dataesr/scientific-paragraphs-categorization huggingface dataset:
In brief, we show on real datasets that singletons may be inferred as drivers of the migration process in an unparsimonious way by structured coalescent model approximations. This result supplements a previous study on the impact of the inclusion of few ghost deme sequences on the inference of migration rates  (Ewing and Rodrigo 2006) ; however, their impact remains unclear and deserves close consideration. Interestingly, the posterior density of the structured coalescent model in BASTA is bimodal for the SARS-CoV-2 dataset. Its major mode corresponds to a migration history close to CTMC-TRAVEL and our expectations of SARS-COV-2 spread at the start of the pandemic, whereas the minor mode corresponds to the non-parsimonious scenario. Such bimodality was not observed for MASCOT in the SARS-CoV-2 analysis.
Practical implications for the analysis of empirical datasets Computation time is an important consideration in real-life situations. The CTMC model is a fast algorithm that can handle many sequences while facing little convergence issues, which made it the predominant approach. For example, CTMC and its extensions have been extensively used during the SARS-CoV-2 pandemic  (Candido et al. 2020; Lemey et al. 2020; Alteri et al. 2021; Butera et al. 2021; Dellicour et al. 2021a Dellicour et al. , 2021b;; Kaleta et al. 2022; Perez et al. 2022). In general, researchers analyzed large datasets whose composition reflected case counts  (Candido et al. 2020; Lemey et al. 2020)  or the number of hospitalizations per geographical location  (Dellicour et al. 2021b ). In our simulations, we did not test sampling strategies based on case counts, but we showed that even though the pool of available sequences is not representative of the underlying transmission process, CTMC inference should be little impacted when using even subsamples of the available sequences.
Besides, sampling strategies based on case counts require the availability of unbiased case count data in all studied locations. With BASTA and MASCOT, computational time can become rapidly cumbersome and even impractical when the number of sequences and locations increases. In parallel, these approaches estimate migration parameters with high uncertainty and can lead to bimodal structured coalescent posterior densities with a major mode and a minor mode, as observed for MASCOT on large samples of 500 sequences in the seven demes framework and for BASTA on the SARS-CoV-2 dataset. Repeating these problematic analyses with different starting values did not redeem these issues. Other studies have reported similar issues  (Richardson et al. 2018 ). However, these problematic inferences can potentially be overcome by informing structured coalescent models with additional covariate data on viral population size dynamics.
Indeed, as a result of adding such data, MASCOT-GLM not only outperformed the other approaches at estimating spatiotemporal parameters but also displayed improved mixing as expected with GLM approaches, which improves the computational burden. However, such improvements depend on the availability and informativeness of the case count data used, notably on the early viral population size dynamics. This is illustrated in our analysis of the SARS-CoV-2 data for which the addition of WHO data led to improved chain mixing and past migration inference compared to the data from Our World in Data, knowing that the dynamics are rather similar in the two datasets, but they go back to 4 January, 2020, for the WHO data and to 23 January, 2020, for the data from Our World in Data.
This framework consists in modeling the migration process with CTMC and the overall population dynamics with the GLM extension  (Gill et al. 2016)  of the skygrid coalescent model  (Gill et al. 2013). In this extension, case count over all locations could be used as a predictor of the viral population size over time. Yet, such an approach assumes a panmictic population and remains rare. It is difficult to generalize our results in regard to the number of demes. Our choice of the number of locations was influenced by the RABV scenario in Morocco. While a scenario with three demes was doable, the one with seven demes turned out to be difficult to analyze, notably due to computational burden (Supplementary Tables  S4-S6 ). More research and development are needed for datasets with a large number of locations (>15), and it currently seems unlikely that such analyses are possible at all with BASTA and MASCOT.
Perspectives In conclusion, sampling bias can be tackled at different levels of data generation and analysis in phylogeographic analyses: sample constitution, inference model choice, and data integration (e. g. through an integrated GLM). Other studies also assess the impact of sampling bias in post hoc analyses  (Chaillon et al. 2020; Vrancken et al. 2020)  or explicitly model sampling patterns  (Guindon and De Maio 2021). Although the exploration of the impact of sampling bias has increased over the recent years and more robust methodologies have been developed, many aspects remain unclear, among which is the impact of unsampled locations, biased epidemiological data incorporation, or the relative performances on even versus representative samples. Whenever possible, we would advise to opt for an even sampling strategy across geographical locations, compare the inferences of the different approaches, or compare the inferences over multiple subsamples when analyzing real datasets.
