dataesr/scientific-paragraphs-categorization huggingface dataset:
The aim of our analyses was not to distinguish between factors (climate, habitat) filtering out species at the establishment stage of the invasion process, but to understand what drives global species movements. As all species records are a reflection of global species flows, we kept all records for the analyses presented in the main part of the manuscript. However, for readers interested in the effects of indoor locations on the global biogeography of alien ants, we have added all figures excluding indoor locations as Appendix S1. We delimited the countries and continents based on the administrative database GADM version 3. 6. For mapping, we used the Mollweide projection. We defined a species "flow" as the number of species introduced from one region to another region, following previous research on global species exchanges  (van Kleunen et al.
| Interception data We have sourced previously published interception records for the United States and New Zealand from 1914 to 2013 (described in detail in  Bertelsmeier et al. , 2018 , the data are available online with the paper). In total, this dataset contains 69 alien ant species intercepted on cargo, goods, mail and baggage and has information on the country of origin for each interception and therefore allows calculating the proportion of secondary interceptions for each species (i. e. the proportion of all interceptions of a species which come from a country where the species is not native).
| Descriptive biogeography of alien ants To quantify to what extent alien species assemblages are dominated by cosmopolitan species, we determined the proportion of Exo1 to Exo6 species in each country. We also determined the number of countries with a majority (>50%) of Exo6 species, for which we have no information about the origin of donor regions. We also calculated the number of species established on one, two, three, four, five or six continents, and we determined the number of countries in which these species are established. We acknowledge the limitation of using the number of countries as a proxy of spread, yet distribution data for most species are only available at that scale.
Therefore, each row contains the compositional data of species flows (for either Exo1, Exo6 or ALL species) to one of the 41 countries. Differences among profiles of geographic origins are represented in the 2-D space of the factorial map. The two first axes represent 60% of the total inertia. To test whether species flows including more cosmopolitan species are more similar among countries than flows of species exotic in a single continent, we assessed the homogeneity of countries' profiles of geographic origins of alien ants. To do this, we tested whether the variances of the coordinates in the CA space differed among Exo1, Exo6 and ALL species flows, using Levene's test. We also tested if the profiles have a stronger regional signature for species exotic in one continent than for species more likely to be introduced via bridgehead effects (Exo6 and ALL species flows).
ALL regions. Indeed, these global assessments do not distinguish between species exotic in one or more continents. Recent advances in building large datasets of species distributions have enabled researchers to make quantitative assessments of intercontinental species exchanges  (Capinha et al. , 2017; van Kleunen et al. , 2015). Although it is interesting to map hotspots of alien species and the regions where these alien species are native  (Dawson et al. , 2017) , labelling the links between native regions and exotic ranges of species as "flows" may suggest that the native regions have indeed acted as direct donors of these alien species. Here, we show that potential flows of species exotic in a single continent were coherent with global trade flows. This coherence between an important invasion pathway of ants and potential global species flows was lost when flows of cosmopolitan species were included.
are associated with differences in invasion risks. Therefore, we used general import flows to represent global flows of potential transport vectors. To calculate import flows to all countries, we used cumulative import data from 1998 to 2017 extracted from the UN Comtrade Database (United Nations Commodity Trade Statistics Database, http://comtr ade. un. org/ db/ (accessed May 2019)). This dataset contains dyadic trade flows between pairs of countries, given in US dollars per year. Such comprehensive data are not available for earlier periods; as most imports over the last two centuries have occurred during this recent period of globalization, we expect these relatively recent imports to have left their footprint on the flows of ants. Because no import data were available for four previously defined administrative units (Puerto Rico, Christmas Island, Norfolk Island and Marshall Islands), they.
