dataesr/scientific-paragraphs-categorization huggingface dataset:
Method Participants and procedure Participants were part of the 2016 Harmonized Cognitive Assessment Protocol (HCAP), an ongoing sub-study of the Health and Retirement Study (HRS). Participants were selected to be a part of the HCAP assessment if they were 65 years or older and completed the 2016 interview of the HRS. Of the eligible participants, a subset was randomly selected and invited to participate in the HCAP assessment (N = 5500). A total of 3496 participants completed at least some part of it. Participants could have the HCAP assessment administered in either English or Spanish. Participants who also completed the personality measure in either the 2014 or 2016 regular HRS assessment were selected for analysis. A total of 2865 participants had complete data on personality and some measures in HCAP.
24, p <. 01), more likely to be Hispanic (χ 2 = 38. 16, p <. 01), more likely to be a race other than white (χ 2 = 59. 85, p <. 01), and had fewer years of education (d =. 38, p <. 01); there was no difference in participant sex. Further, there were differences on all of the cognitive tasks, with ds that ranged from. 30 (p <. 01; Backward Count) to. 71 (p <. 01; Mini Mental State Examination); across all cognitive tasks, participants who also had the personality assessment performed better than participants who did not have personality. More information on the HCAP assessment, sampling, and how to obtain the data can be found at https://hrs. isr. umich. edu/news/2016-harmonized-cognitive-assessment-protocol-hcap-early-version-10. The Health and Retirement Study make their data available to the public, but access to the HCAP data requires an additional authentication process to verify the identity of the person and institute requesting access to the data.
Analysis Perception responses and perception-action belt motion were post-processed using customized MATLAB scripts (Mathworks 2016b), responses were then expressed as a percentage of success (mean±SD). We set the level of significance to α = 0. 05, Bonferroni corrections were used to reduce type I errors, with the adjusted α reported for main effects and interactions in the results  [36]. Further, a Bonferroni correction was applied for post-hoc tests. A Shapiro-Wilk test was performed to evaluate whether data followed a normal distribution. For both experiments, the dependent variable was the rate of success, and a general linear mixed model analysis was used to determine the effect of appearance, type of motion (DM and NDM) and cut off times for the perception task. For the perception-action task, rate of success and response time were the dependent variables to determine the effect of appearance and motion.
Study population The Three-city study is a multi-site community-living cohort of 9,294 participants aged 65 years and over. They were recruited between 1999 and 2001 from the electoral rolls of three French cities (Bordeaux, Dijon, and Montpellier) with the aim of studying the impact of cardiovascular factors on the risk of dementia. A standardized evaluation with a faceto-face interview and a clinical examination was carried out at inclusion and after 2 (wave W1), 4 (wave W2), 7 (wave W3), 10 (wave W4), 12 (wave W5), and 15 (wave W6) years. The PA questionnaire was introduced at W3 (Montpellier center) and W4 (Bordeaux center) that were considered as baseline data for the current analysis. The sample included only participants who lled in the PA questionnaire during the standardized evaluation at these waves.
Dementia diagnosis At baseline and at each follow-up visit, participants recruited in Bordeaux were examined by a neurologist only in the case of dementia suspicion (on the basis of an extensive cognitive and functional examination performed). In Montpellier, all participants were examined by a neurologist at each visit. Then, a panel of independent neurologists with expertise in dementia reviewed all the existing data concerning the participants with suspected dementia, and a consensus on the diagnosis of dementia was obtained according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV), revised criteria and etiology. AD was classi ed according to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS/ADRDA) criteria.
The selected knots corresponded to the 5 th , 35 th , 65 th and 95 th percentiles for both sub-scores. The two median knots were used to transform the scores in classes. The rst class was taken as reference in the adjusted Cox models: low activity level for household/transportation activities; no activity (score = 0) for leisure and sport activities. The proportional hazards assumption over time was veri ed. The nal models with classes were further adjusted for marital status, diabetes, depressive symptoms, and CVD (model 2). These potential confounders were selected on the basis of literature data and according to the strength of their association with the outcome variable (p<0. 1 in model 1). Additional sensitivity analyses were performed to address possible reverse causality and to assess the robustness of the results in the AD subtype.
e. , 2 or 3 years after the baseline) or participants with IADL/ADL limitations at baseline, with unchanged results. However, our results might still partially re ect very early dementia-related behavioral changes. Finally, PA was assessed with the Voorrips questionnaire. Although self-reported PA is susceptible to information bias, it is considered a reliable method to collect data on PA for epidemiologic studies  [16]. Objective methods, such as accelerometers, have known a rapid development. However, data collection and processing criteria are not always well validated, particularly in elderly people. Moreover, these methods do not give information about the activity types  [17]. The strengths of this study include its multi-center longitudinal design, the sample size (n = 1550 elderly participants from the general population), the low attrition rate of the cohort, and the high quality of dementia screening/diagnosis.
