dataesr/scientific-paragraphs-categorization huggingface dataset:
Differences are considered significant at † p < 0. 05, vs. Vehicle (Normal Diet); * p < 0. 05, vs. Vehicle (High-fat Diet). In red characters are all the data significantly different from their corresponding control experiments. Complete individual data can be found in Table  S3. olecules 2021, 26, x 1 of 27 gure 4. Water (A) and food (B) intake in high-fat diet-induced obesity model in C57BL/6 mice. Male C57BL/6 mice were fed a igh-fat diet (HFD, 60% of calories) or a standard chow diet (STD) from 4 weeks of age for 8 weeks before treatment. Once esity was installed, GPS18169, at 5 and 10 mg/kg and vehicle (WFI) was administered by intraperitoneal injection (IP) once aily for 12 weeks after 8 weeks of HFD feeding. Food (B) and water (A) intake were recorded twice weekly. + p < 0. 05, vs. ehicle (High-fat Diet); two-way ANOVA followed by Bonferroni test.
Green squares: vehicle and normal diet; red squares: hicle and high fat diet; orange triangles: GPS18169 (10 mg/kg) and high fat diet; greenish-yellow triangles: GPS18169 mg/kg) and high fat diet. Complete individual data are presented in Table  S4  (Water) and Table  S5  (Food). Once obesity was installed, GPS18169, at 5 and 10 mg/kg and vehicle (WFI) was administered by intraperitoneal injection (IP) once daily for 12 weeks after 8 weeks of HFD feeding. Food (B) and water (A) intake were recorded twice weekly. + p < 0. 05, vs. Vehicle (High-fat Diet); two-way ANOVA followed by Bonferroni test. Green squares: vehicle and normal diet; red squares: vehicle and high fat diet; orange triangles: GPS18169 (10 mg/kg) and high fat diet; greenish-yellow triangles: GPS18169 (5mg/kg) and high fat diet.
5% BSA and rinsed several times with an ice-cold buffer containing 50 mM Tris-HCl (pH 7. 4) and 500 mM NaCl using a 96-sample cell harvester (Unifilter, Packard). The filters are dried and then counted for radioactivity in a scintillation counter (Topcount, Packard) using a scintillation cocktail (Microscint 0, Packard). The results are expressed as percentage inhibition of the control radioligand specific binding. The standard reference compound is MCH, which was tested in each experiment at 8 concentrations to obtain a competition curve from which its IC 50 is calculated. The high level of assay robustness and reproducibility of each control monitored over several decades (internal data base, Eurofins) and included in every assay ensure results' reliability even when performed only twice.
Importantly, however, it was not approved until after this study ended  (European Medicines Agency, 2013; Mahase, 2019; European Medicines Agency, 2019). Additionally, there is no consensus on treatment pathways for TRD, and evidence suggests wide variation between and within European countries  (MacQueen et al. , 2017). The current cohort study was established to collect data from TRD patients with moderate to severe depression being treated in routine clinical practice in a sample of European countries. The aim of the study was to describe real-world clinical features and, as such, did not include a specific hypothesis. Treatment patterns and outcomes were followed for up to 21 months in patients starting a new therapy, having already experienced at least two treatment failures in the current MDE.
Study design A prospective, multicenter, observational cohort study of patients with TRD in Europe was conducted. The study has been described in detail elsewhere  (Heerlein et al. , 2021). Briefly, the study was comprised of baseline data collection, a 12-month observational period with a minimum follow-up of approximately 6 months for each enrolled patient, and an extended observation period up to 6 months from recruitment of the last patient, resulting in a maximum of 21 months of follow-up. Analysis was conducted only on data collected at 6 and 12 months after baseline (Supplementary Fig. 1 ). Patients were enrolled from Belgium, Germany, Italy, the Netherlands, Portugal, Spain and the United Kingdom.
