dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank the financial supports which are ADEME,  Pays de Loire Region  and the  FR CNRS INCREASE 3707 consortium. The mass spectrometry and NMR analyses were performed using the equipment of the  BIBS facility in Nantes  ( UR1268   BIA ,  IBiSA ,  Phenome-Emphasis-FR  (grant number  ANR-11-INBS-0012 )).
Data Analysis Raw data for each participant (gender, age interval, orthonasal smell perception ability, and fVOIQ global score) were stored in a specific database. The mean fVOIQ score was calculated for each participant (sum of answers/16 items). A low mean score (close to 1) indicates a good ability to imagine smells, and vice versa. All statistical analyses were performed using R version 3. 6. 3. The threshold for statistical significance was α = 0. 05. fVOIQ score distribution for all four groups differed from the normal distribution (Shapiro-Wilk test, ps < 0. 001). Therefore, quantitative data were expressed as medians (Med) associated with interquartile range (IQR) and qualitative data by their frequencies. In order to verify the reliability of the fVOIQ, the internal consistency of the questionnaire was assessed.
ACKNOWLEDGMENTS We thank  Dr. Daniel J. Sexton  ( Shire ) for useful interaction and the gift of biotechnological inhibitors,  Ms. Johanne Bouthillier  for technical help, and  DiaMedica, Inc. (Minneapolis, MN,  USA)  for supplying human recombinant KLK-1. Special thanks to  Mr. Charles St-Pierre , president,  Angio-Oedème Héréditaire du Québec , for facilitating this work in several ways.
Acknowledgements This research was supported by a grant from  BPI France/Métropole Européenne de Lille/Région Hauts de France  ( DOS0020745 ) and by a grant from  Association Nationale Recherche et Technologie (ANRT). This work was supported by the  Hauts-de-France region  funding through the  ALIBIOTECH research program. We are grateful to  Lawran Lowenski  for her assistance during experimentations. We also thank  Dorothée Domenger  and  Yanath Belguesmia  for helpful discussions.
Mitochondria study Imaging. Mitochondria were immunolabeled with anti-SDHA antibody (ab14715, Abcam) in combination with a conjugated Alexa Fluor-568 (2124366, Invitrogen) on preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this this version posted February 7, 2020. ; https://doi. org/10. 1101/2020. 02. 06. 937037 doi: bioRxiv preprint 6 fixed preparations. Nuclei were labeled with DAPI. Cell imaging was performed with a confocal microscope (Leica DM5500 TCS SPE). Flow cytometry. Cells were resuspended in PBS + 2 mM EDTA, labeled with 100 nM TMRM (T5428, Sigma-Aldrich) and analyzed with FACSCcalibur (BD Biosciences). Data were quantified with the FlowJo software (FlowJo, LLC).
, 2013). Staining pixel count was performed blindly by setting the threshold to the same value for each section as previously described using ImageJ software (Scion Software). The other hemisphere was used for Western blotting as previously described  (Laurent et al. , 2014). Briefly, tissue was homogenized in 200 µl Tris buffer (pH 7. 4) containing 10% sucrose and protease inhibitors (Complete; Roche Diagnostics, Meylan, France), sonicated and kept at -80 • C until use. Protein amounts were evaluated using the BCA assay (Pierce, Rockford, IL, USA). Proteins were diluted with lithium dodecyl sulfate buffer (LDS) 2× supplemented with reducing agents (Invitrogen) and then separated on NuPage Novex gels (Invitrogen) for Western blot analysis. Proteins were transferred to nitrocellulose membranes, which were then blocked (5% non-fat dry milk in TNT:Tris-HCl 15 mM, pH 8, NaCl 140 mM, 0.
Statistical Analysis Statistical analyses were performed by using IBM SPSS 19 (IBM SPSS, Armonk, NY, USA) and GraphPad Prism 7 (GraphPad Software, La Jolla, CA, USA). Unless otherwise stated, all statistics were done with Two-Way ANOVA or repeated measures ANOVA (RM-ANOVA). For single between-group comparisons, t-tests with Welch's correction were used.
