dataesr/scientific-paragraphs-categorization huggingface dataset:
aureus  (Vonaesch, Anderson and Sansonetti, 2018; Oikonomou, Addis, Chassard, Nader-Macias, Grant, Delbès, Bogni, le Loir, et al. , 2020). The comparative genomics performed using BPGA software yielded 4200 genes. In turn, the core genome is formed by 2008 genes among all strains. It represents 48 % of the S. aureus genes average predicted in this study and a core genome 1,7-fold proportionally higher than a previous study of comparative genomic analysis in 64 S. aureus strains, which resulted in a core genome of 56 % on average of the predicted genes  (Bosi et al. , 2016b). The core genome analysis was also performed among ovine and other host strains, resulting in 2149 and 2119 genes, respectively. It represents 79 % (ovine strains) and 81 % (other hosts) of the average genes predicted by the PATRIC server.
, 2020; van den Brom et al. , 2020). Additionally, exfoliative toxins were found in S. aureus isolated from cows, and these proteins are an agent of a scalded-skin syndrome in humans  (Vautor et al. , 2009; Que and Moreillon, 2014). In addition, the exclusive ovine genes were compared to the VFDB database through BLASTp. They resulted from 14 predicted proteins, such as the coagulase (coa), secreted by S. aureus and causes clotting in the host's plasma. As a result of mechanisms of escape from the immune system  (Salaberry et al. , 2015; Javid et al. , 2018) , hyaluronate lyase precursor (hysA) plays a role in subcutaneous infection  (Makris et al. , 2004; Ibberson et al. , 2014)  and toxins (Le  Maréchal, Seyffert, et al. , 2011; Monistero et al. , 2018). It was previously reported that different levels of iron metabolism, transcriptional regulators, and exoprotein production could contribute to ovine mastitis severity, and different levels of toxin expression were related to the pathogenic potential of genomes isolated from bovine mastitis (ben  Zakour et al.
Acknowledgements We thank the  agencies Coordination for the Improvement of Higher Education Personnel (CAPES) , CNPq  ( Conselho Nacional de Desenvolvimento Científico e Tecnológico)  and  Minas Gerais Research Funding Foundation (FAPEMIG). Our thanks to the  Institut National de recherche pour l'agriculture , l'alimentation et l'environnement (INRAE),  Post Graduate Program in Genetics and Post Graduate Program in Bioinformatics  at the  Federal University of Minas Gerais , and the  Omics Science Network (RECOM).
Lagrangian Analysis and Reanalysis Virtual parcel trajectories and their T-S properties are obtained using the velocity and tracers fields of the eddy-permitting Mediterranean Sea reanalysis  (Simoncelli et al. , 2014). The data set consists of daily mean outputs of zonal and meridional velocities, temperature and salinity fields from 1987 to 2018, defined on the model native grid  (Mesinger & Arakawa, 1976). The subset of data spanning years 2000-2012 is used because it includes the decade of the circulation adjusted with satellite altimetry and Argo. Additionally, this decade partially overlaps with the analysis of García-Lafuente et al. (  2021 ). The ocean model used by the reanalysis is NEMO  (Madec et al. , 2022)  which solves the primitive equations with a spherical coordinate system.
The grid has a horizontal resolution of 1/16° × 1/16° and 72 unevenly spaced vertical layers. The model is nested in the Atlantic within the monthly mean climatological fields of a global model. Air-sea fluxes of momentum, heat and water are computed with bulk formulas based on the ERAInterim reanalysis fields from the European Centre for Medium-Range Weather Forecasts (ECMWF). The temperature and salinity profiles from CTDs, XBTs, MBTs, bottles and Argo floats, provided by different European databases, together with along-track satellite altimetry sea-level anomalies, are assimilated by a three-dimensional variational system  (Dobricic & Pinardi, 2008). Satellite sea surface temperature (SST) is used to correct the model heat flux at the air-sea interface by a relaxation of the surface temperature toward the observed SST.
, 2017; Thomas et al. , 2015). A large number of parcels is required to obtain accurate quantitative diagnostics, as suggested by  Döös (1995). Parcels are released at the Strait of Gibraltar every day from 1 January 2005 to 31 December 2012, during the first cycle of the loop (8 years). The sum of all parcels initialized over these 8 years is about 6 million. The initialization period  (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  is shorter than the looping period  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  to allow backward advection for at least 5 years by the proximal velocities, before looping is introduced. Three-dimensional Lagrangian trajectories are computed using the parcel-tracking software "Ariane," developed by  Blanke and Raynaud (1997).
