dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods: We recruited 353 psychiatric inpatients whose symptom levels and prescribed medications were inputted into the MEmind application. We standardized all medications in the MEmind database using the Anatomical Therapeutic Chemical (ATC) classification system and the defined daily dose (DDD). For each patient, MEmind calculated an average for the daily dose prescribed for antipsychotics (using the N05A ATC code), prescribed daily dose (PDD), and the PDD to DDD ratio. Results: MEmind results found that antipsychotics were used by 61. 5% (217/353) of inpatients, with the largest proportion being patients with schizophrenia spectrum disorders (33. 4%, 118/353). Of the 217 patients, 137 (63. 2%, 137/217) were administered pharmacological monotherapy and 80 (36. 8%, 80/217) were administered polytherapy.
Wirelessly connected technologies have also increased communication and data transfer between clinicians and their patients, which further helps achieve these mHealth goals  [9]. The processing of naturalistic data is especially critical given the enormous complexity of individual conditions  [10]. Data mining techniques can also allow for automatic extraction of meaningful data from large clinical databases, which can help answer important treatment and outcome questions and refine best practices. Moreover, this data mining can help develop algorithms and guidelines to help care providers who require assistance  [11]. These algorithms may be of particular interest to prescribers and can be used to improve the incorporation of prescription guidelines into clinical practice  [12].
Study Procedure During their hospitalization in the psychiatric unit (PU), all patients were assessed with the MEmind Web application after giving written informed consent. The MEmind application was developed for the study by an industrial partner and is available online for download via a secure link provided to the participant via email. The application was designed to gather observational data and to perform ecological momentary assessment (EMA). It has 2 distinct views: the clinician view and the patient view. The "clinician" view is designed to be used by doctors and nurses during their clinical rounds and during their medical or nursing visits (see Figure  1 , which displays the MEmind Web application). MEmind was also designed to capture all the data typically gathered during a standard medical evaluation, including sociodemographic, diagnostic, and pharmacological treatment information.
MEmind's design is based on commonly stored information in mental health management. To this end, the care providers can customize the software to add a number of large relevant scales to suit their individual needs (eg, the specific needs of research vs clinical settings). At the time of discharge, we collected patients' sex, age, diagnosis, and treatment types during their stay in the psychiatric unit. As the study was performed in Spanish mental health centers, the Spanish version of the tutorials for mental health professionals was used. In addition to the clinician view, the MEmind program features a patient view that allows patients to track their symptoms using EMA techniques. This feature was not assessed in our study. Medication compliance was not assessed in this study. For the purposes of this study, we monitored patients' diagnoses and clinicians' prescriptions.
The only users who accessed the app were the clinicians who recorded their prescriptions directly into MEmind's clinician interface. Clinicians could access the Web application either from a computer or their personal mobile phone. Clinical diagnoses and treatment prescription were conducted during hospital admission as part to routine psychiatric evaluations that incorporated data from medical records, other research assessments, and clinical interviews. All diagnoses recorded into the MEmind app were coded according to the International Classification of Diseases, Tenth Revision, for mental disorders alongside the data from these aforementioned psychiatric evaluations.
2 deg (100 px / 27 mm) (average luminance of 60 cd/m²). The center of these targets could appear anywhere within one of three rectangular arrays (1. 8x5deg / 41x113 px / 11x30 mm) with their centers placed on a virtual half-circle of radius 13 cm/482 px/21. 3 deg centered on the finger's starting location (see Fig. 2 ). The mid-rectangular array was placed straight ahead (i. e. , at 90° with respect to the bottom edge of the tablet) with the other two location centers at ±45° on the virtual circle. The center of each rectangle was offset over trials by a random jitter uniformly distributed horizontally and vertically at a range of ±1. 6 cm/59 px. The experimental software controlling the target display and the response recordings was implemented in MATLAB using the Psychophysics toolbox  (Brainard, 1997; Pelli, 1997).
