dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Statistical analyses were conducted within a conventional mass-univariate framework where the evoked hemodynamic response at each voxel was independently modeled using a general linear model (GLM). Each experimental condition (i. e. , encoding, picture recognition, and picture-word recognition) was modeled as a separate session. Functional data from the distractor task were not analyzed as this task was included to disrupt working memory processes and explicit verbal rehearsal of the stimuli encountered in the encoding task (see above). For all conditions, the estimated first-level models shared the following properties. At every voxel, the neural response evoked by each trial was modeled as a boxcar with zero duration convolved with the canonical hemodynamic response function (HRF).
e. , the minimum-T criterion  (Nichols et al. , 2005). To perform region-of-interest (ROI) analyses, each ROI was defined as a voxel cluster. An individual's condition-specific activity at a ROI was obtained by extracting the activity estimates for that condition (i. e. , beta values from the first-level analysis) from the voxels of the ROI. These voxel-wise values were then averaged together to obtain a single value that was treated as the individual's condition-specific activity at the ROI. Brain regions obtained from these analyses were identified using the SPM Anatomy Toolbox  (Eickhoff et al. , 2005) , and surface visualization is shown using the Surf Ice software. 1.
Semantic congruency effects during picture-word encoding We used the data from all participants (irrespective of stimulus language) to evaluate the main effect of semantic relatedness judgments on memory performance. Participants' subjective categorization of picture-word pairs as either Related or Unrelated showed a high degree of agreement to the pre-designed stimulus categories (94. 5 ± 3. 7%). This high degree of the agreement confirmed that participants had correctly interpreted our task instructions. Importantly, it also confirmed that the relatedness judgments, although subjective, were not arbitrary but highly consistent between participants and our pre-designed stimulus categories. The mean response time (RT) for Related judgments (1749 ± 452 ms) was shorter than for Unrelated judgments (1877 ± 431 ms, Z [61] = 657.
As described in Table  1 , the FR and GER subgroups were similar in gender ratio, age, education, language ability, and cognitive status (MMSE scores). Therefore, possible differences in encoding and recognition processes between these subgroups cannot be merely attributed to differences in the types of individuals that selected each language. For clarity, all analyses described below were limited to individuals with valid fMRI datasets, namely, 41 in the GER subgroup and 17 in the FR subgroup (see Supplementary Table  6 ).
Statistical analyses Data are expressed as median with the interquartile range. The Mann-Whitney test was used to compare two groups. Three or more groups were compared using the Kruskal-Wallis test followed by Dunn's multiple comparisons test. Correlation analyses were done using Spearman's rank correlation coefficient. For correlations, r between 0. 2 and 0. 39 was considered a weak correlation, r between 0. 4 and 0. 59 a moderate one and r more than 0. 6 a strong one. The p < 0. 05 was considered statistically significant. Statistical analyses were done using GraphPad Prism 8 for Windows (GraphPad Software, San Diego, California USA).
A moderate correlation was found between myostatin and serum creatinine levels (r = 0. 51; p < 0. 0001; data not shown). After normalisation, the level of myostatin was still statistically significantly lower in IIM patients compared to HD (43. 73  [29. 25; 60. 24]  vs 53. 51 [41. 64; 69. 97]; p = 0. 002) (Figure  1B ). The level of myostatin was also assessed in each of the four myo-.
Sera were collected at diagnosis or during follow-up and kept in our biobank MASC (Myositis, DNA, Serum, Cells, ClinicalTrials. gov Identifier: NCT04637672). Ninety-six patients had two or more serum samples at different time points, giving a total of 447 samples for the 300 patients. HD samples were obtained from a French blood bank (Etablissement Français du Sang ref: C CPSL UNT-N 18/EFS/033).
Related hazard ratios (HRs) were calculated through Cox regression. For the group stratification for the comparisons of the different groups (cMCI, ncMCI, HC, and ADD), it was considered only a single conversion event measured at 30 months, whereas for the survival analysis, we used the data collected every 6 months. Linear regressions models were performed to evaluate the association of LC parameters (independent variable) with neuropsychological scores (dependent variable), both considering the whole sample and considering each diagnostic group separately (please note that, for processing speed and attention domain, scores-which are inversely related to subjects' performance-were reversed to follow the same direction as the other cognitive scores). To ensure fulfillment of model assumptions, outcome and predictor variables were Z-transformed.
