dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimating the time-varying reproduction number and nowcasting reported infections We estimated the instantaneous reproduction number (R t ) using the EpiNow2 R package (version 1. 2. 1) 10 on the last 12 weeks of available data, discarding estimates from the first 14 days globally, for United Nation regions, nationally, and subnationally for 10 countries. The instantaneous reproduction number represents the number of secondary cases arising from an individual showing symptoms at a particular time, assuming that conditions remain identical after that time, and is therefore a measure of the instantaneous transmissibility (in contrast to the case reproduction number -see Fraser (2007)  8  for a full discussion). EpiNow2 implements a Bayesian latent variable approach using the probabilistic programming language Stan 14 , which works as follows.
In mathematical notation, The parameters of the Gaussian process kernnl were estimated during model fitting with the following priors. The length scale was given an inverse gamma prior with shape and scale values optimised to give a distribution with 98% of the density between 2 days and 21 days. The prior on the magnitude was standard normal. Each timeseries was fit independently using Markov-chain Monte Carlo (MCMC). A minimum of 4 chains were used with a warmup of 500 each and 4000 samples post warmup. Convergence was assessed using the R hat diagnostic  14. We used an estimate of the generation time sourced from  18  but refit using a log-normal incubation period with a mean of 5. 2 days (SD 1. 1) and SD of 1. 52 days (SD 1. 1)  19  rather than the incubation period used in the original study (code available here: https://github.
Website, summarised estimates, and interactivity We use Rmarkdown templates and the distill framework to generate webpages summarising these estimates  22, 23. The RtD3 package is used to provide interactive visualisations of all estimates  24. Estimates by country are provided on a dedicated static page along with global, and regional, summaries. More detailed subnational estimates are available for over 10 countries in an flexible framework into which additional subnational estimates will be added as more data becomes available.
For this reason we include estimates based on reported deaths and provide tooling to allow estimates to be produced for alternative datasets. In theory, estimates from disparate sources should be comparable using our approach, however if they in fact represent different sub-populations then there may be variation between them that can potentially be usefully interpreted. In addition, the model is limited by how representative the delay that we use from infection to notification distribution is for a given location. As there is limited data to assess this, we estimate a bootstrapped global delay distribution using the combined data from every country. In particular, the delay from onset to notification can especially impact the upscaling of cases by date of onset that accounts for cases that have onset but not yet been reported.
Acknowledgements This project was enabled through access to the  MRC eMedLab Medical Bioinformatics infrastructure , supported by the  Medical Research Council  ( MR/L016311/1 ). Additional compute infrastructure and support was provided by the Met office. We thank  Venexia Walker  for comments on a version of this draft. The following authors were part of the Centre for Mathematical Modelling of Infectious Disease 2019-nCoV working group. Each contributed in processing, cleaning and interpretation of data, interpreted findings, contributed to the manuscript, and approved the work for publication:  Samuel Clifford ,  Mark Jit ,  Stéphane Hué ,  Eleanor M Rees ,  Petra Klepac ,  Damien C Tully ,  Rachel Lowe ,  Kathleen O'Reilly ,  Nicholas G. Davies ,  Quentin J Leclerc ,  Arminder K Deol ,.
Data availability Latest data: https://dataverse. harvard. edu/dataverse/covid-rt Archived data at the time of publication: https://dataverse. harvard. edu/dataverse/covid-rt License: MIT Software availability Development • Website (Front-end): https://github. com/epiforecasts/covid • Scheduling framework: https://github. com/epiforecasts/covidrt-estimates • EpiNow2 R package (R estimation, data processing, visualisation and reporting): https://github. com/epiforecasts/EpiNow2 • covidregionaldata R package (data aggregation and processing): https://github. com/epiforecasts/covidregionaldata • RtD3 R package (interative visualisation): https://github. com/ epiforecasts/RtD3 Archived at the time of publication • Website: https://doi. org/10. 5281/zenodo. 3841818 • Scheduling framework: https://github.
com/epiforecasts/covidrt-estimates • EpiNow2 R package 10 : https://doi. org/10. 5281/zenodo. 3957489 • covidregionaldata R package 11 : https://doi. org/10. 5281/zenodo. 3957539 • RtD3 24 : https://doi. org/10. 5281/zenodo. 4011841 License: MIT.