544 sec, with the finger movement starting at 0. 31±0. 03 sec (mean and SD across the 13 participants, 4 conditions, and 3 locations) after the target onset, and touching the screen 0. 24±0. 04 sec later. The average estimation time was 0. 97±0. 26 sec. Pointing and estimation performances are given below in mm with 1mm = 0. 16 deg. To quantify the similarity of the participants' consistent errors across the four experimental conditions, we estimated the across-participants variance shared by all conditions, using principal component analysis (PCA, using Matlab©). For each of the four conditions there were 39 measurements (13 participants x 3 locations), forming 4 data series of 39 measurements each. Each series was z-scored before PCA was applied. The analysis shows that for the L consistent error (relative to T), the first principal component explains 76% of the data variance, and for the E consistent error (relative to L) the first component explains 89% of the data variance.
3. 7 and the PowerLabs 8/30 data acquisition system (AD Instruments Pty Ltd. , Castle Hill, Australia), running on a personal computer. EFS was performed through platinum electrodes connected to constant current asynchronous stimulators (Model stm-b01, EMKA Technologies). The tissue samples were allowed to equilibrate for at least 60 minutes, during which time contraction with 80mM KCl was performed on each strip. After a 30-minute washout period, tissues were stimulated by EFS for approximately 30-45 minutes, until a stable plateau of contractile responses was obtained, using the following parameters: constant current, 800 mA; frequency, 10 Hz; train duration, 0. 1 ms; 5 seconds of train pulses, every 62. 5 seconds.
Statistical analyses Values are expressed as mean ± standard error of the mean. Responses to agonist concentrations are expressed as the difference between the basal value of contraction induced by EFS (calculated at the end of the stabilization period) and the maximal amplitude of contraction recorded following the addition of each agonist concentration into the organ bath. This delta value (in grams) was normalized against the amplitude of contractions induced by 80mM KCl (in grams), and expressed in terms of percentage of the overall contraction induced by KCl. Fitting and statistical analysis of the results were performed using GraphPad Prism version 6. 0 (GraphPad Software Inc. , San Diego, CA, USA). The following parameters were determined: the maximal effect induced by the agonist (Emax) and the negative logarithm of the concentration required for a half-maximal response to an agonist (pEC50).
Biological Data After a 12-h fasting period, biochemical analyses were performed from venous blood samples in the hospital's central laboratory with routine methods, using reference measurements from the Department of Clinical Biochemistry. Plasma concentrations of the following proteins were determined: albumin (N: 35-52 g/L), transthyretin (N: 0. 20-0. 45 g/L), CRP (N: <5 mg/L). Trace elements and electrolytes profile included Zinc (N: 9-17 µmol/L), Copper (N: 10-40 mcmol/L), Selenium (N: 0. 90-1. 65 mcmol/L), Magnesium (N: 0. 75-1. 00 mmol/L), Phosphorus (N: 0. 87-1. 50 mmol/L), and Calcium (N: 2. 15-2. 55 mmol/L). Plasma levels of vitamin A (N: 430-800 mcg/L), E (N: 7-17 mg/L), B9 (N: 10. 4-42. 4 nmol/L), and B12 (N: 141-489 pmol/L) were also analyzed.
Statistical Analysis All statistical analyses were performed using SPSS version 10. 0 (SPSS Inc. , Chicago, IL, USA). Continuous data were presented as the mean ± standard deviation (SD) and means comparison were performed using Student's t-test. Categorical data were presented as count and percentage (%) and percentage comparison were performed using Chi2 test followed by Fisher's test for n < 5. Pearson analysis was used to analyze correlations between the groups. p < 0. 05 was considered statistically significant difference.
Surprisingly, after refeeding, folate decreased significantly while zinc increased but did not reach normal value. Further studies are needed to assess micronutrient change after weight gain in AN. Limitations of our study include the lack of biological measurements of antioxidant capacities that may correlate with selenium or zinc deficiency. Iron status was assessed only in the presence of anemia and therefore data are available only in a subset of patients (not shown). The population of patients studied was of intermediate severity, most of patients being followed ambulatory; it is expected that micronutrients status would have been more altered in patients with lower BMI. We also cannot completely exclude that some patients took some micronutrients supplements and denied to declare it.