The high initial values and the decay of activity levels during this phase, which we consider as habituation to the novel environment, are apparently different in Tg vehicle-treated mice from the other genotypes as confirmed by a multivariate ANOVA of the first hour of recording (F (3,33) = 6. 545, p = 0. 002; post hoc Dunnett test vs. vehicle-treated Tg mice, p = 0. 001). Chronic treatment with sodium selenate corrected for the increased initial and nocturnal activity of THY-Tau22 mice. Next, we tested general motor abilities and coordination in the rotarod test and found no differences. Thus, all four groups were equally capable of remaining on the rotating rod (F (3,33) = 0. 747, p > 0. 05; data not shown). Previous investigations of the ambulatory activity of 10-month-old THY-Tau22 mice had demonstrated an overall increased activity in Tg mice  (Van Der Jeugd et al.
Neurocognitive Tests We assessed spatial learning and memory in the established hippocampus-dependent water maze paradigm. Looking at both the time to locate the hidden platform and the path length traveled to reach it, we found significant differences between the groups (overall RM-ANOVA F (3,33) = 44. 542, p = 0. 0001 for escape latency and F (3,33) = 43. 466, p = 0. 0001 for distance traveled). Post hoc Dunnett tests revealed that spatial acquisition in vehicle-treated Tg mice at 12-13 months of age was severely impaired compared with the other groups [Figure  2A , group effect: escape latency (s): p = 0. 001; path length (cm): p = 0. 001, latter data not shown]. This impairment was not due to motor deficits because swim speed did not differ across groups (p > 0. 05 data not shown). Noteworthy, there was a pronounced and significant improvement in spatial learning of selenate-treated Tg mice which was comparable with the WT-groups.
ACKNOWLEDGMENTS We thank the animal care facility (animal facilities of  Université de Lille-Inserm) of ''Plateformes en Biologie Santé de Lille '' and  M. Besegher-Dumoulin ,  J. Devassine ,  Y. Lepage ,  C. Meunier ,  C. Degraeve , and  D. Taillieu  for transgenic mouse production and care. Sebastien Carrier ,  Nele De Ruyck , and  Leen Van Aerschot  are acknowledged for mouse care and genotyping.
FUNDING This work was supported by the  Research Foundation-Flanders  ( FWO grants   G. 0327. 08  to RD'H and DBa, G. 0D76. 14  to  DBa , and  FWO  fellowship to AVDJ  1157812N ). DBl and LB received support from:  I-Site ULNE ,  France Alzheimer / Fondation de France ,  FHU VasCog research network (Lille, France ), programs d'investissements d'avenir  LabEx (excellence laboratory) DISTALZ (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease) ,  Agence Nationale de la Recherche  ( ANR ;  GRAND, SPREADTAU  to LB, ADORATAU and ADORASTrAU to DBl),  Fondation pour la Recherche Médicale ,  Vaincre Alzheimer ,  Fondation Plan Alzheimer as well as Inserm, CNRS, Université Lille, Lille Métropole Communauté Urbaine ,  Région Hauts-de-France ,  DN2M.
Abstract The aim of this integrative study was to investigate the relationships between biochemical traits (total, insoluble and soluble collagens (TCol, ICol, SCol), cross-links (CLs), proteoglycans (TPGs), proportion of fiber types, total lipids (TLips), main fatty acids  (FAs)  families, the n-6/n-3polyunsaturated FA (n-6/n-3PUFA) ratio and the sensory attributes scores (tenderness, juiciness, flavor) of two muscles from beef: Rectus abdominis (RA) and Longissimus thoracis (LT). For robust analysis, a database was prepared using samples from three studies from animals raised under different production systems. The analyses were performed either on each study separately or on pooled data per muscle after removing as many studyal effects as possible. The CLs (across the muscles and studies) and, to a lower extent, type IIA muscle fibers (mainly for RA muscles), saturated FAs (SFAs), monounsaturated FAs (MUFAs) (for the LT muscles) were the components most frequently associated with tenderness.
