dataesr/scientific-paragraphs-categorization huggingface dataset:
Actigraphy (Actiwatch-64; Phillips Respironics, US) was recorded during 10 days, applying the high sensitivity setting for actigraphy scoring. Sleep diaries were filled out by participants, where participants had to report every notable event that would influence recording (eg showers, naps). This prevented any automatic misdetection from misuse of the actimeter (ie, unintentional event marker, forgetting to put back the actimeter after a shower etc. ), as well as quality control and artefact detection. Three sleep parameters derived from computer-generated actigraphy data based on sleep diary information are analyzed in this study, which are described as most characteristic to insomnia  [41] : total sleep time (TST), sleep efficiency (SE) and sleep onset latency (SOL)  [57].
Statistical analysis The data manipulation and analyses were conducted using SPSS 11. Data distributions were checked for normality, outliers and missing data, of which the latter were imputed. Questionnaires and actigraphy encompass different concepts on emotions and sleep that have been grouped in five categories: subjective sleep (PSQI, ISI), objective sleep (actigraphy averages for each variable), comorbid symptoms (BDI, BAI, APS), emotion regulation ('ER'; DERS, CISS), and sleep-related thoughts and emotions ('STE'; TCQI-R, PSAS, FIRST, DBAS-16). Independent sample t-tests on each dependent variable (ie, questionnaires, actigraphy) were conducted to characterize people with and without insomnia (independent variable; cf. hypothesis 1). Effect size and confidence intervals are reported for intragroup differences.
For predictive variables, alpha inflation was addressed by applying False Discovery Rate (FDR) correction, implementing the adaptive Benjamini-Hochberg procedure. Only those results surviving FDR correction are reported. To investigate the association between emotion regulation and objective sleep (cf. hypothesis 2), a multivariate analysis of variance was applied to results of those questionnaires measuring emotion regulation (ER) or sleep-related thoughts and emotions (STE) that showed differences between insomnia patients and controls, and investigate their effect on objective sleep parameters. Specifically, the analyses include the sleep variables as dependent variables (sleep efficiency [SE], sleep latency [SOL] and total sleep time [TST]), and the predictors as covariates in the SPSS command.
Acknowledgments The authors would like to thank  C ecile Klochendler  for her help in developing and managing the project;  Muriel Bacarisse ,  C edric Valtat , and  Aurelien Boiseau  for their help with screening participants and managing the project and students  Marie Bromberg ,  Charly Marteau ,  Salome Fourmon ,  Joyce Reesen ,  Nico Mascaro ,  C ecile Arrieula ,  Laura Dahan ,  Ad ele Jacques , and  Maxime Kohn  for their help in collecting and processing the data.
Alterations in the patterns of use could help clinicians to detect pathological or risky behaviors and individual needs, and increase treatment efficiency. In this article, a nonparametric latent feature model based on the Indian Buffet Process (IBP) explores the response patterns of 2254 psychiatric outpatients to a web-based questionnaire. We aimed to define specific profiles according to response patterns and then link profiles with psychiatric disorders. The study will thus specifically describe patients with psychiatric diagnoses who have used an eHealth application at least once. We hypothesize that data mining techniques, such as IBP, can be used to associate information from different questionnaires and assessments in a plausible model that could serve ultimately to plan health care delivery.
Every patient attending psychiatric consultations independent of diagnosis was considered. Thus, all clinicians in the catchment area were instructed to propose the use of the web application to every outpatient they saw with no restriction whatsoever regarding their diagnoses or their clinical statuses. The total number of outpatients who consulted during the study period was 30,808. For the purpose of this study, we included only participants who voluntarily accessed the application and responded to an open-text field. We made this choice to select proactive participants who completed most of the questions at the user end. We noted a missing data rate of 12%, which resulted from the sum of clinical missing data and a lack of completeness of the questionnaires at the user end of the application.
Acknowledgments This study received grant support from  Instituto de Salud Carlos III  ( ISCIII PI13/02200 ;  PI16/01852 ),  Delegación del Gobierno para el Plan Nacional de Drogas  ( 20151073 ), the  American Foundation for Suicide Prevention  ( LSRG-1-005-16 ), the  Ministerio de Ciencia, Innovación y Universidades  ( RTI2018-099655-B-I00 ;  TEC2017-92552-EXP ), and the  Comunidad de Madrid  ( Y2018/TCS-4705 ,  PRACTICO-CM ).