Numerical results The performance of the marginalized method was investigated on simulated trajectories. Random trajectories were simulated in a 2D box with periodic boundary conditions, a uniform total force, and a triangular diffusivity profile along the x axis (Fig. 2A, B ). Other simulation parameters are given in Appendix A6. For each trial, the simulated trajectories were then analyzed using the TRamWAy software platform (TRamWAy 2018) and following a procedure similar to the one used in El  Beheiry et al. (2015)  and  Remorino et al. (2017)  and consisting of (i) individual spatial tessellation in each trial; (ii) assignment of recorded displacements to spatial domains; (iii) inference of ζ sp and ζ t in each domain; (iv) calculation of the Bayes factor in each domain. The marginalized Bayes factor KM , inferred in each domain, was then plotted against its expected value K M to test the accuracy of the method (Fig.
2008; Floderer et al. 2018). Several VLPs can assemble in parallel in the same observation region. The TRamWAy software platform was used to tessellate the observation region and infer maps of diffusivities (Fig. 4G ) and drift (Fig. 4I , see El Beheiry et al. 2015; TRamWAy 2018). The Bayes factor map was then computed. The localization uncertainty was σ L = 30 nm, requiring corresponding corrections to the Bayes factor (Appendix A4). Inference results and Bayes factors for a 2 μm × 2 μm zone of one T-cell membrane are shown in Fig. 4D-F. Plots of the trajectories, the density of the recorded points and the diffusivity (Fig. 4D, E, G ) indicate that there are two regions of interest (ROI) in the data set. However, the plots of the diffusivity gradient and the drift (Fig. 4H, I ) suggest that the two parameters are of the same scale, hence it is not a priori clear, whether the localization of the particles is due to non-diffusive or spurious forces.
An illustration of this fact for the VLP data can be seen in Fig. 5. Here, our statistical test was applied to the same VLP data set on three different spatial scales. When the bins are much larger than the typical structures present in the biological system (Fig. 5A , 0. 5 μm), the interactions are averaged out and our statistical test confirms the absence of interactions or is inconclusive. On the scale of the structures (Fig. 5B , 0. 25 μm), one may identify the potential regions of interest, but is unable to resolve their internal structure. When the bins are smaller than the regions of interest (Fig. 5C , 0. 05 μm), given enough data, the internal structure of the regions can be resolved. At even smaller scales (not shown), when few points are available per bin, one starts losing the connectivity of the regions of interest, and the statistical tests becomes inconclusive or (by design) favor the model with only spurious forces (H 0 ).
(deceased)   for helpful discussions. This study was funded by the  Institut Pasteur, L'Agence Nationale de la Recherche  ( TRamWAy ,  ANR-17-CE23-0016 ), the  INCEPTION  project ( PIA/ANR-16-CONV-0005 , OG), and the  programme d'investissement d'avenir supported by L ' Agence Nationale de la Recherche   ANR-19-P3IA-0001. The funding sources had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Conflicts of interest.
Source: Built by the authors using the Smart PLS 3. 0 software package. For the estimation of the parameters of the model, we use a method non-parametric. Indeed, on the one hand the sample size is reduced to structural causes related to the characteristics of the research field, and on the other hand, the number of causal links specified is a result in order to avoid the bias of specification. On the methodological plane, and having regard to the estimated non-parametric, the number of observation that must exist in order to estimate an econometric model must at least be equal to 10 observations per variable manifest in the built the most important (to explain) in the model, or 10 times the number of causal links. In both situations, we need to have a larger sample size of 100 observations that we reach from the method of the resampling.
In this respect, our study looked at forty (40) companies that responded to our questionnaire, that we do not allow the use of other software such as LISREL (LInear Structural RELationships) 1  require a minimum of 100 to 150 observations. To achieve this, we tested the structural relationships of our model, even the reflective nature of our built and vocation predictive of our model has led us to retain this method in the framework of of our research model.
