dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome sequences were downloaded from the publicly accessible GISAID database  (23). There were 65 countries (territories) with at least 21 time points and each time point consisting of at least three genome sequences (a time point representing a month) from March 2020 to September 2022. Puerto Rico and Slovakia were excluded from the dataset due to missing data, resulting in a final selection of 63 countries. (B) Monthly Shannon's index of lineage diversity based on PANGO lineage definition  (22)  between March 2020 and September 2022. Shannon's index becomes larger as the number of lineages and uniformness of their frequencies increase  (24). From left to right, the subsampling strategy was: 1) subsample 0. 5% of monthly new confirmed COVID-19 cases in each country, 2) subsample 200 sequences per month in each country, and 3) subsample a total of 2,500 sequences in each country (see more details in Methods).
1B  and SI Appendix, Fig. S12 ). Since lineage diversity may be affected by the PANGO lineage definition, we also aggregated the sublineages based on PANGO nomenclature (see SI Appendix for details). We observed a similar nonlinear effect, but the association was not   B-E , all values of lineage diversity were calculated based on the mean of results from 100 bootstrap subsampled datasets using strategy 1 (Box 1). Shannon's index becomes larger as the number of lineages and uniformness of their frequencies increase  (24)  and is decomposed according to richness and evenness. The richness represents the number of lineages, and evenness represents the relative abundance of the types of lineages, ranging from 0 to 1 (closer to 1 indicates the lineages are more equally distributed). statistically significant (SI Appendix, Fig.
To further assess the robustness of the nonlinear relationship between vaccine rollout and Shannon's index of lineage diversity, we analyzed the distribution of the effect sizes based on 10,000 bootstrap subsamples with replacement from a dataset consisting of 63 countries. Our findings indicated a high proportion of a statistically insignificant association between Shannon's index of lineage diversity and AVC at a medium level. Additionally, the significant associations observed between Shannon's index of lineage diversity and AVC at both low and high levels were not due to random chance (SI Appendix, Table  S2 ). These results suggested that the COVID-19 vaccine can suppress SARS-CoV-2 lineages, while an intermediate level of AVC may not significantly affect lineage diversity. This indicated that the adaptation rate of the virus may be higher in the intermediate levels of immune pressure on population scale  (31).
During the prevaccination era in 2020, PHSM   S3 ), the data for that month were excluded from the DLNMs. Therefore, all of the analyses were based on the genome dataset from subsampling strategy 1 (Box 1 and SI Appendix, Fig. S3 ) across 63 countries worldwide between March 2020 and August 2022. Shaded regions represent 95% CIs. pnas. org had an indispensable role in mitigating the pandemic  (13, (32) (33) (34). We also found that PHSM were effective in controlling the GR of COVID-19 cases, even after COVID-19 vaccine rollout had begun and highly contagious VOCs had emerged. Although these control measures may place a heavy financial and psychological burden on the economy and the population, early lifting of restrictions could diminish the health gains achieved under previous pandemic control policies  (35).
SARS-CoV-2 Genomic Surveillance Data and Subsampling. As of September 24, 2022, a total of 13,243,751 SARS-CoV-2 sequences with metadata were downloaded from the GISAID database  (23). Due to the substantial heterogeneity in the quality of submitted sequences, we performed quality control on the sequences. First, we kept a sequence if it meets one of the following criteria: i) high coverage (less than 1% ambiguity nucleotides sites and 0. 05% amino acid unique mutations and without the insertion/deletion of sites unless verified by the submitter); ii) one of the sequences if duplicates exist (sequences with the same viral name). Second, the sequence was removed if it meets one of the following criteria: i) nonhuman host; ii) length < 29,000 bp; iii) ambiguous temporal information; iv) missing PANGO lineage assignments; v) the collection month from December 2019 to February 2020.
