dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis AMH values were evaluated with a statistical package (SAS 9. 4, SAS Institute Inc. , Cary, NC, USA) and analyzed with a variance model with random and repeated animal effects. Time, age, treatment, stage of the estrus cycle, and some interactions (time by class of age and treatment by class of age) were included in the analysis. Time was indicated in a series from 0 to 8 and classi ed as "time 0" (day of implantation), "1" (implant removal), "2" (day 8 following removal), "3" (day 15 following removal), "4" (day 22 following removal), "5" (day 29 following removal), "6" (day 36 following removal), "7" (day 43 following removal) and "8" (day 50 following removal). Animals were divided into younger or older than 12 months of age. A t-Student test compared the queens that presented estrus resumption during increasing or decreasing photoperiod.
1997). We also analyzed prevalence separately to check if this measure reveals new patterns. Flea prevalence was estimated in the same manner, but the model had binomial error distribution (log link function). Effects of host individual traits on flea abundance were modeled with body mass, sex, and their interactions with mast as explanatory variables. Random intercepts involved host and study site. The model included temporal autocorrelation and had quasipoisson error distribution (log link function). Effects of host individual traits on flea prevalence were estimated using a similar model, but with binomial error distribution (logit link function). We analyzed data in R (R Core Team 2021). Generalized linear mixed models were implemented with package glmmTMB  (Brooks et al. 2017 ) and evaluated with packages performance  (Lüdecke et al.
2010; Kowalski et al. 2014). This also explains why masting did not change this pattern. While changes in food resources associated with masting could, at least in theory, affect the magnitude of sex differences in immunocompetence (through effects on body condition) or in ranging patterns  (Bogdziewicz et al. 2016b) , they are unlikely to affect body size dimorphism because it results from sexual selection  (Moore & Wilson 2002). As a caveat, studying ecological responses to masting is inherently difficult because only multidecade data sets can contain numerous repetitions of masting events. Our research was based on a 4-year series of observations, which included 2 mast years. A longer time series would be needed to exclude the possibility of more complex dynamics, such as long time lags between peaks of the flea and rodent abundance.
ACKNOWLEDGMENTS A. W. was supported by the "Sonatina" grant no. 2020/36/C/NZ8/00013 , M. Z. by the  "Preludium " grant no. 2016/21/N/NZ8/01059 , and R. Z. by the "Harmonia" grant no. 2018/30/M/NZ8/0052. All grants were awarded by the  National Science Centre, Poland.
At mySpot beamline the X-ray beam (Bessy II) was defined to 15. 0 keV (0. 827Å) using a B4C/Mo Multilayer monochromator and focused to (50x50) m 2 using KB mirrors and 50 m 2 pinhole. XRD data were obtained using a 2D Dectris Eiger 9 M detector ((2070 × 2167) pixel²) in transmission geometry, and a single element Si(Li) XRF detector (RAYSPEC (SGX Sensortech) was placed perpendicular to the beam. Acquisition time was set to 60 s/point. Sample to detector distance (around 300 mm) was calibrated using quartz powder in both setups. When present, internal kapton signal at q = 4 nm -1 was used to further verify the calibration for sample. Calibration of XRF detectors was performed using pyMCA (http://pymca. sourceforge. net/) using the argon emission and the main beam scattering energy. XRF intensities corrected by the theoretical absorption cross-section for each element, at 15 or 13 keV (60-63) taking into account (when relevant) the absorption of the XRF by the detector Beryllium window (8 µm thick).
SAXS/WAXS/XRF analysis. Calibration, integration background removal, and peak-fitting of the 2D diffraction patterns were performed using the software DPDAK  (71)  or home-written routines based on pyFAI (https://pyfai. readthedocs. io/en/master/). The fitted parameters as well as the integrated XRF signal around specific emission lines, were exported and plotted in Origin 2020. For 1D presentation, the 1D patterns from specific regions of (ROI)s (X, Y, Z in Fig. 2F ) were averaged and normalized by subtracting the minimum value and dividing by the maximum intensity. The XRF maps were analysed using the software ImageJ  (72). Average atomic densities were extracted by analysing the calibrated grey-level histograms of relevant ROIs in the maps. The correction of the fluorescence intensity signal to relative atomic density was performed taking into account the absorption cross section at E=15 keV (Bessy II data), 13 keV (ESRF data)  (60) (61) (62) (63).