Methods Participants. 34 infants (19 male, 15 female) took part in the EEG experiment at nine months postpartum. Mother-infant dyads were recruited from the 'Child Development Group' database, maintained in the School of Psychology and Clinical Language Sciences at the University of Reading. Before analysis, a number of infants had to be excluded, leaving a final sample of 19 (age: M = 275. 42 days, SD= 7. 88). The infants included in the final EEG sample had all previously been recorded interacting with their mothers at two months postpartum (age: M = 63. 58 days, SD = 3. 75). This research was approved by the University of Reading Research Ethics Committee (31. 07. 14), and was conducted in accordance with the Declaration of Helsinki. Mothers gave written, informed consent before participation.
The videos featuring emotional facial expressions were taken from the Amsterdam Dynamic Facial Expression Set (ADFES), which has been well validated in previous research  64. Equivalent videos were made for the non-emotional (mouth opening) condition, comparable with the ADFES stimuli in terms of onset, duration of movement, size, brightness, contrast, and spatial frequency. Ratings of the mouth-opening videos on a scale of -2 (negative) to +2 (positive) by a panel of 20 adults confirmed that they represented non-emotional facial expressions (M = -0. 10, SD = 0. 07). All videos started with a static/neutral facial expression, followed by 500 ms of movement, and 1250 ms held at the movement peak. Design, procedure, and data acquisition. Infants were seated on their mother's lap approximately 65 cm from a computer monitor.
Data were sampled at 250 Hz with an analogue band-pass filter of 0. 1-100 Hz, and were recorded with the vertex as a common reference. Impedances were kept below 50 kΩ. An experimental block began when triggered manually by an experimenter who was watching the participant on a screen from another section of the room. Trial blocks were triggered as soon as the infant was attentive to the monitor. Synchronous video recordings of the experiment were examined offline to allow exclusion of trials in which the infant was inattentive, and to enable coding of infant expression execution. Behavioural coding. To identify trials in which infants executed the facial expressions presented during experimental blocks, their expressions (happy, sad, and mouth opening) were coded offline from the video recordings.
All videos were coded by a research assistant blind to the experimental condition being presented. Videos were viewed in real-time and frame-by-frame to accurately identify onsets and offsets of movements. A second independent researcher coded a random 20% of the videos to establish inter-rater reliability, with very good reliability obtained (time-unit ĸ = 0. 85-0. 86, event ĸ=0. 92). Data pre-processing and analysis. After viewing the video recordings of infants during the experiment and marking periods of inattention using EGI software (NetStation v4. 3. 1; Electrical Geodesics, Inc. , Eugene, OR), EEG data were exported and analysed using the EEGLAB v13. 3. 2 toolbox  66. More details about the pre-processing steps used before data analysis can be found in the Supplementary Information.
Data collection and analysis Each respondent was sent login credentials to complete the online questionnaires. Missing or potentially ambiguous responses were identified by GS and GdG, and each respondent was sent a detailed list of queries. Respondents were sent up to three email reminders to complete all sections of the questionnaire or clarify the figures provided. If we were still unable to obtain a response, we used data from the original submission. Total population and the percentage of young people below the national legal age of majority were derived from Eurostat databases  16  , collected by GS and GdG.
RESULTS Data were collected between October 1 st 2014 and March 31 st 2015, followed by quality control (from May 1 st 2015 to September 30 th 2015). We approached a total of 34 individuals, and obtained responses from one respondent in all 28 EU countries. Each country respondents was free to ask collaboration to other national colleagues for survey completion (names are included in the appendix). Over 95% of survey items were completed. For six countries (Estonia, Finland, Malta, Romania, Slovenia, Sweden) the original completed questionnaires had missing (n = 20/28) or ambiguous (n = 11/28) data and no answer was received to our requests for clarification. The information was obtained from official national statistics or service reports (64% of countries, N=18/28), but also consultation with colleagues or experts (39% N =11/28), personal knowledge of the field (36%, N=10/28), and web searches (4%, N=1/28).
