dataesr/scientific-paragraphs-categorization huggingface dataset:
Results After preprocessing of the raw sequence data, the final datasets contained in total 8 038 657 bacterial and 14 438 133 fungal sequences representing approximately 47% and 68% of all raw sequences, respectively. Across all 5 countries, 504 taxa with the finest resolution of genera for bacteria and 916 taxa with the finest resolution of species were obtained including those that were assigned to coarse taxonomic levels or were unassignable. Information about relative abundances of most abundant bacterial phyla and fungal classes can be found in Appendix 3 in S1 File. Bacterial and fungal richness and Shannon diversity were significantly different among countries (Tables  1  and 2 ). Average OTU richness per country ranged between 1469 and 2087 for bacteria and between 151 and 444 for fungi (Table  1 ).
Further, indicated by short arrow lengths, the effects of clay content For the European data sets, effects of soil disturbance and country were analyzed. For the country level data sets, the effects of disturbance, year, and vineyard were analyzed where applicable. R2: partial R2; P-value based on 9999 permutations (' ��� ' significant at p < 0. 001; ' �� ' p < 0. 01; ' � ' p < 0. 05; '. ' p < 0. 1). 1 Due to the experimental design of CH, vineyard and disturbance needs to be separated for PERMANOVA https://doi. org/10. 1371/journal. pone. 0280516. t003 and C/N ratio were neglectable. The first axis was dominated by the gradients of soil Cu and OC content, which drove the separation of the Swiss/Romanian cluster (high Cu and OC) from the Austrian/German cluster (low Cu and OC). French samples were separated from the other clusters by the second axis.
Acknowledgments We wish to thank the  Department of Soil Science and Plant Nutrition of Geisenheim University  for conducting soil analyses as well as the  McGill University and Genome Quebec Innovation Centre  for their sequencing services. We are grateful to all technical assistants and student assistants (listed in Appendix 5 in S1 File) of each country for their help in data acquisition in the field and in the lab. We thank the participating wine-growers for granting us access to their fields and for their collaboration in the project.
Results presented here are part of the  PromESSinG  project, funded by the 2013-2014  BiodivERsA/FACCE JPI  joint call for research proposals. With the national funders  Ilona Leyer  received funding from the  Bundesministerium fu ¨r Bildung und Forschung (BMBF, Germany , https:// www. bmbf. de,grant number  01LC1405A ),  Bordeaux Sciences Agro  received funding from  Agence Nationale de la Recherche (ANR, France , https://anr. fr/, ANR-14-EBID-0004 ),  Marius Skolka  received funding from the  Romanian National Authority for Scientific Research and Innovation  (Romania, https://uefiscdi. gov. ro/, CCCDI -UEFISCDI ,  Romania , project number  PN3-P3-61  contract  21/2015 , within  PNCDI III) ,  Astrid Forneck received Funding from Austrian Science Fund (FWF, Austria , https://www. fwf. ac.
Material and methods This study was approved by the Institutional Review Board of the University of Michigan School of Dentistry (HUM00222338) and all methods were performed in accordance with the relevant guidelines and regulations. Informed consent was waived by the Institutional Review Board of the University of Michigan School of Dentistry. The sample consisted of secondary data analysis of 194 de-identified cone-beam computed tomography (CBCT) scans of patients with cleft lip and palate acquired in three different university centers (University of Michigan-School of Dentistry, Hospital for Rehabilitation of Craniofacial Anomalies-University of São Paulo and University of Pacific-Arthur A. Dugoni School of Dentistry). The eligibility criteria included: patients with unilateral or bilateral cleft lip and palate or cleft lip and alveolus, mixed or early permanent dentition, and CBCT scan taken for clinical purposes.
The exclusion criteria were patients with CBCT scans with artifacts produced by orthodontic appliances. The 3D analysis was performed using two open-source software packages ITK-SNAP, version 3. 8 (https:// www. itksn ap. org)  20  , and 3D Slicer, version 5. 1. 0 (http:// www. slicer. org)  21. First, all scans were oriented using the Frankfort horizontal plane perpendicular to the midsagittal plane  22. Secondly, automatic segmentations of the maxilla were obtained using the automatic multi-anatomical skull structure segmentation (AMASSS) algorithm on Slicer Automated Dental Tools  17. 3D volumetric label maps (segmentations) and 3D surface models (vtk files) were obtained for all patients. The shape, height and width of the alveolar bone defect were assessed to determine the severity of the alveolar bone defect and to develop a classification of the severity of the alveolar bone defect using the 3D surface models.
