dataesr/scientific-paragraphs-categorization huggingface dataset:
Gait analysis: Ten trials of gait at a comfortable speed and 10 trials at maximal speed will be recorded in order to evaluate the spatiotemporal, kinematic, kinetic and EMG parameters during gait. Surface EMG will also be performed during the gait analysis. These data will be used to define the patterns of activation of the principle superficial muscles (rectus femoris, vastus lateralis, medial hamstring, biceps femoris, tibialis anterior, medial gastrocnemius and soleus) activated during gait. The EMG data will be used to quantify the activity of the agonist and antagonist muscles (of the hip, the knee and the ankle) in order to determine a possible interaction between muscle activity, variability of COM displacement and joint kinematics Instrumented path-following task (trajectory tracking) From a standing start, the patient will be asked to follow a 2.
Analysis of outcome measures Patient characteristics (age, gender, etc. ) will be described by means, medians and standard deviations for continuous numeric parameters and by frequency tables with 95% confidence intervals for qualitative parameters. A chi-squared test with Yates' correction or the Fisher's exact chi-squared test will be used to compare the distributions of qualitative variables. There will be two possibilities for quantitative and ordinal variables: 1. If the data are close to a normal distribution, means will be compared using Student's t test Comparison of the means or medians will be carried out for all the functional clinical data and the data determined following the movement analyses during the different evaluations. Repeated measures analysis of variance (ANOVA) and/or analysis of covariance as well as t tests (and/or non-parametric sign tests), corrected using the Scheffé adjustment, will be used for multiple comparisons.
Data monitoring This research carries a level-B risk. The clinical research associate (CRA), who represents the sponsor, will visit the investigating centre at a frequency that corresponds to the patient follow-up specified in the protocol. Several monitoring visits will be carried out: An opening visit in each centre: before the first inclusion, to set up the protocol and make acquaintance with the various participants involved During the protocol: the Case Report Forms will be reviewed by the CRA. The principal investigator, who includes and follows the persons involved in the research, will accept regular visits from the CRA. o During these visits, and in accordance with the Good Clinical Practice, the following elements will be reviewed: ■ Compliance with the protocol and defined procedures for research ■ Audit of informed consent by the patients ■ Review of source documents and comparison with data reported in the Case Report Forms: accuracy of data, missing data, consistency of data according to the rules laid down by DRCD procedures Closing visit: review and archiving of the biomedical research documents Adverse event monitoring and reporting Adverse events will be carefully monitored during this study.
Locomotor Rhythms Flies were individually placed into 5 × 65 mm tubes with regular food and placed into constant conditions for 10 days. Locomotor activity was continuously recorded in 30 min bins using the Trikinetics system. Locomotor rhythms were analyzed for 6 or 9 days using MATLAB (Mathworks, Natick, MA, USA) based computational tools designed by  Levine et al. (2002). At least 30 flies were analyzed for each genotype.
Circuit-Dependent Regulation of Sleep by faf and hiw As mentioned, whole genome profiling revealed that faf was increased in ins-l flies compared to normal sleeping background controls identifying it as a potential contributor to insomnia phenotypes  (Seugnet et al. , 2009c). The increase of faf in ins-l flies was confirmed in an independent cohort using QPCR (data not shown). Given this expression profile, we hypothesized that over-expressing faf would recapitulate important features of the insomnia phenotype such as reduced sleep time and sleep fragmentation. The P-element line, faf EP3520 , contains a UAS that can be used to induce functional faf  (DiAntonio et al. , 2001). Thus, we conducted a mini-brain screen to express faf in circuits known to modulate sleep and waking in flies  (Joiner et al.
In plants, PTMs are well known to be critical for regulating cold acclimation or freezing tolerance and different lines of evidence suggest that the disruption of phosphorylation deeply alters the ability of plants to acclimate to low temperature  [10] [11] [12]. Protein phosphorylation, a network of protein kinases and phosphatases and their respective protein substrates, is a pervasive regulatory mechanism that plays pivotal roles in controlling most of cellular processes  13. The mechanism is ubiquitous throughout animals and plants, and countless proteins are phosphorylated by hundreds protein kinases  13. For example, the Human Protein Reference Database lists over 95,000 phosphosites mapped to more than 13,000 proteins and approximately, 1-2% of human and eukaryotic genes encode protein kinases  14.
