dataesr/scientific-paragraphs-categorization huggingface dataset:
1 mm particle size 1. 8 μm) and heated at 40° C in a Pelletier oven. The gradient mobile phase consists of water with 0. 2% of acetic acid (A) and acetonitrile (B). The flow rate was set to 0. 3 mL/min. Initial condition is 98% phase A and 2% phase B. Molecules were then eluted using a gradient from 2% to 95% phase B in 22 min. The column was washed using 95% mobile phase B for 2 minutes and equilibrated using 2% mobile phase B for 4 min. The autosampler was kept at 4° C. Peak detection and integration were performed using the Thermo Xcalibur quantitative software (3. 1. ), exported as tables and processed with R software (version 4. 0. 3) and the GRMeta package (Github/ kroemerlab). In parallel, raw data files obtained by the pseudotargeted analysis described above were used to perform unbiased profiling analyses using the Thermo Scientific™ Compound Discoverer™ small molecule identification software (version 3.
Data analysis using Compound Discoverer™ After sample injection and data acquisition, raw data files were processed with Compound Discoverer software following a customized node-based workflow for identifying unknown compounds in metabolomics. First, spectra selection and retention time alignment were performed, followed by removal of background noise and baseline correction. Next, the processing workflow found chromatographic peaks for unknown compounds (molecular weight, MW, x retention time, RT) extracting all relevant spectral and chromatographic information, to predict the elemental composition of the unknowns. The possible identity of the unknown compounds was then searched against selected MS databases, such as ChemSpider (from MS1 scans by using MW or predicted composition when available), mZcloud (MS/MS spectral library), built-in databases (custom, local libraries), and Metabolika or KEGG databases (metabolic pathway search).
AGING  IDEX-0001. LZ is supported by the  French-German ANR  grant " Ileobiome "  19-CE15-0029-01. A-GG was supported by  Fondation pour la Recherche Médicale  (FRM). F. M. is grateful to the  Austrian Science Fund FWF  ( SFB LIPOTOX F3007  and  F3012, DK-MCD W1226 , as well as grants  P29203 ,  P29262 ,  P27893 ,  P31727 ) and the  Austrian Federal Ministry of Education, Science and Research as well as the University of Graz  for grants " Unkonventionelle Forschung-InterFast and Fast4Health" as well as "flysleep"  ( BMWFW-80. 109/0001-WF/V/3b/2015 ). We acknowledge the support of the field of excellence BioHealth, of  NAWI Graz  and the  BioTechMed-Graz  flagship project "EPIAge". L. D. received support by the  Philanthropia Fondation Gustave Roussy. (ONCOVID;  NCT NCT04341207  has been supported by the  Fondation Gustave Roussy , the Dassault family,  Malakoff Humanis , Agnès b.
For 26S rDNA reads, only forward reads were trimmed and filtered, with truncation at 300 bp, no ambiguous bases allowed and each read required to have less than two expected errors based on their quality scores. ASVs were directly inferred from forward reads. 16S chimera sequences were removed using UCHIME algorithm  (Edgar, Haas, Clemente, Quince, & Knight, 2011)  implemented in VSEARCH v1. 1. 3 (https://github. com/torognes/vsearch) against the ChimeraSlayer reference database  (Haas et al. , 2011)  and the RDP classifier (Q. Wang, Garrity, Tiedje, & Cole, 2007)  was used for taxonomy assignment, which was made with GreenGenes v13. 8 database  (McDonald et al. , 2012)  available in Qiime  (Caporaso et al. , 2010). Each 16S ASV was then classified to the species level using the RDP seqmatch tool (https://rdp.
Statistical and correlation analyses. Statistical analyses were performed using the R software (R Core Team. (  2019 ), 2019). Analyses of variance (ANOVA) and mean comparison were used to determine whether the physico-chemical, microbial and biochemical data significantly changed during the time-course of fermentation. Data from GC-MS were centered and scaled by compound and hierarchically clustered by Ward's minimum variance method and Euclidean distance metric with the hclust R function before being plotted by the heatmap. 2 function of R gplots package. Principal component analysis (PCA) was first used to identify common biochemical profile changes over fermentation times using tea and biofilm data. Then, a multiple factor analysis (MFA) was performed using the MFA function of the FactoMineR package  (Lê, Josse, & Husson, 2008).