Selection acting on the microbiota We investigated the agents exerting abiotic selection on the tropical and subtropical surface-ocean microbiota by analysing β-diversity together with the environmental variables included in the Meta-119 Malaspina dataset (temperature (°C), conductivity (S m -1 ), fluorescence, salinity and dissolved oxygen (mL L -1 )). We used different indices that capture distinct facets of β-diversity (Bray-Curtis, TINA w , PINA w , gUniFrac; see 'Methods' section). Water temperature was the most important driver of selection on prokaryotes (Fig. 2 ), ranging between 15. 7 and 29. 3 °C, with a mean of 24. 5 °C and a standard deviation of 3. 2 °C across the whole Meta-119 Malaspina dataset (Fig. 1a ). Furthermore, water temperature appeared to affect prokaryotic association networks, given that TINA w  [26]  explained ≈ 50% of community variance (ADONIS R 2 ) (Fig.
2 ), while other used β-diversity indices that do not consider species associations explained considerably lower proportions (Fig. 2 ). In contrast, temperature had limited effects on picoeukaryotic community turnover (Fig. 2 ). Analyses using both the Malaspina and TARA Oceans datasets indicated stronger positive correlations between TINA w and watertemperature differences in prokaryotes (Mantel r = 0. 8-0. 5, p < 0. 01) than in picoeukaryotes [Mantel r = 0. 3, p < 0. 05] (Fig. 3 ). In particular, TARA Oceans samples displayed a higher correlation with water temperature than Malaspina samples (Fig. 3 ). Overall, TINA w results indicate that locations with similar temperatures include Fig. 1  Ecological mechanisms shaping the tropical and subtropical surface-ocean picoplankton. a Position of the 120 stations included in this work that were sampled as part of the Malaspina-2010 expedition (green dots) in the tropical and subtropical ocean.
A snapshot of the global sea surface temperature, a main environmental driver affecting microbial distributions, is shown as a general representation of the temperature gradients in the surface ocean (as inferred using the 'optimum interpolation sea surface temperature' dataset from the NOAA corresponding to the 17 of March of 2018). Note that temperatures measured in situ were used in all analyses, not the ones displayed here. b Percentage of the community turnover associated to different ecological processes in prokaryotes and picoeukaryotes in the tropical and subtropical upper ocean as calculated using OTUs -99% and OTUs -ASVs. Note that percentage refers to the percentage of pairs of communities that appear to be driven by a given process prokaryotic species that tend to co-occur, with this pattern disappearing as the temperature difference between stations increases.
Selection acting on single species The previous analyses investigated how selection may operate on the entire assemblage of species, without considering the different responses to selection that are expected in individual species. We therefore evaluated the potential action of selection on single species by determining their individual correlations with multiple abiotic environmental variables using the maximal information coefficient (MIC). In the Malaspina dataset (Fig. 1a ), temperature was the variable with the highest number of associated prokaryotic species (1. 7%), representing ≈ 17% of the 16S rRNA genesequence abundance, while picoeukaryotic species displayed limited associations with temperature (≈ 0. 3% of the species representing ≈ 5% of the 18S rRNA gene-sequence Fig. 3  Temperature-driven selection seems to affect species association networks in prokaryotes but not in pico-/nano-eukaryotes.
Samples for inorganic nutrients (NO 3 -, NO 2 -, PO 4 3-, SiO 2 ) were collected from the Niskin bottles and measured spectrophotometrically using an Alliance Evolution II autoanalyzer (Frépillon, France)  [80]. Chlorophyll measurements were obtained from Estrada et al. [81]. In specific samples, nutrient concentrations were estimated using the World Ocean Database  [82]  due to issues with the measurements. Since not all environmental parameters were available for all stations, two contextual datasets were generated: Meta-119, including 119 stations, five environmental parameters and five spatial features (all except one station in Fig. 1a ) and Meta-57 (Figure  S4 , Additional file 7), including 57 stations and 17 environmental parameters (the five environmental parameters included in Meta-119 were considered here as well).
com/). See additional details on gene amplification and sequencing in Supplementary Methods, Additional file 6. Reads were processed following and in-house protocol  [86]. Briefly, raw reads were corrected using BayesHammer  [87]  following Schirmer et al. [88]. Corrected paired-end reads were subsequently merged with PEAR  [89]  and sequences longer than 200 bp were qualitychecked (maximum expected errors [maxEE] = 0. 5) and de-replicated using USEARCH V8. 1. 1756 [90]. Operational taxonomic units (OTUs) were delineated at 99% similarity using UPARSE V8. 1. 1756 [91] , producing 42, 505 picoeukaryotic and 10,158 prokaryotic OTUs -99%. Taxonomic assignment of OTUs -99% was generated by BLASTing OTU-representative sequences against different reference databases. BLAST hits were filtered prior to taxonomy assignment using an in-house python script, considering a percentage of identity > 90%, a coverage > 70%, a minimum alignment length of 200 bp and an e-value < 0.