3 ). Of these, 81% and 78% were phosphorylated, which represented 1561 and 1668 phosphopeptides in CO and CA, respectively (Fig. 3 ). 97% were phosphoserine or phosphothreonine and 3% were phosphotyrosine in both treatments. This distribution of phosphorylated amino acids corresponds to patterns reported in previous phosphoproteomic studies on Drosophila cells (e. g. 97% on Ser/Thr and 3% on Tyr)  19, 20  or other invertebrates (e. g. 83/12/5% on Ser/Thr/Tyr)  21. Additionally, 1112 (71%) and 1261 (76%) were monophosphorylated, and 449 (29%) and 407 (24%) were multiphosphorylated in CO and CA, respectively. MS data were individually submitted to protein identification (peptide rank = 1; FDR < 1% at the peptide spectrum match level). Comprehensive information regarding the identification of all phosphopeptides, as well as the probability of the localization of the modification(s) and the localization of the phosphorylation in the modified peptides are provided for each replicate samples and treatment in Supplementary Table S-1.
Functional annotation. Two different phosphoprotein sets were used for functional annotation: 1) list_ CA  (+)  comprised phosphoproteins uniquely detected in CA flies (133; see Fig. 5 ) and the differential phosphoproteins upregulated in CA (34; see Fig. 6 ). This list thus represents proteins that were positively regulated by phosphorylation events in response to cold acclimation. 2) list_CA  (-)  comprised phosphoproteins absent in CA (87; see Fig. 5 ) and phosphoproteins downregulated in CA (12; see Fig. 6 ). Hence, this list represents proteins that were regulated by dephosphorylation events in response to cold acclimation. The list_CA  (+)  and list_CA  (-)  are provided in Supplementary Table  S -4 together with annotation, names, symbols and function(s). These two protein sets were used to query STRING database  23  to reveal possible protein-protein interactions.
As shown in Fig. 7 , the phosphoproteins from the list_CA (+) had significant associations and intricate interactions (PPI enrichment p-value = 7 e -06 ) which indicates that, in response to acclimation, many phosphorylation events occurred on proteins that were clearly functionally related. Gene Ontology (GO) enrichment analyses performed on list_CA  (+)  in STRING (considering FDR < 0. 05) resulted in many overrepresented GO-terms: 48 biological processes, 7 molecular functions, and 61 cellular components. The full list of enriched GO-terms is provided in Supplementary Table S-5. To facilitate interpretation, the long lists of significant GO-terms was imported in REVIGO program  24  to reduce the functional redundancy and depict presence of GO superclusters (based on semantic similarity).
The hierarchical treemaps obtained from REVIGO program are shown separately for biological process (Fig. 8a ) and cellular component (Fig. 8b ). For the biological process, four major GO superclusters were detected: microtubule cytoskeleton organization (comprising the most significant GO-terms, see Supplementary Table S-5), positive regulation of transport, mitotic cell cycle and mRNA processing. Within the STRING network, it can be seen that the subsets of phosphoproteins specifically involved in these four GO superclusters were functionally connected (Fig. 7 ). This suggests coordinated phosphorylation events regulating these likely important biological processes for cold tolerance acquisition. Reduction of the cellular component GO-terms in REVIGO (Fig. 8b ) revealed that phosphorylation events occurring in response to acclimation were mainly localized within the microtubule associated complex and within the cell cortex which primarily contains actins network.
Functional annotations revealed that Figure  7. Phosphoprotein interaction network resulting from the set of proteins positively regulated by phosphorylation events [list CA  (+)  ] with acclimation. The phosphoproteins set was analyzed for putative protein-protein interactions using STRING program with default settings except that we only considered high confidence interactions (with score > 0. 7). Disconnected nodes are not shown in the network. Phosphoproteins involved in the four major GO-terms superclusters detected in REVIGO (see Fig. 8 ) are highlighted within the network with different colors: cytoskeleton organization (red), mitotic cell cycle (green), cellular localization (blue), and mRNA processing (dark). cytoskeletal proteins or proteins involved in their assembly/disassembly were particularly targeted by phosphorylation regulation in response to adult cold acclimation.