Thin films with nominal composition of Ni 50 Mn 30 Ga 20 (at. %) and nominal thickness of 1. 5 µm were deposited from a cathode target of Ni 46 Mn 32 Ga 22 (at. %) by DC magnetron sputtering with a sputtering rate of ~0. 2 nm/s. A Cr buffer layer of 100 nm thick was pre-coated on the MgO(1 0 0) monocrystal substrate. The room-temperature crystal structure was determined by X-ray diffraction (XRD) with Cu-Kα radiation. The martensitic transformation temperatures were measured by differential scanning calorimetry (DSC, TA Q100) with a heating and cooling rate of 10 K/min. The microstructural characterization was performed in a field emission gun scanning electron microscope (SEM, Jeol JSM 6500 F) with an EBSD acquisition camera and Channel 5 software. The neutron diffraction measurements were performed using the materials science diffractometer STRESS-SPEC operated by FRM II and HZG at the Heinz Maier-Leibnitz Zentrum (MLZ), Garching, Germany, with a monochromatic wavelength of 2.
Acknowledgments: This work is supported by the  National Natural Science Foundation of China  (Grant Nos. 51431005 ,  51571056 , and  51601033 ); the  863 Program of China  (Grant No. 2015AA034101 ); the  Funding Program of the Education Department of Liaoning Province  (Grant No. L2014094 ); the  111 Project of China  (Grant No. B07015 ); and the  Fundamental Research Funds for the Central Universities of China  (Grant No. N160205002 ). The neutron diffraction results are based upon experiments performed at STRESS-SPEC operated by FRM II and HZG at the  Heinz Maier-Leibnitz Zentrum  (MLZ), Garching,  Germany. The authors appreciate the assistance of MLZ Sample Environment Group during the neutron beamtime at STRESS-SPEC.
Information sources Relevant studies were identified using tailored electronic searches with MeSH and textword terms for the following databases:  Medline  Embase  PsycINFO  CINAHL  The Cochrane Library  Web of Science To maximise the comprehensive nature of information collection, reference lists of appropriate review articles and a book chapter  (Arensman et al. , 2011)  were also screened and a hand search of recent (January 2014 to February 2015) editions of appropriate journals was performed (British Journal of Psychiatry, Suicide and Life-Threatening Behavior).
Search strategy Databases were searched from inception to March 2015. The following terms were used to search all databases: repeat, persist, predict, repetition, self-harm, deliberate self-harm, suicidal behaviour, self-mutilation, self-injury, decision-making, problem solving, aggression, impulsivity, arousal, emotion, facial expression, affect, amygdala, orbitofrontal cortex, anterior cingulate cortex, and dorsolateral prefrontal cortex. All variations of spelling of text terms were used (e. g. self-harm and self harm). Search terms were truncated as appropriate and MeSH or equivalents were used to maximise the comprehensive nature of the searches. Search strategies tailored to each database were checked with a skilled medical librarian. There was no restriction in terms of date of publication.
Study selection The first phase of selection involved screening of titles and abstracts against inclusion criteria to assess eligibility. Each reference was screened by two of four authors (AdeC, BP, KB, KJ) using reference software (EndNote). In the second phase of selection, the full-text of studies determined to be eligible in phase one was examined against inclusion criteria by two of the following authors (AdeC, KR, MB, BP, KB, KJ, EL, FJ). Any disagreements were resolved by discussion and the use of a third author.
Analysis Included studies were reviewed and critically appraised, and data synthesised. Study findings were categorised on the basis of the assessed neurocognitive factor. Groupings for analysis were as follows: (i) cognitive control (including attention, inhibition, interference and working memory); ii) value-based decision-making and related processes (delay discounting, decision-making); iii) problem solving; iv) memory processing (short and long-term and autobiographical); v) emotionalprocessing; and vi) neuroimaging. These groupings were determined by neurocognitive factor categorisation in the literature  (Dixon and Christoff, 2014; Glascher et al. , 2012; Richard-Devantoy et al. , 2013b)  and the expert opinion of the authors. The analysis group, cognitive control, was formed particularly in reference to the previous literature relating to deficient cognitive inhibition and high sensitivity to interference  (Keilp et al.
