dataesr/scientific-paragraphs-categorization huggingface dataset:
Electrospray voltage was set to 2000V and analysis mass ranges were 250-1500 m/z for MS and 200-3000 m/z for MS/MS. DataAnalysis software version 3. 4 from Bruker was used to generate mgf files from raw data, with a precursor intensity threshold of 3. 10 5. The mgf files were submitted to local Mascot server using both PlasmoDB v13 (14 jan 2015, 5542 entries) and human SwissProt (6 march 2013, 20329 entries) databases. Search parameters were the following: MS and MS/MS mass tolerance = 0. 5 Da; missed cleavages = 1; fixed modification = Carbamidomethyl (C), variable modification = Oxidation (M): FDR < 1% (search against the reversed merged database).
mM, 10 mM, 15 mM, 20 mM, 35 mM, 50 mM, 75 mM, 100 mM, 250 mM, 500 mM, et 1 M) accounting for a total of 39x11 = 429 excluded precursors per sample (fraction PIE: F. PIE). In a second time, we divided LC-MS/MS runs into five retention time windows [15 min-30 min], [30 min-40 min], [40 min-50 min], [50 min-60 min], [60 min-80 min] to enable the exclusion of 39 precursors per retention time windows, meaning 39x5 = 195 excluded precursors per salt fraction, leading to a possibility of 39x5x11 i. e. 2145 excluded precursors per sample (enhanced PIE: E. PIE), see the schematic procedures in S1 Fig. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD010427.
Acknowledgments We thank  Liliane Cice  ´ron for her continuous help and expertise for the P. falciparum erythrocyte cultures and  Virander Chauhan  ( ICGEB, New Delhi ) for the anti-PfHMGB2 gift. Also, many thanks to  Omar Harb  and  Susanne Warrenfeltz  (PlasmoDB) and  Hagai Ginsburg  from ( MPM ) for their positive assistance.
Automatized segmentation method We detected all objects (connected areas) within the binary mask. We then used the method "regionprops" of Matlab to fit every object as an ellipse and detected sarcomeres by applying criteria on the geometrical property of the object. These criteria are based on the convexity of the object, the area of the object, and the ratio between the major and minor axes of the fitting ellipse. Minimal and maximal values of each criterion depend on the age of the pupae. This process has been applied to get data for Figs 2G-2J, 4C-4F, 7B, 8G, 8H, 8J and 8K.
Averaged actin molecular order angle Applying a binary mask on its corresponding intensity image, we obtained a mean image value. An individual hemithorax value is given by an average of several mean image values. Number of recorded images by hemithorax is between 10 and 20 (except for 90-h APF kon-IR [KK106680], which were 5 images). We finally calculated the averaged actin molecular disorder angle of the population (between 5 and 31 hemithoraxes per population). This process has been applied to get data from Figs 2K, 7A and 8I.
Acknowledgments Stocks used in this study were obtained from the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ) and the  Vienna Drosophila Resource Center  (VDRC, www. vdrc. at). We are grateful to  Sanford Bernstein  and  Judith Saide  for generously sharing antibodies and Drosophila lines. We thank  Christiane Barz  for help in some sample preparations and  Sebastien Mailfert  for help with the polarization-resolved microscope. We thank the entire  Schnorrer lab  for insightful comments on this manuscript.
Aix Marseille University AMIDEX https://amidex. univamu. fr/(grant number  ANR-11-IDEX-0001-02 ). Received by OL, SB, PFL, FS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. LabEX-INFORM http://labex-inform. com/ (grant number  ANR-11-LABX-0054 ). Received by PFL, FS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. EMBO http://www. embo. org/funding-awards/fellowships/long-term-fellowships (grant number  ALTF 329-2010 ). AKC. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. France-Bioimaging https://france-bioimaging. org/ (grant number  ANR-10-INBS-04-01 ). Received by SB, PFL.
Modelling of the diffusion experiments Parallelepiped out diffusion and through diffusion experiments were modelled numerically using the chemistry-transport coupled model code HYTEC (Van der  Lee et al. 2003) , which is based on finite volumes method. In purely diffusive system and for mobile components, the transport equation writes: dci oe ~dt = diV ^DeV (1) where ci is the total concentration of component i, o  [-]  is the diffusion accessible porosity for mobile components, and De [m2 s-1] is the effective diffusion coefficient with De = oDp, where Dp is the pore diffusion coefficient accounting for the tortuosity of the porous media.