Data Acquisition Periodic field surveys inside the Nyamulagira caldera were carried out between November 2014 and February 2020 with the support of the Observatoire Volcanologique de Goma (OVG) and the MONUSCO (United Nations Organization Stabilization Mission in DRC). During each mission (from a few hours to several days), a comprehensive series of field observations and measurements were conducted to document the physical configuration of the active pit crater, using Laser Range Finder Binoculars. These field campaigns were supplemented by helicopter observations performed by MONUSCO whenever they could fly over this region. which made it possible to estimate the pitcrater floor level at different states of its evolution with an accuracy of about 10% of the absolute value. These estimations were complemented by Sentinel-1 Synthetic-Aperture Radar (SAR) images processed by the MOUNTS volcano monitoring system  (Valade et al.
Unwrapping of the interferograms was done with SNAPHU to recover ground deformation in the radar line-ofsight (LOS) in meters. The ground and satellite data relating to the depth of the pit crater were used to calculate the volume of lava accumulating inside the pit crater (assuming an inverted truncated cone geometry with   = 113 m and   = 250 m; Fig. 2a ). Volumetric estimates have been finally used to calculate growth rate (  ) for each analyzed period (Fig. 2b ). Thermal emissions have been detected using infrared data provided by the moderate resolution imaging spectroradiometer (MODIS), compiled within the Middle InfraRed Observation of Volcanic Activity (MIROVA) system  (Coppola et al. , 2016b; www. mirovaweb. it)  to facilitate their analysis and determine the volcanic radiative power (VRP; Fig.
Code and Data Availability All data generated or analyzed during this study are provided with this article, and are available at https://doi. org/10. 26037/yareta:khivh7h2prgvxauarlckdo7awi MIROVA data, and the associated codes for computing the thickness and time of occurrence of the lava flows, as well as thermal evolution of the lava layers are available at https://doi. org/10. 26037/yareta:5futgivyezdw5gdqqfogybt3ky Sentinel-1 and Sentinel-2 data used in this study are available and freely downloadable from the Copernicus Open Access Hub (https://scihub. copernicus. eu/).
Escherichia coli is an important cause of bloodstream infections (BSI), which is of concern given its high mortality and increasing worldwide prevalence. Finding bacterial genetic variants that might contribute to patient death is of interest to better understand its mechanism and implement diagnostic methods that specifically look for those factors. E. coli samples isolated from patients with BSI are an ideal dataset to systematically search for those variants, as long as the influence of host factors such as comorbidities are taken into account. Here we performed a genome-wide association study (GWAS) using data from 910 patients with E. coli BSI from hospitals in Paris, France; we looked for associations between bacterial genetic variants and three patient outcomes (death at 28 days, septic shock and admission to intensive care unit), as well as two portals of entry (urinary and digestive tract), using various clinical variables from each patient to account for host factors.
Lastly, small sample sizes can lead to insufficient power to find causal variants. Identifying microbial genetic elements that contribute to the outcome of BSI is of interest to i) better understand the molecular mechanisms of microbial infection, and ii) improve patient care and prediction of clinically-relevant bacterial traits based on microbial genomics data, which is increasingly becoming available with very low turnaround time  17. In this context, we performed GWAS on data from two large clinical observational prospective multicentric studies from the Paris area (Septicoli 10 and Colibafi 8 ) involving a total of 910 adult patients with E. coli BSI. We used the clinical information from each patient, such as age, comorbidities and treatment as covariates to reduce the influence of host factors in the association analysis  18.
A combined dataset of 910 BSI patients with matching clinical data and bacterial isolates whole genomes In this study we combined data from two similar clinical studies (Colibafi 8 and Septicoli 10 ), conducted across 11 teaching hospitals, belonging to the same institution, the "Assistance Publique-Hôpitaux de Paris" (AP-HP), across and around Paris, France. The earlier study (Colibafi, 2005) originally included 1,051 patients across the whole of France, with information about bacterial genetic determinants obtained through PCR molecular assays; in this study we kept only those 365 samples originating from 8 hospitals from the Paris area to avoid geographical biases. From the later study (Septicoli, 2016-7) we kept all the 545 samples from 7 hospitals in the Paris area. Bacterial genomes of these samples from both studies, generated by Illumina technology, were available  19.
