dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Ellen Blokland ,  Duaa Elmelik ,  Emeline Gorecki ,  Marlie Jacobs-Camps ,  Charlene Piriou ,  Mariateresa Pizzo , and  Saskia van der Velde-Visser  for technical assistance. We thank  Béatrice Bocquet ,  Dominique Bonneau ,  Krystyna H. Chrzanowska ,  Hélene Dollfus ,  Isabelle Drumare ,  Monika Heusipp ,  Takeshi Iwata ,  Beata Kocyła-Karczmarewicz ,  Atsushi Mizota ,  Nobuhisa Nao -i,  Adrien Pagin ,  Valérie Pelletier ,  Rafal Ploski ,  Agnieszka Rafalska ,  Rosa Riveiro ,  Malgorzata Rydzanicz ,  Blanca Garcia Sandoval ,  Kei Shinoda ,  Francesco Testa ,  Kazushige Tsunoda ,  Shinji Ueno  and  Catherine Vincent-Delorme  for their cooperation and ascertaining STGD1 cases. We thank  Rolph Pfundt  for his assistance in WES data analysis. We thank  ERN-EYE  and  ERDC networks , the  Japan Eye Genetics Consortium , and the  East Asian Inherited Retinal Disease Society.
This suggests that cross-reactions might signal previous exposure to multiple pathogens rather than false positives, which is more in keeping with seroprevalence figures. Finally, our findings support the need for a larger-scale seroepidemiologic study on Reunion Island aimed at assessing the prevalence of Q fever with more accuracy and at better understanding the transmission pathways of C. burnetii. The collection of detailed clinical data will be needed to identify symptomatic vs asymptomatic infections. In the meantime, special attention should be paid to occupations at risk, unprotected ritual slaughtering practices that are popular on the island, and possible high exposure of people living near ruminant farms.
Acknowledgments The contributors are indebted to  Professor Koussay Dellagi  and  Dr. François Favier  for conducting the  CoPanFlu-RUN program. They acknowledge all the members of the  CIC-EC de la Réunion , our beloved and regretted friend  Dr. Alain Michault , and the researchers of the CRVOI. Financial support. The CoPanFlu-RUN program was supported by funds from  CPER-ERDF  ( Contrat Programme Etat/Region  and  European Regional Development Fund) ,  INSERM/IMMI , and CRVOI. Disclaimer. The funder of the CoPanFlu-RUN program did not participate in the design, preparation, data analysis, or decision to publish the manuscript. Potential conflicts of interest. All authors: no reported conflicts of interest. All authors have submitted the  ICMJE Form for Disclosure of Potential Conflicts of Interest.
Phenotypic Studies Cells were fixed in Phosphate Buffer Saline (PBS) containing paraformaldehyde 4% (PFA) for 20 min and dried overnight in 70% ethanol. The number of MT, their diameter and the number of nuclei per MT were counted after hematoxylin/eosin staining in three independent experiments during a 120 h differentiation time course. For each morphometric study, six randomized visual fields at 100 X magnification were studied using ImageJ 1. 45s software (Wayne Rasband, NIH, Bethesda, MD, USA)  [57].
Statistical Analysis All the experiments were performed at least three times and the results were reported as the means ± standard error of the means (SEMs). Statistical comparisons were performed using two-tailed t tests or ANOVAs two-way with Sidak's post-test (Figures  4A  and 5 ) implemented in GraphPad Prism 7 (GraphPad Software Inc. , San Diego, CA, USA). A p value of 0. 05 or less was statistically significant.
In particular, the accuracy of in-plane effective diffusivity is one key property that must be firmly established due to the importance of lateral rib-channel transport mechanisms in the fuel cell. In this study, we propose a fully customizable methodology to extract pore networks from CT images. This method is based on watershed-derived pore network modelling, and it can be used to compute the IP effective diffusivities using the open source pack-age OpenPNM. 16 The code is validated using the experimental IP effective diffusivities reported by Rashapov et al. 36  for two sets of GDLs (SGL 25AA and 34AA) over a range of compression ratios (or compressive strain). The focus of this study is to characterize the IP effective diffusivity, since little attention has been paid to this GDL property despite its influence on how to select an optimal channel-to-rib ratio and GDL thickness.