(f) Statistical analysis Data were analyzed with two-tailed paired (in vitro experiments) or unpaired (in vivo experiments) student's t-tests. p-values < 0. 05 were considered significant (*), values < 0. 01 were considered very significant (**), and values < 0. 001 and < 0. 0001 were considered highly significant (*** and ****). Statistics were performed using GraphPad Prism 6. 01 (Graphpad Software, La Jolla, CA, USA).
TEM Imaging (a) TEM imaging of the inorganic nanomaterials Low magnification images were recorded on a LaB 6 JEOL 2100 electron microscope (JEOL Ltd. , Tokyo, Japan) operating at an accelerating voltage of 200 kV. For the purposes of the TEM analysis, a drop of a diluted colloidal nanocluster aqueous solution was deposited onto a carbon-coated copper TEM grid, and then, the water was allowed to evaporate. All of the images were recorded by the Gatan ORIUSTM SC 1000 CCD camera. In order to estimate the average diameter (D TEM ), statistical analysis was carried out on several low magnification TEM images, with the help of the dedicated software ImageJ (version 1. 52a)  [52].
Acknowledgments: We acknowledge partial support for this work by the project " Advanced Research Activities in Biomedical and Agro alimentary Technologies " ( MIS 5002469 ) which is implemented under the "Action for the Strategic Development on the Research and Technological Sector", funded by the  Operational Programme "Competitiveness, Entrepreneurship and Innovation"  ( NSRF 2014-2020 ) and co-financed by Greece and the  European Union  ( European Regional Development Fund). We would like to thank  Aleka Manousaki  for the SEM and EDS experiments,  Alexandra Siakouli ,  Sevasti Papadogiorgaki  and  Stefanos Papadakis  for the preparation of the biological samples for the TEM experiments and  Maria Bakami  for the nanocluster binding experiments. We are indebted to  Penelope Bouziotis  for the radiolabeling studies, performed at the  Radiochemical Studies Lab, NCSR "Demokritos " and  Maritina Rouchota  for carrying out the imaging data analysis.
LC-MS The analyses were performed using separation on 150 x 4. 6 mm ZIC-pHILIC (Merck) on Ulti-Mate 3000 RSLC (Thermo Scientific) followed by mass detection on an Orbitrap Exactive mass spectrometer (Thermo Fisher) at Glasgow Polyomics. Analyses were performed in positive and negative polarity switching mode, using 10 μl injection volume and a flow rate of 300 μl/min. The samples were run alongside 249 authentic standards at 10 μM each. The data were processed and analyzed using mzMatch software  [57] , IDEOM  [58] , mzMatchISO  [59] , and MetaboAnalyst  [60]. All the MS analyses were performed in 4 replicates, means of which are indicated. For the studies involving U-13 C-glucose labelling, non-labelled samples were run in parallel and the natural labelling subtracted. Metabolites identified based on match with respective standards are indicated, the rest is predicted based on mass and retention time.
Slides were washed three times in 1 x PBS, and incubated with secondary antibody (AlexaFluor 594 anti-rabbit, 1:1000, Molecular Probes) for 1 h at room temperature. Slides were washed three times in 1 x PBS, subsequently mounted with 2. 5 μM DAPI, and covered with cover slides. Cells were visualised using Axioplan2 (Zeiss) and Volocity software.
This work was supported by  Wellcome Trust  with core grants to the  Wellcome Centre for Molecular Parasitology (WCMP)   104111/Z/14/Z  to MPB and the  Institutional Strategic Support Fund (ISSF)   105614/Z/14/Z  MPB, RJSB and SW. JK was funded by the  European Commission   FP7 Marie Curie Initial Training Network "ParaMet " [grant number  290080 ]. FA is funded by a  Leverhulme Trust  early career fellowship. FB was supported by the  Laboratoire d'Excellence (LabEx) ParaFrap  (grant number  ANR-11-LABX-0024 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Software This application is designed on the Android software stack produced by Google. Android is an open source framework designed for mobile devices such as smart phones and tablet computers. It packages an operating system, middleware, and key programs (Android developers. 2017). The Android service development kit provides libraries needed to interface with the hardware at a high level and make/deploy Android applications  (Hoog, 2011). Application are written in Java and data concerning localizations and configuration of the client is synchronized with a server application. The server is written in PHP, run on Apache server and uses a MySQL database to store persistent data. The GSM or WIFI connection is used following their availability. We choose this platform as it is the most widespread (Statista, 2016), and is supported by a large community of developers and also to its compatibility with other Android devices.