Data Availability Statement The reanalysis data are available at https://doi. org/10. 25423/MEDSEA_REANALYSIS_PHYS_006_004. The Lagrangian software employed, Ariane version 2. 3. 0, is available at http://ariane. lagrangian. free. fr/ariane. html. For reproducibility of all major results of the Lagrangian analyses performed for this study, the customized code of the Ariane software, the simulated Lagrangian trajectories, and post-processing scripts are made available through https://github. com/giuli9/Ariane-postprocessing and https://doi. org/10. 5281/zenodo. 7705093.
Almost all slides were imaged on an LSM880 confocal microscope with an Airyscan module (Zeiss) using a 63× objective. The coexpression experiments were acquired on an LSM980 Airyscan 8Y using a 63× objective. Processing of the raw Airyscan images was performed on the ZEN Black software. Postprocessing was performed using the FIJI software  (52). Quantification of RFP-LACV/BUNV-N HsMX1 aggregates HEK293T cells were plated in 96-well plates and cotransfected with constructs of interest. Twenty four hours later, images of the transfected cells were acquired using an EVOS XL Core microscope (Thermo Fisher Scientific), and quantification of aggregated versus cytoplasmic RFP-LACV/ BUNV-N phenotypes was performed manually on FIJI. Results were analyzed using GraphPad PRISM. For representative images, HEK293T cells were plated in 24-well plates on coverslips precoated with Poly-L-lysine (Sigma-Aldrich) and cotransfected with constructs of interest.
Predicted 3D models were compared to crystal (PDB: 3SZR) or low-resolution EM (PDB: 3ZYS) structures for examination of the overall folding of the three main domains (GTPase, BSE, and stalk) and also compared to 3D models based on these templates and built with Modeller v9. 19  (56). Structural alignments of AlphaFold models and X-ray structure gave RMSD values (for all backbone atoms) of 0. 4, 0. 2, and 1. 4 Å for GTPase, BSE, and Stalk domains, respectively. MD simulations were performed using NAMD3 (57) and CHARMM36m force field  (58). Briefly, each protein was immersed in an explicit solvent box (TIP3P water model) using 10 Å of edge in each direction (in respect to protein dimensions), then neutralized with NaCl ions at a physiological concentration (0. 154 M) and energy minimized for 50 ps using the conjugate gradients method.
Statistical analyses Statistical analyses were performed with the GraphPad Prism software. The analysis types performed are indicated in the figure legends. Comparisons are relative to the indicated condition. p-values are indicated as the following: ns = not significant, p < 0. 05 = *p < 0. 01 = **p < 0. 001 = *** and p < 0. 0001 = ****. No indication means the analyses were nonsignificant and were not represented for esthetic purposes.
Acknowledgments-We are thankful to  Prof Georg Kochs  ( Freiburg University, Germany ) for the pcDNA3-RFP-LACV-N and SlMX1 plasmids as well as providing LACV; to  Dr Gert Zimmer  ( Institute of Virology and Immunology, Switzerland ) for the Gpseudotyped VSV*ΔG-fLuc luciferase particles; and to  Dr Yves Gaudin  ( I2BC, Gif sur Yvette, France ) for providing VSV. This work was supported by the institutional funds from the  Centre National de la Recherche Scientifique (CNRS)  and  Montpellier University. We acknowledge the imaging facility MRI, member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). Author contributions-J. M. , O. M. , and C. G. conceptualization; J. M. , M. A. -A. , L. C. , M. B. , O. M. , and C. G. methodology; J.
We converted the two levels of daily water needs  20  to total annual per capita volumes of water required for basic human needs (Table  2 , adapted from a previous study  20  ). The two access levels of 'all needs' represent the demand on the hydrological cycle and include the same domestic needs and the volume of water required to produce food, energy and infrastructure at the two access levels. See ref. 20 for the full methodology used to derive these numbers. 1. Surface water flows to meet minimum human needs We compared the volume of water that was available under the safe and just ESB at the river basin scale with the different volumes of water to meet human needs. The daily per capita water needs were converted to spatially distributed gridded annual volumes by multiplying the demand metrics by a distributed population dataset for 2020  52  and then summed over river basins.
To quantify the extent to which river flows are outside the safe and just ESB in a given river basin, we first calculate the number of months in a year when the contemporary altered flows are more than 20% different from pristine flows using the long-term mean gridded discharge data. We then represent these data as the proportion of months in a year with more than 20% difference for each grid cell in the global river networks. For the purposes of the spatial analyses, we defined a river basin as being outside the safe and just ESB when the long-term mean observed total annual discharge at the river mouth was more than 20% different from the long-term mean pristine total annual discharge  19. We used total annual discharge for this analysis for comparison with the groundwater ESB, which is on an annual time step.