Finally, the data for 63 countries and territories were used for further analysis (Puerto Rico and Slovakia were excluded due to missing data of vaccine coverage or PHSM for nonvaccinated people). In the 63 countries, multiple sources of sampling bias were observed. In the spatial distribution of SARS-CoV-2 sequences, sampling biases exist with some countries overrepresented (e. g. , the United States and the United Kingdom) and some countries underrepresented (e. g. , Egypt). Moreover, temporal sampling bias was also present as more sequences were collected in 2021 compared to the initial outbreak of COVID-19  (48). To reduce the effects of these sampling biases, we randomly subsampled the sequences based on the locations and time to generate a comparable spatial and temporal distribution.
We subsampled the sequence data according to the following three strategies: First, we subsampled sequences by selecting 0. 5% of the monthly new confirmed COVID-19 cases for each country  (9)  to maintain the consistent genomic surveillance intensity among different countries. If the number of sequences collected by a country in a given month was fewer than 0. 5% of the newly confirmed cases, all available sequences were extracted in the analysis. Second, we selected 200 sequences per month for each country to maintain an equal number of sequences among countries per month. Note that if a month had fewer than 200 sequences available, we extracted all the sequences from that month. Lastly, we selected a total of 2,500 sequences from each country between March 2020 and September 2022 to ensure an equal total number of sequences for countries.
We calculated time-series data of Shannon's index, richness, and evenness of lineage diversity for the study countries based on the average of 100 bootstrap subsampling of the three subsampling strategies. Epidemiological Case Data. The global daily number of new confirmed COVID-19 cases was collected from the dataset from Our World in Data on October 6, 2022  (49) , in which the raw data are based on the Center for Systems Science and Engineering at Johns Hopkins University  (50). We calculated the cumulative new cases in each country for a month. The GR of COVID-19 Cases. Based on epidemiological data and the assumption of an exponential model for each country, we calculated the monthly GR of the COVID-19 cases, and that is shown as Eq. 2:  [2]  where N t and N t-1 , respectively, indicate the number of new confirmed infected individuals on month t and the previous month t-1.
We aggregated the daily stringency index for nonvaccinated people to the monthly scale to match the other types of data mentioned above. This indicator measures the intensity and implementation of governments' policy responses in different countries and is comparable globally, ranging from 0 to 100. For each country, the higher value of the stringency index means that the government implemented broader and more stringent policy (0 means no policy response and 100 means the most stringent policy response). AVC. First, we collected daily full-dose (defined as all doses prescribed by the initial vaccination protocol) and booster vaccine coverages for the countries from the global COVID-19 vaccinations database  (53)  on October 6, 2022, and calculated vaccination coverage rates for each country per month.
The structure of α, β j , and log(Cases c,t ), φ c , and PHSM c,t : AVC c,t was the same as the regression model for lineage diversity. CCM. We used the ccm function in R package rEDM  (59)  to decipher the statistically causal relationships between COVID-19 pandemic dynamics and impact factors, including PHSM, IT, and AVC. First, we constructed a single composite time series using the GR of COVID-19 cases, Shannon's index of lineage diversity, PHSM, IT, and AVC. We normalized the time series of each single variable with a mean of 0 and a standard deviation of 1. Then, we determined the optimal embedding dimension for the case GR, Shannon's index of lineage diversity, PHSM, IT, and AVC. Finally, we calculate the cross-mapping skill values, i. e. , Pearson's correlation (ρ) between the observed and predicted values.
As underlined by the candidate's spokesperson Emmanuel Macron, one of the main aims of the digital campaign for the En Marche movement was to "create a mass party in record time. (…) to have 200 000, 250 000 people in a database immediately and with 15-20% of its members able to go door-todoor, to go to a market, to go to a station, to carry the message of the candidate" (E2). It also means that the volunteers have been trained and given the tools they need to organize canvassing activities, etc. As she explains, some people have been employed for managing things at local level: training the local committees and telling them what was expected of them, "telling them 'here you go, you're going to have to go door-todoor' because it's not always intuitive for someone who has never campaigned before".