The data set was constituted of three groups of active variables: pH and density, concentrations of eight organic acids and log values of abundance of 39 volatile compounds from GC-MS analysis plus ethanol from HPLC, for the triplicate samples from d7 to d27. The age of samples and the 25 variables from both culture-dependent and -independent microbial analysis of tea and biofilm were used as supplementary (i. e. illustrative) variables. All analyses were performed with R software using FactoMiner, Factoextra, Hmisc, Psych and gplots packages  (Lê et al. , 2008; Wickham, 2016).
Our microscopic observations suggest biofilm filaments were released from some yeast cells Fig. 8. Multiple factor analysis performed on samples collected at d7, d11, d14, d20 and d27 with three groups of active variables: pH and density, organic acid concentrations and abundance of 39 volatile compounds. Individual factor map (A) and variable factor map (B). Microbial data from culturedependent (n = 6) and -independent (n = 18) analyses of tea and biofilm and biofilm wet mass were used as supplementary variables. The ellipses on plot (A) show the three groups from agglomerative hierarchical clustering on results from MFA. The variables poorly represented on plot (B) are not shown (cos 2 < 0. 5). meaning at least one or more yeast species can produce it during fermentation. Yeast biofilm production, in particular for Dekkera/Brettanomyces as well as Saccharomyces or Debaryomyces species, has already been described in co-cultures with LAB species  (Furukawa, Yoshida, Ogihara, Yamasaki, & Morinaga, 2010; Kawarai, Furukawa, Ogihara, & Yamasaki, 2007; León-Romero, Domínguez-Manzano, Garrido-Fernández, Arroyo-López, & Jiménez-Díaz, 2016; May et al.
Data Source Data used in the preparation of this article were obtained from the ADNI database. 1 The ADNI was launched in 2003 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, private pharmaceutical companies and non-profit organizations, with the primary goal of testing whether neuroimaging, neuropsychological, and other biologic measurements can be used as reliable in vivo markers of AD pathogenesis. A fuller description of ADNI and up-to-date information is available at www. adni-info. org. To test if key findings could be replicated in an independent sample, we used data from the INSIGHT-preAD study  (Dubois et al. , 2018). This study is a monocentric university based cohort of participants enrolled at the Institute of Memory and Alzheimer's Disease (IM2A) at the Pitié-Salpêtrière University Hospital in Paris, France.
Study Participants From the ADNI cohort, we retrieved 600 cases with available 18F-Florbetapir-PET data as well as CSF Aβ42 levels in close temporal proximity to the PET acquisition. Mean temporal distance between 18F-Florbetapir-PET acquisition and lumbar puncture was 13 days (SD 34 days). These cases included data of 152 cognitively normal older individuals, 241 cases with early MCI, 129 subjects with late MCI and 78 subjects with AD dementia. Detailed inclusion criteria for the diagnostic categories are available at the ADNI web site. 2 he INSIGHT-preAD study includes 318 cognitively normal Caucasian individuals, recruited from the community in the wider Paris area, aged 70-85 at baseline, with SMC and defined brain amyloid status  (Dubois et al. , 2018). Fortythree INSIGHT-preAD cases had CSF Aβ42 levels available in close temporal proximity to the 18F-Florbetapir-PET scan.
Imaging Data Acquisition Detailed acquisition and standardized pre-processing steps of ADNI imaging data are available at the ADNI website (see text footnote 2). 18F-Florbetapir-PET data was collected during a 50to 70-min interval following a 370 MBq bolus injection of 18F-Florbetapir. To account for the multicentric acquisition of the data across different scanners and sites, all PET scans undergo standardized pre-processing steps within ADNI. PET data acquisition in the INSIGHT-preAD cohort was performed according to a method previously described  (Habert et al. , 2017). All amyloid-PET scans were acquired in a single session on a Philips Gemini GXL CT-PET scanner 50 (±5) minutes after the injection of approximately 370 MBq (333-407 MBq) of. For anatomical reference and pre-processing of the PET scans we used the corresponding structural MRI scan that was closest in time to the 18F-Florbetapir-PET scan.
