dataesr/scientific-paragraphs-categorization huggingface dataset:
0 represents average normal access;  Calabrèse, Owsley, McGwin, & Legge, 2016). The MNREAD test would benefit from a digital transition, first, by simplifying and standardizing the testing methods; second, by automating the scoring methods that have been applied to calculate the MRS and CPS  (Patel et al. , 2011) ; third by providing easy data sharing; and fourth, by increasing portability, with several test versions (two contrast polarities, several languages) available within a single device. Here we describe a newly developed version of MNREAD, implemented on an iPad tablet with Retina display, resolution 264 pixels per inch (ppi) or 104 pixels/cm: the MNREAD iPad App Ó2017 (https://itunes. apple. com/ us/app/mnread/id1196638274?ls¼1&mt¼8). In the app, sentences are presented one at a time and centered on the screen (Figure  2 ).
Participants One hundred sixty-five normally sighted participants (Experiment1) and 43 low-vision participants (Exper-iment 2) were recruited for this project. The normally sighted participants had normal or corrected-to-normal vision with no reported cognitive or reading impairment. Low-vision participants had heterogeneous diagnoses and levels of visual impairment, and were able to read large print. They had no reported cognitive impairment and no reading deficit diagnosed prior to their visual impairment. All participants were either native or fluent English speakers. Data were collected with informed consent approved by the Institutional Review Board (IRB) of the University of Minnesota.
MNREAD parameters estimation Data collected with the printed chart were analyzed offline in R with the mnreadR package  (Calabrèse, Mansfield, & Legge, 2017)  to extract the four MNREAD parameters (MRS, CPS, RA, and ACC). For each testing run with the iPad app, MNREAD data were analyzed in real time by the app software, which returned the four MNREAD parameters. Both methods used the same calculations: MRS and CPS were estimated using the original algorithm described in  Legge (2007) ; RA and ACC were calculated according to the standard formulas  (Calabrèse, Owsley et al. , 2016). To ensure proper estimation of the MRS and CPS, individual MNREAD curves of log reading speed as a function of print size were plotted (either in R or within the app) and visually inspected. The algorithm estimation was judged erroneous in 4% of the chart tests and 2% of the iPad tests in Experiment 1 and in 11% on the chart and 9% on the iPad in Experiment 2.
We found that MRS, ACC, and CPS were equivalent on iPad and chart measurements. Only the RA was significantly better when measured on the digital version of the test, but by only 0. 03 logMAR. Considering that the coefficient of repeatability for RA in a low-vision population was estimated to be between 6 0. 036 logMAR  (Maaijwee, Figure 7. Agreement between the Critical Print Size (CPS) measured with the chart and the iPad app. (A) shows data from Experiment 1 (normally sighted participants). (B) shows data from Experiment 2 (low-vision participants). Bland-Altman plots show the difference between measured CPS (iPad -Chart) plotted against the mean CPS. To avoid overlapping data points, similar CPS values are grouped and shown with a size code in (A). The red dashed lines represent the average difference.
Acknowledgments The authors would like to thank  J. Stephen Mansfield  and  Charles Bigelow  for their work on the MNREAD iPad app development, as well as  Michael Crossland  for his guiding comments during the app development process. The authors would also like to thank the researchers and clinicians who agreed to test the beta version of the app, providing helpful feedback and suggestions during the debug process. This work was supported by the  National Institutes of Health  (grant  EY002934 ). Commercial relationships: none. Corresponding author:  Aurélie Calabrèse. Email: acalabre@umn. edu. Address:  Aix-Marseille University, CNRS, LPC, Marseille, France.
However, some studies have shown that, in clinical practice, patients who switch antidepressants may have a very different clinical profile compared with those who do not switch treatment. An observational study based on data from the General Practice Research Database (GPRD; now known as the Clinical Practice Research Datalink) in the UK found that patients who switched antidepressants tended to have a more severe psychiatric profile compared with those who maintained their initial therapy  [12]. In addition, differences in the degree of resolution for symptoms of occupational impairment have been noted between patients responding to first-line treatment and those responding to secondline treatment  [16]. Such findings suggest that patients who require a switch in their antidepressant therapy may benefit from a more detailed clinical assessment of their illness and a specific management approach, distinct from that taken with patients who are initiating or maintaining a first-line antidepressant therapy.