Samples were left in 75% epoxy resin overnight. Four changes of 100% epoxy resin followed over the period of 2 days before samples were flat-embedded between Tefloncoated microscope slides. Infiltrated samples were polymerized at 60 °C for two days and defined regions mounted on resin stubs for cutting 70 nm-ultrathin cross-sections through the bacterial colonies, which were postcontrasted with 0. 5 % uranyl acetate and 0. 04% lead-citrate. Samples were investigated on a Morgagni transmission electron microscope (Thermofisher/FEI/Philips) at 80 kV and micrographs acquired with a Morada CCD camera (EmSis/Olympus) with ITEM software (EmSis/Olympus/Sys). Images were analysed using Fiji software (72)).
Acknowledgments Measurements were carried out at the mySpot beamline at the BESSY II electron storage ring operated by the  Helmholtz-Zentrum Berlin für Materialien und Energie  and at beamline ID13 at the  European Synchrotron Radiation Facility. We thank the HBZ and the  ESRF  for provision of synchrotron radiation facilities and for financial support. We thank EM facility of  MPI-CBG  for support, use of equipment and reagents,  Daniel Werner  at the MPIKG for X-ray measurements. Jiliang Liu  for ESRF ID13 beamline assistance at  ESRF. Special thanks to  Prof. Sigal Ben Yehuda  for insightful discussions,  Edery Aharony  for his contribution with data sorting and image analysis and  Daniel Rosenblatt  for assistance with the image analysis. This work was funded by the  Kaete Klausner scholarship.
Ozone Fumigation Treatment and Nitrogen Addition An open-air O 3 enrichment system in each Free-Air-Controlled-Environment (FACE) plot was used for O 3 fumigation. The treatments were ambient ozone concentration (A-O 3 ) and elevated ozone concentration (E-O 3 ) (targeted at ambient O 3 Í 1. 5) with four replicate plots of each treatment. Four E-O 3 plots were separated from others by at least 70 m to avoid cross-contamination. The quantity and direction of the O 3 release was controlled with an O 3 monitor (Thermo Electron 49i, Thermo Fisher Scientific Co. , Waltham, MA, USA) and data logger-controller (Campbell CR 10X, Campbell Scientific Co. , Logan, UT, USA), anemometer and wind vane. The O 3 fumigation ran from May to October since 2018. For more details of the O 3 fumigation system, see  [50].
Evaluation of Poplar Foliar Rust Severity During 26-27 September 2020, when wild poplars were heavily infected by M. laricipopulina, three Mlp-infected '107' poplars and three Mlp-infected '546' poplars were randomly selected from N0 and N60 subplots in four A-O 3 and four E-O 3 plots, respectively (Figure  S1 ). For each selected tree, more than ten Mlp-infected leaves with the total number of 1125 images were captured for rust severity quantification by analyzing the percentage of the abaxial leaf surface covered by uredinia (Table  S1 ). A grid ruler was prepared for size calibration for all images. The leaf area and the number of uredinia were measured by the image analysis software Image-Pro Plus v 6. 0 (Media Cybernetics, L. P. , Silver Spring, MD, USA) (Figure  S2 ). First, we calibrated the geometry size of leaf image with 'Spatial Calibration'.
Then, we outlined along the edge of the leaf with the AOI (area of interest) tool. Select area as measurements and covert the outlined leaf profile to a measure object using 'Convert AOI(s) To Object (s)' menu. The area data of measured leaf could be accessible in 'Measurement data'. Select the outlined leaf profile again, using the color separation method that based on the color histogram to select uredinia, choose 'Measure objects' and 'Apply Filter Ranges' respectively, set at '8-Connect', 'smoothing = 25', 'fill holes' and 'convex hull' and then click 'Count', the results of in range count are the number of uredinia in 'select leaf'. Finally, artificially adjust the uredinia numbers compared to images to avoid mistaking. The severity of poplar foliar rust disease was evaluated by calculating the ratio of uredinia numbers and leaf area (uredinia/cm 2 ).