Statistical analysis The data was split as follows: 70% for training, 10% for validation and 20% for testing (5-folds of 38 patients = 190 testing datasets). A fivefold cross-validation was performed. The performance of the algorithm was assessed using the precision, recall, F1 score, and accuracy. A confusion matrix was also performed to allow the visualization of the performance of the algorithm. The agreement between the ground truth classification and the algorithmpredicted classification was represented by the main diagonal of the table. Cells adjacent to the main diagonal (1 diagonal to the right and 1 diagonal to the left) indicate the classification of the severity index was within 1 group difference. A receiver operating characteristic (ROC) curve was also performed to show the sensitivity of the classification model performance.
Data availability The data analyzed during the current study are available from the corresponding author on a reasonable request. The code and detailed read-me files used in this study are available in our GitHub repository (https:// github. com/ DCBIA-Ortho Lab/ Cl3ft). After additional validation, this algorithm will also be available for clinical and research use in an open-source web-based clinical decision support system (Smart-DOC: https:// dsci. dent. umich. edu) and the 3D Slicer open-source platform (https:// www. slicer. org/).
An Innova Photoacoustic Gas Monitor 1312i (INNOVA 1312, LumaSense Technology Inc. , Denmark) continuously measured N 2 O and CO 2 concentrations at 90 s intervals  Liengaard et al. (2013) ). The INNOVA was calibrated from the factory and validated between measurements to reach atmospheric levels. Measurement drift and gas interferences (CO 2 and H 2 O(g) on N 2 O measurements) were taken into account in the INNOVA software and validated based on absolute gas concentration measurements using gas standards. Soil water content and soil temperature at three different depths (5, 15 and 30 cm) as well as chamber temperature were measured simultaneously with CS616 Water Content Reflectometers and thermocouples, respectively. Data were stored in a Campbell Scientific CR1000 Datalogger. The soil water content and temperature sensors were connected to the datalogger by means of two Campbell Scientific AM16/32B Relay Multiplexers.
Acknowledgements We would like to acknowledge CIRAD, CATIE, Cafetalera Aquiares farm, SOERE F-ORE-T network, Ecofor, AllEnvi, ANAEE-F, and the  ANR-Macacc  project for making this research project possible. Moreover, we would like to thank the following people for the contribution during field and laboratory work:  Patricia Leandro ,  Freddy Esteban ,  Alvarado Acuña ,  Søs Marianne Ludvigsen ,  Maja Holm Wahlgren ,  Esben Nielsen ,  Mathias Madsen ,  Alisa Ambrodji ,  Jonas Ausum Agergaard ,  Alvaro Barquero  and  Alejandro Barquero. We also would like to thank two anonymous reviewers for very useful comments.
Prioritization of drugs Previously using network growing tools, we have identified known and potentially new host targets important for the replication of IAV based on siRNA screens datasets  [11]. Then we applied knowledge-based in silico drug repurposing and identified 343 approved drugs  [11]  targeting the identified cellular host factors using MetaCore™(https://portal. genego. com/). In this study, we used those 343 drugs for prioritization. To prioritize the drugs, we used the following parameters: 1) the killing index of a drug (defined as a drug as its number of targets with serious side effects. These side effects are death, sudden death, sudden cardiac death, cardiac death, cancer, hemorrhagic strokes, heart failure and congestive heart failure)  [20] ; 2) the number of side effects (the predicted number of side effects of a drug are the union of side effects from all its binding targets)  [20] ; The side effects and killing index of each drug were obtained from the DR.
PRODIS database  [20] ; 3) the number of influenza host factors targeted by a drug, 4) the cost of the drug, and 5) the availability on the market (number of manufacturers and packagers). The costs of the drugs and their availability were obtained from the DrugBank database  [21] ; 6) the likelihood ratio of a drug name cooccurrence with words of influenza virus or viruses in PubMed abstracts (see below).