Background. Personality traits have been associated with long-term suicide risk but their relationship with short-term risk is still unknown. Therefore, to address this gap, we explored the moderating effect of personality traits on the relationship between the Suicide Crisis Syndrome (SCS) and short-term suicidal behaviors (SB). Sampling and Methods. Adult participants (N = 459) were administered the Suicide Crisis Inventory (SCI), a validated self-report questionnaire designed to measure the intensity of the Suicidal Crisis Syndrome, the Big Five Inventory for personality traits, and the Columbia Suicide Severity Rating Scale for SB at intake and at a 1-month follow-up. The PROCESS macro in SPSS was used to test the moderation model. Covariates hypothesized to influence the results were added: age, gender, ethnicity, years of education, and depressive symptomatology on the Beck Depression Inventory.
Big Five Inventory The BFI is a 44-item self-report inventory that measures the Big Five personality dimensions, namely Extraversion (8 items), Agreeableness (9 items), Conscientiousness (9 items), Neuroticism (8 items), and Openness (10 items)  [29]. The respondents rate the items on a five-point scale (from "1 = disagree strongly" to "5 = agree strongly"). John and Srivastava reported Cronbach's α from 0. 75 to 0. 80 for the reliability of the subscales and 3-month test-retest reliability from 0. 80 to 0. 90  [30]. In this dataset, the BFI demonstrated a standard level of internal consistency for four of the subscales (Cronbach's α = 0. 799 for Extraversion, Cronbach's α = 0. 751 for Agreeableness, Cronbach's α = 0. 794 for Conscientiousness, and Cronbach's α = 0. 828 for Neuroticism) with a lower alpha for the Openness subscale (Cronbach's α = 0.
Procedure The study participants were recruited from psychiatric outpatient services in four New York City hospitals in the Mount Sinai Health System. The clinicians, who were psychiatric residents working in those settings, referred potential participants after assessing the patients' eligibility as per the inclusion criteria. Compensation was provided for the clinicians' time for each patient referred. Interested patients were contacted within 2 weeks of the referral. All participants were provided informed consent and demographic information at the beginning of the study. Data on participants' psychiatric diagnoses were drawn from the medical record. Trained research assistants administered a psychological test battery to each participant in-person. One-month post initial assessment, a follow-up assessment was administered in-person or via phone call.
Data analysis Data analyses were conducted with SPSS version 25. Nonparametric analogs for parametric tests calculated the descriptive and bivariate analyses. The Mann-Whitney U test and Spearman's correlations were used for univariate analyses involving the measures. Chi-squared statistics and ANCOVA analyses were used to compare groups with and without SB over the lifetime (from initial assessment), during the last 3-months (prior to initial assessment), and in the 1-month after initial assessment (1-month follow-up). For lifetime SB, ANCOVA controlled for age, years of education, and race; for SB during the past 3-months, ANCOVA controlled for age; and for SB at 1-month follow-up, no demographic variable needed to be controlled (for the covariates with significant differences between groups, see Supplementary Tables  S3.
1-S3. 3, respectively). Logistic regression was used to test the hypothesized moderation model, that is, whether the Big Five personality traits moderate the effects of SCI on SB at 1-month follow-up. Logistic regression was conducted using the moderation model in PRO-CESS macro in SPSS. SCI and BFI were centered around their means before computing the interaction term, and all terms were entered into the model together. The Johnson-Neyman Procedure was used to probe the interaction: simple effects coefficients were calculated on three values of each personal trait: 1 SD below the mean, mean, and 1 SD above the mean.
Participants We examined arm kinematic data obtained from a sub-cohort of participants from the experimental group of the REM-AVC clinical trial (NCT01383512), a multi-center RCT of mechanized arm therapy post-stroke  (Rémy-Néris et al. , 2021). The goal of this RCT was to evaluate the medico-economic benefits in post-acute stroke of 4 weeks of standard care and motor arm therapy with ArmeoSpring vs. standard care and selfrehabilitation. The inclusion criterion for the RCT were: age between 18 and 81 years, diagnosis of hemorrhagic or ischemic stroke 3 weeks to 3 months prior to inclusion, and a UEFM score between 10 and 40 points. Exclusion criteria included (1) pain in the affected shoulder >3/10 on a visual analog scale (VAS), (2) a Boston Diagnostic Aphasia Examination (BDAE) score ≤ 3 points, (3) fatigue or visual impairment that would prevent participation in an additional daily hour of therapy, and (4) incapability to sit independently.