A total of 21,970 and 6196 OTUs -ASVs were delineated for the 18S and 16S respectively. OTUs -ASVs were assigned taxonomy using the naïve Bayesian classifier method  [93]  together with the SILVA version 132  [94]  database as implemented in DADA2. Eukaryotic OTUs -ASVs were also BLASTed  [95]  against the Protist Ribosomal Reference database (PR 2 , version 4. 11. 1  [96] ;). Streptophyta, Metazoa, nucleomorphs, chloroplasts and mitochondria were removed from OTUs -ASVs tables. Tables of OTUs -ASVs were rarefied to 20,000 reads per sample with the function rrarefy in Vegan. Only OTUs -ASVs with abundances > 100 reads were used for the calculation of ecological mechanisms (Fig. 1b ). We tested the similarity of OTUs -99% and OTUs -ASVs between themselves as well as against a reference database (SILVA v132) in order to determine whether there were differences in the OTUs delineated by UPARSE or DADA2.
e. UPARSE vs. DADA2) have similar error-rates. We used publicly available data from the TARA Oceans global expedition  [31]  in multiple analyses. This expedition took place between September 2009 and March 2012, and includes samples from the same hemisphere during different meteorological seasons. Due to the nature of the TARA Oceans dataset, we did not perform all the analyses that were run for the Malaspina dataset. Specifically, short V9 18S rRNA-gene reads or 16S rRNA-gene miTags  [97]  from TARA Oceans precluded robust phylogenetic reconstructions, which instead were possible with the longer reads produced for Malaspina. We used data from TARA Oceans surface (≈ 5 m depth) stations only, including 41 samples (40 stations) for pico-nano eukaryotes (0. 22-3 μm [one sample] and 0. 8-5 μm [40 samples]; 18S-V9 rRNA gene amplicon data)  [34]  as well as 63 stations for prokaryotes (picoplankton, 0.
Associations between taxa and environmental parameters We analysed whether OTUs -99% displayed associations with environmental variables and between themselves. Firstly, we used the maximal information coefficient (MIC), which captures diverse relationships between two pairs of variables  [114]. The Malaspina dataset consisted of 119 stations and 17 environmental variables. In the TARA Oceans dataset, prokaryotes were analysed across 63 surface stations (including eight environmental variables), while microbial eukaryotes were analysed across 40 surface stations (including six environmental variables) (see Supplementary Methods, Additional file 6). In both datasets, MIC analyses were run using CV = 0. 5, B = 0. 6 and statistically significant relationships with MIC ≥ 0. 4 (Malaspina) or MIC ≥ 0.
5 (TARA Oceans) were considered (MIC thresholds were adjusted to the characteristics of the datasets). MIC significance was assessed using precomputed p values  [114]. Secondly, we constructed association networks with the Malaspina dataset considering OTUs -99% with > 100 reads using SparCC  [115]  as implemented in FastSpar  [116]. To determine correlations, FastSpar was run with 1000 iterations, including 1000 bootstraps to infer p values. We used OTUs -99% associations with absolute correlation scores > 0. 3 and p value < 0. 01. Networks were visualized and analysed with Cytoscape  [117]  and igraph  [118].
Acknowledgements We thank all scientists from the Malaspina 2010 expedition and crews from the  R/V Hespérides. Bioinformatics analyses were performed at the MARBITS platform of the  Institut de Ciències del Mar (ICM ; http://marbits. icm. csic. es)as well as in MareNostrum ( Barcelona Supercomputing Center)  via grants obtained from the  Spanish Network of Supercomputing (RES)  to RL. We thank the  CSIC Open Access Publication Support Initiative  through the  Unit of Information Resources for Research (URICI)  for helping to cover publication fees.