However, unlike the MBM, the pore space was divided using a customized Watershed 46 algorithm on the inverse of the distance map. A set of 3D matrices of identical shapes were generated as described below. These matrices, once generated, were accessed at various points in the network extraction algorithm (Procedure 1, below). While the algorithms discussed were applied to 3D datasets, the figures accompanying the following sections were created in 2D for illustrative clarity. The extraction algorithm presented here is admittedly slower than similar algorithms, which take advantage of highly optimized watershed sub-algorithms (a great example of which was presented by Gostick  29  ). We utilized the following algorithm written from the ground up in efficient languages, such as C++ combined with Matlab for the interface, thereby providing a highly customizable platform to add rules for preventing pore over-or under-segmentation at any point in the process.
In addition to the pore location, the extraction algorithm was also used to compute the pore diameter, volume, and connectivities of the neighboring pores. The throats (not depicted in Figure  5b ) were extracted from the binary images, and their diameter and connectivity were also computed, as discussed in Section 2. 2. This data was used to define the conductivity between each pore in the network, according to Equations 6 to 9. The pore network was then analyzed with respect to the pore and throat diameter distribution produced via the pore network segmentation. The frequency of pore and throat diameters were computed over a range spanning from 1 to 150 μm every 5 μm for all GDL samples. The resulting distributions are presented in Figures  6a to 6d  and fitted to the log-normal law presented in Section 2.
A linear system of N-equations (corresponding to the N-connectivities that  exist within the pore network) was solved using the pore network modeling package OpenPNM. 16 The mole fraction in each of the pores of the network was similarly computed. A result of these calculations is given in the supplementary materials, where the map of concentration is presented. This shows that the local information (such as pore molar concentration) can also be obtained using our PNM extraction. The molar flow rates in all of the throats of the network, which depended on the local pore and throat structure (see Equation  6 ), were also computed. The total molar flux flowing through the network was then computed at the inlet and used to calculate D e f f using Equation 11. All of these calculations were applied to the nine GDL samples considered in this study.
0004; Fig. 1A ). In contrast, both PCL and the fusion protein induced minimal, if any, IFN- responses in PBMCs from control or vaccinated animals, together suggesting potential utility of defined antigens in in vitro IFN- assays to specifically identify M. bovis-infected cattle (Fig. 1A ). These data suggested that both DST reagents may also be applied to the blood test commonly used as ancillary test to skin testing in cattle. The performances of PCL and the fusion protein as defined skin test antigens were next assessed in animals experimentally infected with M. bovis (n = 24). The results showed that when applying the established strict DST interpretation criteria of 2 mm or more increase in skin induration reaction [using Receiver Operating Characteristic (ROC) analyses to set this cutoff point], PCL was able to correctly classify all of the infected animals as positive, while the fusion protein identified 23 of 24, although this difference was not statistically significant (P = 0.
IFN- enzyme-linked immunosorbent assay IFN- concentrations in PBMC culture supernatants were determined using the commercially available BOVIGAM enzyme-linked immunosorbent assay-based kits (Thermo Fisher Scientific, USA). Results were initially expressed as the optical density at 450 nm (OD 450 ) for cultures stimulated with antigen minus the OD 450 for cultures without antigen (i. e. , OD 450 ). The results from antigen dose titration curves allowed AUC values to be calculated using Prism 7 (GraphPad Software, La Jolla, CA) software.
IFN- ELISpot/FluoroSpot assay Stimulation of PBMCs for these assays was performed as previously described  (40). The production of IFN- by PBMCs was detected using either (i) a secondary biotinylated antibody followed by incubation with streptavidin-linked horseradish peroxidase (Bovine IFN- ELISpot kit; Mabtech, Stockholm, Sweden), with visualization using an AEC chromogen kit (Sigma, St. Louis, MO) and spot-forming units counted using an AID ELISPOT reader and ELISpot 4. 0 software (Autoimmun Diagnostika, Germany), or (ii) a secondary BAMconjugated anti-bovine IFN- antibody (clone MT307, Mabtech) followed by incubation with a fluorophore-labeled anti-BAM-490 (Mabtech) antibody, with visualization using a fluorescence enhancer (Mabtech). Spot-forming units were counted using an ELISpot/ FluoroSpot reader system (iSpot Spectrum, AID, Germany) with software version 7.