Patients receiving a combination of different antidepressant treatments at the time of the initial consultation were also excluded. The physician's choice of the drug used to treat each patient was based on clinical judgement alone and was not influenced by the decision to enter the study. All patients enrolled in the study provided written informed consent. The necessary ethical approvals were obtained for each study site before study initiation. The study was registered at ClinicalTrials. gov: NCT01427439 on 26 August 2011.
Resource-use data were reported for patients who had experienced a depressive episode of at least 8 weeks' duration as: per cent of patients reporting at least one visit (physician or other healthcare professional) or 1 day of hospitalization or sick leave over the recall period; number of visits (physician or other healthcare professional) or days of hospitalization or sick leave for patients with at least one visit or day of the corresponding resource item; and overall number of visits (physician or other healthcare professional) or days of hospitalization or sick leave. The restriction of this analysis to patients with a minimum depressive-episode duration of 8 weeks was applied to allow an appropriate comparison between the two treatment groups, given the between-group difference in duration of the current depressive episode.
Data analysis Patients included in the analysable dataset were those who met the study selection criteria and completed a baseline assessment form as well as at least one post-baseline assessment or outcome questionnaire. Summary statistics (mean, standard deviation, median, minimum and maximum) were calculated for continuous variables, and numbers and percentages were calculated for categorical variables. Analyses were performed for both groups of patients: those switching and those initiating treatment. Differences between these groups were investigated using either Student's t test for quantitative variables or chisquared test (or Fisher's exact test where appropriate) for qualitative variables. All analyses were univariate as the aim was to describe the characteristics of the study populations rather than to explain or identify any associations.
Social disadvantage, greater depression severity and anxiety have previously been linked to reduced likelihood of achieving remission after switching to second-line therapy in patients with MDD  [38]. Previous studies have demonstrated increased healthcare resource use by patients who switch therapy compared with those who maintain their initial antidepressant therapy  [13, 39, 40]. For example, using data from a US medical and pharmacy claims database, Schultz and Joish  [39]  found that patients with MDD who switched therapy incurred statistically significantly more depression-related ambulatory, emergency-room and inpatient visits than those who maintained their antidepressant therapy, leading to almost double the depression-related costs. The greater resource use observed in patients who switch would be anticipated, given their requirement for treatment before switching.
Acknowledgements The authors thank  Julien Chollet  ( Lundbeck ) for his scientific and operational contribution to the study and  MAPI Registrat  for the logistical management of the study. The authors would also like to express their gratitude to all the physicians and patients who kindly provided data for the study. Editorial support in the development of the paper was provided by  Dr. Julie Ponting  of Anthemis Consulting Ltd, funded by  Lundbeck SAS.
The ssn:FeatureOfInterest is fundamental for aligning the different ontologies. The sequence of observations can be analysed, since it forms a time series that can be represented using a combination between the classes offered by the SSN ontology (to specify the values of the time series) and the QUDT ontology (to provide mechanisms for representing specific measures and data conversions). Time intervals, series and temporal parameters of the observations are represented using W3C-Time ontology. This facilitates the representation, abstraction, and conversion of temporal measurements. In detail, the time:Instant and time:DateTimeInterval are used to enable an observation represent a single value at an instant or multiple values (i. e. , time series) during a certain period of time. Extensions related to occupants: OntoH2G Occupants module relies on the combination of Drivers, Needs, and Actions with the representation of the building infrastructure (i.
Participants A total of 54 different participants were recruited: 18 in Experiment 1 (10 females, mean age 22. 7 years, SD 4. 5 years), 18 in Experiment 2 (12 females, mean age 23. 7 years, SD 4. 2 years) and 18 in Experiment 3 (12 females, mean age 24. 1 years, SD 4. 2 years). Two participants had to be excluded due to a technical issue during data recording (one in Experiment 1 and one in Experiment 2) as they could not perform the temporal order judgment task). All participants were right-handed, had normal hearing and no psychiatric or neurological history, and participated in exchange for a monetary compensation (20 CHF per hour). They were naive to the purpose of the study and gave informed consent, in accordance with institutional guidelines and the Declaration of Helsinki. The study was approved by the cantonal ethics committee in Geneva.