Study procedures and evaluations Data were collected at baseline, on scheduled visits every 6 months and at the end of the study, which was planned to run until 6 months after enrolment of the last patient. The data collection planned for the end of the study was also performed in the event of a patient leaving the study before the end of the observational period and, for patients who withdrew consent, were lost to follow-up or died before the end of the study, as many data were collected as possible. Additional data collection was encouraged in case of clinically relevant events, namely: admission to, or discharge from, inpatient settings; relapse of symptoms (including suicidal ideation/intent/behavior); remission of the current MDE (as confirmed by MADRS ≤10); visit/consultation with treating physician due to worsening or improvement of the current MDE (confirmed by Clinical Global Impression of Change [CGI-C; Busner and Targum, 2007] ≤3 or ≥5); change in pharmacological treatment (medication switch; dose changes except those relating to titration during switching; start of augmentation therapy); change in non-pharmacological treatment.
Baseline data were collected on patient socio-demographics, disease history and current clinical characteristics as well as details of all antidepressant treatments used in the current MDE prior to study entry. Assessment at any visit could include documentation of: spectrum and severity of depression symptoms (MADRS; Clinical Global Impression of Severity [CGI-S] score); change in depression severity since initiating last treatment (CGI-C score); suicidality (Columbia Suicide Severity Rating Scale;  Posner et al. , 2011) ; HRQoL (EuroQoL 5-dimension 5-level [EQ-5D-5L] patient-reported questionnaire, including the EuroQoL Visual Analog Scale [EQ-VAS];  Herdman et al. , 2011) ; functional impairment or disability in and outside of work (Work Productivity and Activity Impairment [WPAI;  Reilly et al.
Data processing and statistical analysis The remission or response status of each patient (irrespective of treatment strategy) was assessed at 6 and 12 months after baseline, according to MADRS scores. Remission was defined as a score ≤10, while scores >10 but with improvement from baseline ≥50% defined response without remission. Visits were considered as occurring in Month 6 if they occurred 150-216 days after enrolment, while Month 12 visits were defined as those occurring 330-402 days after enrolment. Data obtained outside these windows were excluded from the relevant time point. Treatment steps for each patient were reconstructed by matching MADRS scores to the start and end dates of each treatment strategy (dates and treatments obtained from medical records), with the end representing a change to a different treatment strategy.
Overall treatment outcomes at Month 6 At Month 6, 58 patients were reported as discontinued: 1 patient ended the study after 99 days 1  ; 37 patients were lost to follow up; 7 withdrew consent; 3 died; 9 discontinued due to 'other' reasons; 1 was of unknown status. Data were excluded from a further 47 patients who were still in the study at Month 6, but whose visits did not meet the defined cut-off dates for a Month 6 visit. All other patients were included in this analysis, irrespective of treatment strategy. Of these patients, 73. 5% showed no response, 9. 8% showed response without remission  and 16. 7% were in remission (Fig. 1A ). As per the definitions used to stratify patients by outcome status, the mean (SD) MADRS score for patients achieving remission was 6. 0 (2. 6) compared to 14.
Overall treatment outcomes at Month 12 At Month 12, 244 patients in total were not included in the analysis set: for 164 patients the study ended prior to their 12-month assessment; 45 were lost to follow up; 9 withdrew consent; 4 died; 22 discontinued due to 'other' reasons; 1 was of unknown status. Data were excluded from a further 21 patients who were still in the study at Month 12, but whose visits did not meet the defined cut-off dates for a Month 12 visit. All other patients were included in this analysis, irrespective of treatment strategy. Of these patients, 69. 2% had not responded, 11. 6% showed response without remission and 19. 2% were in remission (Fig. 1A ). Using KW tests followed by post-hoc Dunn's tests to analyse mean change from baseline, greater changes in EQ-5D-5L, EQ-VAS and SDS were observed in patients who had achieved remission at Month 12 than for those who had not responded (p<0.
Changes in remission or response status between Month 6 and 12 Maintenance of remission or response was analysed in patients for whom data at both timepoints were available. Of patients who had not achieved remission at Month 6, 86. 6% of those that were assessed again at Month 12 still had not achieved remission (Fig. 1B ). Among patients who achieved remission at Month 6, 33. 3% of those that were assessed again at Month 12 were no longer in remission (Fig. 1B ). When these patients were stratified by response vs non-response, 51. 7% of Month 6 responders had lost response by Month 12, and among non-responders at Month 6, 72. 9% remained unresponsive at Month 12 (Fig. 1C ).