Analysis of PFASs in serum and whole blood Blood samples were collected and processed to sera at the data collection site. Maternal PFAS concentrations were measured in maternal serum collected from the mothers during late pregnancy (32nd week of pregnancy) in Cohort 3 and two weeks after parturition in Cohort 5. The maternal serum, cord serum, and cord whole blood samples were analyzed for 17 PFAS concentrations (ng/mL), including 9 Perfluorocarboxylates (PFCAs): Perfluorobutanoate (PFBA), Perfluoron-pentanoic acid (PFPeA), Perfluorohexanoate (PFHxA), Perfluoroheptanoate (PFHpA), PFOA, PFNA, PFDA, PFUnDA, and Perfluorododecanoic acid (PFDoDA); 5 PerfluoroSulfonates (PFSAs): PFBS, PFHxS, Perfluorohexanesulfonate (PFHpS), PFOS (both linear  [n]  and branched [br] isomers), and Perfluorodecane sulfonate (PFDS); and 3 precursors (perfluoroalkane sulfonyl fluoride based substances): perfluorooctane sulfonamide (FOSA: both linear [n] and branched [br] isomers), perfluorooctane sulfonamidoacetic acid (NMeFOSAA), and Nethyl derivative of perfluorooctane sulfonamidoacetic acid (NEtFOSAA).
To take into account any potential non-linearities in the relationship between carbon chain length and transplacental and partitioning ratios, we used generalized additive models (GAM)  (Hastie and Tibshirani, 1990)  including carbon chain length as thin plate regression spline. All statistical tests were two-sided, and significance was set at p < 0. 05. All analyses were performed using STATA and R software packages  (StataCorp, 2013; Team, 2013).
4 for PFOA in a small sample (n = 3-5). Finally, Jin et al. found partitioning ratios between 1. 2 for PFOA and 1. 6 for PFHxS in a small of 60 Chinese mother-child pairs  (Jin et al. , 2016). Biomonitoring studies usually measure PFAS compounds in serum or plasma because it is thought that these substances are largely predominant in serum/plasma, and when PFASs are measured in whole blood, whole blood data are usually converted to a serum/plasma basis by multiplying by a factor of 2. Our results suggesting higher affinity to the serum fraction with increasing carbon chain length indicate that conversion factors should be adapted to the PFAS compound. Moreover, FOSA showed a different pattern, with a higher affinity to the blood cells. Overall, most of PFASs were highly protein bound in blood, and this binding strength increases linearly with increasing carbon chain length in the three investigated PFAS functional groups.
The FEM calculations were performed using ABAQUS software. As illustrated in Figure  5 , the representative unit cell (RUC) of the cubic array was discretized with 10-node tetrahedral solid quadratic elements, whose characteristic size was 2% of the RUC edge length. We took advantage of the symmetric features of the RUC to model 1/8 of it and ended up with 94529 elements (133780 nodes). In the reference model associated with Type A analyses illustrated in Figure  5a , the coating is discretized with four rows of elements. In two other FE models associated with two-level homogenization of Type B, C, the distribution of elastic fields are qualitatively similar in agreement with the quantitative results of Table  5. Knowing that the elastic fields in none of the phases are uniform, contour plots of stress field clearly show that this nonuniformity is more pronounced in the equivalent inhomogeneity than in the core phase of the reference model.
This decrease is mostly observed during winter and spring  (Taibi et al. , 2013). To assess the ability of regional climate models to reproduce rainfall variability in northern Algeria, rain gauge stations, where daily rainfall time series were lacking the least observations since 1961, were selected (Fig. 1 ). The four selected stations are representative of three coastal regions of northern Algeria which present different rainfall regimes, as highlighted by  Taibi et al. (2017) : the eastern region (Annaba station), the central region (Algiers and Tenes stations) and the western region (Oran station). The characteristics of the rainfall stations are presented in Table  II. The daily data was obtained from the National Agency of Water Resources (ANRH) and the National Office of Meteorology (ONM).
Relation between rainfall variability and atmospheric pressure To understand the effect of atmospheric circulation on rainfall in northern Algeria, we analyzed the variability of sea level pressure (SLP) near Oran and Annaba. Data of SLP have been extracted from the site of the National Center for Atmospheric Research (https:// climatedataguide. ucar. edu/climate-data/ncar-sea-level-pressure). A standardized SLP index was computed over the study period and represented in the Fig. 4. The Oran station is located at the western limit of the Mediterranean basin and is therefore influenced by Atlantic variations and the Azores anticyclones, which generally induce calm and dry weather  (Taibi et al. , 2017). The Annaba station is located in a transition zone between the western and eastern sides of the Mediterranean basin, influenced by ENSO and the Asian monsoon  (Rodwell and Hoskins, 1996; Rodo et al.