In Experiment 3, the same setup was used except that the back robot was adjusted to point in the vertical axis so to touch the participants hand instead of their back. Participants could again perform any tapping movements they wanted as long as the robot touched the back of their hand. Auditory stimuli: all experimental sounds were sinusoidal pure tones, with 1 ms rise/fall time and 44100 Hz sampling rate, generated using MATLAB (MathWorks, Natick, MA) with the Psychophysics toolbox  (Brainard, 1997; Pelli, 1997; Kleiner, Brainard, Pelli 2007). Auditory stimuli used for the temporal order judgement task were 600 Hz pitch pairs of sounds, played for 10 ms via headphones either to the left and then to the right ear (Left-Right or LR) or to the right and then to the left ear (Right-Left or RL), with a variable stimulus onset asynchrony (SOA) that was adjusted throughout the experiment using an adaptive one-up two-down staircase procedure  (Levitt, 1971).
Data analysis Reaction times for temporal order judgments longer than 3 s and shorter than 300 ms were discarded (corresponding to 6. 2 % of total trials in Experiment 1, 6. 4 % in Experiment 2, and 11. 4 % in Experiment 3). Reaction times for confidence judgements longer than 6 s and shorter than 300 ms were discarded (corresponding to 3. 0 % of total trials in Experiment 1, 2. 0 % in Experiment 2, and 4. 7 % in Experiment 3). Metacognitive performance was analysed with two different approaches. First, we performed mixed effects logistic regressions between accuracy and confidence, and considered the regression slope as an indicator of metacognitive performance (that is, the capacity for a participant to adapt confidence to performance), and the lower asymptote as a measure of confidence bias (that is, the capacity to report low confidence estimates when perceptual evidence is low).
Acknowledgments This work was supported by  Swiss National Science Foundation   51AU40_125759 , the  Bertarelli Foundation  and a  European Research Council   Advanced Grant RADICAL  to A. C. N. F. was an  Ecole Polytechnique Fédérale de Lausanne Fellow  cofunded by  Marie Skłodowska-Curie. R. S. was supported by the  National Center of Competence in Research  (SYNAPSY: The  Synaptic Bases of Mental Diseases ), financed by  Swiss National Science Foundation   51AU40_125759. A. C. is a Research Director with the  F. R. S. -FNRS (Belgium).
FUNDING TD was funded by the  LEAD Graduate School & Research Network  ( GSC1028 ), which is funded within the framework of the  Excellence Initiative  of the German federal and state governments. YW was funded by the  Zhejiang Provincial Natural Science Foundation of China  ( LY14C090003 ). ML was supported by the  Spanish Government  ( PSI2015-65338-P ) and BCBL acknowledges funding from  Ayuda Centro de Excelencia Severo Ochoa   SEV-2015-0490. We acknowledge support by  Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of University of Tübingen.
Behavioral data analysis Shapiro-Wilk W tests indicated that accuracy to questions was normally distributed in TD children (p =. 64) and children with MLD (p =. 92). Thus, behavioral differences between groups were assessed by entering accuracy to reasoning and memory questions in a general linear model (GLM) with the within-subject factors Question (reasoning versus memory) and Form of the relation (linear-order versus set-inclusion), as well as the between-subject factor Math ability (TD versus MLD). One-sided one-sample t-tests were also used to test whether performance was greater than chance (i. e. , 50%) for each question, group, and form of the relation.