Indeed, the FAs influence a range of diseases, including cardiovascular, metabolic such as type 2 diabetes and inflammatory disorders  (Calder, 2015). It is worthwhile to note that the results about the relationships between the FAs and sensory attributes of beef meat are contradictory  (Cho et al. , 2005; Garmyn et al. , 2011; Hunt et al. , 2016; Hwang and Joo, 2017, 2016). In this context, the aim of this integrative study was to identify the most generic relationships between the main components of IMCT (TCol, ICol, SCol, CLs, PGs), the proportion of fiber types, the TLips, the main families of FAs and the major beef sensory attributes. To do so, three independent studies were utilised to build a database composed of samples from two muscles (Rectus abdominis and Longissimus thoracis) from animals raised under different studyal conditions.
(Annabelle Dubost et al. , 2013a). The data were expressed in mg of hydroxyproline per g of dry matter (mg OH-pro g-1 DM). OH-Prol was not measured in the soluble fraction, because of the low concentrations that made the determination imprecise, but was determined as follows: Soluble Collagen (SCol) = (TCol-ICol)/TCol)* 100. The CLs were determined by the enzyme-linked immunoassay Metra Pyd EIA kit (Quidel Corporation, USA) according to the manufacturer procedure adapted by  Dubost et al. (2013a)  for muscular tissues. The results were expressed in nM of pyridinoline per g of DM (nM pyr g -1 DM).
Myosin heavy chains isoforms quantification by electrophoresis Myosin heavy chain (MyHC) isoforms were separated with sodium dodecyl sulfate glycerol gel electrophoresis according to  Picard et al. (2011) 's method. After migration, the gels were fixed in 30% (v/v) ethanol and 5% acetic acid (v/v) and then stained with colloidal Coomassie Blue R250 for 24 h. After destaining, the gels were scanned and the proportions of the different MyHC bands were quantified by densitometry with ImageQuant Software5500 (Amersham Biosciences/GE Healthcare). The quantification of the bands revealed the existence of MyHC-IIB isoform was found in only 5 animals. Therefore, MyHC-IIB percentage were added to those of MyHC-IIX as described by  Gagaoua et al. (2015)  creating a new variable, muscle fiber type IIX+B.
= 0. 25 mm). The carrier gas was H2, and the oven and flame ionization detector temperatures described by  Scislowski et al. (2004)  were used. Total FA were quantified using C19:0 as an internal standard. The identification of each individual FAME and the calculation of the response coefficients for each individual FAME were performed using the quantitative mix C4-C24 Fame (Supelco, Bellafonte, PA). Data were expressed in mg per 100g of fresh matter.
The expert panellists were trained in accordance with the ISO standards ISO/TC as described by  Gagaoua et al. (2016). The panellists rated global tenderness, juiciness and flavor of the meat on a continuous scale scored from 0 to 10, where 0 refers to extremely tough, dry, not tasty and 10 to extremely tender, juicy, tasty. During sessions, panelists were randomly seated in individual booths, in a sensory analysis room, equipped with individual booths under artificial red light to reduce the influence of the appearance of the samples. The panelists were provided water and unsalted crackers to clean their palate. Each tasting booth was equipped with computer terminal linked to a fileserver running a sensory software (Fizz, version 2. 20h; Biosystemes, Couternon, France) that facilitated the direct entry of assessor ratings.