All models were adjusted for age, sex, and APOE ɛ4 status. and their relationship with LC-related LVs was assessed by considering also the potential effects of age, sex, and APOE status (pooled into bio-LV). For this model, factor scores for LC CR -LV and LC Vox -LV were also calculated  [38]  and analyzed by Kruskal-Wallis test to assess differences among diagnostic groups. Structural model fit indices showed that all the models adequately fitted the data (see Tables  S1  and S3  for details). A full description of SEM analysis methodology is reported in the Supplementary Methods (Appendix S1). All statistical analyses with multiple comparisons were adjusted with false discovery rate correction to rule out type I error. The level of significance was set at p < 0. 05. Stata/SE 13. 1 (SEM algorithm) and SPSS version 24 (all other analyses) were used to address the statistical work plan.
Petrozzi and Annalisa LoGerfo are gratefully acknowledged for the APOE genotyping of patients. Open Access Funding provided by Universita degli Studi di Pisa within the CRUI-CARE Agreement. FU N D I N G I N FO R M ATI O N Funded by Italian Ministry of Health (code: Ricerca Finalizzata 2013, #PE2013-02359574, "In vivo assessment of the role of locus coeruleus in the development of AD and other types of dementia" [principal investigator: F. S. G. ]). CO N FLI C T O F I NTE R E S T H. Ha. is an employee of Eisai, serves as Senior Associate Editor for the journal Alzheimer's & Dementia, and has not received any fees or honoraria since May 2019; before May 2019, he had received lecture fees from Servier, Biogen, and Roche; research grants from Pfizer, Avid, and MSD Avenir (paid to the institution); travel funding from Eisai, Functional Neuromodulation, Axovant, Eli Lilly, Takeda and Zinfandel, GE Healthcare, and Oryzon Genomics; and consultancy fees from Qynapse, Jung Diagnostics, Cytox, Axovant, Anavex, Takeda and Zinfandel, GE Healthcare, Oryzon Genomics, and Functional Neuromodulation; and participated on scientific advisory boards for Functional Neuromodulation, Axovant, Eisai, Eli Lilly, Cytox, GE Healthcare, Takeda and Zinfandel, Oryzon Genomics, and Roche Diagnostics.
Study design and participants The 'Schizophrenia: Treatment Resistance and Therapeutic Advances' (STRATA) Consortium is a UK Medical Research Councilfunded, multi-disciplinary group of researchers working together on stratified medicine for schizophrenia and related psychoses. As part of the STRATA-Genetics workstream (STRATA-G), genetic and phenotypic data was collected from existing observational, prospective, firstepisode psychosis cohorts to perform secondary data analysis. To be included in STRATA-G, cohorts were required to have data on individuals recruited at first episode and prospectively followed for a minimum of 12 months, and to have included DNA collection in the study protocol. Analysis of genetic data is not reported here, and all participants were included in this analysis regardless of whether their genetic data were available.
Predictors Any variables recorded in more than one cohort, at baseline, were considered as potential predictors. We removed variables where >80 % of the data were missing (Supplementary Table  1 ), one variable from every pair with a Pearson's correlation >0. 8 (Supplementary Table  2 ) to prevent multicollinearity (retaining the most clinically informative), and categorical variables where the number of events or non-events per category was less than ten.
Statistical analysis 2. 4. 1. Explanatory model We imputed missing data using the mice package (van Buuren and Groothuis-Oudshoorn, 2011) for multiple imputation by chained equations (MICE). TR status, length of follow up, and an indicator for cohort were included. We ran MICE using 100 imputations, with ten iterations in the burn-in period. Performance was assessed by plotting the residuals and convergence of chains, exploring the range of imputed values, and comparing observed and imputed data  (Nguyen et al. , 2017). We conducted logistic regressions to examine the associations between predictors and TR. Each of the 100 imputed datasets were analysed separately and then the results were pooled together based on Rubin's rules  (Azur et al. , 2011; Rubin, 1987). Regression coefficients were pooled by taking the average coefficient from all the imputed datasets and standard errors by combining the within imputation variance and the between imputation variance.
