dataesr/scientific-paragraphs-categorization huggingface dataset:
The results for Tests 1, 2, 3 and 4 are reported in Figs. 3 4 5 6 , respectively. For each fumarolic source, we provided the ECDFs of the observed and simulated daily averages of H 2 S and CO 2 concentrations (G56, Figs. 3 4 5 6  e-f). The ECDFs of the simulated concentrations are obtained either by randomly varying the gas fluxes (blue curves) or by using fixed estimates (green curves) at source (Figs. 3 4 5 6 ). Regarding the simulated data obtained by randomly changing the gas fluxes, for each source we sampled the H 2 O water flux 11 times a day from the ECDF in Appendix B. Being the investigation period of 13 days, to simulate daily variations in gas flux, we ran 11 simulations per day resulting in 143 total simulation runs, which implies a significant amount of computational time as one simulated day required nearly 3 h on a PC with a i5 dual-core processor.
Hence, the choice of one or another did not impact strongly on our results, implying that either meteorological dataset could be used as input to the DWM. Test 3 showed that a low resolution DEM (25 m) does not affect the model outputs (Fig. 5 ). Notable differences between the daily averages of observed and simulated concentrations are instead observed in Test 4 (Fig. 6 ), when the model accounted for a finer computational grid resolution (5 m). In Tests 1, 2 and 3 we noted that the gas concentration variability shown by the simulated daily averages obtained by randomly varying the water vapour fluxes at source (blue curves) well captured the observed data (red curves) at TAS (Figs. 3 4 5 ), but showed marked over estimations at G56 (Figs. 3 4 5 , panels a-b). The simulated daily averages for CS tend to underestimate the observations in all Tests, especially when a higher resolution of computational grid is taken into account (Test 4; Fig.
g. ,  Erfurt-Cooper, 2018)  where residents and visitors commonly expose themselves to unhealthy levels of toxic gases without adequate understanding of the health risks. In this respect, the choice of a specific modelling tool and its validation represent the very first step for achieving unbiased hazard quantifications. Here, we showed that modelling using a new implementation of a gas dispersion code (DISGAS-2. 0) and varying DEMs and meteorological and field data, is able to capture the order of magnitude and the statistical property of the natural variability displayed by the averages of the observed CO 2 and H 2 S concentrations over the entire period of simulation. Due to lack of simultaneous data from gas sources (i. e. , gas flux at specific times, for a period of few days at least) and distal gas concentration measurement points (at hundred of meters to few km away from the source), we could not extend our validation up to a larger spatial scale.
Considering a limited area of investigation, we showed that meteorological data obtained from local weather stations and reanalysis datasets (as those from ERA5) are capable of reproducing realistic wind fields on a terrain such as around the edifice of La Soufrière de Guadeloupe, as inputs for the gas dispersion modelling. Moreover, it is well known that the topography can significantly influence plume transport and diffusion  (Kaneston Scientific, 1998). In particular, topography and surface roughness can change wind speeds, directions and turbulence characteristics; for example, the presence of significant valleys can restrict horizontal movement and dispersion encouraging the development and persistence of drainage flows  (Douglas et al. , 1990). Previous applications of DISGAS concerned simple, relatively flat topographies such as calderas (i.
Acknowledgements SM, FD, LS, JS, DJ, RM, JCK, AC were supported by the  European Union 's  Horizon 2020 project EUROVOLC  (grant agreement no. 731070 ). FD has also been supported from  UK National Capability funding (BGSInnovation Flexible Fund). This work is published with permission of the Executive Director of  British Geological Survey (UKRI). We thank the  OVSG-IPGP  for providing additional data acquired in the framework of recurrent funding in France from the  IPGP , the  Service National d'Observation en Volcanologie (SNOV, INSU-CNRS) , the  Ministère de la transition écologique (MTE) , and the  PREST INTERREG EU  project. This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001 I. E. Université de Paris ANR-18-IDEX-0001  and the  Laboratory of excellence ClerVolc contribution 487.