Few studies have examined the link between PA and dementia risk in very old people as we did here. PA was assessed using the Voorrips questionnaire that was validated in elderly populations and explores different types of activity. To be more clinically relevant, two sub-scores speci c for two PA domains were generated, and sitting unloaded activities were excluded because they do not involve physical capacities and are mainly cognitive. To reduce the information loss these scores were analyzed as continuous measures; the exposure-response curve shape and the optimal location of the PA level thresholds were determined by a data driven method. The literature on the effect of PA on dementia risk is abundant, but not always conclusive. Several meta-analyses have shown a protective effect of PA on dementia onset.
In an e-health context, combining traditional structured clinical assessment methods and routine electronic health based data capture may be a reliable method to build a dynamic clinical decision support system (CDSS) in suicide prevention. Our objective was to describe the data mining module of a web-based CDSS and to identify suicide repetition risk in a sample of suicide attempters. A database of 2802 suicide attempters has been analysed. Clustering methods were used to identify groups of similar patients while regression trees are applied to estimate the number of suicide attempts among these patients. Three groups of patients are identified by clustering methods. Relevant risk factors explaining the number of suicide attempts are highlighted by regression trees. Data mining techniques can help to identify different groups of patients at risk of suicide reattempt.
The sub-scores range respectively from 11 to 44 for motor and attentional impulsivity, and from 12 to 48 for non planning impulsivity. Uni and two-dimensional analyses for both quantitative and qualitative variables were carried out. Fisher-Snedecor procedures were used to compare two sub-groups (e. g male/female) when needed. An unsupervised approach was conducted to extract homogeneous patterns from the data without any prior hypothesis. It is first based on a Multiple Correspondence Analysis (MCA) of qualitative variables to reduce the dimensionality. It consists in representing the patients in a factorial space where each dimension is a combination of the initial variables. Quantitative variables (e. g age) are not used during the calculations but they are also projected onto the factorial space.
Hierarchical Clustering on Principal Components (HCPC) is then performed from the patient's' representation in the initial factorial space. Hierarchical clustering has many advantages, including the construction of a hierarchical tree called dendrogram that enables a visual interpretation of the dataset. The dendrogram depicts the emergence of groups of patients who share common risk patterns. In addition, it facilitates the discussion between statisticians and practitioners for the choice of the optimal number of clusters. Each cluster was then interpreted through the association between the cluster and the list of qualitative and quantitative variables (v test). In a second step, a focus has been made on a variable of interest, the number of suicide attempts. Recursive partitioning has been used as a multivariable procedure that classifies individuals (patients) by successively splitting into subpopulations.
Population description From the original database, the first step relied on data qualification. Lots of redundancies (eg. duplicated surveys or alternative coding) have been observed between the 263 initial variables. Then, a completion threshold has been applied to the resulting variables and only 23 variables satisfied a 70% minimum completion rate. Three additional variables related to types of impulsivity (as described above) have been added. With respect to the 2802 initial patients, it was decided to keep only suicide attempters with a 100% completion rate for the 26 variables. As a final filtering, 5 variables have been disregarded for redundancy or useless purpose (type of the patient, assessment date, source, year and day of birth). This rigorous process ensures a high data quality for both patients and variables.
It provides a final dataset of 681 patients and 21 variables. Participants were predominantly young (mean age=40. 1 years), female, employed and married. Most patients included in the final analysis had history of mental disorder including major depression (70. 3%), bipolar disorder (23. 0%), dysthymic disorder (4. 4%), obsessive compulsive disorder (8. 5%) and alcohol misuse (26. 1%) Table  1  : Clinico-sociological main features of the post-filtering dataset of 681 suicide attempters.
Main result and integration into a dynamic CDSS Systematic assessment before discharge from hospital has allowed a large database to be built, suitable for modern data mining techniques. In our study, we were able to identify clusters of suicide attempters and variables that may explain repetition of suicide attempt in suicide attempters. This study shows how a simple structuration of the assessment of discharged patients after a suicide attempt may provide relevant data for clustering methods. The clustering may help clinician to allocate a patient into a risk cluster. Therefore, it stands as the first step of a CDSS design. This model may lead to a stratified approach in decision making for suicide prevention. Furthermore, analysing larger datasets could allow discovery of new risk factors that are not currently considered as relevant during clinical interviews.
However, we did not propose a model for suicide prediction: our model mining big databases is a prerequisite toward a better decision making for suicide prevention. This model could also be applied on other data sources like personal health records or ecological momentary assessment. Our results are in line with recent results showing how suicide risk assessment coul lead to patient clustering in a preventative perspective. Lopez-Castroman et al. analyzed a sample of 1,009 hospitalized suicide attempters that were recruited between 1999 and 2012. Eleven clinically relevant items related to the characteristics of suicidal behavior were submitted to a Hierarchical Ascendant Classification. The results shows that most individuals were included in a cluster characterized by less lethal means and planning ("impulse-ambivalent").