We focused on three outcomes for the patients represented in the combined dataset, namely death at 28th days, presence of a septic shock and admission to an intensive care unit (ICU); we note that these outcomes are not mutually exclusive. We found that the prevalence of these outcomes in the two studies was 10. 7%, 24% and 14. 5%, for death, septic shock and admission to ICU, respectively (Figure  1a ). We found that the prevalence of death and admission to ICU was very similar between the two studies, with 12. 3% and 9. 5% of deaths in the Colibafi and Septicoli studies, respectively, and admission to the ICU reported for 12. 3% and 16% of patients. Conversely, we found a much higher incidence of patients experiencing septic shock in the Septicoli cohort: 32. 5% of patients versus 11. 2% in Colibafi (Figure  1b ).
These variations may be due to the different hospitals contributing the clinical data between the two studies: indeed, even though both studies are exclusively focused on the AP-HP teaching hospitals in Paris, only 4 out of 11 hospitals are included in both studies  19. We additionally focused on the reported portal of entry of the BSI, which has been previously found to be predictive of patient outcome; urinary and digestive tract portals of entry were the most prevalent in the combined dataset -58. 2% and 35. 7% of patients, respectively. The other reported portals of entry all had a prevalence below 5% (Figure  1a ), and we therefore chose to only use the urinary and digestive tract portals of entry for all subsequent analyses. We found that entry through the digestive tract was reported for 41.
8% patients in the Septicoli study, compared to 26. 6% in the Colibafi study, which again may be due to differences in the hospitals providing the data for both studies (Figure  1b ). The age distribution between the two studies is comparable, with median age of the patients being 67 and 69 years in the Colibafi and Septicoli studies, respectively (Figure  1c ). To reduce the influence of these differences between the two studies on our analyses, we introduced the study provenance as a covariant in the combined dataset (Supplementary Table  1 ).
002). This analysis underscores the influence of the E. coli portal of entry on BSI outcomes, which in turn could have an association with specific bacterial genetic elements. We found that no E. coli phylogroup was associated with patient death (p-value > 0. 01), consistent with earlier analyses from the two separate studies  8, 10  , in contrast to what we previously observed in a mouse model of BSI, in which we found that the B2 phylogroup was associated with the death of the animal  20. This difference suggests that host and clinical factors may have a larger influence on patient outcomes than the genetic background of the bacterial isolate, at least in this dataset. We also observed no association between an isolate's phylogroup and a septic shock or admission to the ICU. The absence of these genetic background effects does not imply that there are no "locus effects", meaning that individual genetic variants may still be found to be associated with patients' outcomes.
This is unsurprising, as the decision to admit a patient to the ICU can depend on the subjective assessment of a physician considering a patient's comorbidities, as well as other subtle differences in care protocols. Conversely, the estimated heritability due to genetic effects for the portals of entry varies in magnitude between the combined dataset and the two cohorts alone, but we nonetheless found it to be > 0 in the three datasets. In particular, we found that genetic effects could explain 23% of the variance of both urinary and digestive tract portals of entry (95% CI 3. 8% -71. 3% and 3. 9% -71. 4%, respectively), which is more than double the variance explained by the isolates' phylogroup (Figure  3a ); this suggests that a genome-wide association analysis is likely to discover genetic variants associated with the portal of entry for BSI.
The papGII/papGIII operon is associated with the pathogen portal of entry In order to account for both core and accessory genome genetic variability, which is one of the main differences between GWAS studies in human and bacterial datasets, we associated unitigs generated from a de Bruijn graph of all the bacterial isolates against the target variables  22, 23  ; namely the three patient outcomes and the two major portal of entry for BSI. We used a linear mixed model for the association, which has been shown to better correct for the influence of bacterial population structure in the association  24. In order to account for the host and clinical factors on target variables, we conducted the association with the clinical variables as covariates  25. Since our earlier analysis indicated that the portal of entry can influence patient outcome, we added this information as covariates when looking for bacterial genetic factors associated with the three patient outcomes.
