dataesr/scientific-paragraphs-categorization huggingface dataset:
Data processing and analysis Collected data were coded, introduced and entered to the software Statistical Package for Social Sciences (SPSS) version 25 (SPSS TM Inc. , Chicago, IL USA). At first, descriptive analyses were performed using numbers and percentages for qualitative variables and means with standard deviation (SD) for continuous variables. And one way repeated measures Analysis of Variances (ANOVA) was important to assess the cognitive function over time. Second, the univariate analyses were conducted. The Student's t test was used for comparison of means of the quantitative variables with a normal distribution and equal variances, whereas the non-parametric test for quantitative variables with an abnormal distribution and the unequal variances were used. The Chi-square test was used for the comparison of percentages between two qualitative variables.
2 proximal microdeletion. 31 On the other hand, among 16p-DEL the prevalence of ASD has been reported to vary from 11% 8 to 27%  29, 30  when assessed with both the ADOS and the ADI-R, and to vary from 22%  32  to 43%  33  when assessed with the ADI-R alone. Within the 16p-DEL population, specific construct alterations have been described. Excluding the 16p-DELs diagnosed with ASD, 60% of the remaining 16p-DEL presented impairments in the ADI-R Social Interaction sub-domain, and 73% in the Communication sub-domain. 33 milar data have been reported in independent cohorts  29, 30  where 16p-DEL carriers showed higher Social Interaction and Communication impairments compared with siblings. 29 Socialization was significantly impaired when measured with the VABS as well but not consistently. 30, 34, 35 Interestingly, Moreno-De-Luca et al.
97 Thus, these data suggest that discrepant results could be related to the genetic background of the tested mice. For example, the Herault mouse line has a C57BL/6N Â C3B background and the corresponding wild-type mice show higher levels of basal social interaction. 98 This suggests that for social deficits to manifest in 16p11. 2 CNV carriers, a second "hit" (in this case genetic) may be needed. More consistent impairments were observed in 16p11. 2 deletion mice in relationship to malefemale interactions. In particular, in the Dolmetsch line, lower nose-tonose sniffing duration, nose to nose sniffing bouts, nose to anogenital sniffing duration, nose to anogenital sniffing bouts and a lower number of ultrasonic vocalizations were recorded. 136 Similarly, another study reported reduced anogenital sniffing and ultrasonic vocalizations, but no changes in nose-to-nose sniffing in the same model,  96  during the first exposure to an unfamiliar estrous female as well as after the female was removed.
ACKNOWLEDGMENTS We thank  M. Morini ,  D. Cantatore ,  G. Pruzzo ,  B. Chiarenza ,  A. Monteforte  and  C. Chiabrera , for technical support. We thank  C. Stubbendorff  for careful English proofreading. This work was supported by funding from the  Istituto Italiano di Tecnologia , the  Ministero della Salute italiano  (project  GR-2016-02362413 ), and  Fondazione Telethon Italia  (project  GGP19103 ) to FP. The authors declare that they have no conflict of interest.
Apparatus and Set-Up All stimuli were programmed on an ACER computer running Windows 7 with Matlab 2016b, using the Psychophysics Toolbox extensions  (Brainard, 1997; Pelli, 1997; Kleiner et al. , 2007) , and displayed on a gamma-corrected CRT Silicon Graphics monitor with 1,280 × 960 pixels resolution at 120 Hz refresh rate. The whole display (38. 5 × 29. 5 cm) subtended 38. 5 × 29. 5 • of visual angle at a viewing distance of 57 cm. All experiments were carried out in a dark room, with no lighting other than the display screen. Ad hoc software in Mathematica (Wolfram Inc. ) was used for the extraction of stimuli, curve fitting, and statistical analysis. Participants' manual responses were provided on a standard Dell keyboard.
Procedure In all experiments, participants were asked to choose which of the two compounds presented on each side of the screen was the most salient, in a 2AFC procedure. Participants were sitting in a dark room at 57 cm distance from the monitor. Each trial started with the presentation of a gray display for 800 ms, during which subjects were asked to fixate a cross in the center of the screen. The compound stimuli were then shown for 26 ms on a gray background. After the stimulus presentation, subjects indicated the more salient compound by pressing a computer key. There was no time limit for the response (Figure  2A ). All data for each subject were collected in one single session of about 1 h, divided into four blocks (one block/experiment). In Experiment 1 the preference for a compound of optimal features (target) with respect to a compound of non-optimal features (distractor) was measured as a function of the number of features presented.
Apparatus and Set-Up All stimuli were programmed on a MacPro computer running OS 10. 6. 8 with Matlab 2016b, using the Psychophysics Toolbox  (Brainard, 1997; Pelli, 1997; Kleiner et al. , 2007) , and the Eyelink Toolbox extensions  (Cornelissen et al. , 2002) , and displayed on a Samsung SyncMaster 2,233 LED-monitor with 1,680 × 1,050 pixels resolution at 120 Hz refresh rate. The whole display (47. 2 × 29. 5 cm) subtended about 47 × 29 • of visual angle at a viewing distance of 57. 3 cm. All experiments were carried out in a dark room, with no lighting other than the display screen. Eye movements were recorded using an Eyelink 1,000 video-based eye tracker (sampling rate 1 kHz). The viewing was binocular, but only the right eye was tracked. A chin and forehead rest stabilized the head.