After various research and the opinion of several cardiologist doctors, one came to the conclusion that for these types of person there were no fixed standards and the thresholds can vary from one individual to another. And so tried to set them by ourselves in our algorithm was an error that will generate several false positives, and that will cause our application can not be used by any type of user profile. The solution offered by our system is to allow the doctor or the person in charge of patient safety to enter the standards, and thus the minimum and maximum heart rate (Hmin and Hmax) specific to the patient. These two thresholds will be stored in a database with the rest of the patient profile information. And used later in the detection of possible cardiac disorders (Tachycardia, Bradycardia, and cardiac arrest).
Data Base A database is a device for storing a set of information in a structured manner. The databases for Android are provided using SQLite  (Owens and Allen, 2010). The advantage of SQLite is that it is a very compact DBMS and therefore very efficient for embedded applications. SQLite does not require a server to run, which means that it runs in the same process as the application. Therefore, a massive operation launched in the database will have visible consequences on the performance of your application. Thus, we had to know how to master its implementation so as not to penalize the rest of our execution. For our application we have created a database to record user profiles with several information (Name, date of birth, max and min heart rate, etc. ) (Figure1). Some of these data such as minimum heart rate and maximum heart rate are used in algorithms for the detection of various disorders such as tachycardia or bradycardia.
Experimental Setup For reasons of safety, this study was carried out on two persons of different sex and an average age of 29 years in good physical condition. And the max and min thresholds used comply with safety standards. During the tests the cardiac belt was placed on the chest of the subject. Once the Android app is running and the profile data for the user counting the min and max thresholds recorded. The application checks to see if the belt is positioned on the person. If yes, a connection is established and an interface opens automatically displaying the change of the BPM (Figure  2 ). The algorithm for detection of cardiac disorder is then launched. If a heart trouble is detected, a toast is displayed for a moment on the application interface.
Experimental Result For the evaluation of our application, we first set the max and min thresholds and verify the correct functioning of the database. Once the data is recorded they are used in our algorithm of detection of cardiac disorder. The Figure  3  shows a screenshot of the cardiac monitoring interface, where we can see the thresholds previously recorded in the profile as well as the BTMs can be seen. Knowing that a tachycardia is by definition an acceleration of the heart rhythm, in our case study we asked the subjects to make a physical effort As a result, the observed increase in beats was observed and once the maximum threshold was exceeded, the Toast appears showing the detection of a tachycardia (Figure  4 ). The tests were performed several times with different thresholds and the result was that, whenever the beats are lower or above the set threshold a trouble is detected after a short time t.
Hydrological processes In this study, observed water table dynamics were used to better understand the dominant hydrological processes taking place in two locations of a restored peatland (rewetted and control), by calibrating a conceptual model. Though simple (6 calibrated parameters), the model was able to correctly reproduce the specific water table dynamics in each location of the studied area, using the same input data (precipitation and potential evapotranspiration). This difference in observed water table dynamics (24 cm of difference for the maximum water table drawdown) is reflected in the calibrated parameter values for each location (Tab. 3). In addition, and in order to better assess the dominant processes, a sensitivity analysis of the model was performed for each location (Tab.
This paper is a contribution of the  Labex VOLTAIRE  ( ANR-10-LABX-100-01 ) and of the  PIVOTS  project ( ARD 2020  of the  Centre Val de Loire region ,  CPER and FEDER). This study was undertaken in the framework of the  Service National d'Observation Tourbières (French Peatland Observatory ), accredited by the  INSU/CNRS. The authors would like to thank  A. Guirimand-Dufour  and  F. Le Moing  for their help in fluorescence analysis,  N. Lottier  for DOC analysis and  E. Rowley-Jolivet  for revision of the English version.