A larger sample size could reveal additional bacterial factors involved in bacteremia Our heritability estimates and association results are in good agreement both with previous results about the difficulty of finding bacterial genetic elements associated with virulence from clinical cohorts  16  and with the importance of the pap operon in enabling severe UTI  13. We next asked whether it would theoretically be possible to find even more associations from cohorts measuring E. coli BSI; would an increase in sample size lead to the discovery of more bacterial genetic factors able to affect the establishment and the outcome of BSI? To answer this question, we generated a dataset of 10,000 simulated genomes -one order of magnitude higher than the dataset presented in this study -with mutation and recombination rates similar to those of E.
coli, and two phenotypes with either "high" or "low" heritability (0. 2 and 0. 05, respectively)  26. For each phenotype we selected 28 causal variants with a range of effect sizes. We then ran a GWAS on the full dataset and in two smaller samples, in order to determine the empirical statistical power (Figure  5 ). We found that in this simulated dataset an increase in sample size by an order of magnitude would be needed to discover most of the causal variants (mean recall 57%) for the phenotype with high heritability, which is a large increase from the sample size most similar to this study (1,000 samples, mean recall 5%). Conversely, we found that for the low heritability scenario only a relatively low statistical power (mean recall 10%) could be achieved with a large sample size of 10,000 samples, and no power when using 1,000 samples (mean recall 0).
In fact the animal model is a controlled environment, as the individual tested are healthy and homogeneous (same sex, age, weight and diet); furthermore the standardized inoculation uniformizes the portal of entry, thus allowing for an unbiased evaluation of the intrinsic virulence of each strain  33. The data presented here once more seems to point to either a negligible influence of bacterial genetic variation on infection outcomes when compared to host and clinical factors, a complex trait influenced by multiple loci, or to a lack of statistical power due to a relatively low sample size. The results from the heritability analysis from this dataset of combined cohorts is mixed in this regard, as we found that the variance in a patient's death or septic shock is not explained by bacterial genetics, while we found that locus effects may explain up to 19% of the variance in admission to ICU.
Dataset The Colibafi and Septicoli studies were prospective observational cohort studies conducted in tertiary-care teaching hospitals in the Paris area. Adult patients with E. coli BSI were included. Only patients previously included in the study for a previous BSI episode, and patients receiving vasopressors before the onset of BSI were excluded. E. coli BSI was defined as the isolation of E. coli from at least 1 blood culture bottle. Data were prospectively collected by clinicians in each centre on two separate visits: Visit 1 corresponded to the time of BSI (the day the blood culture was drawn; data were collected retrospectively 24-48h hours later, once the blood culture had grown) and Visit 2 corresponded to the day of discharge or in-hospital death (or day 28 if the patient was still hospitalized).
For each episode, the first E. coli strain collected in the blood culture was identified. The primary endpoint was vital status at discharge or Day-28 (i. e. Visit 2). In each centre, an infectious diseases clinician and a microbiologist were in charge of including patients and completing the case report form (see Colibafi and Septicoli groups in the Acknowledgments section). A steering committee was in charge of implementation and a scientific committee responsible for scientific overview. From the combined dataset we removed those variables with more than 15% of missing values (whether the patient had received a transplant, neutropenia, pregnancy status, body mass index, patient discharge route), and we added a binary variable to record the study provenance of each sample. We imputed the remaining missing values using the MICE package, v3.