Statistical analysis The data from the three studys were analyzed together using  XLSTAT 2017. 19. 6 (AddinSoft, Paris, France). Normal distribution and homogeneity of the dataset was tested by Shapiro-Wilk test (P > 0. 05). Data were standardized in the same way for all studies to remove the effects of muscle (St. 1), of breed (St. 2) as suggested by  Gagaoua et al. (2015)  and of breed and gender (St. 3) as in  Listrat et al. (Listrat et al. , 2020a) , which are the major parameters affecting eating quality of beef. The standardization of the data was based on Z-scores, which represent the number of standard deviations for each observation relative to the mean of the corresponding data group amongst the studied factors of each study. Therefore, after this transformation, the data had a mean of 0 and standard deviation of 1.
Subsequently, Partial Least Squares (PLS) regression based on Z-scores for the whole variables of the database were used to explain tenderness, juiciness, and flavor traits for each muscle using IMCT, IMF and muscle fiber characteristics. The PLS method aimed to predict data by relating two data matrices X and Y to each other, where in our case, the X consists in the explanatory variables (X-matrix, 14 variables) and Y consists in sensory beef quality traits (Y-matrix, 3 variables). The relationships for each sensory quality (or PLS models) were built first per muscle of each study and second per muscle across studys (St1 + St2 = LT muscle or St1 + St3 = RA muscle). The filter method with the variable importance in the projection (VIP) set at the level of VIP > 0. 8 was used for variable selection as described by  Gagaoua et al.
Funding St 1 was funded by ADEME (Agence de l'Environnement et de la Maîtrise de l'Energie, France). St 2 was partly funded by the European « ProSafeBeef » Integrated Project  (FOODCT-2006-36241). St 3 was funded by a financial support of "régions Bretagne and Pays de la Loire". Declaration of competing interest measurements and analyses. The authors are further thankful to the French Livestock Institute Meat Quality Team, namely Laurent Allais, Catherine Malayrat, Chloé Flattard and Valérie Hardit for muscle sampling and sensorial analyses. We also thank Terrena and Elivia who allowed this project happened and sampled the muscles.
Acknowledgments For St 1 and 2: The authors express their thanks to the staff of the  Studyal Unit, Herbipôle (UERT) of INRA (Theix)  for the animal management, slaughtering and the staff of Biomarmarkers team for their skilled technical assistance, particularly  Arnaud Delavaud ,  David Chadeyron ,  Blandine Fréchet  for the collagen, cross-link and proteoglycans content measurements and  Agnès Thomas ,  Sylvie Bardou-Valette  and  Mélissa Vauris  for the muscle lipid and  FAs determination. The authors also express their thanks to the staff and the panellists of the  INRA Unit (Le Magneraud ) for the sensory quality evaluations. For St1, we also thank  Emmanuel Bedier  and  Mathieu Pondet  from  Idena  and  Bérengère Hoez  from  Thivat Nutrition Animale , who allowed this project happen.
Identification of Bacteria by MALDI-TOF MS Biotyper Bacterial strains from the fecal microbiota of male and female rats, before and after restriction, were isolated on Luria-Bertani medium and identified by analysis of the total proteome using an Autoflex III Matrix-Assisted Laser Desorption/Ionization-Time-Of-Flight mass spectrometer (MALDI-TOF MS) (Bruker, Marcy-l'Etoile, France) coupled to the MALDI-Biotyper 3. 1 system, as previously described  [12, 13]. Formic acid was used on the bacterial spots as a quick extraction procedure  [14] , then the MALDI target plate was introduced in the mass spectrometer for measurement and data acquisition. For each sample, 600 spectra were pooled, and the generated spectra were compared with the MALDI-Biotyper 3. 1 database. A score was calculated based on the matching between the reference spectrum and the unknown spectrum.
Data and Statistical Analysis Results were analyzed using GraphPad Prism 5. 02 (GraphPad Software Inc. , San Diego, CA, USA). Normality was evaluated by the Kolmogorov-Smirnov test. Group differences were analyzed by the analysis of variance (ANOVA) or the non-parametric Kruskal-Wallis (K-W) test with the Tukey's or Dunn's post-tests, according to the normality results. Where appropriate, individual groups were compared using the Student's t-test or the Mann-Whitney (M-W) test according to the normality results. Data are shown as means ± standard error of means (SEM), and for all tests, p < 0. 05 was considered statistically significant.