Parameterization of the between-region mobility matrix To avoid computational difficulties and over-parameterization of the different discrete phylogeographic models, we aggregated the fifteen official Moroccan regions retrieved from the GADM dataset (https://www. gadm. org) into three or seven locations (in two simulated scenarios, that are based on human demographics and ecological features (Supplementary Fig. S23 ). Dog mobility was defined across locations by fitting a radiation model with a raster of human population distribution (WorldPop (2022)) using the R package movement  (Golding, Schofield, and Kraemer 2015). In the radiation model, commuting is determined by the job-seeking behavior modeled as an absorption and radiation process  (Simini et al. 2012)  where  , is the total population in the circle of radius  , centered at  (excluding the source and destination population).
Discrete phylogeographic analysis in BEAST Generation of BEAST XML files and phylogeography inference set-up. Tailored XML template files for the BASTA and MASCOT structured coalescent models, as well as for the DTA (CTMC) model, were edited using the lxml Python package to add sequence alignments along with their metadata. Bayesian phylogeographic analyses were performed using BEAST v1. 10. 5  (Suchard et al. 2018 ) for the CTMC model  (Lemey et al. 2009) and BEAST v2. 6. 4 (Bouckaert et al. 2019: 5)  for MASCOT v2. 2. 1  (Müller, Rasmussen, and Stadler 2018) and BASTA v3. 0. 1 (De Maio et al. 2015) , making use of the BEAGLE library v3. 1. 1  (Ayres et al. 2012 ). We assumed an HKY substitution model with a strict molecular clock. When the sampling scheme resulted in a deme not being sampled, the discrete phylogeography algorithm was parameterized with the sampled demes only, i.
Analysis of phylogeographic inference output. For each BEAST analysis, adequate mixing was assessed based on the ESS values of the continuous parameters. We calculated ESS values using a Python function adapted from Tracer v1. 7. 2  (Rambaut et al. 2018). When at least one continuous parameter had an ESS value below 200, chains were resumed to reach at most 120 million iterations. Analyses that exhibited ESS values lower than 200 at this point were discarded (Supplementary Tables  S5  and S6 ). Due to the higher computational burden of BASTA, the ESS cut-off was reduced to 100. We discarded a 10 per cent burn-in in the selected chains. The combined posterior tree distributions were summarized into MCC trees using TreeAnnotator for BASTA and the CTMC, and the Python library DendroPy  (Sukumaran and Holder 2010)  for MASCOT and MASCOT-GLM (Supplementary Materials Section 6b).
RABV expansion in the Philippines We extended our comparative analysis of the CTMC model, BASTA, and MASCOT by analyzing a set of RABV genetic sequences using the three approaches. In total, 233 sequences corresponding to the RABV glycoprotein gene were sampled in the Philippines from 2004 to 2010  (Saito et al. 2013 ). In the original discrete phylogeographic analysis, the authors studied viral spread across eleven out of the seventeen Philippines regions and showed that the genetic diversity was highly spatially structured, notably at the island level  (Tohma et al. 2014). Here, we evaluated the spread across the six sampled islands (Luzon, Catanduanes, Oriental Mindoro, Cebu, Negros Oriental, and Mindanao) to compare the reconstructions on a highly structured dataset and limit the number of demes that considerably slow down BASTA and MAS-COT.
The early dynamics of SARS-CoV-2 worldwide spread Tracking viral disease spread in animal populations faces many challenges, and to our knowledge, no reliable incidence data are available for zoonoses such as rabies. In this context, MASCOT-GLM cannot readily be used. We analyzed the early worldwide spread of SARS-CoV-2 to compare the inferences of the CTMC, BASTA, MASCOT, and MASCOT-GLM. Lemey et al. (2020)  analyzed this dataset to characterize SARS-CoV-2 spread across forty-four location states by incorporating individual travel histories of sampled individuals to help correct for sampling bias and unsampled locations. By using the carefully obtained results of  Lemey et al. (2020)  as a reference, we can evaluate how the four algorithms are impacted by sampling bias. The dataset comprises 282 SARS-CoV-2 genomic sequences sampled in the five continents from 24 December, 2019, to 4 March, 2020.