Cold treatment of certain cultured Drosophila cells induces reversible disassembly of microtubule arrays  34. Insect proteomic and genomic data also detected changes in cytoskeleton genes or proteins correlated with cold tolerance  4, 6, 35. A cytoskeleton remodelling is presumably fundamental to maintain the structure, function, and organization of cells upon low temperature. Our observation together with recent phosphoproteomic report  5  strongly support that phospho-regulation plays a crucial role in cold-induced cytoskeleton remodelling. Many unique or differential phosphoproteins involved in cellular localization and transport were detected in CA flies. This was the case for AP-2alpha which is involved in protein transport and endocytosis, and Tud which is involved intracellular mRNA localization.
Ltd, Munich, Germany). Flies were then allowed to recover at 25 ± 1 °C, and recovery times were individually recorded. Flies were considered recovered when they stood up. Data were used to generate temporal CCR curves, which were compared between CO and CA with Mantel-Cox analysis. Second, climbing activity tests were used to assess the medium-term recovery as previously described  52. Briefly, for each treatment (CA and CO), 50 flies were individually transferred to a 9. 5 cm plastic vial and the height flies reached within 7 s after a mechanical stimulation was noted. Flies were divided into three categories: injured, recovering, and fit. This test was performed repeatedly on the same individuals after 2, 4 and 6 h of recovery (at 25 °C). Flies were maintained on food during this period.
For the NL method, masses specified for further CID fragmentation were 32. 6, 49, 65. 3, 98 and 147 Da with a precursor selection window of 2 Da. For the MSA method, neutral loss masses specified for further MSA fragmentation of the precursor were 24. 49, 26. 65, 32. 65, 39. 98 and 48. 98 Da with a precursor selection window of 3 Da. For OrbiTrap measurements, an external calibration was used before each injection series ensuring an overall error mass accuracy below 5 ppm for the detected ions. MS data were saved in RAW file format (Thermo Fisher Scientific) using XCalibur 2. 0. 7 with tune 2. 4. The experimental approach is outlined in Fig. 1. Protein identification. Proteome Discoverer 1. 2 software (Thermo Fisher Scientific) supported by Mascot (Mascot server v2. 2. 07; http://www. matrixscience.
com) database search engine was used for peptide and protein identification using its automatic decoy database search to calculate a false discovery rate (FDR). MS/MS spectra were compared to the UniProt D. melanogaster reference proteome database (UniProt UP000000803 release May 14, 2016, 22023 sequences). Mass tolerance for MS and MS/MS was set at 10 ppm and 0. 5 Da, respectively. The enzyme selectivity was set to full trypsin with one miscleavage allowed. Protein modifications were fixed carbamidomethylation of cysteines, variable oxidation of methionine, variable acetylation of N-terminus and variable phosphorylation of serine, threonine or tyrosine. Identification validation and spectral count label-free quantification. Proline Studio 1. 3 software was used for the validation and the spectral count comparisons of the identified proteins in each samples (http://proline.
profiproteomics. fr/)  53. After importation of the mascot. dat files from each query, each search results were validated with a peptide rank = 1 and a FDR of 1% on mascot score at the peptide spectrum match (PSM) level. All the identification summaries (i. e. , the validated Mascot search results) of all the MS analyses of all the elution fractions corresponding to one biological replicate were merged and a protein inference list was created. Proteins identified with exactly the same set of peptides or with a subset of the same peptides were grouped in a protein set. This protein set was then represented by a typical protein, which is the best identified protein (best score) or in case of same set proteins, the SwissProt one if possible. For the spectral count comparison, a parent dataset corresponding to the merge of the individual biological replicate identification summaries was created to define the shared and specific peptides and the protein set list to compare (CACO protein set, Table  S1 ).
For each protein, weighted spectral counts were calculated, as suggested in Abacus  54  , where shared peptides are combined and weighted according to the associated protein sets. To detect significant difference between the two conditions (CO vs. CA) a beta-binomial test was performed on the weighed spectral counts calculated for each biological sample and a p-value was calculated for each protein set using the R package BetaBinomial 1. 2 implemented in Proline Studio  55. For each protein, an expression ratio was calculated between CA and CO samples, which represented the ratio of the mean of the weighted spectral counts in the four CA replicates to the mean of the weighted spectral counts in the four CO replicates. In order to be able to calculate such ratio, even when a protein is not identified in one condition, one spectral count is added to the whole weighted spectral counts, meaning that when a protein is not identified in one sample its weighted spectral count is equal to one.