, 2014; Richard-Devantoy et al. , 2014a; Richard-Devantoy et al. , 2012). For the group, we included all studies meeting inclusion criteria and where the neurocognitive measure related to attention, inhibition, interference and / or working memory. Within each neurocognitive factor, study findings were subdivided into studies addressing objective 1 (retrospective: "multiple vs. single" or "repeaters vs. nonrepeaters") or objective 2 (prospective: "repeaters vs. repeaters"). A priori, it was expected that data synthesis would be narrative as the search would result in included studies with heterogeneity in terms of study type, participants and methods.
Study selection 6990 records were identified from database searches after de-duplication and 35 records from hand-searching of reference lists, relevant journal searches and expert advice. 6857 records were excluded following appraisal of title and abstract. 168 fulltext articles were assessed for eligibility. 153 full-text articles were excluded, with reasons for each given in the PRISMA diagram (figure  1 ). The majority of studies were excluded because they did not examine repetition of self-harm. Two articles were excluded because of a lack of clarity about inclusion after we had attempted to contact the study authors. 15 unique studies met inclusion criteria. Full details are presented in the PRISMA flow diagram (figure  1 ).
Overview of neurocognitive results There were 15 studies included in this review. Seven studies assessed cognitive control (including attention, interference and response inhibition), two studies valuebased decision-making, eight studies problem solving, and two studies autobiographical memory. There were no data available for other neurocognitive factors. Only one quarter (n=4) of studies involved adolescents. Two studies assessed participants with diagnosed affective disorders at baseline, and only one each with personality disorder and schizophrenia. Overall, the results were inconsistent for cognitive control, although one longitudinal study suggests a potential predictive value of the IAT in terms of repetition. Results were also inconsistent for autobiographical memory, although there were too few and heterogeneous studies to draw conclusions from this.
In our review, only one included study exclusively recruited patients with depression  (Oldershaw et al. , 2009) , and so it was not possible to assess the extent of depression on traitdependent neurocognitive vulnerability. Another issue with research examining repetition of self-harm is that self-harm and particularly suicide are rare events. To be comprehensive and maximise data available for analysis, all types of self-harm were included. Therefore, studies assessing non-suicidal self-injury (NSSI) / self-injurious behaviour (SIB) were included as a type of self-harm at baseline and at follow-up. Some may argue that NSSI is not related to suicide attempts, especially in the United States where NSSI has been segregated from "suicidal behaviour syndrome" in the new DSM-5  (APA, 2015). However, the majority of the evidence indicates that any form of self-harm, regardless of intent, increases the risk of future self-harm and suicide  (Brent, 2011; Groschwitz et al.
Acknowledgements AdeC was in receipt of a  National Institute for Health Research (NIHR)   Academic Clinical Fellowship  at the time of this review KH is a  National Institute for Health Research (NIHR) Senior Investigator KR  is supported by the  National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care West Midlands The  NIHR  had no other input into the research described or the drafting of this article.
Sample Data come from 22 community epidemiological surveys in the larger WMH series that assessed lifetime PTSD after randomly-selected traumas. By randomly selected we mean that one occurrence of one lifetime trauma reported by each respondent was selected at random for retrospective assessment of PTSD. We adjusted for individual differences in number of lifetime trauma exposures by weighting random trauma data a the individual level by the respondent's number of lifetime traumas, generating a weighted sample representative of all trauma occurrences in the population rather than of all individuals in the population. The joint associations of earlier lifetime traumas and history of psychopathology with PTSD after the subsequent random trauma are the focus of the current report Three of the 22 surveys were in low/lower-middle income countries (Colombia, Peru, Ukraine), seven in upper-middle income countries (Brazil, Bulgaria, Colombia [administered after the previously-mentioned Colombian survey, when the World Bank country income rating had increased], Lebanon, Mexico, Romania, South Africa), and 12 in high income countries (Belgium, France, Germany, Israel, Italy, Japan, Netherlands, New Zealand, Northern Ireland, Spain [separate national and regional surveys], United States)  18  Each survey was based on a multi-stage clustered area probability sample of adult household residents.