As a consequence, the MIC can be seen as a distributed variable within the same bacterial species, underpinned by a mapping of each strain genome to a unique MIC. These MIC distributions are experimentally assessed on a log 2 -discretised scale (see e. g. the EUCAST database  [6] , usually with a low skewness that spans over two or three order of magnitudes of antimicrobial concentrations). For instance, a recent statistical model of MIC explained by genomic data has shown, in the case of Neisseria gonorrhoeae, that independent exponential contributions of distinct substitutions provide a good set of regressors for estimating MIC  [7]. Therefore, we here use the log difference in MIC as a phenotypic distance between bacterial strains, with respect to antimicrobial susceptibility. This is particularly suitable because the log scale allows the additivity of independent mutation effects, which will later support symmetric mutation kernels.
Acknowledgements This work was supported by the  ANR  (https://anr. fr/en/)grant  QUASAR  (grant agreement  ANR-21-CE45-0004 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This article has been peer-reviewed and recommended by  Peer Community In Mathematical and Computational Biology  (https://doi. org/10. 24072/pci. mcb. 100007).
Background: The worldwide epidemic of weight gain and obesity is increasing in response to the evolution of lifestyles. Our aim is to provide a new predictive method for current and future weight status estimation based on individual and behavioral characteristics. Methods: The data of 273 normal (NW), overweight (OW) and obese (OB) subjects were assigned either to the training or to the test sample. The multi-layer perceptron classifier (MLP) classified the data into one of the three weight statuses (NW, OW, OB), and the classification model accuracy was determined using the test dataset and the confusion matrix. Results: On the basis of age, height, light-intensity physical activity and the daily number of vegetable portions consumed, the multi-layer perceptron classifier achieved 75. 8% accuracy with 90.
Subjects This statistical study was carried out on existing data collected by a sample of the French population through their smartphone. This sample is representative of the French population for the proportion of the three major weight statuses: normal-weight, overweight and obese. This study was conducted on 273 data sets. The subjects were adults (older than 18 years), owned an Android smartphone and provided consent to the statistical processing of their data. A total of 170 women and 103 men, either normal weight (NW, n = 139), overweight (OW, n = 74) or obese (n = 60), were studied in free-living conditions (Table  1 ). A total of 108 subjects were used for model development (constituting a training group consisting of 36 normal weight, 36 overweight and 36 obese subjects), and a total of 165 subjects (constituting a test group consisting of 103 normal weight, 38 overweight and 24 obese subjects) were used to evaluate the validity of the weight status estimation.
Database The data come from an existing collection. As a reminder, the WellBeNet application is presented below. the WellBeNet app  [11]  can be downloaded at the Play Store. This application has two parts to evaluate sedentary and active behaviors and food choices: eMouve and NutriQuantic. The time spent in four activity categories (immobility, light-, moderate-and vigorous-intensity activity) is expressed in percentages of the total recording time. eMouve provides an accurate estimation of time spent in the four categories, i. e. , with errors in absolute value between 0. 6% and 6. 6%, and between 0. 1% and 5. 3%, respectively, in normal-weight and overweight subjects  [12, 13]. NutriQuantic is used to collect the number of portions consumed in 12 food categories  [14].
Statistical Models The statistical analyses were performed using the SAS software version 9. 4: normality tests, descriptive statistics, analyses of variance and mean comparison tests, analyses of correlation coefficients (Pearson). For the implementation of the neural network, Visual Studio (development environment), Python (computer language) and Scikit-Learn (main library of tools dedicated to learning) were used to find the best predictions of the three weight statuses. All of the variables studied in the training group were tested for normality using the Shapiro-Wilk test. For normally distributed variables, differences between weight statuses (normal weight, overweight and obese) were evaluated by analyses of variance (GLM procedure of SAS). Variations in other non-normally distributed variables were examined using Chochran-Mantel-Haenszel statistics (cmh, based on the difference in row mean ranks; FREQ procedure of SAS).
A significant p-value (<0. 05) indicates that the association between weight status and variable value is strong. When differences between the three statuses were significant, multiple mean comparison tests were performed (LSMEANS) on normally distributed variables. In non-normally distributed variables, the differences between NW and OW or between OW and OB were examined using Chochran-Mantel-Haenszel statistics. Correlation coefficients (Pearson) were then calculated between BMI and physical activity and food variables from the data collected by all the subjects in the training group. These preliminary statistical treatments provided information on variables relevant to the neural network. The neural network, multi-layer perceptron classifier (NN-MLP), was then used to learn the weight statuses among NW, OW and OB subjects based on individual and behavioral data.