Global surface water hydrology We derived the pristine and altered monthly river flow datasets from the water balance model (WBM) river discharge outputs  53  at a 6 min grid-cell resolution using the TerraClimate high-resolution dataset of monthly climate forcings  54  for the period 2000-2020 using Python version 3. 9. 7. River basin delineation and flow routing configurations are defined by the WBM 6 min topological river network used to establish local discharge and river flow  53. The pristine and altered WBM runs use the same climate forcings for the 2000-2020 time period, but the altered runs use human alterations to the water cycle, including water extraction for irrigation and large reservoirs as an indicator of flow alteration. As the WBM disturbed model runs do not include smaller alterations not associated with dam operations, such as direct extraction for stock and domestic water, we may under-estimate the extent to which a given basin is outside the surface water ESB.
Global groundwater dynamics Hydrological measurements of volumetric changes in aquifer storage are critical in assessing groundwater status, but these measurements are considerably limited in several regions of the world  10. Given that the aquifers in some regions are typically not monitored, global-scale assessments of baseline aquifer volumes are difficult. To circumvent this, the Gravity Recovery and Climate Experiment (GRACE  59  ) mission has been used to track changes in several large aquifer systems around the world  60. In this study, changes in groundwater were quantified using the GRACE data covering the period 2003-2016 (data files are accessed at http://www2. csr. utexas. edu/grace/RL06_mascons. html). GRACE measures monthly changes in terrestrial water storage (TWS), which is the sum of soil moisture, groundwater, surface water, snow water and canopy storage and is expressed as: TWS = SMS + GWS + SWE + SWS + CS ( 1 ) where SMS is the soil moisture storage change, GWS is the change in groundwater storage, SWE is the change in snow water equivalent, SWS is the change in surface water storage (that is, inland surface and reservoir storage) and CS is the water storage change in canopy.
In addition, uncertainties caused by residual ice and snow cover from areas (for example, Patagonia, Alaska, the Himalayas, the Swiss Alps) with large variations were minimized by masking such regions using the world distribution of glaciers and ice-cap extents (geospatial data layer showing boundaries of such glaciers). This decision acknowledges the higher uncertainties in the simulations of these quantities by the GLDAS model. Furthermore, some glaciers are small and may be obscured, but a buffer zone of 1° was created to help flag and remove such glaciers. Overall, the groundwater estimation process here is based on the water budget approach, which has been widely used in GRACE-derived groundwater storage studies (for example, refs. 65,66). There are several GRACE-TWS products available from different providers, but the TWS data used in our study are a mass concentration (mascon) product (GRACE RL 06 version 02) obtained from the Center for Space Research.
These mascon products are preferred to other GRACE solutions (for example, those based on spherical harmonics) as they exhibit less signal leakage and a posteriori filtering is unnecessary as the mascon product relies on geophysical constraints to suppress noise in the data (for example, ref. 67). The estimated annual recharge volume used in this study was based on the time series of groundwater anomalies. For each groundwater pixel, annual recharge was calculated by quantifying the difference between the maximum groundwater depth in a given year and the shallowest observation of the preceding year (Supplementary Fig. 3 ). The annual recharge depth at each GRACE pixel was converted to a volume by multiplying the area of the pixel. As such, our estimate of annual recharge volume is the net of aquifer discharge, recharge and any groundwater abstraction that may have occurred at a given location.
Trends in annual rainfall volume The trends (mm yr -1 ) in annual rainfall were based on monthly Global Precipitation Climatology Centre (GPCC) precipitation data (mm). The GPCC-based precipitation is one of the widely used gridded rainfall products because it consists of quality-controlled observational data from 67,200 gauged stations worldwide  69. The GPCC data are available on a 0. 25° spatial resolution and were accessed from the NOAA repository (https://psl. noaa. gov/data/gridded/data. gpcc. html). The monthly grids (spatial and temporal dimensions) of GPCC rainfall were generated from the scientific file format (popularly called Network Common Data Form) and accumulated to annual values using scripts written in Matlab R2018A version, underpinned by the Mapping and Aerospace Toolboxes.