fMRI data acquisition Functional and anatomical images were acquired with a Siemens 3 T Prisma (Siemens Healthcare, Erlangen, Germany) at the CERMEP Brain Imaging Center in Lyon. A high-resolution anatomical scan was collected for each participant with a standard MPRAGE sequence (TR = 3500 ms, TE = 2. 24 ms, flip angle = 8°, slice thickness = 0. 90 mm, number of slices = 192, isovoxel resolution = 0. 875 mm). Functional sequences were collected with a gradient-echo, echo-planar sequence (TR = 2000 ms, TE = 24 ms, flip angle = 80°). Thirty-two interleaved transverse slices were acquired per volume (slice thickness = 3. 48 mm, voxel size = 1. 72 × 1. 72).
fMRI data preprocessing Images were analyzed with SPM12 (Welcome department of Cognitive Neurology, London, UK). The first 4 images of each run were discarded to allow for T1 equilibration effects. Functional images were corrected for slice acquisition delays and spatially realigned to the first image of the first run to correct for head movements. Realigned images were smoothed with a Gaussian filter (4 × 4 × 8 mm full-width at half maximum). ArtRepair (http://cibsr. stanford. edu/tools/ArtRepair/ ArtRepair. htm) was used to identify volumes with excessive head motion. Volumes showing rapid scan-to-scan movement > 1. 5 mm were substituted by the interpolation of the 2 nearest non-repaired volumes. Runs with > 10% of repaired volumes were excluded from the analysis. Based on these criteria, one run was discarded for eleven participants (i.
e. , 4 TD children and 7 children with MLD). Finally, functional images were normalized into the standard Montreal Neurological Institute (MNI) space. This was done in two steps. First, after coregistration with the functional data, the structural image was segmented into grey matter, white matter, and cerebrospinal fluid by using a unified segmentation algorithm  (Ashburner and Friston, 2005). Second, the functional data were normalized to the MNI space by using the normalization parameters estimated during unified segmentation (normalized voxel size, 2 × 2 × 3. 5 mm 3 ).
, they were part of background noise). Because the task was self-paced, different regressors were constructed for each participant based on their own timings. Behavioral results indicated that the form of the relation (linear-order versus set-inclusion) did not interact with group (TD versus MLD) (see below). Therefore, to improve the reliability of the estimate of the neural response, linear-order and setinclusion relations were merged and modeled within the same regressors. All epochs were convolved with a canonical hemodynamic response function (HRF). The time series data were high-pass filtered (1/128 Hz), and serial correlations were corrected using an autoregressive AR(1) model. For each subject, the effect of Relation (transitive versus non-transitive) on brain activity was evaluated by contrasting brain activity associated with transitive relations to brain activity associated with non-transitive relations.
Acknowledgments We thank the  Hospices Civils de Lyon  for sponsoring the research, as well as  Anne Cheylus ,  Auriane Couderc ,  Inès Daguet , and  Romain Mathieu  for their help during the conception of the study. Finally, we are grateful to the MRI engineers ( Franck Lamberton  and  Danielle Ibarrola ) at the  CERMEP-Lyon  platform for their assistance in collecting the fMRI data.
Participants The IMAGEN sample is comprised of > 2000 14-years-old adolescents that were tested at one of eight assessment centers (London, Nottingham, Dublin, Berlin, Hamburg, Paris, Dresden, and Mannheim)  (Schumann et al. , 2010). WM data were collected from 2048 of the participants at age 14 (WM14). Out of this sample 756 participants from six assessment sites (London, Dublin, Berlin, Paris, Dresden and Mannhaim) completed the WM task also at age 19 (WM19) (between visits range = 3-7 years, mean = 4. 5 years, sd = 0. 7 years). 698 of these individuals had complete genetic data and 487 of these individuals had quality controlled structural MRI data. These latter individuals made up the sample of the present study.
Genetics Polymorphisms were selected on the basis of previously published studies which have found association between genetic variability in dopamine-related genes and WM training  (Söderqvist et al. , 2012; Bellander et al. , 2015). DNA was extracted from blood samples. Five single nucleotide polymorphisms from four genes (rs27072 and rs40184 from DAT1/SLC6A3, rs1800497 from DRD2/ANKK1, rs4680 from COMT and rs11246226 from DRD4; Table  1 ) were extracted using the software Plink  (Purcell et al. , 2007). Genotyping procedures have previously been published  (Nymberg et al. , 2013) , further details are provided in the supplemental information.