A second cluster featured more carefully planned attempts ("well-planned"), more alcohol or drug use before the attempt, and more precautions to avoid interruptions. Finally, a third cluster included individuals reporting more attempts ("frequent"), more often serious or violent attempts, and an earlier age at first attempt. Differences across clusters by demographic and clinical characteristics were also found, particularly with the third cluster whose participants had experienced high levels of childhood abuse. Overall, a systematic structured assessment may help clinician to better characterize suicide risk and personalize prevention strategies. We believe that e-health and data mining techniques may help us to reach this goal. In this study, participants were assessed by trained clinicians before discharge from the ED.
Data were captured using paper based formularies of the actual MeMind online EHR (www. memind. net). These data could have been captured by our MeMind web application (www. memind. net) designed to gather observational data trough an HER interface and to perform ecological momentary assessment (EMA)  [20]. This online software has 2 distinct views: the EHR view (for clinicians) and the EMA view (for patients). The "EHR" view is designed to be used by doctors and nurses during face-to-face assessment. As current EHRs, the application also collects sociodemographic, diagnostic, and pharmacological treatment information within the "assessing suicide" protocol. Sociodemographic variables included age (defined as the age at the index episode), sex, profession, current working status, marital status, number of children (if any) and educational level.
Family history of suicidal behaviour, age at the first suicide attempt, and violence of the suicide attempt were also measured. The EMA view for patients to track their symptoms was not used in this study. Clinicians and patients can access the web application either from a computer or their personal mobile phone. EMA involves repeated sampling of subjects' behaviors and experiences in real time, in their natural environment. EMA has been successfully used for real-time self-reporting of symptoms and behaviour. For example, Husky et al. showed the utility and feasibility of using EMA to study suicidal ideation  [21]. For this study purposes patients did not have any access to this interface. Such a web application may represent the future of suicide risk assessment, as it allows for the data mining of static data (EHR data provided by a single clinical assessment) and dynamic data (Smartphone data provided by EMA techniques).
These data can be processed in order to build a dynamic CDSS  [20]. Limitations of the study 1. Focus on suicide attempters: In this study, patients were recruited after a suicide attempt. We postulate that the development of CDSS would be more relevant in a population of suicide attempters. Suicide attempters are also defined as an "indicated population"  [2]  who warrant a maximal attention of health care service due to the risk of reattempt. Treatment and follow-up strategies are well described in guidelines for suicide attempters. Therefore we were able to propose to each participant the treatment based on actual recommendations. However, applying these strategies to samples of patients with suicide ideation but without history of suicide attempt would be hypothetical and ethically controversial.
Due to the epidemiological specificities of both populations, a specific model would have to be built for each population. In terms of multiple suicide attempters, a relevant perspective is to focus on the temporality of suicide attempts rather than the absolute number. To achieve this objective, finer data are needed. 2. Missing data, the data mining challenge: This study illustrates the need for high quality and large databases for extracting significant patient profiles or risk factors. In this study, starting from an initial set of 2802 patients with 263 variables, the data qualification process resulted into a final sample of 681 patients with 21 complete variables. Although this volume of data already ensures statistical significance, it underlines the importance to better standardize the data collection in participating institutions.
acknowledge the  French "Groupement d'Etude et de Prevention du Suicide" (GEPS)  and the  French Embassy in Madrid, Spain. This study received an  Hospital Clinical Research Grant  ( PHRC 2009 ) from the  French Health Ministry. None of the funding sources had any involvement in the study design, the collection, analysis or interpretation of data, the writing of the report or the decision to submit the paper for publication. This study was funded partially by  Instituto de Salud Carlos III  ( ISCIII PI13/02200 ;  PI16/01852 ),  Delegación del Gobierno para el Plan Nacional de Drogas  (20151073);  American Foundation for Suicide Prevention (AFSP)  ( LSRG-1-005-16 ).
Possible avenues for translational studies Several studies have investigated the networks involved in fear conditioning in human using functional brain imaging. Most of these studies show that the amygdala is part of the circuit, thus corroborating the data from the animal literature (for a review see  Phelps and LeDoux, 2005). Only a few studies have used odor fear conditioning in the literature. Among them,  Li et al. (2008)  reported that aversive olfactory learning enhances perceptual acuity of the sensory signal. Indeed, initially indistinguishable odor enantiomers become discriminable after aversive conditioning. In parallel, functional brain imaging revealed that the amygdala response to the conditioned odor increases sharply in early trials. Interestingly, the authors also measured changes in activity in the piriform cortex.