Of note, the study design meant that the later a patient enrolled in the study, the less likely they were to reach 12 months before the study end date; this was the case for approximately 70% of patients. A small proportion of Month 6 or Month 12 visits did not happen within the post-study defined cut-off dates, and in those that did, some did not have a MADRS score (or other outcome measures) recorded at one or more visits. Furthermore, some assessments and/or questionnaires were not completed fully, so numbers of responses for some outcome measures vary. A number of patients were lost to follow up after attending only some visits, possibly due to reduced functioning caused by TRD. Thus, overall, patient numbers are much lower for analysis of Month 12 data than for data collected at Month 6, and results for Month 12 should therefore be interpreted with caution.
Patient outcomes at Month 6. Dataset includes all patients, irrespective of treatment strategy. *p<0. 05; **p<0. 01; ***p<0. 001 when compared to no response (Kruskal Wallis test followed by posthoc Dunn's test). Remission: MADRS score ≤10; response without remission: MADRS improvement from baseline ≥50% and MADRS score >10. CGI-C: Clinical Global Impression of Change scale; EQ-5D-5L: European Quality of Life (EuroQol)-5-dimension 5-level; EQ-VAS: EuroQol-visual analog scale; MADRS: Montgomery-Åsberg Depression Rating Scale; SD: standard deviation; SDS: Sheehan Disability Scale.
Acknowledgements The authors thank the patients, the investigators and their teams who took part in this study. The authors also acknowledge  Julia Stevens, Ph. D. , and  Emma Phillips, Ph. D. , from  Costello Medical, UK , for medical writing and editorial assistance based on the authors' input and direction,  Yerkebulan Kambarov  for publication coordination, and  GAMIAN-Europe  for their review and input into the manuscript. This study was funded by  Janssen EMEA.
44 (7. 76)** *Dataset includes all patients, irrespective of treatment strategy. *p<0. 05; **p<0. 01; ***p<0. 001 when compared to no response (Kruskal Wallis test followed by posthoc Dunn's test). Remission: MADRS score ≤10; response without remission: MADRS improvement from baseline ≥50% and MADRS score >10. CGI-C: Clinical Global Impression-Change scale; EQ-5D-5L: European Quality of Life (EuroQol)-5-dimension 5-level; EQ-VAS: EuroQol-visual analog scale; MADRS: Montgomery-Åsberg Depression Rating Scale; SD: standard deviation; SDS: Sheehan Disability Scale.
Participants Study participants were recruited as part of a longitudinal population-based cohort designed to evaluate the risk factors of dementia, the Bordeaux subset of the Three-City (3C) study  17. During the 1999-2000 inclusion period, a personal letter including a brief description of the study protocol and an acceptance/refusal form were sent to non-institutionalized individuals aged 65 years and older randomly selected from electoral lists. Partner was also invited to participate in the study if meeting eligibility criteria. In case of no response, an attempt was made to contact subjects by telephone. After inclusion, people were then followed-up prospectively for up to 14 years. Data regarding demographic characteristics and vascular risk factors was collected at baseline. Of the initial cohort of participants with baseline MRI data (n=663), only non-demented participants with a MMSE >23, who agreed to have a second MRI 4 years later were included in the present hippocampal subfields analyses (n=364).
upv. es)  18. Next, the segmentation of hippocampal subfields was performed with the HIPS (HIPocampus subfield Segmentation) pipeline  19  , based on a combination of non-linear registration and multi-atlas patch-based label fusions with systematic error correction. HIPS has been shown to significantly outperform other publicly available software such as FIRST or Freesurfer  20. It uses a training library from a public repository (www. nitrc. org/projects/mni-hisub25) composed of manually labeled high resolution T1-weighted images  21  (Kulaga-Yoskovitz dataset). We used the Kulaga-Yoskovitz protocol instead of the Winterburn protocol (the other available segmentation protocol in the HIPS pipeline) because its segmentations were more reliable (0. 88 vs 0. 71) due to the use of a larger number of training cases (25 vs 5)  19  and a lower number of subfields (3 vs 5).
Statistical analyses Statistical analyses were performed with Prism software 8 (Graphpad) and XLstats 19. 4 (Addinsoft). First, patients were classified into three subgroups based on their WMH volume (cross-sectional measures at baseline, Fig. 2 ) or according to the progression of WMH volume between the two MRI (Fig. 3 ). We defined a group with a low level of WMH volume or WMH progression (≤ 25th percentile), a moderate level (25th-75th percentile), and high level (≥75th percentile). In univariate analyses, the χ 2 test was used to compare categorical variables and Mann-Whithney or analyses of variance (ANOVAs) were performed to compare quantitative variables among groups, followed by Sidak multiple comparisons tests. Then, hippocampal subfields volumes and annualized rate of.