Different settings (number of neurons, number of epochs) were investigated in order to find the best configuration, i. e. , the configuration that provides the highest number of correct classifications. The configuration was tested with two hidden layers. The number of neurons on the first layer varied from 100 to 250 in increments of 2. The second layer was composed of 20 to 100 neurons in increments of 10. Random weight was tested between 1 and 70. The number of epochs varied from 1 to 50. All the variables by groups of four to seven were introduced in the NN-MLP. All data collected by the subjects in the test group were used to validate this learning model.
While the reconstructed spatiotemporal histories were impacted by sampling bias for the three approaches, BASTA and MASCOT reconstructions were also biased when employing unbiased samples. Increasing the number of analyzed genomes led to more robust estimates at low sampling bias for the CTMC model. Alternative sampling strategies that maximize the spatiotemporal coverage greatly improved the inference at intermediate sampling bias for the CTMC model, and to a lesser extent, for BASTA and MASCOT. In contrast, allowing for time-varying population sizes in MASCOT resulted in robust inference. We further applied these approaches to two empirical datasets: a RABV dataset from the Philippines and a SARS-CoV-2 dataset describing its early spread across the world. In conclusion, sampling biases are ubiquitous in phylogeographic analyses but may be accommodated by increasing the sample size, balancing spatial and temporal composition in the samples, and informing structured coalescent models with reliable case count data.
Introduction Over the past decade, Bayesian discrete phylogeographic inference has greatly benefited viral epidemiological studies in unraveling the origin and subsequent spread of viral epidemics  (Faria et al. 2019; Lemey et al. 2020; Lu et al. 2021) , the spatial processes driving the viral spread  (Müller et al. 2021) , and environmental or human-related factors associated with the viral spread  (Lemey et al. 2014; Dudas et al. 2017; He et al. 2022). BEAST is a popular Bayesian phylodynamics software package commonly used in the analysis of time-stamped viral molecular sequences. It offers different discrete phylogeography approaches: a popular and computationally efficient discrete phylogeographic Downloaded from https://academic. oup. com/ve/article/9/1/vead010/7028398 by guest on 07 March 2023 inference approach that makes use of continuous-time Markov chain (CTMC) modeling  (Lemey et al.
2015). In parallel, several studies tested alternative strategies to mitigate the potential effects of sampling bias, mostly focusing on CTMC as it was shown to be potentially less robust to sampling bias compared to the structured coalescent model  (De Maio et al. 2015). Downsampling that was tested early on but was limited to large datasets  (Lemey et al. 2014; Yang et al. 2019 ) rapidly became a prerequisite in any SARS-CoV-2 data analysis study due to the large number of available sequences and the high sampling heterogeneity between countries  (Hodcroft et al. 2021). However,  Magee and Scotch (2018)  showed that inference accuracy rapidly plateaus when using up to 25-50 per cent of the sequence data available  (Magee and Scotch 2018). Other studies aimed at improving inference accuracy by integrating additional reliable epidemiological data.
Here, we compare the performance of the different phylogeographic methods using viral epidemics simulated under a stochastic metapopulation model, based on RABV epidemics in dogs in Morocco. We investigated the impact of sampling bias on the spatiotemporal reconstruction of these viral epidemics using the CTMC model, BASTA, and MASCOT, with the latter two assuming populations to remain constant over time. Next, we explored different approaches to mitigate sampling bias, maximizing the spatial and/or temporal coverage of the sample and informing the deme sizes under MASCOT with the true (time-varying) case count data per location. The latter is to test to what degree biases originating from assuming constant population sizes over time can be mitigated by allowing them to vary over time. Finally, we applied the three algorithms to two empirical datasets: a dataset of RABV sequences isolated in the Philippines between 2004 and 2010 and a global dataset of SARS-CoV-2 genomes associated with the early spread of the pandemic.