Data availability. The raw mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium 56 via the PRIDE partner repository  57  with the dataset identifier PXD005311 and doi:10. 6019/ PXD005311. Datamining. We used InteractiVenn tool to detect and visualize the amount of overlap between lists of proteins from both treatmetnts  58. From this, were retrieved a set of phosphoproteins that were unique in CA, another set where phosphoproteins were lacking in CA (only found in CO), and a common set of phosphoproteins that were detected in both treatments. Non-phosphorylated proteins were not considered in further analyses. To reveal possible protein-protein interactions, sets of phosphoproteins of interest [list_CA  (+)  and list_CA  (-)  ] were used to query STRING database  23.
The STRING algorithm links proteins into networks based on published functional or informatics-predicted interactions  23. Default parameters were used except that we increased the minimum required interaction score to 0. 7 (high confidence). Also, to increase visibility, disconnected nodes in the network were not displayed. Gene Ontology (GO) enrichment analyses were performed in STRING (considering FDR < 0. 05). Non-redundant and significant overrepresented GO-terms were then reduced and visualized using REVIGO program with the default similarity value (0. 7) and Resnik (normalized) as semantic similarity measure  24. Data were then exported in R statistical software (v. 3. 0. 3. ) to generate treemap views using the "treemap" package, in which related terms were joined into loosely related "superclusters" visualized with different colors.
Acknowledgements This study was supported by actions incitatives "defis scientifiques émergents" from  University of Rennes1  and  SUZUKILL  project ( ANR-15-CE21-0017-01  and  FWF I 2604-B25 ). This work was also supported by grants from  Biogenouest ,  Conseil Régional de Bretagne and IBiSA  awarded to CP. We are grateful to  Nick Teets  and  David Denlinger  for constructing discussions in the preparation of this manuscript.
Statistical Analysis Statistical analyses were performed with Prism (GraphPad), SPSS (v. 14. 0, SPSS), Statistica (v. 6. 0, StatSoft), and/or Excel software. When two groups were compared, we first performed a normality test. Parametric data were compared using a two-tailed Student's t test (with equal or unequal variance, as appropriate). Non-parametric data were compared using a two-tailed Mann-Whitney U test. For one-way ANOVA, if statistical significance was achieved, we performed post-test analysis to account for multiple comparisons. Statistical significance was set at p < 0. 05. The data are presented as mean ± SEM. The graph represents the average number of hind leg slips per trial (±SEM) (*p < 0. 05, **p < 0. 01; two-way ANOVA). Learning was assessed by the number of slips at day 5 relative to day 1 (##p < 0.
Data are represented as the mean (±SEM). Total protein was visualized by stain-free protocols and used as loading control. (D) Rate of glutamate-induced neurite collapse of WT and DMSXL neurons. Mouse primary neurons were cultured with WT astrocytes, control DMSXL astrocytes, DMSXL astrocytes transfected with GLT1, or DMSXL astrocytes treated with 10 μM ceftriaxone (CEF) (n = 4 independent co-cultures, each combination).
ACKNOWLEDGMENTS We thank  Dr. Thomas Cooper  for providing the DMPKS,  DT960 ,  CELF1 , and  CELF2  plasmids;  Dr. Nicolas Reyes  for the GLT1-EGFP-expressing plasmid;  Dr. Rob Willemsen  for FXTAS mouse brain slices; and  Dr. Jeffrey Rothstein  for anti-GLT1 antibody. We are grateful to the personnel of  CERFE (Centre d'Exploration et de Recherche Fonctionelle Expérimentale, Genopole, Evry, France)  and  LEAT (Laboratoire d'Experimentation Animale ,  Imagine Institute, Paris, France ) for attentively caring for the mice. We thank  Léonard Bertrand  and  Elodie Dandelot  for help with the graphical abstract. This study was supported by grants from  AFM-Téléthon  ( France , project grant  16161  to M. G. -P. ),  INSERM (France) ,  Université Paris Descartes (France) , and  Fondation ARC (France) ; as well as PhD fellowships from  Ministére Français de la Recherche et Technologie (France , to G.