Prediction model We imputed missing data using the missForest package  (Stekhoven and Buhlmann, 2012)  for imputation by random forests  (Breiman, 2001). Imputation performance was assessed using the out-of-bag imputation normalized root mean squared error (NRMSE) for continuous imputed data, and the proportion of falsely classified entries (PFC) for the categorical imputed data set (in both cases, a value close to 0 % indicates good performance and a value close to 100 % poor performance; we considered a value around 50 % to indicate a moderate performance)  (Stekhoven and Buhlmann, 2012). We conducted a 'Least Absolute Shrinkage and regression Operator' (LASSO) logistic regression to identify a model capable of predicting TR. LASSO is a form of regularised regression which can reduce overfitting when there are a large number of predictors  (Hastie et al.
The optimal cut-off point was defined as the threshold which maximised overall correct classification rates and minimised misclassification rates, while choosing the point on the receiver operating characteristic curve farthest from chance. In the absence of an independent dataset with which to externally validate the model, we conducted internal, repeated cross-validation (5 folds, 50 repeats) to estimate the optimism of the model (the degree to which the accuracy of the model is inflated by overfitting) and recalibrated the beta coefficients  (Steyerberg, 2008). The difference between the training and test performance estimates of each fold were averaged to obtain an estimate of the optimism. The optimism was then subtracted from the apparent performance measures to obtain the corrected (internally validated) performance measures.
Software Pre-processing of individual cohorts was conducted in STATA version 14 (Stata Statistical Software: Release 14, StataCorp LP. , College Station, TX). Analyses were performed in RStudio (v1. 3. 1073) using R (v4. 0. 2) with the exception of MICE and optimism-correction which used R (v4. 0. 0) in the Hawk cluster of Supercomputing Wales. Scripts used for analysis can be found at: https://github. com/sophiesmart/str atagprediction.
Results The final dataset consisted of N = 2216 participants, from ten cohorts, of which 385 (17 %) were TR (Table  1 ). Data preparation is described in the Supplementary Material, after which we retained 19 predictors. The descriptive statistics for these predictors, as well as length of follow up, clozapine use, and schizophrenia diagnosis, are presented in Table  2. The mean proportion of missing data was 45. 68 % (ranging from 0 % to 75. 30 %; see Supplementary Table  1 ).
Explanatory model Missing data imputation performed well: chains of trace plots showed convergence, the distributions of the imputed values were consistent with the distributions of the non-imputed values, and the residuals of each imputed dataset showed that the assumptions were met (see Supplementary Material). In univariable logistic regression models, eight predictors were associated with TR. Younger age of onset, being male, higher BMI, not being in a relationship, fewer years in education, higher SAPS score, lower GAF score (worse functioning), and being of African ethnicity (compared to European ethnicity) all increased the odds of being TR (see Supplementary Table  3 ). However, in the multivariable logistic regression, controlling for all other predictors as well as cohort and length of follow up, only younger age of onset and fewer years in education increased the odds of being TR (Table  3 ; Supplementary Table  4 ).
Strengths and limitations Our study uses only data collected at first-episode, encompassing the full range of first episode presentations of psychosis. Many studies rely on cross-sectional or retrospective case-control designs which suffer from selection bias since they tend to include only patients who remain in mental health services and exclude those with good outcomes. Furthermore, cross-sectional or retrospective studies cannot exclude reverse causation. We cannot therefore assume that findings from these studies will generalise to first-episode patients. One of the strengths of this analysis is that we have estimated, separately, the strength of associations, between characteristics at firstepisode and TR, and the predictive accuracy of these same characteristics  (Shmueli, 2010). A LASSO regression is an optimal method for prediction in this context.
Bologna (Italy) cohort was funded by the  European Community 's  Seventh Framework Program  under grant agreement (agreement No. HEALTH-F2-2010-241909 , Project  EU-GEI ). The  GAP (London, UK ) cohort was funded by the  UK National Institute of Health Research (NIHR) Specialist Biomedical Research Centre for Mental Health ,  South London  and  Maudsley NHS Mental Health Foundation Trust (SLaM)  and the  Institute of Psychiatry, Psychology, and Neuroscience at King's College London ;  Psychiatry Research Trust ;  Maudsley Charity Research Fund ; and the  European Community 's  Seventh Framework Program  grant (agreement No. HEALTH-F2-2009-241909 , Project  EU-GEI ). The Lausanne (Switzerland) cohort was funded by the  Swiss National Science Foundation  (no. 320030_135736/1  to P. C. and  K.
