dataesr/scientific-paragraphs-categorization huggingface dataset:
Individuals with excessive missing genotypes (failure rate >5%) were also excluded. Population homogeneity was examined with the Structure software using HapMap populations as reference groups. 18 Individuals with divergent ancestry (from Utah residents with ancestry from northern and western Europe) were excluded. Identity-by-state clustering and multidimentional scaling were used to estimate cryptic relatedness for each pair of individuals using the PLINK software  19  and closely related individuals were eliminated from the subsequent analysis. We applied principal component analysis to remove remaining outliers, 20 defined as individuals located at more than four s. d. of the mean principal component analysis scores on one of the first 20 dimensions. Finally, the integrated genotypes from both Illumina Human610 Quad BeadChip and Human660-Quad BeadChip were combined and platform-specific SNPs were removed.
Magnetic resonance imaging Full details of the magnetic resonance imaging acquisition protocols and quality checks have been described previously. 21 Brain images were segmented with the FreeSurfer software package (http://surfer. nmr. mgh. harvard. edu/) and the entire cortex of each individual was inspected for inaccuracies. Individuals with major malformations of the cerebral cortex were excluded from further analysis. Out of 1909 images, 1584 passed these quality control checks. In addition to global mean thickness of the left and right cerebral hemispheres, neuroimaging measures included cortical thickness for 33 individual regions per hemisphere. These were combined to produce weighted average thickness (weighted for surface at each region) for the four cerebral lobes (that is, frontal, temporal, parietal and occipital).
RNA extraction and microarray analyses and SNP selection RNA was extracted from triplicate SPC04 differentiation experiments using the RNeasy Mini Kit (Qiagen), according to the manufacturer's instructions. Total RNA samples were processed using the TargetAmp-Nano Labeling Kit (Cambio, Cambridge, UK) and hybridized to Illumina HumanHT-12 v4 Expression BeadChips according to the manufacturers' instructions at the Biomedical Genomics microarray core facility of the University of California, San Diego, CA, USA. Raw data were extracted by the Illumina BeadStudio software and further processed in R statistical environment (http://www. rproject. org) using the lumi  24  and limma  25  Bioconductor packages. Raw expression data were log2 transformed and normalized by quantile normalization. Differential expression between each differentiated versus undifferentiated conditions was assessed using the linear model for microarray analyses package.
P-values were adjusted for multiple testing according to the false discovery rate procedure of Benjamini and Hochberg, and differentially expressed genes were selected at false discovery rate o5%. See Supplementary Table  1  for the list of differentially expressed genes. The functional annotation clustering tool, part of the Database for Annotation, Visualisation and Integrated Discovery  26  was used to determine enrichment of functional groups in genes' list generated from the microarray analyses. SNPs (n = 59 643) lying within ±10 kB of each differentially expressed autosomal genes were selected for genetic association studies; of these, n = 54 837 passed genetic quality controls and were used in further association analyses.
Meta-analytic association of rs7171755 with brain volumes in ENIGMA We have used the ENIGMA data set, the largest meta-analysis of gene × neuroimaging phenotypes, to investigate association of rs7171755 with total brain volume, the brain phenotype most closely related to cortical thickness available in this data set. Association of rs7171755 with brain volume was performed using the online tool EnigmaVis,  30  generating an interactive association plot. Only the healthy subsample (N = 5775) of ENIGMA, for which this brain volume was available, were included in the meta-analysis.
NPTN expression on mouse brain samples RNA samples extracted from CD1 mouse brains at embryonic day 10 (E10), E14, E18 and at postnatal (P) stages 1 week, 1 month or 6 months were obtained from AMS Biotechnology (Abingdon-on-Thames, UK). Wholebrain mouse RNAs extracted from pools of five and three embryos were used for the E10 and E14 stages, respectively. RNAs extracted from the frontal cortex were used for later developmental stages (that is, E18-P6 months). In this case, triplicate samples from independent brains were analyzed for each stage, except for the P6 month stage for which data were derived from a single mouse brain. Complementary DNAs obtained by reverse transcription using the SuperScript III First-Strand Synthesis System (Invitrogen, Paisley, UK) following the manufacturer's instructions were amplified by PCR with GAPDH as an internal control, using the following forward and reverse primers: GAPDH-F 5′-TGTTCCTACCC CCAATGTGT-3′; GAPDH-R 5′-CCTGCTTCACCACCTTCTTG-3′; NPTN-F 5′-GCCTTTCTTGGGAATTCTGGC-3′; NPTN-R 5′-AGAGTTGGTTTTCATTGGTC CAG-3′.