F o r S t r o k e P e e r R e v i e w. D o n o t d i s t r i b u t e. D e s t r o y a f t e r u s e. 8 atrophy found to be significantly associated (p<0. 05, before Sidak correction) with WMH volumes or rate of WMH progression were predicted with multivariate linear regression models. For each hippocampal subfield, the first model included WMH volumes (or rate of progression) and demographic variables (age, gender and educational level) known as nuisance variables in MRI volumetric studies. The second model included the variables of model 1 and additionally vascular risk factors including high blood pressure, body mass index, diabetes, smoking and alcohol consumption. Finally, we performed a sensitivity analysis on the longitudinal MRI data by running the same regression models without excluding the 49 patients who developed neurodegenerative diseases (n=298 participants).
Funding The 3C Study is conducted under a partnership agreement among the  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Bordeaux University , and  Sanofi. The  Fondation pour la Recherche Médicale  funded the preparation and initiation of the study. The 3C Study is also supported by  Caisse Nationale Maladie des Travailleurs Salariés ,  Direction Générale de la Santé ,  Mutuelle Générale de l'Education Nationale ,  Institut de la Longévité ,  Conseils Régionaux d'Aquitaine et Bourgogne ,  Fondation de France , and the  Ministry of Research-INSERM Programme "Cohortes et collections de données biologiques ". The follow-ups have also been funded by  ANR   2007LVIE 003 , the " Fondation Plan Alzheimer " and the  Caisse Nationale de Solidarité pour l'Autonomie (CNSA). This work benefited from the support of the project DeepvolBrain of the  French National Research Agency  ( ANR-18-CE45-0013 ) and by the  Spanish   DPI2017-87743-R grant  from the  Ministerio de Economia, Industria y Competitividad of Spain.
In addition, this study was achieved within the context of the  Laboratory of Excellence TRAIL   ANR-10-LABX-57  for the  BigDataBrain  project. Finally, we thank the  Investments for the future Program IdEx Bordeaux  ( ANR-10-IDEX-03-02 ,  HL-MRI  Project),  Cluster of excellence CPU  and the  CNRS. VP also received grants from  Fondation Bettencourt Schueller  ( CCA-Inserm-Bettencourt ). The sponsors did not participate in any aspect of the design or performance of the study, including data collection, management, analysis, and the.
Experimental set-up Monkeys were seated in a primate chair in a sphinx position, with the head immobilized via a surgically implanted plastic MRI-compatible head post (CE and GE) or a non-invasive head restraint helmet (CA and GU)  (Hadj-Bouziane et al. , 2014) , in front of a computer screen (distance: 57 cm). Eye position was sampled at 120 Hz using an infrared pupil tracking system (ISCAN Raw Eye Movement Data Acquisition Software) interfaced with a program for stimulus delivery and experimental control (Presentation®).
Each category comprised 5 original images and 5 horizontally flipped images, in order to control for possible biases in the lateralized distribution of objects or salient features across the images (see Fig. 1A  for an example of images presented in one testing session). The monkey face images were the same across all sessions, whereas new landscape and scrambled images were presented at every session. Within each session, the order of the stimuli (and categories) presentation was randomized. Monkey face images were collected from the internet (criteria: rhesus macaque face with a neutral emotion) and the landscape images were drawn from the MIT places database (from 2 categories: badland and cabin outdoor)  (Zhou et al, 2014 (Zhou et al, , 2017)). The scrambled landscape images were generated by taking the two-dimensional Fourier transform of the natural landscape images, scrambling the phase, and then taking the inverse Fourier transform.