The proportion of CLR01 in the brain was determined as the percentage of radioactivity in the perfused brain, relative to that of the blood, at three different doses and three time points in 12-month-old animals. Our results indicate that 10-35% of the drug concentration available in the blood is present in the brain, and that this is a time-and dose-dependent process (Supplementary Fig. 8A ). Based on these data and previously published blood kinetics  14  , we first predicted the extraand intracellular accumulation of the compound in the brain (Supplementary Fig. 8B ), then simulated a 2-month treatment spread across 16 subcutaneous injections at 40 µg/kg/day (Supplementary Fig. 8C ), to obtain pharmacokinetic parameters (Supplementary Table  1 ). We found that the intracellular time averaged area under the curve was 240 ng/ml, with a predicted maximum concentration of 510 ng/ml, which resulted in cells being 93% of the experimental time above the EC 50 (85.
Free-floating SNc sections were mounted on gelatinized slides, counterstained with 0. 1% cresyl violet solution, dehydrated, and cover-slipped, whereas striatal sections were mounted on gelatinized slides and cover-slipped. The extent of the lesion in the striatum was quantified by optical density (OD). Sections were scanned in an Epson expression 10000XL high-resolution scanner and images were used in ImageJ open source software to compare the grey level in the putamen. TH-positive SNc cells were counted by stereology, blind with regard to the experimental condition, using a Leica DM6000B motorized microscope coupled with the Mercator software (ExploraNova). α-Syn pathology: Synucleinopathy was assessed using a primary antibody that recognizes both mouse and human α-syn (mouse anti-α-syn monoclonal antibody, BD Transduction Laboratories, clone 42, #610787; BD Biosciences, SYN1, 1:1000) and aggregated α-syn antibody (mouse anti-α-syn monoclonal antibody, BioLegend, Syn-F1, 1:2000).
The sections with most p-α-syn+/TH+lesions were blindly selected, and the resulting number of lesions was averaged for independent animals. Imaging. Samples were imaged with the Opera Phenix confocal microscope and Harmony software (Perkin-Elmer) or the EVOSflAUTO (Thermo). For standard cell culture, images were automatically captured with the Opera Phenix and analyzed with ImageJ or Cell Profiler for the detection of puncta (AS-PLA signal) or cell nucleus/body (TO-PRO3, DAPI, TH counts), respectively. To analyze process morphology, average length and number of puncta were analyzed. For microfluidic and tissue samples, images were taken blinded to treatment on the EVOSflAUTO. We used automatic measurement with ImageJ for puncta counts (AS-PLA signal) or stained areas (GFAP signal). To count PLA puncta inside cells of interest, puncta were counted blindly by eye in 25 cells per animal.
Mice were placed on a transparent glass platform illuminated with green light on a red background. Runs were acquired and accepted for analysis if they were between 0. 5 and 5 s in duration and there was <35% variation in speed. The first five compliant runs were analyzed with Catwalk software, and the average data for each mouse was taken for analysis. For rotarod analysis, mice were gently placed on an open rotarod (Med Associates, Vermont, USA) facing away from the experimenter. Mice were tested under the same accelerating conditions (4-40 r. p. m. over 5 min) for two consecutive days starting at 9:00. The first day was considered acclimatization; on the second day, the latency to fall was recorded for analysis. For muscle strength and coordination analysis, mice were placed on a 43 cm 2 wire mesh attached to a 4 cm deep wooden frame.