Future evolution of seasonal rainfall Seasonal rainfall outputs of ENSEMBLES-RCM were analyzed during two projection periods, 2021-2050 and 2070-2099. The CNRM and ETHZ were the only models that efficiently simulated data during these two periods. Simulated data of the GKSS model were only available for the 2021-2050 the period. The simulated seasonal rainfall of both projection periods was compared to simulated rainfall for the 1961-1990 reference period. The change in rainfall amounts is presented in Figure  6. The Kolmogorov-Smirnov (KS) test has been used to assess significant changes in projected seasonal rainfall. It is a non-parametric test that compares the cumulative distributions of two datasets. The KS test reports the maximum difference between the two cumulative distributions, and calculates a p-value.
The assessment of the ENSEMBLES model outputs for rainfall in northern Algeria by comparing the simulated rainfall of 12 RCMs and the observed data during the reference period showed that the majority of models tend to underestimate/overestimate rainfall during the wet/dry period, which affects the distribution of wet days between both periods. The models simulate rainfall differently from one season to another and from one station to another. To deepen this study, investigations should be conducted particularly to explain the underestimation of models. Some factors must be considered such as the density of the observation network, hence requiring more stations for a better inter-comparison of the models at the scale of the study area. The analysis of projected rainfall by three models, CNRM, ETHZ and GKSS, over the periods 2021-2050 and 2070-2099, allows to compare various models for the same stations (temporal variability), as well as projections simulated by a model from one station to another (spatial variability).
Models simulate a significant decline in rainfall during winter and spring for 2050, which worsens in 2099. In autumn, the ETHZ model simulates an increase in precipitation at all stations, while the CNRM and GKSS models simulate a decrease. In summer, the GKSS model simulates an increase in rainfall for all stations and the CNRM and ETHZ models indicate a decrease in precipitation. These conflicting results prevent us from reaching a definitive conclusion from model predictions for the studied areas. It is important to consider the uncertainties affecting the results of this study, which relate to the RCMs and parameterization, and also to the quality of observed data and the uncertainties due to station location on the RCM grids.
We acknowledge that the present data were collected solely in astrocytes from hippocampal cultures and organotypic slices, which may not fully capture the in vivo polarization of AQP4 distribution on astrocyte endfeet. Yet AQP4 subtypes were differentially organized, in agreement with the well-described larger AQP4-M23 assemblies  (Crane et al. , 2010). Furthermore, AQP4-M23 clusters with similar organization were previously described in the brain parenchyma; clusters that were significantly smaller compared with AQP4-M23 endfeet in perivascular areas  (Smith and Verkman, 2015). Thus, the described surface dynamics and spatial distribution of AQP4-M23 are consistent with previous work and surely shed light on AQP4 regulation in brain networks. Although the molecular mechanism underlying this regulation remains unknown, one may speculate that the activation of astrocytic metabotropic glutamate receptor and subsequent calcium-calmodulin-dependent protein kinase II (CaMKII) phosphorylate AQP4  (Gunnarson et al.
, 2016). According to the French law on bioethics  (July 29, 1994; August 6, 2004; and July 7, 2011, Public Health Code) , the patients' written informed consent was collected. Moreover, data confidentiality was ensured in accordance with the recommendations of the French commission for data protection (Commission Nationale Informatique et Liberte, CNIL decision DR-2014-558). Sex and age of patients were unknown, as experimentations were performed from blinded sera provided by the Lyon biobank, only characterized by their AQP4-Ab positive serostatus. Briefly, IgG were purified and concentrated to obtain an IgG concentration of 2 mg. ml -1. For cell experiments, IgG were incubated for 30 min.
5 mM D-glucose; osmolarity was 250-260 mosml kg -1 ; ph 7. 4). The lateral diffusion of AQP4-M1 myc and AQP4-M23 myc was recorded with a Nikon microscope (NIKON Eclipse TE2000-U) with a stage heated to 37 C using an air blower (World Precision Instruments) and an objective heater (Bioptechs). QD were detected using a mercury lamp and appropriate excitation/emission filters. Images were recorded with an integration time of 50 ms for 500 consecutive frames (25 s) and signals were detected using an EM-CCD camera, and image analysis performed using MetaMorph software (Molecular Devices). QD were followed on randomly selected GFP-expressing astrocytes and tagged with AQP4-M1 myc or AQP4-M23 myc. The instantaneous diffusion coefficient ''D'' was calculated for each trajectory, from linear fits of the first four points of the mean square displacement versus time function using MSD(t) = < r 2 > (t) = 4Dt.