The PCR mixtures contain 5 × TransStart FastPfu buffer 4 µL, 2. 5 mM dNTPs 2 µL, forward primer (5 µM) 0. 8 µL, reverse primer (5 µM) 0. 8 µL, TransStart FastPfu DNA Polymerase 0. 4 µL, template DNA 10 ng, and finally ddH 2 O up to 20 µL. PCR reactions were performed in triplicate. The PCR product was extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to manufacturer's instructions and quantified using Quantus™ Fluorometer (Promega, Madison, WI, USA). The qualified PCR products were mixed, and paired-end sequenced on an Illumina MiSeq PE300 platform (Illumina, San Diego, CA, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. , Ltd. (Shanghai, China). The raw sequences are available in the NCBI Sequence Read Archive (SRA) database with the accession code PRJNA776974.
qzv" file. The bacterial AVSs were taxonomically classified using the qiime2 v2020-6 plugin "qiime feature-classifier classify-sklearn" with the pre-trained Naïve Bayes Greengenes classifier trimmed to the V5-V7 region of the 16S rDNA gene. The fungal ASVs were analyzed by UNITE classifiers against the UNITE reference database. Weighted Unifrac principal component analysis (PCoA) was used to assess the βdiversity across different treatments, followed by the significance test by permutational multivariate analysis of variance (PER-MANOVA)  [56]. The Mental tests and Spearman's correlation coefficients among Nitrogen addition, ozone concentration, leaf properties, rust severity, fungal α-diversity and bacterial α-diversity were analyzed and visualized by R package MatCorPlot  [57]. To determine the effects of elevated O 3 , N addition, and Mlp-infection on phyllosphere associations in the two clones of poplar, the underlying co-occurrences between bacterial and fungal taxa were depicted through network analysis using the R library igraph  [58].
The network analysis was performed at the class level to reduce the complexity of calculation as well as to ensure the accuracy of taxonomic information. Data filtering was performed prior to network construction in that only highly abundant ASVs that were in the top 10% in terms of relative abundance across all samples were reserved to mitigate the random variances  [59]. The resulting correlations were then imported in Gephi software  [60]  and visualized by the Frucherman Reingold algorithms, the topology property parameters of the network the clustering coefficient, network density, and modularization were calculated automatically in Gephi.
The Variation of Bacterial and Fungal β-Diversity under Elevated O3 Concentration and N Addition The Weighted Unifrac distance matrices for both bacterial and fungal communities were calculated and visualized using PCoA analysis. As expected, the leaf condition (non- ) with nitrogen addition (N60) and without nitrogen addition (N0). Box plots showed the range of estimated values between 25% and 75%, the median, the minimum, and the maximum observed values within each dataset.
Recruitment. By the end of the study, 226 patients had been recruited (Scheme 1). e dropout rate was 7. 5% (17 patients). e following were the main reasons for leaving the study: actual duration of ECC was less than 90 minutes, 10 patients (5 in group CS, 5 in group C); last-minute decision was taken to perform off-pump bypass surgery instead of onpump, 5 patients (3 in group CS, 2 in group C); and failure of sufficient postoperative data collection, 2 patients (1 for group CS, 1 for group C). Five patients of the cell-salvage group did not receive the collected blood as their hemodynamics and the oxygen delivery-consumption equilibrium were adequately assured by intraoperative RBC transfusions due to delays in preparing the centrifuged product. Consequently, these 5 patients were transferred to group C.
Clinical study design, patient population and ethics approval Critically ill patients admitted to two ICUs (Medical and Surgical ICUs of the Edouard Herriot University Hospital) of Lyon university-affiliated hospitals (Hospices Civils de Lyon, Lyon, France) who presented with SARS-CoV-2 pulmonary infection confirmed by RT-PCR were included in the RICO (REA-IMMUNO-COVID) study. 2  This study was approved by ethics committee (Comit e de Protection des Personnes Ile de France 1 -N°IRB / IORG #: IORG0009918) under agreement number 2020-A01079-30 and was registered at ClinicalTrials. gov (NCT04392401). The committee waived the need for written informed consent because the study was observational, with a low risk to patients, and no specific procedure, other than routine blood sampling, was required.
Upon ICU release, convalescent COVID-19 individuals were requested to participate to the ancillary study: NOSO-COR-IMMUNO. The clinical protocol was approved by ethics committee of Ile de France on October 14 2020 (N°IDRCB: 2020-A02128-31). This study was registered at Clinicaltrials. gov under number NCT04637867. Inclusion criteria were (i) patients aged > 18 years included in NOSO-COR study  4  (ii) signed written informed consent (iii) affiliation to social security system. Exclusion criteria were: (i) opposition to participation (ii) breast feeding and pregnancy (iii) currently hospitalized patients (iv) patients under legal assignment. Patients who agreed to participate were invited for two follow-ups medical visits in an ambulatory setting in Edouard Herriot Hospital, Hospices Civils de Lyon, 9 and 13 months after SARS-CoV-2 infection.