Background: Waterborne disease outbreaks (WBDO) associated with tap water consumption are probably underestimated in France. Aim: In order to improve their detection, Santé publique France launched a surveillance system in 2019, based on the periodical analysis of health insurance data for medicalised acute gastroenteritis (mAGE). Methods: Spatio-temporal cluster detection methods were applied to mAGE cases to prioritise clusters for further investigation. These investigations determined the plausibility that infection is of waterborne origin and the strength of association. Results: Between January 2010 and December 2019, 3,323 priority clusters were detected (53,878 excess mAGE cases). They involved 3,717 drinking water supply zones (WSZ), 15. 4% of all French WSZ. One third of these WSZ (33.
Introduction Waterborne disease outbreaks (WBDO) are still a public health issue worldwide  [1] [2] [3]. They are generally caused by the microbiological contamination of tap water, and acute gastroenteritis (AGE) is the most common syndrome in affected people. Faced with this issue, many countries have implemented dedicated surveillance systems  [1, [3] [4] [5]. However, notification processes (voluntary or mandatory) vary, as do definitions for WBDO. Standardised information is collected including epidemiological, clinical and, occasionally, biological data, as well as data on the drinking water supply zone (WSZ) in question and operating and distribution incidents. A WSZ refers to a geographically defined area within which water intended for human consumption comes from one or more sources, and where water quality may be considered as approximately uniform.
They are usually notified to health authorities through voluntary reporting by general practitioners or pharmacists following official drinking water monitoring results, or following consumer complaints (smell, taste, etc). Rarely, WBDO are also notified through the Food-borne Infectious Outbreak (FIO) mandatory surveillance system, which is also managed by SpFrance. The lack of a specific WBDO surveillance system leads to underestimation of their health impact. Studies based on improving sensitivity, by using health insurance data to record medicalised acute gastroenteritis (mAGE) cases, have proven both their utility in the study of infectious risk attributable to tap water, and their applicability in retrospective WBDO detection systems  [13] [14] [15] [16]. In this context, SpFrance, in partnership with the Ministry of Health and regional health agencies (ARS), designed a national French WBDO surveillance system based on health insurance data.
Health data sources The National Health Data System (SNDS) database is managed by the French National Health Insurance system. It covers almost all the French population (99%) and records individual sociodemographic information about healthcare services received and pharmacybased reimbursements for medicines. Partial or full systematic reimbursement for purchased medicines is a key feature of the country's insurance system  [17]. For the WBDO surveillance system, the SNDS is used to identify mAGE cases, defined as follows: any AGE case who consulted a general practitioner and purchased medications prescribed to treat AGE at a pharmacy within 2 days of the consultation. A specific algorithm built in 2011 and regularly updated is used to identify mAGE cases  [18, 19]. Cases are aggregated according to.
Surveillance system management and implementation The French national WBDO surveillance system is run under the supervision of SpFrance (Figure  2 ). Its general framework and the requirements for the organisation of data collection between local stakeholders are detailed in a governmental directive  [25]. The directive stipulates a 3-year start-up period (2019-2021) during which at least one cluster per year and per county must be investigated (i. e. almost 100 clusters per year nationally). The detection algorithm mentioned earlier is routinely updated every 4 months, over a retrospective period ranging from 3 to 7 months to take into account the consolidation delay for SNDS data (see Health data sources sub-section above). During the surveillance system's start-up period 2019 to 2021, the criteria to define priority clusters for environmental investigation were: (i) clusters associated with at least 10 excess mAGE cases and a risk ratio ≥ 3 and (ii) the repeated detection of clusters for the same WSZ.
Data management and visualisation: EpiGEH web application Standardised tools are used together with a web application (using the Shiny R package) named EpiGEH, developed by SpFrance to share analyses and investigation results between regional and national agencies  [26, 27]. EpiGEH has two primary functions: (i) centralisation of data from WBDO surveillance system such as mAGE cases, environmental information forms, cluster epidemiological information and cluster classification and (ii) visualisation of detected clusters (Figure  3 ). Data from environmental investigations and the classification of the level of plausibility of contamination from waterborne origin can be entered into the application using an interactive purpose-built form which is then integrated into the national cluster database.