In ADNI, MRI scans were acquired on multiple 3T MRI scanners using scannerspecific T1-weighted sagittal 3D MPRAGE sequences. Similar to the PET data, MRI scans undergo standardized preprocessing steps aimed at increasing data uniformity across the multicenter scanner platforms (see text footnote 2 for detailed information on multicentric MRI acquisition and preprocessing in ADNI). In INSIGHT-preAD, MRI scans were acquired on a Siemens Verio 3T scanner at Pitié-Salpêtrière Hospital, Paris. A T1-weighted image was acquired using a fast three dimensional gradient echo pulse sequence using a magnetization preparation pulse (Turbo FLASH)  (Habert et al. , 2017).
Positron Emission Tomography Data Pre-processing and Analysis Images were preprocessed using Statistical Parametric Mapping software version 8 (SPM8) (The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London) implemented in Matlab 2013. The pre-processing pipeline followed a routine described previously  (Grothe et al. , 2017). First, each subject's averaged PET frames were coregistered to their corresponding T1-weighted MRI scan. PVE were corrected in native space using two approaches, a 3-compartmental voxel-based post-reconstruction method as described by  Müller-Gärtner et al. (1992)  (henceforth termed PVC-3) and a simpler 2-compartmental approach as proposed by  Meltzer et al. (1996)  (henceforth termed PVC-2). Both PVC approaches are implemented in the SPM-based PETPVE12 toolbox  (Gonzalez-Escamilla et al.
, 2017). The uncorrected and corrected PET images were spatially normalized to an aging/ADspecific reference template using the deformation parameters derived from the normalization of their corresponding MRI. The composition of the cortical SUV mask has been described in  Teipel et al. (2014). In brief, this composite mask consisted of frontal, parietal, and temporal ROIs known to be vulnerable to amyloid accumulation  (Thal et al. , 2002). Anatomical masks for these ROIs were derived from the Harvard-Oxford structural atlas (distributed with the software package, FSL;  Desikan et al. , 2006)  and high-dimensionally warped into the reference space of this study based on a DARTEL registration of the MNI152 template (the template space of the Harvard-Oxford atlas) to the aging-AD reference template.
In this way, groups of interest gradually moved down the scale of CSF Aβ42 levels, always using the same initial reference group of 23 cases for comparison. This procedure was repeated 36 times until the three healthy controls with the lowest CSF Aβ42 levels were included. For each comparison, voxel-wise twosample t-tests were performed using SPM12 software with the spatially normalized 18F-Florbetapir-PET SUVR maps from the different PVE correction methods. All voxel-wise analyses were corrected for multiple comparisons using a family-wise error (FWE) at p < 0. 05 and cluster size k = 150. With this approach we addressed two questions: First, does the PVC method affect the sensitivity of 18F-Florbetapir-PET for early signs of cerebral amyloidosis as indicated by CSF Aβ42 levels? Second, where does regional amyloid signal occur first in people with low levels of amyloidosis? Since the selection of the upper 15% quantile of CSF Aβ42 levels for the reference group was purely arbitrarily (with the goal to achieve a reasonable group size), in sensitivity analyses we used other cut-offs, specifically the 20% (N = 30; with a slide of 5 cases) and 10% quantiles (N = 15; with a slide of 2 cases), respectively.
The regions detected in our study form a subset of the regions that showed early accumulation of amyloid over two years of follow-up in 18F-Florbetapir-PET data from the ADNI and the BioFINDER cohorts  (Palmqvist et al. , 2017). FIGURE 6 | Detection of early regional amyloid accumulation across all cases of the ADNI sample. Effect of partial volume correction (PVC) method on first detectable signal increases in voxel-wise 18F-Florbetapir-PET SUVR WC values as determined by the sliding-window analysis with decreasing CSF Aβ42 levels across all cases in the ADNI sample. Clusters of >150 voxels passing a family wise error corrected level of significance of p < 0. 05 are plotted on rendered cortical surfaces and a midsagittal brain section. Effects are shown for the index sample (group mean CSF Aβ level of 1,287 pg/ml) where first regional signal increases compared to the reference sample (group mean CSF Aβ level of 2,280 pg/ml) were detectable in standard uncorrected 18F-Florbetapir-PET data ('no PVC').