Prioritization of targets The 349 cellular host targets of the 343 FDA approved drugs were prioritized based on three parameters: 1) the degree of a target node within the network of the cellular host targets (degree is the number of edges to other cellular targets that are connected to the target node). This was obtained by network analysis of all cellular host targets targeted by the 343 FDA approved drugs using the STRING (v10) database  [22]. Highly connected targets (hubs) tend to have several cellular activities and are usually essential  [23] , hence, targeting such genes may impose toxicity, 2) the relation of the target with influenza or other viruses. This was obtained by calculating the likelihood of co-occurrence of the names of targets and influenza virus or viruses in the PubMed abstract (see details below).
The higher the number of publications that reported target and influenza virus cooccurrence would indicate the higher confidence of the target associated with influenza virus infections. 3) The druggability of the targets. The drug-gene interaction database (DGIdb 3. 0) is used to select only druggable targets  [24]. The druggable targets have been defined as the genes or gene products that are known or predicted to interact with drugs, ideally with a therapeutic benefit to the patient (https://www. dgidb. org/about)  [24].
Prioritization of drug-target pairs Text mining of the published articles in PubMed or other databases has been used to extract drug-gene relationships and automatic text mining was equal to or better than manually curated knowledge  [25] [26] [27]. This text mining approach assumes that the higher the co-occurrence of two terms within the literature databases, the more significant the association is likely to be  [28] [29] [30]. Hence, we hypothesise that the higher the number of publications in which the drugs and targets co-occur, the more likely the drugs are active on the targets. To quantify the drug-target associations in literature, we used online PubMed "esearch" tool and counted the number of publications related to the drugs and their corresponding targets. To account for unequal frequency distribution of the words of the drug, the target, influenza virus or viruses in the pubmed abstracts, we estimated the co-occurrence scores as the likelihood ratio: 𝑳𝑯𝑹 = 𝟐 𝑨𝑩𝒊 𝑨𝒊 + 𝑩𝒊 Where, LHR = Likelihood ratio, ABi = the number of PubMed abstracts that both Ai and Bi co-occur; Ai = the number of publications in Ai (e.
Microscopy and image analysis The immunofluorescently stained cells on the 96 well plates were imaged using a Leica DMi8 inverted wide-field microscope at the Centre for Structural Systems Biology (CSSB), Advanced Light and Fluorescence Microscopy (ALFM) facility. Before imaging, two or more focal points were defined for each well. After adjusting optimum exposure times for DAPI (blue) and Alexa Fluor 488 (green) channels, the microscope was automated to take images covering each well using a 10x NA 0. 32 objective. Three independent replicates of the immunofluorescence experiments and imaging were carried out for each drug. A customized Java script was used to count the number of cells (DAPI, blue) and virus-infected cells (NP, Alexa Fluor 488, green) in each image. The antiviral activity of the drugs in different concentrations was then evaluated by the percentage of virusinfected cells relative to the corresponding solvent controls.
Prioritization of drug-target pairs We used the systems biology and systems pharmacology approach to prioritize drugs, targets, and drug-target pairs combinations of the 343 FDA approved drugs that we previously predicted to inhibit host factors important for influenza virus replication  [11]. Primarily, a total of 11 parameters were used to prioritize the drug-target pairs, such as the drug-killing index and the side effects of the drugs, cost, availability, the number of host factors targeted by the drug, the connectivity of the targets, and LHR of drug-target co-occurrence in PubMed abstracts (see Material and Methods). The ranked drug-target pairs with calculated individual and total z-scores are described in Supplementary Data 1A. Among the 534 prioritized drug-target pairs, 399 targets were druggable (Supplementary Data 1B).
A549 and MDCK cells were infected with A/WSN/1933(H1N1) at MOI of 0. 1 for 1 hour and treated with the drugs for 24 hours. Cells were stained with anti-influenza NP and DAPI. The anti-influenza activity of the drugs in different concentrations was evaluated as the percentage of cells exhibiting NP staining relative to the solvent controls. The experiment was performed in triplicate and GraphPad Prism V8 was used for dose-response curve fitting. Each data point represents the average % of infection relative to the solvent controls. A. Anti-influenza activity of the drugs in A549 cells at increasing concentrations. B. Anti-influenza activity of the drugs in MDCK cells at increasing concentrations. In the second scenario, A549 cells were pre-treated with different concentrations of drugs for 8 hours; after which the drugs were removed and the cells were infected with the virus for 1 hour, followed by treatment of the drugs for 24 hours with the corresponding concentrations.