Flow cytometry T lymphocyte immunophenotyping was performed on an automated volumetric flow cytometer from Beckman Coulter (Aquios CL) as previously described. 5 The expression of monocyte HLA-DR was determined using the Anti-HLA-DR/Anti-Monocyte Quantibrite assay (BD Biosciences, San Jose, USA). Total number of antibodies bound per cell (AB/C) were quantified using calibration with a standard curve determined with BD Quantibrite phycoerythrin (PE) beads (BD Biosciences) as described elsewhere. 6 B and NK lymphocyte immunophenotyping was performed using lyophilized antibody panel from Beckman Coulter (Duraclone kit). Data were acquired on a Navios flow cytometer (Beckman Coulter, Hialeah, FL) and flow data were analyzed using Navios software (Beckman Coulter).
Statistical analyses Data are presented as numbers and percentages (qualitative variables) and medians and 25th/75th percentiles (quantitative variables). Fisher's exact test was used for qualitative variables comparison. Quantitative variables were compared with Mann-Whitney U test. For all pairs of immune parameters, Spearman's rho correlation coefficients were estimated and summarized in a correlation matrix. Comparisons between immune parameters measured in patients with short and long hospital length of stays (hospital length of stay below or over 25 days) were limited to values measured at D0, M9 and M13 when all patients from both groups could be sampled. The level of significance was set at 5%. Data were analyzed using Graphpad Prism version 5. 03 (Graphpad Software, La Jolla, USA) and R version 3.
Acknowledgments The authors would like to thank the clinical teams from all ICUs in HCL who were involved in this project while dedicated to their clinical duties during COVID-19 pandemic as well as patients and their families who agreed to participate to this clinical study. The authors would also like to thank  Val erie Cerro ,  Laurie Bignet ,  Marion Provent ,  Lauredana Baboi ,  Jessica Rousson ,  Florence Raynal ,  Laure Giambra  ( Services de R eanimation, Hospices Civils de Lyon ),  Sabrina Bennia  ( Service Hygi ene, Epid emiologie, Infectiovigilance et Pr evention, Hospices Civils de Lyon ),  Anne Termoz  et  Julie Haesebaert  ( Pôle de sant e publique, Hospices Civils de Lyon ),  Camille Mena ,  Catherine Planckaert ,  Nad ege Trehet-Mandez ,  Nathalie Paquin ,  Pr Roland Chapurlat (Pr evention des Maladies Osseuses ,  Hospices Civils de Lyon ) for clinical trial management.
Population Patients with a primary diagnosis of PTSD according to the text revision of the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria (Structured Clinical Interview for DSM-IV, PTSD module) and in the absence of contraindication to propranolol provided written consent to participate in the research (main characteristics and inclusion/exclusion criteria are reported in Table  1 ). No racial or genetic data were collected in this study. They all took part in an RCT testing for the efficacy of traumatic memory reactivation under the influence of propranolol versus placebo. This trial received approval from an ethics committee  (CPP 2009-012976-29)  and was registered on clinicaltrials. gov (NCT01713556). The main results for this clinical trial (i.
MRI acquisition and data preprocessing Patients underwent two MRI sessions at rest with their eyes closed (at baseline and posttreatment); a 3 T Philips Achieva scanner with an 8channel head coil was used. Each of these sessions included a 4-min T1-weighted (T1w) 3D anatomical run (124 transverse slices, field of view = 256 mm 3 , vox = 0. 8 mm 3 ) and a 15-min T2*-weighted 3D principles of echo shifting with a train of observations (3D-PRESTO) sequence  (Liu et al. , 1993; Neggers et al. , 2008; van Gelderen et al. , 2012). This last sequence is a functional sequence (dynamic scan time = 1000 ms, TR = 22 ms, TE = 9. 6 ms, flip angle = 9 • , vox = 3. 3 mm 3 ) allowing for full functional brain coverage with a temporal resolution particularly suited for effective or directed functional connectivity analysis  (Deshpande and Hu, 2012).
Anatomical and functional MRI data were preprocessed using the FMRIPrep pipeline v. 1. 5  (Esteban et al. , 2019) , a tool based on Neuroimaging in Python: Pipelines and Interfaces (Nipype v. 1. 2. 2)  (Gorgolewski et al. , 2011). T1w images were corrected for nonuniform intensity and skull stripped. Brain tissue segmentation of cerebrospinal fluid, white matter and gray matter was performed on the brainextracted T1w image. Volume-based spatial normalization to the Montreal Neurological Institute ICBM-152 (MNI) template was performed through nonlinear registration using brain-extracted versions of both the T1w reference and the MNI template. For functional images, a reference blood-oxygen-level-dependent (BOLD) volume and its skull-stripped version were generated and coregistered to the T1w image using a boundary-based registration algorithm with 9 degrees of freedom.