Microscopy A 6-µl drop of E. coli BL21 (DE3) co-transformed with the pUA66 promoter containing one of the different promoters and the pET29a-erxR plasmid (Supplementary Table  3 ), resuspended in LB growth medium, was spotted onto a Superfrost Plus slide (Fisher Scientific Ltd, UK) and visualized using a Nikon Eclipse 80i epifluorescence microscope (Nikon, France). Fluorescent images were acquired with a Nikon DXM1200F digital camera (Nikon, France), using Nikon ACT-1 software (Nikon, France). Fluorescence intensity was calculated by measuring the area, integrated intensity and mean gray value of the fluorescent bacteria and the background with ImageJ (National Institute of Health, USA). Corrected total cell fluorescence (CTCF) was calculated using the following formula: integrated density-(area of the cell × mean background readings).
Identification of One ecxR Ortholog in the E. ruminantium Genome We used the ecxR sequence (YP_507593. 1) from E. chaffeensis to search NCBI databases using the BLAST tool. With this approach, we identified ERGA_CDS_03000 (YP_196226. 1) in the genome of E. ruminantium as the closest ortholog to ecxR. The results of the BLAST search revealed a putative conserved domain belonging to the HXT_XRE superfamily of DNA binding proteins (cl17200). We identified a helix-turn-helix structure by comparing it with the structure found in Wolbachia  (Li and Carlow, 2012)  and further confirmed by Pfam (https://www. ebi. ac. uk/Tools/hmmer/search/hmmscan). This protein comprising 124 amino acids has a predicted molecular mass of 14. 25 kDa. Alignment of the deduced amino acid sequences of the various orthologs is shown in Figure  1A.
Analysis of erxR Expression during the Life Cycle of E. ruminantium To determine the relative expression of erxR throughout the developmental cycle of E. ruminantium, erxR mRNA expression was analyzed by qRT-PCR. Compared to 96 hpi, the expression of erxR decreased at 24, 48, and 72 hpi, and peaked at 120 hpi, having increased 4 fold (Figure  2A ), which corresponds to the time of lysis (early time point in the following round of infection). Data from previous independent microarray experiments  (Pruneau et al. , 2012)  also demonstrated that the maximal expression of erxR in the post-exponential growth phase is significant and reproducible (data not shown). These results suggest that the up-regulation expression of erxR correlates with early stages in the development cycle in vitro before the bacteria enter the host cell.
9˚C for 10 min, and 4˚C on hold. Undiluted pre-amplification products (1x final concentration) were used to prepare the real-time qPCR samples with 2x TaqMan Master Mix II, no UNG (Thermo Fisher Scientific). All the TaqMan Array Cards were run on the Applied Biosystems 7900HT Fast Real-Time PCR System according to manufacturer's protocol. All Ct values above 35 cycles were considered as undetectable. We normalized the data with a universal method for miRNA RT-qPCR data normalization using the mean expression value of all expressed miRNAs in a given sample  [28]. Firefly multiplex circulating miRNA assay. To exclude any potential confounding effect due to the blood collection method (use of heparin or citrate EDTA-coated tubes) on the assay results, peripheral blood was collected from 6 patients in one tube containing heparin and one tube containing citrate EDTA at the same timepoint.
Exosomes were isolated from the plasma of these matched samples as previously described. The Oncology panel of the Firefly Multiplex Circulating miRNA Assay (Abcam) was used to analyze these samples. Samples were scanned on a Guava 8HT flow cytometer (EMD Millipore). The signals measured by flow cytometry were converted into microRNA expression data by the Firefly Analysis Workspace software. The data was normalized with the geNORM algorithm which uses the signal of the three probes with the most stable expression across all samples  [29]. The algorithm chose let-7i-5p, miR-103a-3p and miR-20a-5p as normalizer probes. Furthermore, a custom panel comprised of 33 miRNAs (33-plex) was built to measure the expression levels of these miRNAs with the Firefly Multiplex Circulating miRNA Assay. The assay was performed in exosomes isolated from peripheral blood plasma and cell lines as previously described as well as in RNA from cell lines and bone marrow selected WM cells.