Further assessment of priority clusters is a necessary step to complete this surveillance system. In the pilot study, the implementation time for environmental investigation was estimated at between 0. 5 to 2 days per cluster  [16]. When data finally become available, environmental investigations of priority clusters by various ARS and more detailed and frequent information by water suppliers should continue to provide important insights into the circumstances surrounding WSZ pollution, especially in terms of whether it is related to a resource, treatment and/or distribution incident. This information, even if only occasionally collected for a priority cluster, will help identify possible avenues for preventive actions. Besides underestimating the real health impact of WBDO, the new system has other limitations.
The first is its lack of reactivity, as the consolidation period for the health data (mAGE/SNDS) used is quite long  (2 months). This delay between the onset of a WBDO and the ability to detect it, means that the new system is not suitable for promptly stopping the course of an outbreak, or indeed for preventing new cases. Instead, it is more adapted to identifying at-risk WSZ, preventing recurrence and contributing to prevention strategies. Maintaining existing surveillance through voluntary notification is therefore essential to ensure that public health authorities can respond quickly to WBDO. Secondly, although the retrospective detection method used by the system is able to detect clusters potentially associated with a common route of exposure (i. e. tap water), any detection needs to be validated using additional environmental investigations focusing on, for example, microbiological data in tap water or potential technical incidents.
Acknowledgements Our thanks to the epidemiologists of Santé publique France who contributed to data collection and the development of the surveillance tools used at the national and county levels, to  Farida Mihoub  of Santé publique France, and to  Jude Sweeney  for the English revision of the manuscript. We also thank the various ARS involved, the  French Ministry of Health , and the  French Agency for Food, Environmental and Occupational Health and Safety , for their contribution to the implementation of the surveillance system and waterborne disease outbreak field investigations.
, date of consultation and age group (1-15 years and > 15 years). A 2-month data consolidation delay is required until mAGE indicators are available, as it takes this amount of time for the health insurance system to update its records. The Information System for Health Security (SIVSS) is managed by the Ministry of health. This national database centralises, in real time, all health and envi- ronmental events voluntarily notified. Notified AGE outbreaks typically occur in nursing homes or schools. Suspected WBDO, voluntarily notified by general prac- titioners, and tap water restrictions following microbio- logical tap water contamination, are also recorded. For each notification, investigation results (epidemiologi- cal, environmental), if any, are recorded in the SIVSS database. The National Food-borne Infectious Disease (FIO) Mandatory Reporting System (NFIOMRS) is managed by SpFrance.
Health professionals must report FIO to ARS [20]. The FIO notifications include information on cases, symptoms, date of symptom onset and sus- pected meals. The SIVSS and NFIOMRS databases are not specific to WBDO and have very low sensitivity for detecting them. They are cross-referenced with detected mAGE clusters to determine whether certain clusters have previously been notified. Environmental data sources The Health and Environment Water Information system (SISE-Eaux d'alimentation) is managed by the Ministry of Health and regularly updated by ARS. This database includes information on WSZ infrastructure, such as size of each population served at the geographical intersection between WSZ and the municipality. In April 2020, ca 24,200 WSZ supplying 35,573 munici- palities were listed throughout all French territory.
A Time-To-Occlusion (TTO) of 6. 92 months on average was observed with an SE of 0. 24 months in the aneurysm population surveyed. TTO was estimated with statistical significance from the resulting model for the data examined and was capable of explaining 92% of the data variation. Conclusions: Porosity was found to have the most correction power when assessing TTO, proving its importance in the process of aneurysm occlusion. Still, further Verification and Validation (V&V) of treatment simulation in more extensive, multi-center, and randomized databases is required.