The increased cellular concentration of K + and the switch from Na + to K + trigger the uncoating of influenza and other RNA-enveloped viruses through endosomal acidification  [55, 56]. Thus, ranolazine, as the inhibitor of Na+ channel protein SCN5A and other channel proteins, may inhibit endosomal maturation and IAV infectivity. This study is not without limitations. Although the tested drugs have their protein targets annotated in databases, we have not confirmed whether these targets are indeed responsible for the effects on the IAV infection. Also, as the experimentally tested drugs targeted host factors important for different stages of the influenza infection cycle, testing drug combinations could show higher synergistic antiviral activity. Finally, and most importantly, the tested drugs showed anti-viral activity only at higher concentrations, and hence these drugs are less likely to be considered as lead compounds for drug development.
We measured temperature each night every 15 min using a Data Logger RC-5 (accuracy: ±0. 5°C; Elitech), mounted at the 3 m poles. In case of missing data (<30%), we used data provided by the Department of Meteorology, Cyprus. We manually identified each bat pass-defined as a series of minimum two echolocation calls with pulse interval (s) <1-using a full spectrum, 15 s WAV file in BatSound 4. 1. 4. (Pettersson Electronic). Over 90% of bat passes were identified to species level while ambiguous calls were assigned to species-complex (e. g. Pipistrellus pipistrellus/P. kuhlii) or grouped by genus (e. g. Myotis spp). Identification criteria were based on call characteristics (see Appendix S2 for details). We used bat activity (i. e. number of bat passes) as a surrogate of bat abundance  (Froidevaux, Boughey, et al.
| Local habitat structure and landscape analysis To control for any potential cofounding variables known to drive bat activity, we conducted local and landscape analyses. We delimited a stand of 15 × 15 m around each sampling site (i. e. centre of the citrus orchard where bat detector was installed) to document the local habitat variables, including tree height and ground vegetation cover (Table  S5  in Supporting Information). Local habitat variables collected at the stand scale were representative of the farm. We also noted the presence/absence of linear features (i. e. hedgerow or tree line) along field boundaries. There was little variation in tree height and this variable was disregarded for the statistical analysis. To get finer details on ground vegetation cover, data were collected within each of the nested square (7.
5 × 7. 5 m). Only one surveyor (PCF) conducted the survey, thus eliminating potential observer bias. We extracted landscape variables within three buffers (1, 2 and 3 km radii) that we created around the sampling sites using ArcGIS Desktop 10. 5 (ESRI; Figure  S2  in Supporting Information). The scales were selected to represent both site-specific characteristics (1 km) and main core foraging zones of bats (2 and 3 km;  Laforge et al. , 2021). We reclassified feature classes obtained from CORINE Land Cover data 2012 supplied by the European Environment Agency (www. eea. europa. eu) into 10 categories: urban area, arable, vineyard, fruit orchard, olive, other agricultural area, mixed and deciduous forest, coniferous forest, semi-natural area and water bodies (Table  S6  in Supporting Information).
organic-certified as interactions with the amounts of semi-natural habitats as independent variables. We also included several covariates to control any confounding factors as this pair was not controlled in our design, namely structural orchard features (presence/absence of woody linear feature, ground vegetation cover), landscape characteristics (amount of semi-natural habitats, urban areas and distance to the nearest water bodies) and Julian days. We compared a series of models containing all possible preselected predictor variable combinations (see Appendix S2 for more details) using the dredge function (mumIn package;  Bartoń, 2016)  for model selection. From the most parsimonious models, we only retained the models with the lowest number of parameters and (whenever possible) that included farming systems (i.