PCRs were run in triplicate in the Applied Biosystems real-time PCR device (7900HT Fast Real-Time PCR system) in 20 μl reactions containing 4 μl complementary DNA, 0. 5 μM of each forward and reverse primers and 1 × Power SYBR Green Mix (Applied Biosystems, Paisley, UK) using the following cycles: 95 °C for 15 min and 40 cycles at 95 °C for 30 s and 59 °C for 30 s. The PCR reaction products were evaluated by a melting curve analysis. Relative quantification of the PCR products was performed using the SDS software (Applied Biosystems) comparing threshold cycles (Ct). NPTN mRNA levels were first normalized to that of GAPDH (ΔCt = Ct NPTN -Ct GAPDH ) at each developmental stage, and changes in expression relative to E10 were calculated as 2 -(ΔCt -ΔCtE10). Statistical analysis (one-way analysis of variance, followed by Bonferroni-based post hoc analysis with α = 0.
NPTN expression in human brain samples Expression of NPTN in the human brain was investigated using two databases. To study effects of rs7171755 on NPTN expression (probe 33624, targeting NM_001161363 and NM_012428), we used the publicly available BrainCloud database (http://BrainCloud. jhmi. edu/), which includes data on gene expression and genotypes from post-mortem dorsolateral prefrontal cortex samples collected from 272 subjects across the lifetime. In this database, transcript expression levels were measured on Illumina Oligoset array of 49 152 probes, and genotyping was performed using Illumina Infinium II or HD Gemini 1M Duo BeadChips. 33 The genetic data sets were obtained from dbGaP at http://www. ncbi. nlm. nih. gov/gap through dbGaP accession number phs000417. v1. p1. Submission of the data phs000417.
v1. p1 to dbGaP was provided by Drs Barbara Lipska and Joel Kleinman. Data collection was through a collaborative study sponsored by the National Institute of Mental Health Intramural Research Program. Initial report on this data set is from Colantuoni et al. 33  For this study, we considered only samples with good RNA quality (RNA integrity number ⩾ 8). Statistical analyses measuring effects of rs7171755 on the postnatal expression of NPTN were performed on 147 samples (individuals ⩾ 0. 5 year old) by general linear models controlling for age, ethnicity and RNA quality. To investigate possible differences in NPTN expression between brain hemispheres, we analyzed a database (GEO series GSE25219) containing genome-wide gene expression data from 16 brain regions on both hemispheres, collected from 57 subjects across the lifetime (N = 1340 postmortem brain samples).
To confirm this, we investigated patterns of NPTN expression in the brain. First, we investigated changes in NPTN expression in the mouse brain during stages of embryonic and postnatal development. One-way analysis of variance indicated that, although levels of NPTN mRNA are low in the mouse neocortex during embryonic development, expression of this gene is markedly increased in the first week after birth, reaching maximum levels 1 month after birth (F(3, 8) = 53. 83; P = 1. 2 × 10 -5 ), a time period that corresponds to adolescence in mice (Figure  4a ). To confirm relevance of our findings to human brain development, we interrogated the BrainCloud database, which contains genome-wide expression data of the prefrontal cortex of 272 individuals across the lifespan as well as their genotype information.