Analysis of the spread of RABV in the Philippines As a case study to compare the performance of the three algorithms, we analyze the spread of RABV in dog populations among six Philippine islands using 233 sequences of the RABV glycoprotein gene isolated between 2004 and 2010  (Saito et al. 2013; Tohma et al. 2014 ). Discrete phylogeography is particularly adapted here to model transmission in animal populations across an archipelago. In this dataset, sampling is highly heterogeneous across the different islands: Luzon represents up to 65 per cent of the total dataset, while Oriental Mindoro is represented by a single sequence (Supplementary Fig. S14 ). This heterogeneity is very unlikely to be representative of the underlying transmission but rather due to the case under-reporting outside Luzon.
Analysis of the early spread of SARS-CoV-2 across the world While there is typically no or scarce reliable data on the number of new cases in wild and domestic animal populations, such estimates are generally widely available for pathogens infecting the human population, as is the case for SARS-CoV-2, dengue virus, HIV, and West Nile virus  (Gill et al. 2016; Dellicour et al. 2020 ). Here, we compare the phylogeographic reconstructions of the four algorithms tested earlier on a dataset of SARS-CoV-2 genomic sequences from the early stage of the pandemic  (Lemey et al. 2020). MASCOT-GLM is informed using the seven-day moving average of case count data either from Our World in Data  (Mathieu et al. 2020)  or from the WHO (World Health Organization (WHO) 2023). MASCOT-GLM is then referred to as MASCOT-World In Data (WID) and MASCOT-WHO, respectively (Supplementary Fig.
S16  and S21 ) and of total migration counts (Fig. 6 ). As previously shown, BASTA and MASCOT lead to more uncertain ancestral migration histories with the extreme case of BASTA for which the posterior evolutionary rate and the structured coalescent density are bimodal. The first mode of BASTA infers a tree topology and a migration history that are similar to the CTMC model and CTMC-TRAVEL (Supplementary Fig. S17 ). For example, the reconstructed location of the most recent common ancestor of lineage B. 4 is China for CTMC-TRAVEL, the CTMC model, and the first mode of BASTA, whereas it is located in Oceania for MASCOT and the second mode of BASTA (Supplementary Table  S1 ). For the latter two reconstructions, most of the ancestral branches were not inferred to occur in China and, similar to the RABV dataset, they predict the least sampled locations (Africa and Oceania) to play a major role in the transmission process.
When we incorporate incidence data into MASCOT-GLM, reconstructions differ strongly between the Our World in Data and WHO datasets. While MASCOT-WID reconstruction is uncertain with multimodal total migration counts (Fig. 6 ) and does not reflect the original spread from China (Supplementary Fig. S20 ), the MASCOT-WHO estimated migration counts that are close to the estimates of CTMC-TRAVEL (Fig. 6 ), and its maximum clade credibility (MCC) tree is in agreement with the origin of the pandemic (Supplementary Fig. S21 ). Importantly, the two datasets differ strongly in how well early cases are covered (Supplementary Fig. S15 ), with the WHO dataset being more representative of the incidence over time. Overall and as also suggested by our simu-Downloaded from https://academic. oup. com/ve/article/9/1/vead010/7028398 by guest on 07 March 2023  For CTMC, BASTA, and MASCOT, posterior distributions of the total migration counts with a BF < 3 are not depicted but marked as non-significant.
Analysis of empirical RABV and SARS-CoV-2 datasets We further compare the approaches on real datasets of RABV and SARS-CoV-2. As dog case counts were not available for RABV, we compare only CTMC, BASTA, and MASCOT. CTMC predicts a highly spatially structured migration process, whereas BASTA and MASCOT predict a non-parsimonious scenario. We observe similar results for the SARS-CoV-2 dataset. As we have set equal deme sizes in BASTA and MASCOT, but a single tip is sampled for Oriental Mindoro in the RABV dataset and for Africa in the SARS-CoV-2 dataset, BASTA and MASCOT compensate for location under-representation by estimating high backward-in-time migration rates to the under-represented location (Oriental Mindoro and Africa). Our results are in line with those given in previous studies reporting strong differences between the CTMC model and the structured coalescent model on real datasets (De Maio et al.