For the present study, we had access to ArmeoSpring kinematic data and to clinical data of 53 participants with a single stroke in the territory of the middle cerebral artery (MCA) (30 males, 19 females, 4 gender not available; 59. 3 ± 13. 9 years old; baseline UEFM 24. 7 ± 9. 1, final UEFM 37. 2 ± 15. 1, days since stroke 56 ± 21 days -all reported values are mean ± SD). UEFM scores, which were unavailable for three participants, were measured by trained physical or occupational therapists. In REM-AVC, the participants were scheduled to receive training with the more affected arm on the ArmeoSpring, twice/day, 5 days/week, for a total of 40 sessions over a period of 4 weeks. Each training session lasted 30 min. A session consisted of several different video games (selected by the therapist and the patient in each session), and the ArmeoSpring vertical reaching tests "Ladybug" test (Figure  1A ; see below for details) given at the beginning and at the end of each session, for a scheduled total of 80 tests (4 weeks × 5 days × 2 sessions × 2 tests).
Each test session was separated into trials to parse out movement trajectories between consecutive targets. For participants in the stroke group, the therapist adjusted the test difficulty based on the participant's performance and motivation. If a participant could catch more than 90% of the lady bugs in two consecutive sessions, then the therapist would increase the difficulty level. If the participant did not maintain this 90% success rate, the difficulty level was decreased. In the control group, difficulty was set to the highest level. Note that this study only analyzed end-effector trajectory and joint angle data from the Ladybug tests, but not from the training games in between the tests.
Movement Data Processing We used a second-order low-pass Butterworth filter  (Butterworth, 1930)  to filter the raw data (end-effector trajectory and joint angle trajectories) with a cutoff frequency of 5 Hz. We calculated end-effector velocities for each session by finding the derivatives of the end-effector displacement trajectories. We then calculated the number of peaks in the end-effector tangential velocity profiles for each trial. A peak was defined at any point where the velocity value was higher than the previous time-point and higher than the next time-point. End-effector performance in each test was assessed by the mean number of peaks using all trials in the test. We also measured smoothness in arm movements using a more robust metric, the spectral arc-length, which has been proposed as an alternative method to evaluate task space performance  (Balasubramanian et al.
, 2012). We found that the mean number of peaks per trial and the mean spectral arc-length metric per trial were highly correlated in the stroke participant (r = -0. 91). Thus, we reported the mean number of peaks to be consistent with our previous study  (Schweighofer et al. , 2018)  (Note that task-space results slightly differ between this current study and this previous study because here we selected all trials, not only the successful trials). We calculated the angular velocities across six joint angles by finding the derivatives of the respective joint angular displacements. We removed data from the first trial for every session because this trial often showed unrealistic high velocities caused by initial adjustment of cursor position at the beginning of sessions. We further removed data from 337 trials out of a total of 100,422 trials (0.
3%) in which recording errors led to any of the following issues: the maximum angular velocities for any joint being higher than 10 rad/s at any point in a trial; the maximum angular velocities for any joint being less than 0. 01 rad/s throughout a whole trial; or a trial lasting longer than 20 s. When more than half the data were removed for a session, we treated the session as a missing data-point for any further analysis. Nineteen out of a total of 3916 sessions (0. 5%) were removed for this reason. We analyzed angular velocity data instead of angular displacement data to remove any differences in starting positions across sessions from our analysis. Based on our visual observations of the participants performing the task, we suspected a negligible contribution of wrist movements to the overall movements.
Indeed, the mean variance across the participants post-stroke for the angular velocities for the wrist and pronation/supination joints were much smaller than that of any of the other joints by a factor of at least 8. The mean session variance for angular velocities of the wrist and pronation/supination joints were 0. 00163 and 0. 00917 (rad/s) 2 , respectively, whereas the minimum mean session variances for any of the other joints were 0. 0426 (rad/s) 2. We therefore analyzed velocity data from four ArmeoSpring joints: SH rotation, SE rotation, Forearm rotation, and Elbow rotation.
