For the purpose of the empirical analysis, teacher quality is measured as the average of the students to teaching staff ratios in primary and secondary schools. Ideally, such a ratio should be adjusted for hours worked but no such cross-country data is available. This variable ranges from less than ten students per teacher in Luxembourg and Greece to about 20 in Turkey, France, the United Kingdom and Chile. The number of students per teaching staff in Mexico is almost double the OECD median of 15 (Figure  2 , Panel B). An additional variable is also used to check for the robustness of the results: teachers' qualification measured as the share of full-time teachers fully certified.
Relying on a system of central examination provides a powerful instrument to increase school accountability. Such a system makes cross-school comparisons easier and fosters competition between schools for students  (Braga et al. , 2013). The OECD's PISA dataset contains numerous measures of school autonomy, which tend to be highly correlated with each other. The indicator measuring a wide range of aspects of autonomy of primary schools is selected for the empirical analysis. The PISA index of school autonomy suggests a divide across OECD countries. In some countries such as Turkey, Greece and Italy, the education system is very centralised and schools have little decision power (Figure  2 , Panel C). By contrast, schools enjoy a large amount of freedom in taking decisions with regard to resources and teaching content in the United Kingdom, the Netherlands and the Czech Republic.
Data In order to assess the pedestrian accessibility around bus stops, we conducted access audits around sample bus stops. We used the Public Transport Accessibility (PTA) toolkit  [24]  for the assessment, as it is an objective survey instrument, which measures accessibility along footpaths on which the bus stop is located, and also regarding the type of crossing to access the bus stop. These pedestrian accessibility audits were conducted from June 2015 to July 2015. In addition, to study the pedestrian safety, we used the traffic fatality data reported by Delhi Traffic Police for 2010 till 2016.
Road traffic fatalities The pedestrian fatalities in Delhi for the period 2010-2016 have been considered. We have considered the fatalities for this period of 6 years, to bring stability in the pedestrian fatality counts for disaggregated spatial units in Delhi. Injury data is not used due to its high underreporting in Indian cities  [25]. The traffic fatalities data for the years 2010-2016 were collected in tabulated format from first-information reports (FIR) compiled by the Delhi Traffic Police. FIRs are the first set of information pertaining to the road traffic crashes reported by those involved in the crash, or by anyone who knows about the crash, or the police  [2]. These contain the details of the date, time, location, police station which is responsible for the jurisdiction in which the crash occurred, impacting vehicle, and the victim type.
Sampling strategy In order to determine the sample of bus stops to be studied, we undertook multi-criteria stratified random sampling. We divided the bus stops in Delhi into strata based on the following types of indicatorssafety, socio-economic, and geographical representativeness. Given the significance of safety for PT users, it is important as a stratification factor. Stratification by safety indicators is done to ensure that we obtain a mix of bus stops with varying levels of risk for pedestrians. Data for safety indicators and socio-economic indicators were available at the ward level. We have thus assigned ward-level values to the bus stops located in the respective wards for these two indicators. Safety was assessed in terms of relative risk of pedestrian death per PT trip for each bus stop.
We chose PT trips as the base of our rate (estimated from  [17] ) so as to ensure that a measure of exposure is taken into account. For estimating the ward-level risk, we calculated the ratio of pedestrian deaths to the number of PT trips, in the ward. This was further divided by the number of bus stops in the ward, to estimate the risk for each bus stop. Risk at the city level was also obtained similarly, by dividing the ratio of all pedestrian fatalities to overall PT trips by the number of all bus stops in Delhi. Relative risk for each bus stop was then calculated with respect to city-level overall risk. This estimation was done through GeoDa  [26] , a geographic information systems (GIS)-based software. Relative risk ranged from 0. 00 to 4. 73, with a  mean (±standard deviation) of 1. 51 (±1.
For the 18 strata (3 relative risk strata × 2 literacy strata × 3 distance strata) thus obtained, 3 strata did not have any bus stops assigned. For the remaining 15 strata, we selected 30 bus stops in each stratum (or all the bus stops in a particular stratum if it had less than a total of 30 bus stops), and arrived at the total sample size of 360 bus stops. We selected the bus stops in each stratum based on random sampling. Thus, we conducted a total of 360 pedestrian accessibility audits. The sample of bus stops is indicated in Fig. 2. The audits for three bus stops were identified to be incomplete during the data cleaning process, and were removed from subsequent analyses. Thus, the analyses have been conducted for 357 bus stops.