We analyzed this further, investigating possible asymmetries in cortical thickness and NPTN expression. For this purpose, we performed paired samples t-tests that indicated that, although cortical thickness correlated well between hemispheres (r (1583) = 0. 864) in our sample, the cortex was on average 0. 012 mm thicker on the right hemisphere (t(1582) = 9. 818, P = 3. 977 × 10 -22 ). To test for asymmetric expression of NPTN in the human brain, we analyzed a database (GEO series GSE25219) containing gene expression data from 16 brain regions on both hemispheres (n = 1340 post-mortem brain samples). 34 Paired samples t-tests comparing expression on NPTN in the right vs the left hemisphere indicated that RNA levels of this gene were higher in the right hemisphere than in the left (NPTN left -NPTN right = -0.
63 t should be noted that the effect sizes observed in our experiments are small, as might be expected from mutations in human genes that regulate late events in neural differentiation. Such mutations may not cause gross cortical malformations, but rather more subtle cognitive and behavioral defects. Given this and the age specificity of our observations, a major challenge remains to generate additional studies to replicate our findings. Nonetheless, we have partly overcome these limitations, further testing the relevance of NPTN genotypes for interindividual variations in brain structure in our sample and in the ENIGMA consortium for meta-analysis of large neuroimaging and genetics data set, and demonstrated the negative association of the rs7171755 risk allele with brain volume, further supporting a role for NPTN in influencing brain structure.
ACKNOWLEDGMENTS We thank  Dr Gary Hardiman  for the microarray hybridizations and  Professor Jack Price  for giving us the SPC04 cells. This work was supported by the  European Unionfunded FP6  Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  German Ministry of Education and Research (BMBF  Grant #  01EV0711  and  eMED 'Alcoholism') , the  FP7  project  IMAGEMEND (Development of effective imaging tools for diagnosis, monitoring and management of mental disorders ) and the  Innovative Medicine Initiative Project EU-AIMS  ( 115300-2 ), as well as the  Medical Research Council   Programme Grant  ' Developmental pathways into adolescent substance abuse ' ( G93558 ), the  Swedish Research Council (FORMAS)  and the  United Kingdom National Institute for Health Research (NIHR) Biomedical Research Centre Mental Health.
Pattern recognition techniques, robust clustering There exists a wide range of techniques termed pattern recognition or clustering techniques all aiming at two distinct goals. They can either be used (1) to automatically classify events or observations after a proper training when a gold standard is available (e. g. , the automated recognition of hand written addresses on postcards  [18] ), or (2) to retrieve homogenous forms in data, without any a priori knowledge and in the absence of a gold standard (e. g. , the identification of distinct profiles of users of the healthcare system). Clinical researchers show a growing Figure  1  Reduction of two complex datasets A (a roll) and B (a helicoidal tore) with two methods, classical linear method (PCA) versus nonlinear method (ISOMAP). PCA relies on projections: original data (left) are represented in a three-dimensional space.
Techniques for attractor reconstruction A specific system may be characterised by the nature and the shape of its attractor rather than by a mean. The issue consists in identifying the shape of the attractors. According to Takens' theorem, attractors can be retrieved in datasets  [21]  and reconstructed from experimental data  [22]. In the biomedical area, the homeostasis theory due to Cannon and Bernard has been later provided with a theoretical framework as an implementation of the theory of systems and Wiener's cybernetics theory  [23, 24]. In this framework, a so-called normal state is regulated by the system using a negative feedback and the integration of various signals. The complex systems theory provides tools which derived from an extension of the cybernetics theory. It includes the long-term past of the system to process the present signals, whereas cybernetics operates in an absolute and never-ending present.
Collected data become more heterogeneous when getting bigger. Big data are characterized by a lot of information and some of them, such as genetic and imaging data, are heavy, i. e. they gather more data than a classical library would do  [30]. For example, imaging databanks are made of thousands of digital images, which are made of millions of colored pixels. Each pixel gives information to process. Collected data involve different aspects of a person and their environment. If the age, sex and height are the three dimensions used to identify a person, we can locate different persons relatively to each other according to these three parameters in a three-dimensional space. If other parameters are taken into account, e. g. , symptoms, behaviors, biological or social data, we need a much higher dimensional space to locate persons relatively to each other.