We assumed an HKY nucleotide substitution model with a proportion of invariant sites, an among-site rate heterogeneity modeled by a discretized gamma distribution, and a strict molecular clock. For the CTMC, we assumed an exponential growth model for the viral demographics. MASCOT and BASTA assume a constant size model for the viral demes demographics. Contrary to the original study, we analyzed migration among six discrete locations: Africa, the USA, Asia, China, Europe, and Oceania. For MASCOT-GLM, we used the daily number of confirmed cases at the continent level from Our World in Data  (Mathieu et al. 2020)  or from the World Health Organization (WHO 2023) as a predictor of the deme sizes. The former is referred to as MASCOT-WID, and the latter as MASCOT-WHO. We smoothed the number of new confirmed cases using a seven-day moving average.
Figure5. MCC trees and median total migration counts estimated on the rabies dataset. (A-C) MCC trees and a median number of total migration counts estimated on the rabies dataset by the CTMC model, BASTA, and MASCOT, respectively. Branch width is proportional to the maximal ancestral location probability estimated by the algorithms, and branches are colored by the maximal ancestral location. Posterior support of the Negros Oriental, Catanduanes, Mindanao, and Luzon Island lineages is reported. Pie charts displayed at root nodes represent the posterior probability distribution of the root location. Median estimates of the total migration counts are reported as heatmaps. Gray tiles correspond to transitions associated with a migration rate that is not statistically supported, i. e. with a BF lower than 3.
Funding SD acknowledges the support from the  Fonds National de la Recherche Scientifique (F. R. S. -FNRS, Belgium ; Grant Number  F. 4515. 22 ) and from the  European Union Horizon 2020  project  MOOD  (Grant Agreement Number  874850 ). SD and GB acknowledge the support from the  Research Foundation-Flanders (Fonds voor Wetenschappelijk Onderzoek-Vlaanderen, FWO, Belgium ; Grant Number  G098321N ). GB also acknowledges the support from the  Internal Funds KU Leuven  (Grant Number  C14/18/094 ) and the  Research Foundation-Flanders (Fonds voor Wetenschappelijk Onderzoek-Vlaanderen ,  FWO, Belgium ;  G0E1420N ). SC acknowledges the financial support from the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases program  (Grant Number  ANR-10-LABX-62-IBEID ) and the  INCEPTION  project (Grant Number  PIA/ANR-16-CONV-0005 ).
Human engagement in protected areas We developed indicators of human engagement in recreational, educational, and scientific activities in California's state MPA network using a mixture of citizen science, naturalist, and state agency datasets (Table  S3 ). We focused on recreational, educational, and scientific engagement given that they are specific objectives of the network (Marine Life Protection Act, 1999) and given the lack of data on other cultural, spiritual, or emotional types of human engagement. We used data from two citizen science programs (MPA Watch and Reef Environmental Education Foundation) and two naturalist social networks (iNaturalist and eBird), which provide spatially referenced records of activities  (e. g. , surfing, swimming, boating, tidepooling, diving, etc. )  or observations of wildlife submitted by individual users, as indicators of recreational and educational engagement in MPAs.
) does not require permits; thus, the permit data may underestimate the amount of research occurring in the network. From 2012-2021, 5,329 scientific permits were issued for research in all 124 of California's state MPAs (Figs. S14  & S15 ). We quantified human engagement by MPA in terms of the (1) number of permits issued and (2) number of years in which permits were issued. We used records of citations issued by the CDFW Law Enforcement Division for regulatory violations occurring within California's MPA network as an indicator of compliance. From 2016-2021, 2,812 citations were issued for violations occurring within 85 of California's state MPAs (Figs. S16  & S17 ). We quantified non-compliance by MPA in terms of the (1) number of citations issued and (2) number of years in which citations were issued.
e. , the iNaturalist, eBird, and REEF indicators). We tested whether the mean log-ratio of these sums differed from zero using t-tests (i. e. , whether MPAs and non-MPAs generate different levels of human engagement). Log-response ratios were calculated after adding 1 to the engagement values occurring in both the numerator and denominator to avoid non-finite ratio values. All data analysis and visualization was done in R (R Core Team, 2021) and all data and code are available on GitHub here: https://github. com/NCEAS/ca-mpa 3. Results.