The Santander (Spain) cohort was funded by the following grants (to B. C. F):  Instituto de Salud Carlos III ,  FIS 00/3095 ,  PI020499 ,  PI050427 ,  PI060507 ,  Plan Nacional de Drogas Research Grant   2005-Orden sco/3246/2004 , and SENY  Fundatio Research Grant   CI 2005-0308007 ,  Fundacion Marques de Valdecilla   A/02/07  and  API07/011. SAF2016-76046-R  and  SAF2013-46292-R  ( MINECO and FEDER). The West London (UK) cohort was funded The  Wellcome Trust  (Grant Number:  042025 ;  052247; 064607 ).
Each trial had a maximum duration of 60 s. If the mouse did not find the escape platform in 60 s, it was physically placed on the platform to learn for 20 s. For the retention test, on the 5th day, the platform was removed, and the mice were allowed to swim for 60 s. The tendency of the mice to search for the platform was evaluated by quantifying the time spent in the target quadrant where the escape platform had previously been located. The time spent by each mouse in the target quadrant and the distance traveled by the mice were recorded and processed using a video tracking device and computer-equipped analytics management system (Kinovea. org software, 0. 8. 15). The MWM protocol was applied twice, the first time before the first injection, at 7 months of age, and the second time after the last injection, at 11 months.
2 NA, objective lens with an optical section separation (z-interval) of 0. 3 µm. Fluorescence signal collection, image construction, and scaling were performed using the control software (FluoView FV-1000, 4. 2. 1. 20 Olympus). Multiple fluorescence signals were acquired sequentially to avoid crosstalk between image channels. Fluorophores were excited with the 405 nm line of a diode (for DAPI), and the 543 nm line of a HeNe laser (for TRITC). Emitted fluorescence was detected through spectral detection channels and images were merged as an RGB image.
The membranes were washed again and exposed to the chemiluminescence Luminata Forte Substrate. After two washes in TBST, the membranes were put in contact with mouse antibodies against β-actin overnight at 4 • C, washed with TBST and incubated with HRP-conjugated secondary antibodies for 1 h before being exposed to the chemiluminescence Luminata Classico Substrate. All luminescent signals were captured by the Gbox system (GeneSnap software, Syngene, Ozyme distributor). Automatic image analysis software was supplied with Gene Tools (Syngene, Ozyme distributor). In each case, the protein of interest/βactin ratios were calculated. Results were then normalized as a percentage of expression in PEG treatment for both groups (WT and AD).
Statistical Analysis Results were expressed as means ± standard error (SEM). The statistical program GraphPad (GraphPad Software, San Diego, CA, United States) was used to compare quantitative variables. For all comparisons, the level of significance was p < 0. 05. To compare quantitative variables between two groups of mice, Mann-Whitney test was used. Comparisons between all groups of mice were performed using a Kruskal-Wallis test followed by a post hoc Dunn's test. For imaging experiments, intergroup comparison was performed using a two-tailed unpaired Student t-test. Effect size was evaluated for each significant difference observed and expressed as d-values corresponding to a large effect size for d values superior to 0. 80  (Cohen, 1988). The signals extracted using the ROIs on the z-score maps were considered for further analysis when representing at least 50 contiguous voxels for a statistical threshold set at p < 0.
, 2005; Garcia-Alloza et al. , 2006; Ruan et al. , 2009) , supporting the preventive effect of viniferin on the appearance of amyloid deposits  (Caillaud et al. , 2019). Thus, in the present study, AD and WT mice were treated by resveratrol or viniferin, at 20 mg/kg, or their vehicle, PEG-200, by i. p. injections once a week from 7 until 11 months, when amyloid deposits were already present. Moreover, it was necessary to determine the effects of viniferin on memory decline in comparison to resveratrol. Like many models of mice modeling Alzheimer's disease, these APPswePS1dE9 mice display amyloid deposits in the whole brain including the cerebellum with age  (Sérrière et al. , 2015). Here, we compared data obtained in the hippocampus by immunofluorescence assay and micro-PET imaging. The classic AD pattern consists of early prominent involvement of the entorhinal cortex and the hippocampus.