In such a space, the classical tools used to measure the distance or similarity between two persons are inoperative. All individuals tend to melt into a single and unique average person, who cannot be properly distinguished from another one. This phenomenon, which is known as the "curse of dimensionality"  [31] , hinders high dimensional datasets to be directly analysed with standard techniques. It is therefore necessary to reduce the dimensionality of the initial dataset to its lowest needed, while preserving the complexity of relationships between observations. The kind of complexity we are interested in here could be termed as a topological complexity: two persons close to each other in reality must remain close in the constructed map. The reduced dataset should preserve these neighboring relationships.
Statistical techniques for dimensionality reduction are available, such as the principal components analysis (PCA) and PCA-like techniques  [32]. These techniques should not be used because they may rule out any complex relationships between observations. The different kinds of collected data are different ways to observe and qualify a person. These data are linked to each other and present at least partially redundant or correlated information. A lower dimensional space can be studied, in which redundancy is dramatically reduced, e. g. , when an initial threedimensional space is reduced to a two-dimensional space. The reduced dimensionality is termed "true" or "intrinsic" dimensionality. The intrinsic dimensionality of a dataset can be approached by different techniques, e. g. those used in dynamical systems  [15].
Data analysis: global and local analysis of the psychological map The search for specific regions starts once the map is built. These regions constitute parts of the map that Figure  3  Building the psychological landscape, from raw data to the determination and the causal analysis of its dynamical patterns. A. The whole dataset is constituted of various data layers collected on persons, which include behavioral, biological, genetic, and imaging data. It may exhibit complex relationships that cannot be preserved or explored appropriately with classic statistical tools. B. Reduced dataset produced by nonlinear reduction techniques (NLDR), which allow minimal complexity loss (step 1). C. A raw map is produced by pattern recognition techniques and robust clustering (step 2). Homogeneous groups of persons may be identified, according to similar behaviors and psychological traits.
Such groups are termed nosological areas. D. Zooming in on a specific nosological area. E. Applying algorithms for attractor reconstruction should allow the dynamic pattern of this area to be identified. F. The causal pathways leading to an attractor can be analysed with Bayesian networks or similar methods (steps 4 and 5). The links of the dynamic pattern to other items from the whole dataset, e. g. , age and sex, can be also analysed. G. Once the main nosological areas are identified, pathways between these areas can be searched for. Bayesian networks can be used as well. naturally gather people sharing some characteristics, e. g. , depressed persons sharing traits of sadness or anhedonia. Clustering techniques could be used to partition the map into specific homogeneous regions  [18]. These techniques are widely used in several areas of research and industry  [34] , and currently contribute to identify the functions of "junk DNA" in the ENCODE project  [35].
Conclusion An atheoretical approach to improving the validity of psychiatric classification systems may be grounded on appropriate techniques. Reliable techniques currently exist which can support the rigorous integration of huge amount of data while preserving the real-world complexity, leading to the building of a psychological landscape. Existing databases provide only a limited amount of the data needed for the psychological landscape construction. Yet, the RDoC project, which integrates the fundamental genetic, neurobiological, behavioral, environmental and experimental components that comprise mental disorders, could provide a starting point for our hypothesis to be tested. The second British National Survey of Psychiatric Morbidity could serve as another useful basis  [25] , data should be linked to external surveys, e.
Experimental stimuli were 24 consonant strings built-up from 10 consonants (BPTFLMDSRH). An additional 10 different letter strings were used for training. Score was the number of accurately reported letters, regardless of order (maximum score: 144). The VA span performance of the participants was compared to normative data from the EVADYS diagnostic tool  (Valdois et al. , 2014a). Every control participant scored within 1 SD of the norm on the VA span task. The dyslexic participants' VA Span abilities were at least 1. 65 standard deviations below adult norms. Performance on the 6-letter whole report task indexing ME processing capacity (VA Span) was significantly lower for dyslexic (3. 5 letters per trial on average) than for control (5.