Description of testing visits and outcome measures Table  2  presents the timing of the testing visits and outcome assessment measures. The baseline assessment (pre-test) visit (V1) will take place on the first day of the 3-week in-patient hospital rehabilitation stay. A specialist trained therapist will run the clinical assessments (WMFT, FM-UE, Box and Block Test (BBT), Bell's test, MMSE, Barthel Index (BI); more details in the "Clinical data and outcome measures" section). Patients UL kinematics and motor cortical region activation will then be evaluated by one of the investigators on three functional motor tasks: a seated reaching task (Proximal Arm Non-Use, PANU), a circular steering task, and the Ladybug game of the Armeo VRT platform used for rehabilitation. The next day (day 2), patients will start the standard in-patient stroke rehabilitation program that will last for 13 complete days (excluding weekends) over 3 weeks.
Clinical data and outcome measures Clinical data Clinical data will be collected by the medical doctor during the routine clinical visit, including date of birth, age, sex, hand laterality before and after the stroke, date of the stroke, delay from the stroke to the inclusion, and characteristic of the brain lesion (type and location). Treatment, medication, and health events will also be reported. The source documents are the original data, sheets, and files, from which the data concerning the research are copied in the electronic case report form (eCRF) by the clinical investigation centre (CIC).
A practice trial will be done to familiarize the patients on the timing of reaching (~5 reaches/20 s based on pilot testing) by following a recorded voice of investigator to start and return phase of the reaching movement. After a 2-min rest period, the patients will be required to repeat the reaching movements first with the paretic UL then with the nonparetic UL over 3 trials (20 s with 20 s rest between trials). The spontaneous condition will be undertaken first followed by the maximal condition. Kinect V2 (Microsoft, USA) and LSL-Kinect software (see "Availability of data and materials" for details) will be used to stream and analyse the UL kinematics to calculate PANU score and other kinematic variables according the methods outlined in our previous work  [4].
Other/exploratory outcome measures Functional motor tasks The experimental setup for the Seated Reaching and Circular Steering tasks will allow synchronous measurements of motor cortical region activation (functional near infrared spectroscopy-fNIRS and electroencephalography-EEG) and UL kinematics using lab streaming layers (LSL, https://github. com/ labstreaminglayer/App-LabRecorder). In the circular steering task (see Fig. 5b ), a computerized version of a circular steering task (LSL-Mouse; see "Availability of data and materials" for details) based on the speed-accuracy trade-off  [8]  will be used to determine performance improvements of the paretic UL. Patients will be comfortably seated in front of a table with a graphical tablet (A3 size, Wacom, Kazo, Japan) and stylus moulded onto a mouse pad for ease of hand placement for stroke patients.
Collecting and data management The participant will only be identified by a unique identification number, the first letter of the name, the first letter of the first name, his gender, and the year of birth. An identification list of subjects will be kept in the investigator's file. The investigator will ensure that the anonymity of each person participating in the study is guaranteed. Information will be collected for each participant in a standardized observation booklet filled out by the investigator or the co-investigator. The source documents are the original documents, data, and files, from which the data concerning the research participants are entered in the electronic case report form (eCRF) by dedicated employees at Montpellier University Hospital. The investigator undertakes to authorize direct access to the source data of the study during control, audit, or inspection visits.
Data from the actimetry, from the Armeo and from the functional motor tasks (kinematics, performance, EEG/fNIRS) will be extracted from the local password-protected computers and stored on the EuroMov centre private server that can be accessed only by the registered investigators (KB, CM, DM, MM, SP, PJ, GD) with reinforced authentication during the duration of the project. All data will be checked monthly by the PI to ensure that all protocols and ethical guidelines for data collection and analysis are followed. Study data are entered through the e-CRF developed using ENNOV CLINICAL software, which allows realtime data quality control. In order to meet regulatory requirements, this software complies with the recommendations concerning computerized systems for the management of clinical trials and electronic signature and standards.