Discussion Understanding the ability and prerequisites for MPAs to achieve human use objectives is central to designing MPA networks that provide multiple benefits to people and nature. California's MPA network supports a diverse array of recreational, educational, and scientific activities. MPAs are commonly used for recreational activities such as walking, playing, or relaxing on the beach or boating, surfing, swimming, or SCUBA diving in the ocean. Engagement in these activities makes important contributions to local economies  (Pendleton & Kildow, 2006)  and to cultural, emotional, and physical health  (Hipp & Ogunseitan, 2011; Jacobson, 2020). Wildlife viewing is also common within California's MPAs and provides a platform for education and research. Many visitors engage in MPAs through citizen science programs that provide opportunities both to learn about the natural world and to contribute to meaningful scientific datasets  (Freiwald et al.
, 2013). Furthermore, the types of engagement evaluated here, especially engagement in science and tourism, likely undercount underserved and disadvantaged communities, as the geoscientific community remains largely white  (Dutt, 2020)  and the expense of tourism and even coastal parking can be a barrier to engagement. Notably, our analysis does not explicitly account for tribal and indigenous engagement with MPAs, which is an important consideration for California's MPA network. In addition, some of our datasets have known biases. For example, iNaturalist observations require the use of a smartphone, which may exclude some user groups. Understanding the ability and enabling conditions for MPAs to achieve human engagement objectives is important as entities around the world aim to protect 30% of the ocean by 2030 to meet objectives for people and nature  (CBD, 2021).
Acknowledgements This research was funded by the  California Ocean Protection Council (OPC)  and  California Department of Fish and Wildlife  as part of the  Decadal Review of California's Marine Protected Areas. CMF was funded through the  Arnhold UC Santa Barbara-Conservation International Climate Solutions Collaborative. JC was funded through  BiodivERsA METRODIVER and Fondation de France MultiNet projects. JED is grateful for support by the  California Ocean Protection Council  and the  Santa Barbara Coastal Long Term Ecological Research project (National Science Foundation   OCE-1831937 ). The paper improved from the feedback of  Freya Croft  and an anonymous reviewer. We thank  Sophie Morgan  for help with data collection,  Angela Kemsley  for sharing the  MPA Watch data, Christy Pattengill-Semmens  for sharing the REEF data,  Amanda Van Diggelen  ( CDFW ) for sharing the citations data, and  Stephen Wertz  (CDFW) and  Sara Worden  ( CDFW ) for sharing the scientific permit data.
Materials and methods Ethics declaration. The reported study (RCB # 2017-A02755-48) was reviewed by the French ethics committee, "Comité de Protection des Personnes Nord-Ouest III" (Caen, France), who granted approval on November 4th, 2017 (CPP dossier: 2017-74). All individuals enrolled in the InSPIRe cohort provided signed informed consent that included authorization for the collection of medical information and biological samples. The present work includes only the non-human fraction of DNA extracted from vaginal swabs from expectant mothers. Clinical metadata of patients was collected in a secured electronic database. Exports from the database used for analysis were anonymized. Processed vaginal swabs were labeled with corresponding anonymous inclusion numbers.
DNA was eluted in 60 µl Qiagen AVE buffer into 96-well plates containing 95 samples and one negative control. Whole-genome metagenomic sequencing. Metagenomic libraries were prepared using Illumina (San Diego, CA) TruSeq kits. Extracted DNA was fragmented to an average size of 500 bp using a Covaris sonicator. Ends were repaired prior to ligation of sequencing and barcoding primers. Following PCR amplification of the libraries, insert size was confirmed with an Agilent 2100 and qPCR was performed to ensure an effective library concentration of > 3 nM. Libraries were sequencing on a NovaSeq6000 instrument. An average of 6 Gb of highquality data was targeted for each sample, corresponding to 2. 0 × 10 7 paired-ends reads (2 × 150 bp). Customized database and taxonomic assignment. We performed taxonomic profiling of 600 + human samples (oral, fecal, breast milk and vaginal) using MetaPhlAn3  32.