MRI data acquisition and analysis Structural MRI data was acquired at six IMAGEN assessment sites with 3T MRI scanners of different manufacturers (Siemens, Philips, General Electric, Bruker). The scanning variables were specifically chosen to be compatible with all scanners. The same scanning protocol was used at all sites. High-resolution T1-weighted 3D structural images were acquired for each subject (TR = 2300 ms, TE = 2. 8 ms, 256 × 256 × 160 matrix, voxel size 1. 1 × 1. 1 × 1. 1). T1-3D images were skull-stripped using the Brain Extraction Tool (BET)  (Smith, 2002). Subcortical nuclei (caudate nucleus, putamen, nucleus accumbens) were automatically segmented from T1 images using the FMRIB imaging registration and segmentation tool (FIRST) (http:// www. fmrib. ox. ac. uk/fsl/first/index.
Statistical analyses Statistical analyses were performed in SPSS version 22 and R (https://www. r-project. org/). We computed the total size of the striatum by adding the volumes from both left and right hemisphere of the nucleus accumbens, caudate and putamen and tested 5 models for the 5 different SNPs. WM performance at age 19 (WM19), WM performance at age 14 (WM14) and striatal volume were mean centered and then outliers (defined as point below 1. 5 times the lower interquartile distance and above 1. 5 times the upper interquartile distance) were set to 1. 5 time the value of the relevant interquartile distance. All the SNPs except two were coded for an additive effect: the minor allele was coded as 0, the heterozygote was codes as 1 and the major allele was coded as 2. In coding rs27072 (DAT1) and rs1800497 (DRD2) the minor allele was lumped with the heterozygotes as our sample contained less than 5% of the former, so that in the model the minor allele and the heterozygote were coded as 1 and the major allele as 2.
A second limitation of the study is the age range of the sample we included: previous studies have shown how fastest rate of WM development happens at younger ages that the ones included in our sample (e. g. between 6 and 11-12 years old;  (Luna et al. , 2004) , between 8 and 20 years old  (Roalf et al. , 2014)  and  (Dumontheil et al. , 2011) ). The IMAGEN study was indeed devised with a focus on factors related to mental health during adolescence, and as such included subjects between 14 and 19 years old. However, the unique nature of the dataset, following hundreds of subjects longitudinally while including imaging, genetic and cognitive measures, allowed us to test relevant and specific hypothesis about the physiological underpinning of WM development. It is worth mentioning that the subjects participating both at baseline and follow-up had higher WM performance at baseline that subjects that did not participate in the follow-up.
Acknowledgements This work received support from the following sources: the  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant 'STRATIFY' (Brain network  based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the  Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways)  ( PR-ST-0416-10004 ),  BRIDGET  ( JPND: BRain Imaging, cognition Dementia and next generation GEnomics ) ( MR/ N027558/1 ), the  FP7  projects  IMAGEMEND ( 602450 ;  IMAging GEnetics  for MENtal Disorders) and MATRICS ( 603016 ), the  Innovative Medicine Initiative Project EU-AIMS  ( 115300-2 ), the  Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  Swedish Research Council FORMAS , the  Medical Research Council , the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministeriumfür Bildung und Forschung (BMBF  grants  01GS08152 ;  01EV0711 ; eMED SysAlc01ZX1311A; Forschungsnetz AERIAL), the  Deutsche Forschungsgemeinschaft (DFG  grants  SM 80/7-1 ,  SM 80/7-2 ,  SFB 940/1 ).
Further support was provided by grants from:  ANR  (project  AF12-NEUR0008-01 -WM2NA , and  ANR-12-SAMA-0004 ), the  Fondation de France , the  Fondation pour la Recherche Médicale , the  Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA) , the  Assistance-Publique-Hôpitaux-de-Paris  and  INSERM  (interface grant),  Paris Sud University  IDEX 2012; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ),  U. S. A. (Axon, Testosterone and Mental Health during Adolescence ;  RO1 MH085772-01A1 ), and by  NIH Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to Knowledge Centres of Excellence.