5 min, delivered at 0. 6 mA, for 200 µsec  12. Data were acquired and analyzed with Axoscope 11. 0 (Molecular Devices). Western blotting. Dissected brain regions were lysed in RIPA buffer (SDS-polyacrylamide gel electrophoresis (PAGE)) or non-denaturing lysis buffer (native PAGE). For immunoblot analysis, 10 μg of total protein per lane was loaded on 4-15% gels (Bio-Rad) and blotted onto polyvinylidene difluoride membranes (Bio-Rad). For SDS-insoluble 0. 5 μg of total protein was loaded. Blotted samples were probed with antibodies against α-syn (MJFR1 Abcam), PGC1α (Santa Cruz Biotechnologies sc-13067), Lamp2A (Abcam, ab18528) and Lamp1 (Abcam, ab24170), all 1:1000. After ON incubation with primary antibody at 4 °C blots were washed in TBS-T and incubated in secondary horseradish peroxidase antibody (Bio-Rad, 1:5000) for 1-2 h at room temperature.
Blots were analyzed with enhanced chemiluminescence reagents (Millipore) in a Chemidoc (Bio-rad). Beta-actin was used as a loading control. Uncropped blots are available in the Source Data File. Statistical analysis. Statistical analyses was performed with GraphPad Prism 6. 0 (GraphPad Software). Two-way analysis of variance (ANOVA), one-way ANOVA, t-test, and Mann-Whitney U-tests were used as required (specified in figure legends). In all analyses, statistical significance was set at p < 0. 05. Unless otherwise stated, all error bars depict SEM. For ANOVA analysis, F is expressed as F (degrees of freedom, degree of freedom error) = x. In silico modeling of CLR01 pharmacokinetics. Supplementary Methods detailing the in silico modeling of the pharmacokinetics of CLR01 are available. The code for the model is available upon request.
Acknowledgements We thank  Sally Cowley ,  Jane Vowles ,  Phillippa Carling , and  Marta Cherubini  for their support with iPSCs and the manuscript. We thank the  Network of Centres of Excellence in Neurodegeneration (COEN)   MR/P007058/1 , the  Monument Trust Discovery Award from Parkinsonʼs UK  ( J-1403 ), the  National Institutes of Health  ( NIH/NIA R01AG050721 ),  Team Parkinson/Parkinson Alliance , and the  Medical Research Council Dementia Platform UK Stem Cell Network  grant for support. The samples were obtained from the  Brain Bank GIE NeuroCEB  (BRIF number  0033-00011 ), funded by the patients' associations  France Alzheimer ,  France Parkinson ,  ARSEP , and ' Connaître les Syndromes Cérébelleux ' to which we express our gratitude.
Membrane Fluidity The membrane fluidity of nanoliposomes was measured by fluorescence anisotropy measurements as described by Maherani et al. [51]. In brief, TMA-DPH solution (1 mM in ethanol) was mixed with the liposomal suspension to reach a final concentration of 4 µM for the probe and 0. 2 mg/mL for the lipid. Then, the fluorescent intensity was measured with Tescan INFINITE 200R PRO (Warriewood, Sydney, Australia) equipped with fluorescent polarizers. Samples were excited at 360 nm and emission was recorded at 430 nm under constant stirring at 25 • C. The Magellan 7 software was used for data analysis and to calculate the polarization value (P). Membrane fluidity was defined as 1/P.
Each FVI consisted of a grid of 50 × 50 force curves measured by adopting a 2 µm s -1 approach rate of the tip toward the sample. The cell stiffness (Young modulus) E was evaluated by analyzing the force-indentation curves within the framework of the Sneddon model. In this model, the Young modulus is related to the applied force according to the equation given below: F = 2E • Tan(α) π(1 -ν 2 ) R 1/2 δ 2 • f BECC ( 1 ) where δ is the indentation depth, ν is the Poisson coefficient, α is the semi-top angle of the conical tip, and f BECC is the bottom effect cone-correction function that considers the stiffness of the petri dish substrate that supports the cells  [52]. All FVI were analyzed using an automatic Matlab algorithm detailed elsewhere  [53] , and the average Young moduli values given in this work were derived from at least 800 force curves.