, 2005). Consistent with these two studies, it was recently demonstrated that homozygous mice expressing human E2 exhibit impaired extinction of contextual fear memory  (Olsen et al. , 2012). Several PTSD-like behavioral, cognitive and neuroendocrine alterations are more pronounced in mice expressing E2. These alterations include a resistance to exposure therapy (fear extinction), alterations in anxiety-like and home cage behavior, circadian disturbances, and hippocampus-dependent cognitive impairment following a stressful event. Importantly, data collected on the severity of PTSD symptoms in combat veterans were consistent with the mouse data. In a group of 92 veterans with combatrelated PTSD, those with E2/E3 showed significantly higher Clinician Administered PTSD Scale (CAPS) and PTSD checklist (PCL) scores compared to E3/E3 and E3/E4 individuals.
Administration of THC and THC + CBD resulted in a similar effect  (Stern et al. , 2015). These data are in support of a role for CBD and THC on modulation of fear in PTSD by facilitating extinction learning through a reconsolidation blockade. CBD may also decrease anxiety and anhedonia and THC may reduce nightmares and insomnia in PTSD (reviewed in  Loflin et al. , 2017). Two randomized controlled clinical trials of THC, CBD, and the combination of THC + CBD to assess efficacy to treat PTSD are ongoing (e. g. , NCT02759185 and NCT02517424). Recent evidence also suggests that the endocannabinoid and the neurosteroid systems are inter-related. Evidence shows both allopregnanolone and the endocannabinoid, anandamide (AEA) levels are low in depression and PTSD and in animal models of anxiety and depression  (Hill et al.
Acknowledgements Part of this study was supported by the  United States U. S. Department of Defense  Grant  W81XWH-15-1-0521  to  G. Pinna ,  W81XWH-17-1-0193  to  J. Raber ,  NASA   NSCOR NNX15AK13G  to  M. Weil  ( OHSU subcontract  to  J. Raber ), and  James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human.
Data Analysis The data analysis for this article was conducted by the first author, who did not participate in the data collection process, with support from the other authors for the conceptual aspects and interpretation of results. Following an initial familiarization, the material was coded following a mix of deductive and inductive coding, along a priori codes based on the conceptual framework, the themes of the interview guide, and field notes recorded by the interview teams, but allowing additional codes to emerge while proceeding through the material. A matrix approach to coding was applied, whereby material was simultaneously coded by themes pertaining to organizational factors and by psychological needs satisfaction (relatedness, competence, autonomy). In interpreting the coded material, we triangulated responses of health workers  from the same facilities with the aim of disentangling individual motivational responses from those related to the broader organizational context.
Acknowledgements The  research project is part of the "Community research studies and interventions for health equity in Burkina Faso. " We thank the  Canadian Institutes of Health Research (CIHR)  for funding the program (Grant number  ROH-115213 ). VR holds a CIHR-funded Research Chair in Applied Public Health. AMTT obtained a bursary from the  University of Montreal Hospital Research Center  and the  University of Montreal Public Health Research Institute. AMTT received a training bursary from the  CIHR. This study would not have been successful without the contributions of  Maurice Yaogo  and  Sylvie Zongo , who managed and supervised the data production, and  Guillaume Kambire ,  Saliou Sanogo ,  Mariétou Ouedraogo ,  Souleymane Ouedraogo ,  Vincent Paul Sanon ,  Idriss Gali Gali ,  Vincent Koudougou ,  Assita Keita ,  Maimouna Sanou ,  Elodie Ilboudo , and  Ahdramane Sow , who collected data in the health centers.
Furthermore, based on the evidence of alcohol-induced glutamatergic excitotoxicity and due to neuronal death  (Lovinger et al. 1989; Tsai et al. 1995) , we hypothesized that basal insular glutamate plus glutamine concentration would be lower in individuals with higher scores on alcohol use measures, as a consequence of reduced neuronal density impacting on synaptic vesicles and glutamateglutamine storage. Finally, we also explored if alcohol-related metabolites changes (e. g. , glutamate-glutamine reduction) will be predicted by structural measures (e. g. , gray matter reduction), suggesting an insular atrophy in alcohol users. Here, MRS data were measured within a three-session within-subject experimental protocol that also tested for functional effects of a hormone, intranasal oxytocin, on interoceptive processes in alcohol users.
Procedure The study was conducted at the Clinical Imaging Science Centre in Brighton, UK. Demographic, psychometric, and spectroscopic data were measured within a three-session within-subject experimental protocol that also tested for functional effects of a hormone, intranasal oxytocin, on interoceptive processes in alcohol users. Given the natural menstrual fluctuation of endogenous oxytocin in women, we decided to only recruit men to simplify the within-subject design procedure (e. g. , women would need to be tested on the same day of their menstrual cycle). Demographic and trait psychometric data were measured during the baseline session (the first session which was not involving drug). Spectroscopic data were acquired at the end of the third (and last) session which was following the baseline session by maximum of 7 days.