Acknowledgements The Earth Commission is hosted by  Future Earth  and is the science component of the  Global Commons Alliance. The  Global Commons Alliance  is a sponsored project of  Rockefeller Philanthropy Advisors , with support from  Oak Foundation ,  MAVA ,  Porticus ,  Gordon and Betty Moore Foundation ,  Tiina  and  Antti Herlin Foundation ,  William and Flora Hewlett Foundation , and the  Global Environment Facility. The Earth Commission is also supported by the  Global Challenges Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper. C. N. is supported by the  Australian Research Council  ( DE230101327 ). J. G. acknowledges funding from the ' Water Allocation and Rights Study ' funded by the  Dutch Ministry of Infrastructure and Water Management (IenW) , under grant number  5000005700  and case number  31184622 , and the ' Water Commission Report on Water Economics ' project, contributing to the  Global Commission on the Economics of Water (GCEW)  and funded by the  Netherlands Enterprise Agency (RVO)  with reference  UNWCS22002.
Data availability The groundwater data were derived from GRACE (http://www2. csr. utexas. edu/grace/RL06_mascons. html) and the GLDAS (https://disc. gsfc. nasa. gov/datacollection/GLDAS_NOAH025_3H_2. 1. html). Annual rainfall data came from the GPCC precipitation data (mm) available at https://psl. noaa. gov/data/gridded/data. gpcc. html. The data for the surface water hydrology were derived from existing published datasets  53, 57.
Article neutralization and binding phenotypes of each hmAb (Figures 2D-2F and S4 ). Positive and negative correlations with coefficients I between ± (0. 51 and 0. 70), ± (0. 71 and 0. 90), and ± (0. 91 and 1. 0) were respectively considered as moderate, strong, and very strong. In vitro activities may show negative correlations with protection when they are measured on the basis of hmAb concentration, such as in the lethality assay (log EC 50 ). In this case, potent effectors with lower values of EC 50 will associate with higher protection. Ptotal most strongly correlated with lethality (r = À0. 97, p < 0. 0001), motility inhibition (r = 0. 91, p < 0. 0001), and Pskin (r = 0. 88, p = 0. 0004). These data suggest that the hmAbs that most potently slowed or killed sporozoites in vitro were most protective against in vivo skin challenge.
Background subtraction of nonspecific binding was performed through measurement of association in buffer alone. Data analysis and curve fitting were performed using Octet software, version 7. 0. Experimental data were fitted with the binding equations describing a 1:1 analyte-ligand interaction. Global analyses of the complete datasets, assuming binding was reversible (full dissociation), were carried out using nonlinear least-squares fitting allowing a single set of binding parameters to be obtained simultaneously for all concentrations of a given hmAb dilution series.
22 At least 5,000 events in the sporozoite gate were acquired. Viability was defined as the percentage of GFP+PIÀ sporozoites to the sum of GFP+PIÀ and GFPÀPI + sporozoites. The Circumsporozoite Precipitation Reaction (CSPR) seen in the presence of anti-CSP antibodies was determined by the intensity of the forward scatter-width signal (FSC-W), which was observed to indicate the length of the sporozoites. CSPR span was determined by subtracting the bottom from the top value of the sigmoidal FSC-W curve. Data were analyzed using the CytExpert 2. 0 software (Beckman Coulter).
The resulting suspension was transferred to an 18-well slide (iBidi) and centrifuged at 400 g for 3 min at 4 C. The slide was then allowed to equilibrate at 37 C, 5% CO 2 for 3 min in the incubation chamber (Incubation System S, Zeiss) of an inverted epifluorescence wide-field microscope (AxioObserver Z. 1, Zeiss) equipped with a LED illumination system (Colibri2, Zeiss), a CCD camera (AxioCam MR, Zeiss) and controlled by the AxioVision software (version 4. 8. 2. 0, Zeiss). Time-lapse movies were then recorded for 2-4 min at a rate of one image per second with an EC ''Plan-Neofluar'' 10x/0. 3 objective (Zeiss) using a 470 nm LED and a matching filter cube (43HE, Zeiss) to excite and detect GFP and thus visualize sporozoites. Average sporozoite velocity over the first 2 min of the acquisition was determined using the MTrack2 plug-in from Fiji.
ACKNOWLEDGMENTS We would like to thank the team of the  Center for Production and Infection of Anopheles (CEPIA, C2RA, Institut Pasteur)  for providing mosquitoes, the staff of  Central Animal Facility (C2RA, Institut Pasteur)  for animal care, and the team of the  Photonic BioImaging platform (UTechS PBI ,  C2RT ,  Institut Pasteur)  for providing access to the spinning-disk microscope and support. This work was supported by funds from the  Institut Pasteur , the  Agence Nationale de la Recherche  ( ANR ,  French National Research Agency)/Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -project number SporoSTOP  ANR-19-CE15-0027 , and the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence ''Integrative Biology of Emerging Infectious Diseases' '  - project number  ANR-10-LABX-62-IBEID.