Identified genera were used to filter the 'maxikraken2_1903_140GB' Kraken2 33 database; archaea, bacterial, fungi and protozoa species from these genera were retained. Assemblies for species not present in Kraken2 libraries were obtained from NCBI. When multiple assemblies were available, we prioritize the RefSeq tag "reference genome" over "representative genome" over "na. " Remaining "excluded_from_refseq" species were used if labeled "genus undefined", "derived from environmental source", "derived from metagenome", "derived from single cell" or "sequence duplications. " For assemblies, "Complete Genome" was preferred over "Chromosome", then "Scaffold" and lastly "Contig". The most recent assembly was selected for any redundant datasets. 13 species could not be found using this method; all but Coprobacter secundus and Flavobacterium thermophilum had at least one other representative of their genus already present.
The Kraken2 script kraken2-build was used to build the customized database, Cus-tomDB, from the resulting FASTA sequences, comprising 1302 species from 225 genera. Kraken2 was then used, with CustomDB, to assign taxonomy to sample reads. Phylogenetic tree for taxonomic diversity. We identified 16S sequences from either SILVA  34  or NCBI nucleotide databases for 1215 out of 1302 species present in CustomDB. Sequences were aligned using the Multiple Sequence Alignment tool, MAFFT (Multiple Alignment using Fast Fourier Transform)  35  , with the option -auto. A phylogenetic tree was generated with RAxML (Randomized Accelerated Maximum Likelihood)  36  using a random seed (-p 1234), under the General Time Reversible model of nucleotide substitution and the Gamma model of rate heterogeneity (-m GTRGAMMA).
This model includes different rates of substitution for each pair of nucleotides and across the sequence, as well as different frequencies of occurrence of nucleotides. We retained the tree with the best likelihood upon 100 bootstraps (-N 100). The remaining 87 species were added in three steps: (1) For each missing species, the Most Recent Common Ancestor (MRCA) of species from the same genus, present in the tree, was calculated using the python library dendropy. If no common genus was found, the family level was used. (2) The mean distance of all the species of the same genus (or family) to the MRCA was calculated; (3) Missing species were added at the MRCA node with a length equal to the calculated mean distance. Diversities computation. Alpha-diversity (intra-sample richness) and beta-diversity (inter-sample diversity) were each computed using two methods which differed in their inclusion, or not, of phylogenetic distances.
PCoA was computed in Python (ver. 3. 7) using the statsordination module of scikit-bio (http:// scikit-bio. org, ver. 0. 5. 6). The pcoa function was used to calculate plot coordinates, taking a Bray-Curtis dissimilarity matrix as input. The pcoa_biplot function was used to extract component (taxa) contribution vectors. Bray-Curtis dissimilarity was calculated using the diversity. beta-diversity module of scikit-bio. Intermediate data including sample coordinates for each principal component, and vector for each taxon component, were collected as Pandas (https:// pandas. pydata. org, ver. 1. 0. 1) data frames and visualized using Plotly (https:// plotly. com, ver. 5. 3. 1).
Random forest modeling and prediction. Machine learning analysis of the data was implemented by training a random forest (RF) classifier. Features used to train the classifier were normalized counts of species for each sample. We used the RandomForestClassifier in the Python package sklearn (https:// scikitlearn. org, vers. 0. 24. 3) for model training and sample classification. Results were tested and interpreted using the model_selection and metrics functions of sklearn. The matplotlib package (https:// matpl otlib. org, ver. 3. 4. 2) was used to plot the results. Training of the RF classifier was run with 10 5 estimators. In order to obtain features with the strongest signal, the classifier was trained with features present in at least 25% of samples and with a minimum mean of 50 counts.
Results A prospective study of pregnant women. We established a prospective cohort of 2313 pregnant women with the goal of studying the vaginal microbiota, its community composition and the presence of pathogenic bacteria in the peripartum period. Inclusions and data collection were performed in accordance with applicable laws and ethical standards. Ethical approval for the study was obtained in November of 2017. Patients were enrolled, and samples collected, at three French hospitals in the Paris region (AP-HP): Hôpital Bichat Claude-Bernard, Hôpital Louis-Mourier and Hôpital Port-Royal. Informed consent was obtained prior to inclusion. In order to be included in the study, individuals had to have passed their 22nd week of gestation and be at least 18 years old. This work reports on 749 mothers whose pregnancies stared between 2018 and 2020, where vaginal swabs were collected through June of 2021 and whose clinical data records had been completed and validated.