It is only very recently that imaging modalities and the use of additional radiopaque markers on FDs allowed visualizing individual wires after implantation. Still, FD porosity in vivo is a cumbersome and time-consuming task and cannot be performed in a predictive manner by the interventionist  (14). Recent works have described simulation-based methods capable of such assessment in vivo, with high accuracy (+95%) and low average error (∼3%) compared to the actual device porosity measured in vivo  (15). In this study, we used device simulation software ANKYRAS (Galgo Medical S. L. , Barcelona, Spain) to assess local FD porosity, FD expansion, and FD mesh angle. The main hypothesis driving this study is that the FD morphology of the device implanted in the patient, obtained using computational simulation technology provided by ANKYRAS, will have a statistically significant relationship to the treated aneurysm TTO.
MATERIALS AND METHODS In this study, we used imaging data from cerebral aneurysms treated with FDs in three different medical centers in Europe, namely, The University Hospitals of Tours, Toulouse, France, Hospital Clínic de Barcelona, Barcelona, Spain, and Hospital Universitari Germans Trias i Pujol, Badalona, Spain. The institutional review boards of the three institutions approved the data collection and analysis protocol for this study. From the clinical database at each institution, we retrospectively identified cerebral aneurysm treatments using FDs that had a pre-treatment 3D Rotational Angiography (3DRA), a posttreatment angiogram (2D or 3D) with visible FD radiopaque markers and had undergone at least one follow-up catheter angiogram, irrespective of the follow-up time. Follow-up time ranged from 6 to 12 months throughout the sample.
FD Porosity Porosity was simulated using ANKYRAS software (www. ankyras. com, Galgo Medical S. L. , Barcelona, Spain). A thorough explanation of the validation of the porosity simulation is not presented here. That matter deserves a proper study and is currently undergoing. Still, we provide the currently available validation results for the sake of completeness. The accuracy of porosity simulation was validated by comparing the simulated porosity, which is rendered as a scalar field indicating the ratio of [free surface]/[total surface] at each point of the FD surface, to the porosity measured from the device. Devices were implanted physically (on 3D silicon phantoms) and virtually (on identical digital replicas of the silicon phantoms). The silicon phantoms were scanned in 3D (using 3DRA images), and the porosity of the implanted FD was extracted by manually segmenting each FD wire from the 3DRA images of the silicone models with the deployed FD inside.
Local Porosity Assessment at the Aneurysm Region For each aneurysm, simulation was done using standard ANKYRAS software workflow. The ANKYRAS software workflow involves segmentation of the 3DRA image, mesh cleaning (to eliminate spurious vessels and noise from the image), centerline extraction of the vessel 3D model, and placement of the device at the location of the aneurysm (Figure  1 ). The FD is placed by defining the distal position of the implanted device. The same FD brand and size used on the patient were virtually placed on the pre-treatment 3D model, simulating FD length, porosity, and expansion  (20). At a glance, the computation of the local device porosity is performed by considering the local braiding angle of the FD wires, the local expansion of the device (the local braiding angle is modified when the expansion changes), and the location of each point on the vessel (the region of the device on the inner side of a vessel curve has a more closed mesh than the region on the outer side).
Statistical Data Analysis Two different analyses were performed on the data. The first one searched for a relation between the FD morphology and the occlusion/patency of the aneurysm at follow-up, such as the whole cohort (occluded and patent aneurysms). In a second analysis, device morphology and TTO were related, only considering occluded aneurysms in the cohort. Patients and deployed device data analyzed were: • Patient data: sex and age. • Aneurysm data: location, depth, volume, width, and maximum and minimum neck diameter. • Flow diverter morphological data: • Mesh porosity: total surface coverage computed as metal covered area/total area. • Mesh angle along the longitudinal direction: It is the angle between the FD wires at the crossings using as reference the longitudinal direction of the device.