S. and D. M. D. ),  AFM-Téléthon (France , to G. S. ) and  Imagine Foundation (France , to S. O. B. ). This program received a state subsidy managed by the  National Research Agency  under the  "Investments for the Future" program  bearing the reference  ANR-10-IAHU-01  and under the program  ANR-10BLAN-1121-01. L. S. , G. C. , G. G. , and M. G. -P. have a patent on GLT1 upregulation in DM1.
THE ASSOCIATION BETWEEN PHYSICAL ACTIVITY AND DEPRESSION IN OLDER ADULTS WITH PARKINSON'S DISEASE M. Ventura 1 , J. K. Johnson 1 , D. E. Barnes 1,2 , 1. University of  California, San Francisco, San Francisco, California, 2. San Francisco VA Medical Center, San Francisco, California  It is important to understand the associations between physical activity (PA) and mood in the early stages of Parkinson's disease (PD) progression. The purpose of this study was to explore the relationship between PA and depression in newly diagnosed, untreated individuals with PD. We analyzed data from the Parkinson Progression Markers Initiative and examined depression (Geriatric Depression Scale; GDS) in 260 de novo individuals with PD. Participants with a GDS score ≤5 were considered "not depressed" and those with a GDS score >5 were considered "depressed.
THE INTERPLAY OF LIFE EVENTS, RELIGIOUS EXPRESSION, AND SOCIAL SUPPORT WITH DEPRESSION A. M. Ogletree, R. Blieszner, L. P. Sands, Human Development, Virginia Tech, Blacksburg, Virginia Based on Pearlin's stress process model, we examined relationships among religious expression/participation, social support, and depressive symptoms in adults who experienced significant life events (SLEs) within the past year. Data came from the ORANJ BOWL study and included 5,468 randomly-dialed New Jersey residents aged 50-74 in 2008. We explored which events were the strongest indicators of the SLE construct and most associated with higher CES-D scores. We hypothesized more depressive symptoms among individuals with more stressful events, less social support, and lower religious expression. Religiosity was measured using 6 items from the Multidimensional Measurement of Religiousness/Spirituality.
The 10-item CES-D indexed depressive symptoms; life events were recorded on a 14-item Cochrane-Robertson Life Event Inventory; perceived social support was measured using 4 items. Structural equation modeling assessed construct factor loadings and hypothesized associations; fit indices revealed good model fit to the data (x 2 =3637. 32, p=0. 000, df=221; RMSEA=0. 053, NNFI=0. 911, CFI=0. 922, SRMR=0. 047). The strongest indicators of SLE were three items reflecting relational family events. Maximum likelihood estimates partially supported the hypotheses: individuals with more stressful events, lower public religious expression, and less perceived social support reported significantly more depressive symptoms. However, individuals with more private religious expression also had more depressive symptoms.
Statistical analysis. Results were expressed as means ± SEM. Normality and variance homogeneity were tested and data were subsequently analyzed either by the non-parametric Kruskal-Wallis and Mann-Withney U tests, or a two-way analysis of variance, with IH and lipectomy as the independent variables. Subsequent multiple comparisons were made with the Bonferroni post-hoc test. Correlation between subcutaneous and epididymal fat volumes was evaluated by the non-parametric Spearman rank correlation test. Statistical significance was set at p < 0. 05.
Phylogenetic analysis In phylogenetic reconstruction, we employed sequences that have been retrieved from the NCBI (http://www. ncbi. nlm. nih. gov), Ensembl (http://www. ensembl. org/ index. html) and Aniseed (http://www. aniseed. cnrs. fr/) [46]  databases excluding uninformative proteins (see Additional file 3). Ciona robusta Tyr and Tyrp. a were the initial query sequences used for TBLASTN  [35]  in other tunicates, and reciprocal blasts were performed on every genome/transcriptome  [36, 46, 47]. The protein set was aligned by ClustalW with default parameters  [48]. The phylogenetic tree inference was computed employing the maximum likelihood estimation (MLE) using MEGA6 with the WAG + γ + I matrix  [49] , in order to improve sensitiveness in our analysis. We assessed the robustness tree with 1000 bootstrap replicates, and the numbers at the branches indicate replicates obtained using the maximum likelihood estimation method (Fig.