MR ACQUISITION A whole-body 3T MR scanner was used (Bruker MedSpec S300) with 41 mT/m maximum gradient strength and 120 mT/m/s maximum slew rate. For functional scans, the manufacturer-provided gradient-echi/T2 * weighted EPI method was used. Thirty-nine adjacent axial slices parallel to the bi-commissural plane were acquired in interleaved mode. Slice thickness was 3. 5 mm. The in-plane voxel size was 3 mm × 3 mm (216 × 216 field of view acquired with a 72 × 72 pixels data matrix; reconstructed with 0 filling to 128 × 128 pixels). The main sequence parameters were: TR = 2. 5 s, TE = 30 ms, flip angle = 80 •. To correct images for geometric distortions induced by local B0inhomogeneity, a B0 fieldmap was derived from two gradient echo data sets acquired with a standard 3D FLASH sequence TE = 9.
DATA PROCESSING Both preprocessing and statistical analyses of the data were performed using the Statistical Parametric Mapping software (SPM5, Wellcome Department of Imaging Neuroscience, London, UK; http://www. fil. ion. ucl. ac. uk/spm; Friston et al. (1994). Functional volumes were time corrected using the 20th slice as reference. All volumes were then realigned using rigid body transformations to correct for head movement, using the first ER-fMRI session as the reference volume. The T1-weighted anatomical volume was co-registered to the realigned mean images and normalized to MNI space using a trilinear interpolation. The anatomical normalization parameters were then used for functional volume normalization. Finally, each functional volume was smoothed by an 8-mm FWHM (Full Width at Half Maximum) Gaussian kernel.
Traces analysis and indicators The implemented system uses all significant traces : game traces (interactions, movement, etc. ) and activity traces within the PLM system (document consultation, creation, etc. ). The aim of these traces is to improve the user monitoring for individual actions but also and especially during interactions and group actions. Annotation traces will complement game traces and activity traces. The interest of these annotation traces is twofold : on the one hand, they are used to validate PLM data and Fig. 4  The Learning Adventure Serious Game Environment business rules that were identified as holder of knowledge by expert. On the other hand, they can implicitly define an "organizational mapping" of knowledge. Here, the meaning of mapping concerns actors' identification with a level of professional expertise in relation to the formalized knowledge.
Eye-tracking data were also collected, which allowed us to further examine the attentional power of the alerts by decomposing the validation time into two parts: the time between notification onset and fixation on the notified plane, and the time between perceiving (fixating) the notified plane and responding to it. We used a FaceLab 5 device without a chin rest, and gaze was tracked remotely using an infrared source and two infrared cameras. The set up involved a 30 inch display with a resolution of 2560 × 1600, located 70 cm in front of the participant. Facelab was configured to output 60 Hz eye-data and head-position data. The calibration phase involved fixating 9 points in turn and took about 5 minutes. A fixation was defined as lasting more than 50 ms. To identify which aircraft was fixated, we looked for planes whose boundary box intersected with a square area of side 140 pixels (2.
Acknowledgements We would like to thank  Roland Alonso  (researcher in ergonomics); and  François Marais ,  Philippe Ribet ,  Daniel Etienne  and  Anthony Marion  (research engineers) for programming the LABY microworld. This work was supported by the  R&D department of the French civil aviation  ( DSNA/DTI/R&D ). Support was also provided by a  R&D partnership grant  from the  National Sciences and Engineering Research Council of Canada , awarded to  Sébastien Tremblay.
PARTICIPANTS Eight AD participants (mean MMSE = 25. 2/30; range 23-27) and seven healthy controls participated in the study. Healthy controls were recruited from the participant database of the Research Center of the Geriatric Institute of the University of Montreal (CRIUGM). We recruited AD participants from the Alzheimer Society of Montreal (N = 6) and from a cohort of patients followed at the CRIUGM (N = 2). AD participants met the NINCDS-ADRDA research criteria for probable AD  (McKhann et al. , 1984)  and the DSM-IV clinical criteria for dementia of the Alzheimer's type  (APA, 1994). Mixed dementias were excluded. Cognitively healthy individuals were used as controls. They were screened for cognitive impairment and selected to match AD patients for age and education. In all participants, exclusion criteria included history of psychiatric or neurological disorders, cerebrovascular diseases, hearing impairment, alcoholism, and dyslexia.