DISCUSSION The data were collected in three centers from different countries, using various protocols and four disparate FD brands. The time between treatment and follow-up (FU) varied depending on the patient and the center. Due to the retrospective nature of this study, data heterogeneity in FU was unavoidable. Although the data are heterogeneous, and the number of cases for different brands is not balanced, the overall sample size is sufficient to produce statistically significant results. The results obtained from the simulation were compared to the porosity indicated by the manufacturers in the product tag. This might be considered as trivial, since we expect personalized simulation to provide a better estimate. Still, this is the only baseline available. We expected simulation to be better, but this remained to be proven.
A second analysis was run on the data excluding patent aneurysms (i. e. , non-occluded) at 12 months from the dataset. On this subset, the time when the aneurysm was observed occluded (TTO) was modeled as the response variable, with FD brand and patient age as factors, and mesh porosity, mesh angle, mesh expansion and aneurysm morphology (MAAI) as correction coefficients. The resulting ANCOVA model showed that patient age had statistically significant predictive value, but not the FD brand. From that, no differences in the response variable TTO were associated with the FD brand, as opposed to patient age, which showed that both younger (between 34 and 47 years) and older (between 53 and 66 years old) patients presented shorter response times than mid-age ones in our cohort. These findings are non-conclusive and deserve further study in a larger cohort.
First, a proper question of interest will need to be defined. In this context, the question could be the identification of the ideal device size that ensures a safe and early occlusion of an aneurysm. Secondly, a context of use (COU) will need to be defined. This COU will consider the availability of pre-treatment data, available device brands, and sizes at the intervention site, and a post-treatment data acquisition protocol that ensures a sustained follow-up of the treatment. In the third place, model risk will need to be assessed in the context of the patient resulting harmed from the use of FD simulation for sizing. The definition of the model's risk should consider the effect of the simulation output on the interventionist decision-making process (model influence) and the consequence of negative treatment outcomes resulting from inaccurate or incorrect decision-making due to model output (decision consequence).
A comprehensive, accurate functional annotation of genes is key to systems-level approaches. As functionally related genes tend to be co-expressed, one possible approach to identify functional modules or supplement existing gene annotations is to analyse gene co-expression. We describe TopoFun, a machine learning method that combines topological and functional information to improve the functional similarity of gene co-expression modules. Using LASSO, we selected topological descriptors that discriminated modules made of functionally related genes and random modules. Using the selected topological descriptors, we performed linear discriminant analysis to construct a topological score that predicted the type of a module, random-like or functional-like. We combined the topological score with a functional similarity score in a fitness function that we used in a genetic algorithm to explore the co-expression network.
INTRODUCTION In model organisms, geneticists produced a large amount of functional data that attributed one or more functions to many genes. Yet, numerous human, murine, fly, worm, arabidopsis, and yeast genes still have no or sparse functional annotations  (1) ; the situation is obviously worse in non-model organisms. The available information on gene function was categorized in projects such as the Gene Ontology (GO)  (2) , the Kyoto Encyclopaedia of Genes and Genomes (KEGG)  (3) , and the Reactome pathway database  (4). These projects are key to genome-wide approaches as they help organize the thousands of genes harboured by prokaryote and eukaryote genomes into functional 'pathways' or 'biological processes'. The resulting gene classification is widely used to infer functional knowledge from omics data, e.
(21). Functional annotations were also used in gene co-expression network analysis as in EGAD  (22). Recently, a machine learning approach, 'Multi-Features Relatedness'  (23) , was developed; it combined co-expression information, functional annotations, protein-protein interactions, and textual information. It was limited to study the multi-feature relationship between pairs of genes, not multi-gene functional modules. Finally, the most classical approach was to use functional annotations a posteriori to assign one or more function(s) to the different topologically defined clusters using classical network algorithms such as WGCNA  (13) , CoEx-pNets  (24) , ARACNE  (25) , GeneMania  (26) , etc. In the present work, we used a different strategy and favoured a gene set-centred approach. Starting from a seed gene set, we aimed at exploring co-expression networks to test whether these genes, or a subset thereof, were significantly co-expressed, and to identify novel genes significantly co-expressed with the seed genes.