Univariate and multivariate analysis We tested the association between clinical variables and patient outcome in a similar way as it was done in the original studies  8, 10  ; briefly, we first applied a min-max scaler to the age variable to bring it in the [0-1] range. For each patient outcome we then tested each clinical variable using a logistic regression as implemented in the statsmodels package, v0. 11. 1, using the study provenance as a covariate. We used those variables with association p-value < 0. 1 to run a multivariate logistic regression, using a backward stepwise selection method to construct the final model, using the MASS package v7. 3_51. 3  48.
Heritability estimates We estimated narrow-sense heritability for the five target variables, using 2 different covariance matrices; one built from the phylogroup membership of each strain and another using a kinship matrix built from the core genome phylogeny, in which the distance from the most recent common ancestor is used for each pair of samples. For the latter covariance matrix we also used the same clinical covariates as in the GWAS analysis (see below). We used Limix v3. 04  55  , assuming normal errors for the point estimate and we computed the 95% confidence intervals using the ALBI package (commit 90d819e)  56.
Acknowledgements We thank  John Lees  and  Lars Barquist  for providing suggestions on the final version of the manuscript. We are grateful to  Jean-Marie Lacroix  for having kindly shared his knowledge on opgE. This work was partially funded by a  Translational Research Grant  from the  Agence Nationale de la Recherche (ANR) ,  Ministry of Higher Education and Research, France 2015  (grant  n°ANR-15-CE-17-0019-01 ). ED was partially supported by the  "Fondation pour la Recherche Médicale " (Equipe FRM 2016, grant number  DEQ20161136698 ). GR was supported by a " Poste d'accueil " funded by the  "Assistance Publique-Hôpitaux de Paris" (AP-HP)  and the " Commissariat à l'énergie atomique et aux énergies alternatives" (CEA ) personal grant for his PhD. MG was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC 2155project number  390874280.
Code availability Apart from the software packages mentioned in the previous sections, the following were used to run the analysis and generate the visualizations presented in this work: pandas v1. 2. 2  68  , numpy v1. 20. 0 69 , scipy v1. 6. 0 70 , matplotlib v3. 3. 4  71  , seaborn v0. 11. 1  72  , biopython v1. 79  73  , reportlab v3. 5. 68  74  , gffutils v0. 10. 1, jupyterlab v3. 0. 7  75. Most of the analysis were incorporated in a reproducible pipeline using snakemake v6. 5. 0 76 and conda v4. 10. 3  77, 78  , which is available as a code repository on GitHub (https://github. com/microbial-pangenomes-lab/2021_ecoli_pathogenicity) under a permissive license (MIT).
54 [1. 96-3. 33] 5E -12 pulmonary tract 2. 10 [1. 25-3. 51] 0. 005 admission to ICU digestive tract 1. 57 [1. 19-2. 08] 0. 002. Univariate analysis on the combined dataset. Only clinical variables significantly associated with BSI outcomes are shown. CI, confidence interval.
Further analyses are required to confirm if the endometrioid subtype is associated with outcome and how this impact is independent of other usual prognostic criteria  [10]. The ESME (Epidemiological Strategy and Medical Economics) Research program aims to centralize existing real-world patient data in oncology  [11, 12]. ESME data platforms provide independent and highquality aggregated real-life data available for analysis to the scientific and medical community, as well as to the pharmaceutical industry and health authorities. It compiles data from patient's electronic medical records, inpatient hospitalization records, and pharmacy records. Several studies have already been published related to the ESMEmetastatic breast cancer database with more than 22,000 patients included  [13] [14] [15].
The ESME OC data platform is an electronic health records-derived database populating data on consecutive patients diagnosed and/or treated (whatever the stage of OC disease) between 2011 and 2017 in one of 18 French Comprehensive Cancer Centers. It offers a large real-world multicenter cohort, with demographics, tumor characteristics, clinical features, clinical events, and treatment-related data that may lead to a better knowledge of ovarian cancer evolution. We propose in this work to explore the subset of patients with endometrioid EOC included in the ESME-OC database and to compare their clinical and pathological characteristics to that of patients with serous carcinoma included in the same time period.