These zones will subsequently be called accessibility zones for the bus stops. For all the 3818 bus stops in Delhi, there are corresponding accessibility zones, with the areas ranging from 220 m 2 to 22 km 2 , presented in Fig. 3. Smaller zones indicate closer proximity of bus stops, and larger zones are typically present only near the periphery of the city, owing to low bus stop density. The range of sizes for sampled zones is from 760 m 2 to 3. 3 km 2 (of which, 92% of the zones cover an area less than the area of a circle with radius of 500 m). For each accessibility zone, the pedestrian fatality counts associated with them were estimated by using the Spatial Join tool in the Analysis Tools toolbox of ArcGIS. Since we estimated the number of pedestrian fatalities in each accessibility zone, Poisson regression models are applicable, as they are used when count data is available and where the probability of occurrence of event is rare  [28].
Log-linear model is most commonly used to express the relationship between the independent variables and the Poissondistributed dependent variable, expressed as y i ¼ Poisson λ i ð Þ ð1Þ log λ i ð Þ ¼ log e i ð Þ þ β 0 þ βX i ð2Þ where y i are the observed numbers of pedestrian fatalities, λ i are the expected fatality counts, e i is the exposure, β 0 is the intercept, β is the vector of estimable parameters, and X i is the vector of independent variables. However, according to Fotheringham et al. [29] , this is a type of global regression, and suffers from the shortcomings of such models. Geographic analysis for assessing road safety is an established method, with various studies employing GIS-based datasets and tools for their research  [30] [31] [32] [33] [34] [35] [36]. Previous studies have also determined that it is important to take spatial autocorrelation into account, particularly for road safety assessments, as crashes are often clustered spatially  [36] [37] [38].
Thus, geographically-weighted Poisson regression (GWPR)  [29]  was applied. The use of this regression model allows the estimation of parameters which are unique to each location i. Global Poisson regression is applied to the neighbouring points to location i, and a new subset of corresponding points is chosen for the next location. Calibration of the parameters is weighted based on the distance of neighbouring points from location i. The statistical expression of GWPR is given by: log λ i ð Þ ¼ log e i ð Þ þ β 0 u i ; v i ð Þþβ u i ; v i ð ÞX i ð3Þ where (u i , v i ) signify the coordinates of the i th point in space. The software GWR4. 0  [39]  was used in combination with ArcGIS to apply the GWPR model and for the visualisation of the results. The parameter estimates were obtained by the adaptive Kernel function.
Independent variables The purpose of applying PCA is to reduce and simplify a large set of variables to a smaller set of meaningful latent variables  [44]. We used IBM SPSS Statistics version 22  [45]  to execute factor analysis, with extraction method of principal components with direct oblimin rotation. The correlation matrix of association was used. We selected the direct oblimin for the factor rotation as it a type of oblique rotation method which allows for the factors to be correlated, as opposed to orthogonal methods of factor rotations  [46]. We used two criteria to determine the number of factors to retain-a) eigenvalue N1, and b) parallel analysis using Monte Carlo simulations, executed through MonteCarloPA programme  [47]. To assess whether PCA is applicable, the Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test of sphericity were estimated.
The KMO value was 0. 79, supplemented with Bartlett's test significant at N99%, which indicated that PCA was suitable. Five components (henceforth referred to as 'factors') were generated from the PCA which had eigenvalue N1. Together they explained ~70% of the variance in the data. Parallel analysis confirmed the significance of the eigenvalues. The details of the factors are illustrated in Table  3. The contribution of a variable to a factor is indicated by the pattern matrix and the structure matrix, presented in Table  4. They highlight that the variables with high correlations (N0. 50 and lesser than -0. 50) belong to the same unique factors. Note that Factor 5 is related negatively to the pattern matrix and structure matrix. Factor scores were generated by SPSS for the five latent factors for each bus stop.
Discussion We have estimated the number of pedestrian fatalities as the dependent variable for two safety models-a) where the impacting vehicle was known, and b) where the impacting vehicle was unknown. For both the models developed, GWPR perform better as compared to global Poisson regression. This is reflected both in terms of the reduced AICc and the improved percent deviance explained for the GWPR. This is because geographically-weighted regression models address the problem of local-level multicollinearity observed in the data points which cluster together spatially. Our hypothesis that the nature of hit-and-run cases differ from those where the impacting vehicle is known is also proven to be correcta) considering the effect of exposure variable of log of PT trips, and b) different factors being significant in the respective models, especially   in the GWPR.
Without entering too much in the details, Roth's idea is the following: the first step is to create a database, ideally at the national level, to register all the non-compatible pairs. Then, a "Kidney Exchange Clearinghouse" will organize exchanges between these pairs of non-compatible patient and donors. Patients choose the most were deemed insufficient, notably because the structure and thus the choices delivered by the software were not clear enough (see Piketty "Le scandaleApb", Le Monde, july 12, 2016. suitable kidney among those available on the database; or they opt to be given priority on a waiting list for a good match with kidneys coming from the post mortem procurement process; or, finally, they can choose to wait for the next matching process if they believe there to be currently no good match.