ACKNOWLEDGMENTS This work has benefited from the facilities and expertise of PREBIOS platform ( University of Poitiers, France ). The authors thank  Johnny Vercouillie  and  Gabrielle Chicheri  ( UMR 1253 , iBrain,  University of Tours ), and  CERRP  ( Centre d'Etudes et de Recherches sur les Radiopharmaceutiques, University of Tours ) for the preparation of PET tracers,  Damien Chassaing  ( EA3808 Neurovascular Unit and Cognitive Disorders, University  of Poitiers),  Sylvie Bodard  ( UMR 1253 , iBrain,  University of Tours ), and  Julie Godet  ( Poitiers University Hospital ) for their technical skills. The authors also thank the  LNEC Inserm U1084 laboratory (M. Jaber)  for the loan and advice of the VideoTrack system for automation of behavioral animal experiments and  E. Rowley-Jolivet  who edits the manuscript to eliminate possible grammatical or spelling errors and to conform to correct scientific English (Language Editing service available from  Elsevier Author Services.
Randomisation intervention Eligible patients will be randomised on the day of inclusion by the main investigator and by using the Ennov Clinical software system. Randomisation will be balanced in both groups with a 1:1 allocation ratio without stratification. The randomisation list will be established by a statistician from the methodological support department before the start of the research. The method used will be a randomised block approach using the SAS Proc Plan system. We will build blocks of variable sizes and the investigator will perform the randomisation step directly via the electronic case report form (eCRF) using the CS Random module of the Ennov Clinical eCRF system on the day of inclusion.
Open access Data collection Data will be collected using an eCRF system. The database will be created by a data manager at the Methodology and Data Center using EnnovClinical software. All information required by the protocol will be recorded in the eCRF and explanations will be given for any missing data. Data will be collected as it is obtained and subsequently legibly transcribed into the eCRF. Reasons for premature exit from the study will be as follows: (1) premature discontinuation of treatment, (2) participant unavailable for follow-up, (3) withdrawal of consent, (4) abandonment of the research project and (5) major protocol deviation (absence from individual and group training sessions). Caregivers who discontinue research treatment, are unavailable for follow-up, or who drop-out of the study will all be included in the intention-to-treat (ITT) analysis.
Data analysis The sample size of 60 caregivers (30 per group) will be determined following the calculation recommendations for a pilot study (2005)  39  and will be considered to be large enough to gauge whether or not the study will be feasible for a full-scale clinical trial intervention. This sample will allow Open access us to estimate an adherence rate of 0. 8 with confidence limits±0. 1 around the point estimate. The primary analysis of this pilot trial will focus on the outcomes assessing feasibility by descriptive analysis. Objectives are key and will be focused on recruitment, intervention adherence and dropout rates. The recruitment rate will be calculated as the number of participants enrolled in the trial divided by the number of eligible patients to whom participation in the study is proposed.
The comparison of the number of healthcare consumptions (including consultations and hospitalisations) will be compared between the two groups using a Student or Mann-Whitney U test depending on the conditions of application. Changes in stress, burden, sleep, fatigue level and QoL in the intervention group will be estimated using a linear mixed model to account for intraindividual variability. All hypotheses will be tested two sided at the 5% alpha threshold. Analyses will be performed on an ITT basis and a sensitivity analysis on a per-protocol basis will also be performed. Patients who stopped research treatment, died, were lost to follow-up or dropped out of the study will all be included in the ITT analysis. Analyses will be performed using SAS V. 9. 4 software (SAS Institute).
Data monitoring A clinical research associate mandated by the sponsor will check the participant files four times: (1) after inclusion, (2) 8 weeks post-inclusion, (3) 16 weeks postinclusion and (4) at the end of the research. Following the risk-based monitoring plan during these visits (participant, logistics, impact and resources), the following elements will be reviewed: a collection of informed consent, compliance with the research protocol and the procedures defined therein, the quality of data collected in the CRF namely accuracy, missing data and quality of the data collected in the observation book such as accuracy, missing data and the consistency of data with source documents. All visits will be documented in a written monitoring report.