Secondly, the data manager performs additional computerized consistency tests based on the presence of non-standard, missing, aberrant, or inconsistent data and which are performed regularly during patient recruitment and follow-up. All consistency tests are defined in advance in specifications proposed by the data manager and validated by the investigator, the statistician, and the CRA. Each inconsistency identified on the eCRF ("Queries") is the subject of a request for correction or justification from the investigator (response to "Queries"). The latter undertakes to make himself available to the members of the research team and to provide them with all the additional information required to resolve these errors. This information is reported in the study database. Once the study is complete, namely the data required for the protocol and any additional data entered (self-questionnaires), the data monitored and validated, the study database is frozen.
The maintenance of the database is the responsibility of the Clinical and Epidemiology Research Unit of the Innovation and Research Department of the study sponsor. The data is kept in an ASCII type format. The data encoding is integrated into eCRFs. The computer files and the follow-up of any modification are saved and made available by the Innovation and Research Department of Montpellier University Hospital. The database freezing is carried out according to the Innovation and Research Department procedures. The freeze is documented by a database freeze certificate. It takes place once all the data is verified and the corrections requested from the investigators are obtained. The closure of the trial including the closure of the centres will be carried out in accordance with Good Clinical Practice and International Conference on Harmonization.
Monitoring and quality control The project will be monitored by the CRA. Appropriate monitoring will be implemented based on a risk grid related to the project. In accordance with this grid, the CRA will carry out regular follow-ups of the project's investigation centres (establishment visit, follow-up according to the rhythm of inclusions and a closing visit). All checks will be the subject of a monitoring report by written report (traceability of visits). The investigators agreed to comply with the requirements of the sponsor and the competent authority with regard to an audit or inspection of the research. The audit might be applied to all stages of research, from the development of the protocol to the publication of results and the classification of data used or produced in the context of the research.
Primary outcome measure analysis The aim of the primary outcome analysis will be to compare the functional motor capacity of the UL (WMFT) in the intervention group comparatively to the placebo group at V3. The normality of the data from the WMFT will be analysed by the Shapiro-Wilk test. The variance homogeneity will be assessed using the Bartlett test. If the normality is respected, a parametric Student t-test will be performed for mean comparisons. If the distribution of the data is not normal, a non-parametric Wilcoxon signed-rank test will be used.
Acknowledgements The authors acknowledge  Claire Chauveton  ( Direction Recherche et Innovation ),  Sophie Bentz ,  Bernard Moreno ,  Christel Guiraudie  ( Direction de la Coordination Générale des Soins ),  Valerie Driss  (CIC), and  Lucie Gamon  ( Clinical Research and Epidemiology unit) from Montpellier University Hospital  for their contribution on the administrative aspects of the project. The authors acknowledge the medical doctors and therapists from Montpellier and  Nîmes University Hospitals  for their commitment in the project for recruitment and training of patients, respectively. We would like to thank  Gilles Dusfour  from the Cartigen platform at Montpellier University Hospital contributing in the development of the analysis pipeline for the actimetry data. We would also like to thank  Benoit Prigent  from  EuroMov at University of Montpellier  for contributing in the development of the LSL-Kinect software application.
Funding The ReArm project is funded by the Direction générale de l'offre de soins (DGOS) in the French Ministère des Affaires Sociales et de la Santé (Programme hospitalier de recherche infirmière et paramédicale-PHRIP-2018-0731). CM and GF are supported by PhD scholarships from the Doctoral School of Human Movement Science at the University of Montpellier. The requested budget was calculated according to the design of the study (number of subjects, number of centres and visits planned/patient and number of specific examinations carried out), the applicable regulations and additional and specific examinations planned in the framework of this study. As a reminder, this study is based, in 2 centres, on the inclusion of 58 patients in total. The design, management, analysis, and reporting of the study are entirely independent of the funding received for the ReArm project.