M. C. A. -B. is funded by the  Pasteur -Paris University International PhD Program. A. S. was supported by contract  HHSN261200800001E  from the  National Cancer Institute ,  NIH. Y. F. -G. and F. Z. thank the  Bill and Melinda Gates Foundation and Bloomberg Philanthropies  for their continued support. The antibodies provided by the Center for AIDS Reagents repository at the  National Institute for Biological Standards and Control, UK  ( 6F8  and  2C4 ) were produced through the  European Commission   FP7 European Research Infrastructures for Poverty Related Diseases (EURIPRED) project  ( INFRA-2012-312661 ), funded by the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 312661 -EURIPRED. The graphical abstract was created with  BioRender. com.
Hence, elaborating on the HCC tumor biology is of great value for HCC patients  [5] , namely for diagnosis or identifying possible treatment targets. Moreover, epigenetic molecular markers can be easily detected and quantified in peripheral blood liquid biopsy, which is a minimally invasive tool that can identify cancer at an early stage as well as monitor disease progression  [6]. Micro-RNAs (miRs) are a group of epigenetic small nonprotein coding RNA (ncRNA) with 18-22 nucleotide length  [7]. According to miRBase https://www. mirbase. org/ (accessed on 28 October 2022), which is one of the microRNA databases (Release 22. 1), the miR count is 38,589 entries. MiRs bind to messenger RNAs (mRNA), sponging them to control their intended protein production  [8] , a mechanism linked to carcinogenesis for one of the different cancer hallmarks  [9].
Bioinformatics Databases Analysis Bioinformatics identification of the investigated miRs panel (Table  1 ) (accessed on 28 April 2022) retrieved from miRDB https://mirdb. org/mirdb/index. html and human ncRNA gene database GeneCaRNA https://www. genecards. org/genecarna as well as the miRPathDB v2. 0 https://mpd. bioinf. uni-sb. de/overview. html.
hsa-miR-155-5p/hsa-miR-199a-5p Min-Max median (inter-quartiles) 2. 7-29,737. 5 12. 3 (3. 8-29. 9) 0. 15-41,764. 80 17. 4 (6. 9-207. 9) NS Data are median (inter quartile range (1st-3rd quartile)), statistics were computed using SPSS software. * Statistical significance p-value < 0. 05; NS, non-significant for comparison of AFP-HCC and AFP+HCC sub-classification. HCC, hepatocellular carcinoma; LC, liver cirrhosis; AFP, alpha fetoprotein.
ucsc. edu/cgi-bin/hgGeneGraph?gene=MIR21&supportLevel= ppi&geneCount=25&geneCount=25&geneAnnot=gnf2&1=OK&lastGene=MIR21). For miR-155, top interacting genes that have known inhibitor drugs are calcium-binding protein P calmodulin (S100P), which is targeted by cromoglicic acid (from DrugBank); angiotensin II receptor type 1 G-protein coupled receptor (AGTR1), targeted by the Sartan drug group; chemokine (C-C motif) ligand 2 (CCL2), targeted by Danazol; and mimosine (http://genome. ucsc. edu/cgi-bin/hgGeneGraph?gene=MIR155&supportLevel=text& hideIndirect=on&geneCount=25&geneAnnot=none&1=OK&geneCount=25). However, hsa-miR-192-5p and hsa-miR-199a-5p genes (miR192 or miR199a) are not present in this current gene interaction database.
hsa-miRs Target(s) Analysis Through the analysis of hundreds of miRNA-target interactions from high-throughput sequencing experiments, the bioinformatics tool MirTarget was able to create miR database (miRDB), an online library for miR target predictions and functional annotations (https: //mirdb. org/custom. html). For hsa-miR-199a-5p target expression analysis, the expression levels of predicted hsa-miR targets retrieved in miRDB (accessed on 28 October 2022) are epidermal growth factor (EGF), mitogen-activated protein 3 kinase 11 (MAP3K11), cell-division-cycle-associated 7-like, and zinc finger proteins. Mirpath uses reverse search to identify miRs involved in KEGG pathways using the DIANA-TarBase v7. 0 method and by searching clusters/heatmap results for KEGG targeted pathways. The investigated miRs are shown in Figure  3  (https://dianalab.