Methods The information received by the French population on the obligation to vaccinate was collected in the traditional media: articles in the written press, radio and television broadcasts. Articles and broadcasts were selected from the 10 media in each category with the highest audience rate  (16. 17) , from January 2016 to May 2018 (Table1). The search for articles and broadcasts was carried out on the media websites, entering the keywords "vaccination obligation" or "compulsory vaccination" in their search engine. The radio and television programs were listened to, viewed and then transcribed word by word. Once the articles were selected and broadcasts transcribed, they were analyzed by inductive coding according to the grounded theory with the help of NVIVO 12 software. Articles and broadcasts were included until the data saturation.
Thus, isotopic analysis is also a way to quantify vegetational criteria, which otherwise are primarily qualitative. To date, the isotopic structure of extant equatorial African mammals  (3) (4) (5) (6)  has been the only broadly sampled system for understanding tropical ecosystems. Although phylogenetically and size-biased, this African system has become the de facto model to infer closed-canopy rainforests from fossil mammal data on all continents. However, the absence of comprehensive isotopic data from other closed-canopy mammalian communities impedes confident attribution of an isotopic range for this type of ecosystem across landmasses and over time. Thus, is there a unique mammalian isotope signal for all closed-canopy rainforest ecosystems? Do different phylogenetic, geologic, and biogeographic histories influence the isotopic structure of these ecosystems? To answer these questions, a broad suite of modern western Amazonian mammalian taxa were sampled for isotopic analyses.
Grass species are present in the Amazon rainforest, and most of them utilize C4 photosynthesis  (21). All mammals sampled in this study, except for two specimens, are from localities in Peruvian western Amazonia (mainly from the Madre de Dios, Ucayali, and Loreto regions). Although some of these sampling areas (e. g. , Manu National Park in Madre de Dios) span a large altitudinal gradient (Andean highland, cloud forest, and lowland rainforest), our sampling has been restricted to localities below 700 m above sea level (i. e. , lowland rainforest), with the exception of four samples coming from the cloud forest (SI Appendix). All samples therefore come from wet forest localities exhibiting analogous and relatively homogeneous environmental conditions. Aiming to include all available data from the literature for other western Amazonian sites, two specimens of the largest extant Amazonian mammal, Tapirus terrestris, from Colombia and Bolivia have also been included  (22).
Our Amazonian localities span a latitudinal gradient of ∼13°(from 0°40'S [Rio Curaray, the northernmost locality] to 13°6'S [Manu, the southernmost locality], although 68% of the samples are from Madre de Dios and Ucayali, encompassing a narrower 4°latitudinal range). Virtually all samples are from undisturbed habitats in national parks. Selection criteria for data from African and Amazonian localities were the same (SI Appendix). Therefore, the African mammals assessed in this study were sampled from equatorial lowland rainforests (primarily from the Democratic Republic of Congo, but also from Uganda and Gabon). We have not included data from any other African ecosystem (e. g. , savannas or woodlands). The African localities span <5°of latitude (from 0. 5 °N [Kibale, Uganda] to 3. 3 °S [Mwenga, Congo]).
Results Mammals analyzed herein (Fig. 1 ) were collected from as long ago as 1912 to as recently as 2015, and δ 13 C atm has decreased by ca. 1. 7‰ during that time interval. Consequently, all data described are normalized to the preindustrial atmospheric δ 13 C of the year 1750 (-6. 3‰, subsequently referred as δ 13 C 1750 ; the SI Appendix provides details on this "Suess Effect correction"). Data for African mammals are used for broader comparative interpretations (Discussion), but, as these data have been published previously (Dataset S1), the results herein described focus on the Amazonian data (Table  1  and Fig. 2  also summarize prior results for African taxa). δ 13 C diet of Western Amazonian mammals. The taxa examined in this study included representatives of all nonvolant mammal groups present in western Amazonia (terrestrial: Artiodactyla, Carnivora, Didelphimorphia, Lagomorpha, Perissodactyla, Primates, Rodentia, Xenarthra [Pilosa, and Cingulata]; and aquatic: Cetacea and Sirenia).
in SI Dataset). δ 13 C 1750 refers to raw values corrected for anthropogenic CO 2 set to preindustrial values (the year 1750) ± 1 SD. δ 13 C diet refers to the reconstructed diet, which, for herbivores, refers to the δ 13 C of the vegetation on which they feed. δ 13 C diet was not calculated for secondary consumers (Table  2  shows secondary consumer δ 13 C 1750 values only). C3, taxa consuming C3 plants; span, total range in ‰. canopy closure or incorporating a variety of food items in their diets), thereby averaging the δ 13 C of their energy pool to yield values closer to the typical median for rainforests. The median δ 13 C diet values of artiodactyls in South America and Africa are not significantly different (Table  1  and SI Appendix). The isotope range exhibited by artiodactyls in Africa (>21‰, ranging from -35.