Funding RL was supported by a  Ramón y Cajal fellowship  ( RYC-2013-12554 ,  MINECO, Spain). IMD was supported by an  ITN-SINGEK fellowship  ( ESR2-EU-H2020-MSCA-ITN-2015 , Grant Agreement  675752  [ ESR2 ] to RL), PCJ by  Fundação de Amparo à Pesquisa do Estado de São Paulo-FAPESP  (PhD grant  2017/ 26786-1 ) and CR-González by a  Juan de la Cierva  ( IJCI-2015-23505 ,  MINECO, Spain)  fellowship. MM was partially supported by  CONICYT  ( FONDAP-IDEAL 15150003 ), Chile. This work was supported by the projects Malaspina 2010 Expedition ( CSD2008-00077 ,  MINECO , Spain to CMD),  INTERACTOMICS  ( CTM2015-69936-P ,  MINECO , Spain to RL),  REMEI  ( CTM2015-70340-P ,  MINECO , Spain to JMG) and MicroEcoSystems (240904, RCN, Norway to RL).
Availability of data and materials DNA sequences and metadata from the Malaspina expedition are publicly available at the European Nucleotide Archive (http://www. ebi. ac. uk/ena; accession numbers PRJEB23913 [18S rRNA genes] & PRJEB25224 [16S rRNA genes]). The data used from TARA Oceans is publicly available through Pangaea (https://doi. org/10. 1594/PANGAEA. 873275) as well as in (http:// ocean-microbiome. embl. de/companion. html) [34, 56]. The code for generating OTU -99% tables is available in: https://doi. org/10. 5281/zenodo. 259579. R-Scripts for calculating the β-Nearest Taxon Index and the Raup-Crick metric are available in https://github. com/stegen/Stegen_etal_ISME_2 013. The code for calculating the TINA and PINA indices is available in https://github. com/defleury/Schmidt_et_al_2016_community_similarity, while the code for calculating MIC is available at http://www.
INTRODUCTION The operation of selection on heritable traits leaves distinct signatures in the genes that code for them. These include, for example, depletions in amino-acid changing mutations in genes whose function is crucial. Therefore, analyzing selection fingerprints at the molecular level while considering phenotypic changes can reveal the identity of the genes that are associated with the phenotype, their novel functionalities, or which ones are no longer required. The ongoing advances in highthroughput sequencing and increasing efforts to collect phenotypic trait data (e. g. ,  Parr et al. 2014 , Tree Of Sex Consortium et al. 2014 , Rice et al. 2015 , Kattge et al. 2011)  provide the opportunity to detect associations between evolutionary patterns at the genomic level and whole-organism phenotypic traits.
Simulating data sets under the TraitRELAX model Simulations were used to investigate the power and precision of our method and to assess its accuracy in inferring the selection intensity parameter k. In general, the simulations were performed by first simulating character evolution along a given tree. As part of these simulations, the exact locations of character state transitions were recorded, thereby yielding branch partitioning based on the simulated (i. e. , true) character histories. This partitioning was used to simulate codon sequences using the RELAX sequence model. Specifically, random trees with different numbers of taxa  (16, 32, and 64)  were generated according to a birth-death process using INDELible  (Fletcher and Yang 2009)  with default parameters (speciation rate 0. 3 and extinction rate 0.
The latter serves as a measure of the effect of the reduction of multiple stochastic mappings to a single expected history on the accuracy of estimated character evolution. More details on these two measures are available in the supplementary materials. The mean values of each of these measures were computed for simulations from scenario 1. For each simulated dataset, three executions of TraitRELAX were carried out; one for each approximation approach. In the first two executions, 1000 stochastic mappings were sampled. In all approximations, the distance between the estimated character histories and the true history increases with the number of transitions in the true history (i. e. , higher values of ). When comparing the derived partitions (Fig. 2a ), all three approximations exhibit similar patterns.
Duration The mean run time of the method was computed for while allocating each run a single core in a Linux server. The run time of the method varies both with the number of taxa (mean values of 10. 5 and 14. 77 hours for simulations of 32 and 64 taxa with 300 positions) and with the number of positions in the coding sequence alignment (mean values of 9. 15 and 10. 5 hours for simulations of 150 and 300 positions with 32 taxa). Importantly, the average number of cycles required to converge to optimal assignment of the alternative model parameters remains approximately 2 consistently throughout the simulation study.