85 ms in a total of 100. 000 consecutive frames. Particle detection threshold was set to 20 in the Leica LAS software and multicolor fluorescent microbeads (T7279 TetraSpeck, Life Technologies) were used for lateral drift correction. dSTORM images were reconstructed using a fitting algorithm by the Leica LAS software using a 2-dimensional Gaussian fitting Cell Reports 27, 3860-3872. e1-e4, June 25, 2019 e3 algorithm. These images were then analyzed using ImageJ software (ImageJ 1. 50b, NIH). Briefly, images were thresholded and the analyze particles function used to determine the number and area of AQP4-M1 and AQP4-M23 clusters, respectively.
Time-lapse imaging and analysis For time-lapse experiments, cells were transfected with GFP and AQP4-M1 myc or AQP4-M23 myc before live immunostaining for AQP4 myc using a monoclonal antibody to myc (10 mg/ml, 15 min; clone 9E10, Origene Technologies) followed by a secondary goat antibody to mouse conjugated with Cy3 (15 min; Jackson ImmunoResearch). For time-lapse experiments with labeled synapses, presynaptic terminals were labeled with 10 mM of FM4-64 dye (Molecular Probes-T13320) in a solution containing 60 mM of KCl at the end of each acquisition. Time-lapse imaging was performed on a Spinning-disk LIFA microscope (Leica DMI6000B) with a 40X dry objective, with four diodes laser  (408, 491, 561, 638 nm)  and a stage heated to 37 C. Z-stacks of GFP labeling were acquired every minute during 30 min using MetaMorph software (Molecular Devices).
Images were acquired at 20 Hz with 50 ms of exposure time. DL-APV (50 mM; Tocris-0105) was added at the end of experiments for 5 min before images recorded. Calcium signal was detected from randomly selected GCaMP3 neurons facing astrocytes tagged with AQP4-M1 myc or AQP4-M23 myc and recorded with MetaMorph software. Data analysis were carried out as described in Patel et al. Briefly, ROIs of spines were first delineated and the fluorescence versus time trace was calculated with MATLAB script  (Patel et al. , 2015). The fluorescence ratio (DF/F) was quantified and a threshold at 0. 2 was used to determine peak calcium fluorescence.
QUANTIFICATION AND STATISTICAL ANALYSIS All quantitated analysis were performed with the researcher blind to the condition. All data were acquired from at least three independent batches of cells. Detailed for experiment parameters are provided in the Supplemental Data Analysis Table  S1. Data are presented as mean ± s. e. m. , median ± IQR or standard deviation. All statistical tests were carried out using GraphPad Prism 6. 0. Significance was assessed by performing the Mann-Whitney U, the Pearson correlation test, One-way ANOVA and Student's t test.
ACKNOWLEDGMENTS This work was supported by  Centre National de la Recherche Scientifique ,  Agence Nationale de la Recherche ,  Fondation pour la Recherche Medicale ,  Conseil Re  ´gional d'Aquitaine, and  French Agence Nationale de la Recherche  within the context of the  Laboratory of Excellence TRAIL program  (referenced  ANR-10-LABX-57  named  TRAIL ) (project  IBIO-NI  and  GM-COG ),  Labex BRAIN, ARSEP Foundation , and Fondation pour l'Aide ala Recherche sur la Sclerose en Plaques. We thank the  Bordeaux Imaging Center (service unit of CNRS-INSERM and Bordeaux University , member of the  national infrastructure France BioImaging ) for confocal and STORM imaging. We thank  Pauline Durand  for technical assistance on molecular biology and lab members for constructive discussions.
The recent explosion of transcriptomics and proteomic data have resulted in vast amounts of datasets without connection and sometime too large to be easily analysed. Integration between datasets and analysis of an extracted datasets are limiting factors which need to be solved in order to make full use of the data and to connect data. ExpressWeb is an online web tool that combines a Taylor clustering of expressed data sets to extract gene network with gene annotations to visualise the co-expression network. Data sets can become from personal or publically experiments. ExpressWeb allows to easily compute clustering on expression data and provides friendly and useful visualisation tools as heatmaps, graphs and networks, generating output images which can be used for scientific publications.