P (i; j) , A i , and D i were expressed in units of z-transformed correlations, and τ i was expressed in units of sessions and were all assumed to be normally distributed to reflect the inter-subject variability. The initial performance was estimated by A i + D i. We then modeled the change in the mean number of velocity peaks per trial for each session, using a double-exponential mixed effect model, as in our previous work  (Schweighofer et al. , 2018). In this model, the first exponential corresponds to a fast component, and second exponential to a slow component. Both joint and end-effector performance mixed models were fitted using the "nlmefitsa" function in MATLAB 2020a, which estimates the parameters using a stochastic Expectation-Maximization algorithm. We used Root Mean Square Error (RMSE) to measure the goodness of fit.
Limitations A first limitation of our study was that we did not use a motion capture system to collect the actual upper-limb kinematics during the clinical trial, but used a commercially available exoskeleton. An advantage to this set-up is the simplicity and reliability of data collection across the multiple sites who participated in the REM-AVC clinical trial. A disadvantage is that we recorded, and analyzed, exoskeleton angles, and there is no clear/direct mapping between these angles and anatomical angles for all joints. SH and SE were similar to anatomical shoulder abduction/adduction and shoulder flexion/extension, respectively, but the Elbow and Forearm angles were a combination of elbow flexion/extension and shoulder rotation. However, the exoskeleton joint angles allowed us to decompose the movements to joint synergies either aligned with or perpendicular to the gravitational field.
Statistic Graph Pad Prism version 7. 00 (Graph Pad Software, La Jolla, CA, USA) and SPSS version 24 (IBM Corp. , Armonk, NY, USA) was used for data graphing and statistical analysis, respectively. All data are expressed as mean ± SEM. For all experiments, one-way, two-way or three-way analysis of variance (ANOVA) was used. When a significant effect of main factors or interactions was found, a Tukey's multiple comparison test (post one-way and two-way ANOVA) or Fisher's LSD post hoc test (post three-way ANOVA) was used. Significance was set at P≤0. 05.
These changes in DAX-1 are also constent wih the effects of LPS treatment in ATC7-THP1 cells. The data are important as they provide an insight of the role of specific cytokines in regulating immune and steroidigenic response in adrenal glands exposed to inflammatory stimulus. We have also assessed the effect of glucocorticoids on immune-adrenal interactions. This approach is novel since, to our knowledge, the effects of glucocorticoids on the HPA axis responses to inflammation has only been investigated at a system level, and not directly in the adrenal gland cells. We found a significant effect of a high dose of LPS on the increase of IL-6 mRNA expression. This increase was suppressed by low and medium doses of DEX. A similar dose-dependent suppression was noted in the StAR mRNA expression as a result of LPS stimulation.
Identification of ligand-receptor pairs involved in the dialogue between SVZdNP and microglia We used our single-cell RNA sequencing data, focusing on secreted extracellular molecules or membrane receptors reported to have immunomodulatory properties in the Gene Ontology database, to identify candidate ligand-receptor pairs for involvement in SVZdNP/microglia dialogue. According to the literature  (De Feo et al. , 2012; De Gioia et al. , 2020) , immature neural progenitors (cluster 5 in  Figures 3E and 3F)  are the SVZdNP population most likely to display immunomodulatory properties. The three genes displaying the most significant enrichment in cluster 5 (Figure  3F ) were Egfr (2. 7-fold; p = 1. 41 3 10 À16 ), Mfge8 (2. 7-fold; p = 5. 87 3 10 À13 ), and Ascl1 (2. 1-fold; p = 2. 80 3 10 À1 ).
Single-cell library generation and sequencing Single-cell library generation (Chromium Single-cell controller; 10X Genomics) and sequencing (NextSeq 500; Illumina) were performed by the HalioDX Company (Luminy, Marseille). The cDNA, library preparation and sequencing all passed quality control tests. NextSeq data were demultiplexed with the 10X Genomics suite Cell Ranger 1. 2, and the mkfastq function. This first level of analysis generated quality metrics (e. g. , Q30, number of reads per sample) and FASTQ files. Cell Ranger 1. 2 count was then performed on each library (sample) demultiplexed with Cell Ranger 1. 2 mkfastq. Finally, Cell Ranger 1. 2 count was performed on a mouse reference genome, determining the number of cells and the number of genes per cell, quantifying gene expression (count tables) and performing t-SNE analysis.