Explorations parameters First, we calculated the total duration of the exploration as the percentage of time that monkeys spent exploring the image per trial, i. e. the number of samples recorded inside the stimulus-image (±2 • ) divided by the total number of samples recorded during image presentation. Then, we defined fixation events, using EyeMMV toolbox implemented in MATLAB  (Krassanakis et al. , 2014) , as eye positions lasting at least 70 ms within a location of 1. 2 • of radius. The first fixation was excluded from the analysis as it was a direct consequence of the preceding central fixation period. All fixations which fell outside the image were excluded from the analysis (±2 • tolerance). We calculated the fixation number per trial and the duration of each fixation. To assess the effects of ATX on the exploration parameters, we calculated for each trial within each category of images the difference between the mean fixation duration or number of fixations in ATX and saline conditions: ΔFixation_Duration= FixationDuration ATX -meanFixationDuration Saline |meanFixationDuration Saline | ×100 ΔFixation_Number= FixationNumber ATX -meanFixationNumber Saline |meanFixationNumber Saline | ×100.
Statistical analysis We used linear mixed models (using the 'lme4' package for R,  Bates et al. , 2014)  to examine the effect of ATX on the different dependent measures described above, for each monkey. As a first step, we defined a model containing the most appropriate random effects (i. e. grouping factors and hierarchical structure of the data). Random effects were thus introduced sequentially, and their effect on model fit was assessed through Likelihood Ratio Tests (LRT): residuals of each model were compared, and the one with significantly lower deviance as assessed by a chi-squared test was chosen (Supplementary Table  S1 ). We then tested the effect of the pharmacological condition and image category as fixed factors to evaluate the effect of ATX on the exploration duration, fixation number and fixation duration.
In addition to the linear mixed-effect models carried out at the individual level, we computed a linear mixed-effect model for each main dependent variable tested (the exploration duration, fixation number and fixation duration) across all monkeys by adding monkey as a random factor. As in monkeys CA and GU, we tested four doses of ATX (0. 1, 0. 5, 1. 0 and 1. 5 mg/kg) whereas in monkeys CE and GE, we tested two doses of ATX (0. 5 and 1. 0 mg/kg), the linear mixed-effect model across monkeys included only these latter two doses that were common across the four animals (0. 5 and 1. 0 mg/kg). Finally, post-hoc comparisons were carried out using pairwise comparisons through the 'lsmeans' package for R  (Lenth, 2016 , p-adjusted with false discovery rate method,  Benjamini and Hochberg, 1995)  to assess the effect of the different doses of ATX and the different categories of images.
Funding and disclosure This work was funded by the  French National Research Agency (ANR)   ANR-14-CE13-0005-1  grant. It was also supported by the  NEU-RODIS Foundation. It was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Lyon University  within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  ANR. EB was supported by: the  European Union 's  Horizon 2020 research and innovation programme (Marie Curie Actions)  under grant agreement  MSCA-IF-2016-746 , 154 ; a grant from  MIUR (Departments of Excellence  DM May 11, 2017 n. 262) to the  Department of General Psychology, University of Padova. EM is supported by a chair  INSERM-UCBL1.
Thin gray lines represent the 5th, median, and 95th from the distribution made of all median PMTH obtained from surrogate raster plots from the sessions included in the group. Black asterisk indicate that the median value is above the 95th percentile or below the 5th percentile from the surrogate dataset (B). Same as (A), but illustration is made with one P12 Gad1 Cre/+ mouse pup and PMTHs are built with all imaging sessions from pups aged between P10 and P12 (N = 7 mice, n = 9 imaging sessions). Note the presence of red labeled processes in the neuropil of the stratum pyramidale of P12 in contrast to P5. Results to build the PMTHs, as well as Calcium Imaging Complete Automated Data Analysis (CICADA) configuration files to reproduce the analysis, are available in      , 2007). We focused on changes that may occur around the end of the first postnatal week.
Thin gray lines represent the 5th, median, and 95th from the distribution made of all median PMTH obtained from surrogate raster plots from the sessions included in the group. Black asterisk indicate that the median value is above the 95th percentile or below the 5th percentile from the surrogate dataset (B). Same as (A), but illustration is made with one P12 Gad1 Cre/+ mouse pup and PMTHs are built with all imaging sessions from pups aged between P10 and P12 (N = 7 mice, n = 9 imaging sessions). Note the presence of red labeled processes in the neuropil of the stratum pyramidale of P12 in contrast to P5. Results to build the PMTHs, as well as Calcium Imaging Complete Automated Data Analysis (CICADA) configuration files to reproduce the analysis, are available in Figure3-source data 1.