e. the variable of interest; Table  S9  in Appendix S1). We considered activity of the most active species (i. e. Pipistrellus pipistrellus, P. kuhlii, Hypsugo savii and Miniopterus schreibersii) as response variables in our models. Models were fitted with the appropriate error distribution, that is Poisson or negative binomial family, when overdispersion was present, with a logit link function  (Zuur et al. , 2009). Models were diagnosed and validated using the DHARmA package  (Hartig, 2018). Residual spatial autocorrelation in the final GLMMs models was inspected using Mantel tests and no spatial autocorrelation was found (Table  S7  in Supporting Information). We used output from the full models and considered an effect to be significant at p < 0. 05. We have checked the consistency of our results by implementing an alternative approach using the whole dataset in a unique model framework (see Appendix S2 form more details).
| Bat acoustic sampling We recorded 9826 bat passes from nine taxa (Table  S4 ) within 44 citrus orchard plots (22 conventional, 11 certified-organic and 11 transitional-organic). Nearly 88% of all bat passes (8461 passes) belonged to the Pipistrellus genus, with P. kuhlii being 70% more frequently recorded than P. pipistrellus. Only four out of nine species detected were retained for analysis due to the fact of insufficient data for the remaining species.
, 2011 , Casas et al. , 2018 , Carey et al. , 2018 , Clague et al. , 2018]. Here, we present new petrological and geochemical evolution of the 2018-2021 submarine eruption that occurred off the eastern coast of Mayotte. This major eruption provided a unique opportunity to carry out frequent and targeted sampling of eruptive products whose emplacement date was constrained with repeated bathymetric surveys. In addition, one active incandescent submarine lava flow was sampled during a dredge (DR18). This sample collection permits constituting one of the most valuable petrological and geochemical databases for any known submarine eruption and particularly given that the eruption occurred at a depth between 2500 and 3500 m below sea level  (BSL). We first report an improved dredging protocol that allowed "surgical strikes" at >2500 m BSL to individually sample each eruptive unit of the newly emplaced volcanic material.
A tensiometer monitored the load on the ship's cable to follow the dredge behavior on the seabed, and to identify potential "hooks" when the dredge was supposedly sampling. For each hook, we recorded on the dredge profile (i) the position of the BUC, (ii) the time, and (iii) the tension of the cable. The known length between the BUC and the dredge (e. g. , 365 m during MAYOBS 15 campaign) allowed us to precisely locate the dredge on the seafloor on a 2D plot of the dredge profile. For the latest MAYOBS 21 campaign, dredging was also followed with the software VGraph3D in which the topography is shown together with the position of the BUC and the vessel. Careful attention was paid to the time-series of the hooks along the dredging profile to interpret in real-time the behavior of the dredge, whether it was stuck, and whether it had lifted off the seabed, potentially empty.
Lava flows chronology Repeated bathymetric surveys with the ship's multibeam echosounder have systematically been gathered during the MAYOBS campaigns over the areas of eruptive activity following specific data acquisition protocols, leading to bathymetric grids with a resolution of 7 m to 30 m. Some of the key objectives of these repeated surveys were to be able to track the evolution of the active lava flow field along with the eruptive activity. Due to the depth of the volcano, new volcanic materials can be inferred when depth changes between two surveys exceed 10 m over a sufficiently large area. In some conditions, this depth differential threshold can be as low as 5 m. After each survey, a preliminary analysis of depth differences was achieved, and the results announced by the REVOSIMA.
A re-examination of the whole bathymetry dataset is now in progress to quantify more accurately the volumes and flux of erupted material and their evolution over time. Figure  2  synthesizes the spatio-temporal evolution of the overall contours of the different active lava flows. The compound lava flow field was mapped after each bathymetric survey using a combination of bathymetric data, differential time-lapse bathymetric analysis and ocean bottom reflectivity data. Several phases of lava flow eruption can be identified, ordered chronologically, and matched with the corresponding dredges performed during the different surveys (Figure  2 ). Phase 1 corresponds to the volcanic field and the new edifice discovered during the first MAYOBS campaign  [Feuillet et al. , 2021]. The structure, evidenced by comparing their data to those acquired during a 2014 survey by the French Naval Hydrographic and Oceanographic Service (SHOM), is composed of a main central edifice with radial ridges, up to 300 m thick and extending up to 5 km north and south, emplaced between July 2018 and May 2019  [Figure 2a, Feuillet et al.