Granger causality analysis Directed functional connectivity was assessed using Granger causality analysis (GCA), which allows for the data-driven exploration of a reference region's influence over the brain as well as targets of influence on the same given area  (Roebroeck et al. , 2005). We used this approach to account for the multiple regions naturally connected to the AI  (Menon, 2015)  and to avoid missing brain areas that could not have been retained in a more traditional theory-driven framework. Here, we used the implementation proposed in the BrainVoyager software suite (v21. 4, BrainInnovation, Maastricht; Granger Causality Mapping plugin V1. 5). We defined the bilateral AI, according to the meta-analysis from Laird  (Laird et al. , 2011) , as the reference region for later analyses (right anterior insula (x = 35.
Intrinsic network spatial stability measure In parallel, we also explored the spatial stability of the DMN and the CEN posttreatment (i. e. , the "posttreatment" time-point) using a "goodness-of-fit" (GoF) procedure. This method refers to the rate of spatial concordance between the resting-state networks and a given template  (Jardri et al. , 2013). After decomposing each posttreatment functional dataset using independent component analysis (ICA), we selected the components exhibiting the highest spatial correlation with an a priori template. For each participant, this procedure was repeated twice: with the DMN and the CEN template  (Greicius et al. , 2004). The resulting GoF scores were assumed to reflect posttreatment DMN and CEN spatial stability. We tested for an association between these GoF scores and the severity of re-experiencing symptoms using Pearson's r correlation test, considering p < 0.
Numerical model To achieve high vibration accelerations (Γ > 11), discrete element simulations (DEM) are also performed using the open-source software LIGGGHTS  [23, 24]. This software is an extension of LAMMPS (widely used in molecular dynamic) based on the work of Cundall and Strack  [25]  to simulate the mechanical behaviour of each particle in a granular assembly. The geometry of the silo (created and meshed using the open-source software FreeCAD) is the same as the one used in experiment. As in experiment, non-cohesive spherical glass beads  of diameter d = 1. 1 ± 0. 1 mm are inserted 1  in the silo and then discharged while vertical and sinusoidal vibrations are applied. The only difference with the experiment is the limit for the vibrations, which was fixed at Γ = 30 in simulation. Above this limit, the flow becomes highly unsteady while a transition to a granular gas is observed due to the extreme values of vibrations.
Flow analysis To study the flow during the discharge, the position and velocity of the particles are recorded in both experiment and simulation. In simulation, all information can be directly extracted and stored. In experiment, the particle centers appear as bright spots on the images from the camera (see Fig. 2 ). Those spots can be detected and tracked to obtain the trajectory of each particle, and thus their position and velocity. The detection and tracking are performed with the software Matlab, using functions developed by Daniel Blair and Eric Dufresne 3  , adapted from the free software "Particle tracking using IDL" 4  of John C. Crocker and Eric R. Weeks  [27]. This program has the advantage of allowing to follow a very large number of particles and to measure displacements with a sub-pixel resolution (1/10 pixel precision).
Acknowledgements We acknowledge support from the  Agence Nationale de la Recherche/DFG  (Project " pastflow "  ANR-19-CE08-0030-01 ). A part of this study is conducted in the framework of the " PowderReg " project, funded by the  European programme   Interreg VA GR  ( ANR-017-4-08-061 ) within the priority axis 4 "Strengthen the competitiveness and the attractiveness of the Grande Région / Grobetaregion.
Objective: The aim of the study is to compare the health benefits of EGs versus those of CEs for the physical function and mental health of older adults. Methods: A comprehensive search was conducted from the earliest available date to February 2023 in the following 6 databases: PubMed, Web of Science, Embase, Cochrane, CINAHL, and PsycINFO. All English-language randomized controlled trials comparing the effects of EGs versus those of CEs on the physical function and mental health of older adults, with nearly same physical activity between the 2 interventions, were included. Risk of bias was independently evaluated by 2 authors using the Cochrane risk of bias in randomized trials tool. Two authors independently extracted data. We followed the Cochrane Handbook of Systematic Reviews of Interventions to process and analyze the data for meta-analysis.