Samples were scanned on a BD FACS CANTO II flow cytometer with a HTS unit (BD Biosciences). The signals measured by flow cytometry were converted into microRNA expression data by the Firefly Analysis Workspace software. The data was normalized using the geNORM algorithm  [29]. The algorithm chose let-7i-5p, miR-20a-5p and miR-223-3p as normalizer probes.
fMRI data acquisition All MRI scanning took place on a 3T Siemens Tim Trio MRI scanner at the Wolfson Brain Imaging Center in Cambridge, UK. Functional imaging data were acquired with a multi-echo echo planar imaging (EPI) sequence with online reconstruction (repetition time (TR), 2000 ms; field of view (FOV), 240 mm; 28 oblique slices, descending alternating slice acquisition, slice thickness 3. 8 mm; echo times (TE) = 13, 31, and 48 ms, GRAPPA acceleration factor 2, BW = 2368 Hz/pixel, flip angle, 90°, voxel size 3. 8 mm isotropic). Resting state data were collected using a 10 min ''eyes-open'' run (i. e. , 300 volumes), where participants were asked to stare at a central fixation cross and to not fall asleep. Anatomical images were acquired using a T1-weighted magnetization prepared rapid gradient echo (MPRAGE) sequence for warping purposes (TR, 2300 ms; TI, 900 ms; TE, 2.
fMRI preprocessing Data were processed by ME-ICA using the tool meica. py as distributed in the AFNI neuroimaging suite (v2. 5), which implemented both basic fMRI image preprocessing and decomposition-based denoising. For the processing of each subject, first the anatomical image was skull-stripped and then warped nonlinearly to the MNI anatomical template using AFNI 3dQWarp. The warp field was saved for later application to functional data. For each functional dataset, the first TE dataset was used to compute parameters of motion correction and anatomical-functional coregistration, and the first volume after equilibration was used as the base EPI image. Matrices for de-obliquing and six-parameter rigid body motion correction were computed. Then, 12parameter affine anatomical-functional coregistration was computed using the local Pearson correlation (LPC) cost function, using the gray matter segment of the EPI base image computed with AFNI 3dSeg as the LPC weight mask.
Matrices for de-obliquing, motion correction, and anatomical-functional coregistration were combined with the standard space non-linear warp field to create a single warp for functional data. The dataset of each TE was then slice-time corrected and spatially aligned through application of the alignment matrix, and the total non-linear warp was applied to the dataset of each TE. No time series filtering was applied in the preprocessing phase. Data were analyzed with no spatial smoothing. ME-ICA denoising was used to identify and remove non-BOLD signal fluctuation  [29] [30] [31].
Group independent components analysis and dual regression To assess large-scale intrinsic functional organization of the brain we utilized the unsupervised data-driven method of independent component analysis (ICA) to conduct a group-ICA, and then utilized dual regression to back-project spatial maps and individual time series for each component and subject. Both group-ICA and dual regression was implemented with FSL's MELODIC and Dual Regression tools (www. fmrib. ox. ac. uk/fsl). For group-ICA, we constrained the dimensionality estimate to 30, as in most cases with low-dimensional ICA, the number of meaningful components can be anywhere from 10-30  [32]. From these components, we manually selected the components that best represented networks typically involved in emotion (amygdala), reward (ventral striatum), language (superior temporal gyrus, inferior frontal gyrus, insula), and social cognition (default mode network) functions.
Between-component connectivity analysis Time courses for each subject and each component were used to model between-component connectivity. This was achieved by constructing a partial correlation matrix for all six components using Tikhonov-regularization (i. e. , ridge regression, rho = 1) as implemented within the nets_netmats. m function in the FSLNets MATLAB toolbox (https:// fsl. fmrib. ox. ac. uk/fsl/fslwiki/FSLNets). The aim of utilizing partial correlations was to estimate direct connection strengths in a more accurate manner than can be achieved with full correlations, which allow more for indirect connections to influence connectivity strength  [32] [33] [34]. Partial correlations were then converted into Z-statistics using Fisher's transformation for further statistical analyses.
The lower diagonal of each subject's partial correlation matrix was extracted for a total of 15 separate component-pair comparisons. For each of these 15 comparisons we ran robust regression (to be insensitive to outliers)  [35]  (https:// github. com/canlab/RobustToolbox) to examine correlations between connectivity and FT variation. This analysis was conducted twice-once with FT only and again with FT and controlling for variability in current levels of testosterone. These correlations were computed separately for males and females. Finally, we computed Z-statistics and p-values for difference between male and female correlations utilizing the paired. r function in the psych R library. False positive control was achieved with FDR q < 0. 05, implemented with the p. adjust function in R.