Statistical analyses Nonparametric Mann-Whitney and Wilcoxon tests were used for two-group comparisons (unpaired and paired samples, respec-tively). For the comparison of multiple groups, ANOVA by score permutation was performed, followed by Kruskal-Wallis tests with StatXact software (Cytel Studio). Differences were considered significant if p < 0. 05 (*), and highly significant if p < 0. 01 (**).
ACKNOWLEDGMENTS We thank  C. Maurange ,  C. Faivre Sarrailh , and  L. Kerkerian Le Goff  for critical reading of the manuscript. This work was funded by  CNRS ,  Aix-Marseille University , the  Fondation pour la Recherche Medicale  ( DEC 20140329501 ) and the ARSEP foundation. We also thank France-bioimaging/ PICSL infrastructure  ( ANR-10-INSB-04-01 ). This work received support from the  French government  under the '' Investissements d'Avenir'' program ,  Initiative d'Excellence d'Aix-Marseille Universitevia AMidex funding NeuroMarseille   AMX-19-IET-004 , and NeuroSchool  ANR-17-EURE-0029. BB was funded by the  ANR  and an  ARSEP  fellowship.
Somatic and dendritic quantal EPSCs were obtained from experiments where [Ca 2+ ] was lowered to 0. 5 mM while [Mg 2+ ] was increased to 5 mM to obtain an evoked EPSC success rate <10% known to produce a qEPSC with a <10% amplitude error  (Silver, 2003). Trials with a synaptic event could be clearly selected by eye. The stimulation artifact was removed by subtracting from single success traces the average obtained for the traces with failed synaptic transmission. Current-clamp recordings were performed using a Multiclamp 700 amplifier. Patch electrodes were coated with dental wax and series resistance was compensated by balancing the bridge and compensating pipette capacitance. Current was injected to maintain the resting potential near -70 mV. Data were filtered at 10 kHz, and digitized at 100 kHz.
4nm gold-conjugated streptavidin (Nanoprobe, 1:100 in Tris-buffered saline (TBS) and 0. 03% Triton X100). After washing in TBS and dH2O, slices were treated with HQ silver enhancement kit (Nanoprobe) for 5 minutes, fixed in 1% OsO4 in PB for 30 minutes, and block stained with 1% uranyl acetate for 40 minutes. After dehydration through a series of ethanol solutions  (50, 70, 80, 90, 95, 99, and 100%)  and propylene oxide twice for 10 minutes, slices were embedded into Durcupan (Fluka) and flat embedded. The labeled SCs were trimmed and 300~400 serial ultrathin sections were cut at 70 nm using ATUMtome (RMC Boeckeler). Serial sections containing immunogold labeled profiles were imaged with a scanning electron microscope (Merlin Compact, Zeiss) and Zeiss Atlas package at X 22,000 for whole-cell reconstruction and at X 55,000 for synapses.
Dendrite tracing: The image analysis software IMARIS 9. 5 (Bitplane) was used for dendritic tracing and fluorescence puncta detection. Fluorescence images were filtered using a 3x3 px median filter to remove noise. Image stacks were then further combined using the IMARIS stitcher tool to create one contiguous file that permits tracing of the entire dendritic tree. Dendrites, but not axons, were traced in IMARIS using the filament tool in a semi-automatic mode using the AutoPath method and the options AutoCenter and AutoDiameter activated. The soma center was chosen as the starting point with centripetally tracing along dendrites. Dendritic lengths were estimated as the dendritic path distance from the center of the soma and Sholl analysis was performed with IMARIS from all reconstructed SC.
PSD-95 Venus puncta detection: Fluorescence puncta were detected using the IMARIS spot creation tool, using background fluorescence subtraction to compensate for different levels of fluorescent intensity along the z-axis of the stack. The initial minimal spot search size was set to 300x300x1100 nm, slightly smaller than the PSF at 810 nm. No further thresholds or criteria were applied inside IMARIS. Parameters describing the fluorescent puncta, including the intensity at the center, their spatial coordinates, and their diameters, were exported as excel files via the Statistics tab and further analyzed using custom python scripts. In order to separate the PSD95 puncta from false detection of noise, two threshold criteria were applied. 1) All spots with a diameter smaller than and equal to the PSF (300x300x1100nm) were rejected.