Eye Movements Data Analysis Ad hoc software in Matlab and Mathematica (Wolfram Inc. ) was used for extraction of oculomotor parameters and statistical analysis. Recorded horizontal and vertical gaze positions were low-pass filtered using a Butterworth (acausal) filter of order 2 with a 30-Hz cutoff frequency and then numerically differentiated to obtain velocity measurements. We used an automatic conjoint acceleration and velocity threshold method to detect saccades (see for instance  Damasse et al. , 2018) , and we visually inspected all oculomotor traces to exclude aberrant trials. We excluded from the analysis saccades with latencies below 140 ms, considered anticipatory and not guided by visual information in this type of choice-saccade tasks (e. g. ,  Walker et al. , 1997) , and very late saccades, above 500 ms (less than 6% of the first detected saccades overall).
FUNDING This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement No. 832813  GenPercept " Spatio-temporal mechanisms of generative perception "). AM and SC were partly supported by the  PICS "APPVIS"  grant of the  CNRS  ( 2018CNRS ( -2020 )). AM has received support from the  French government  under the  Programme "Investissements d'Avenir", Initiative d'Excellence d'Aix-Marseille Université via AMidex funding  ( AMX-19-IET-004 ), and  ANR  ( ANR-17-EURE-0029 ).
ACKNOWLEDGEMENTS The author group acknowledges and specifically thank  Dr. Ben Serrien  for his invaluable input in the areas of physics, classical and quantum mechanics and assistance in manuscript editing. Without his input this article would not be Gabel CP et al. Slacklining: Origins, neuromechanics and therapy WJO https://www. wjgnet. com11 June 18, 2021 Volume 12 Issue 6 completed.
Gene expression analyses The following datasets were retrieved from public sources: GSE39582 ("CIT cohort", n = 566), GSE13294, GSE18088, GSE14333, GSE13067, GSE17536, GSE17537, GSE33113, GSE26682, TCGA, altogether forming the "validation cohort" (n = 1647). Gene expression analysis for the PETACC8 cohort was performed through 3ʹRNA sequencing. Samples were selected so as to yield a near even distribution among CMS subgroups. Data were normalized as transcript per million reads. Subtype classification systems assignments were performed using original published predictor methods as described in. 2  Kaplan-Meier plots were generated using the "survival" (Cancer Genome Atlas Network, 2012) R package.
Gene Set Enrichment Analysis (GSEA) GSEA  9  was performed on the GSE39582 dataset. 10 This cohort is composed of n = 91 CMS1, n = 232 CMS2, n = 69 CMS3, n = 127 CMS4 n = 47 unclassified and n = 19 non tumor samples (NT). The GSEA was performed using the Broad Institute platform (http://www. broadinstitute. org/gsea/index. jsp; Version 2. 0. 14).
Analysis of plasma samples Characteristics of plasma samples from the PRODIGE9 trial have been described in. 5 Collection of material was approved by the Committee for the Protection of Persons Ile de France VIII. The patients and methods for the PRODIGE9 trial have been described previously (trial registration number NCT00952029). 11 Blood samples of 275 healthy individuals without any indication of malignancy were collected as a control group. We verified that control values did not differ according to age groups (>65 and ≤65 years). Experiments were all carried out under blinded conditions.
Integrin-linked kinase correlates with PrP C expression and is enriched in mesenchymal colorectal tumors. We employed gene set enrichment analyses (GSEA) to get further insight into the pathways enriched among the genes whose expression is correlated to that of PRNP in colorectal patients ("CIT cohort", GSE39582). We found that the expression of PRNP was highly associated with integrin binding, cellsubstrate adhesion and cell-substrate junction (Figure  1a ). Using leading-edge analysis to identify overlapping genes in these 3 gene sets, we derived a short list of highly correlated genes, most of which encode integrin subunits (Figure  1b ). Because distinct integrins may share common partners and functionally compensate for each other,  13, 14  we chose to focus on one of their proximal effector, namely ILK,  15  which features in the leading edge-derived list of overlapping genes (Figure  1b ).
Funding: This research was funded by ( ANR-11-LABX-0054 ) (Investissements d' Avenir-Labex INFORM ),  ANR   PEPTIMET  ( ANR-18-CE15-0018-02 ),  ANR BACNEURODRO  ( ANR-17-CE16-0023 ),  Equipe Fondation pour la Recherche Médicale  ( EQU201903007783 ) and  Institut Universitaire de France  to J. R.
DESIGN PROBES INTO DATA VISCERALIZATION To explore and better define the concept of data visceralization, we developed a set of VR prototypes using the Unity3D game engine. We critically reflect on these prototypes, with each prototype requiring 1 to 2 weeks to create, test, and critique. These design probes were conducted using an ASUS Windows Mixed Reality Headset (HC102) in an open space free of obstructions. Each prototype was adapted either from existing stories published in online journals and news articles, or from popular data-driven graphics/visualizations. We strove to test a range of different scenarios to explore data visceralizations as much as possible. In this section, we describe six of these design probes, which we refer to as examples for simplicity and abbreviate as E1 , E2 , E3 , E4 , E5 , and E6.
The first half focuses on scenarios with common types and scales of physical phenomena, not requiring any scaling or transformation to be readily perceived in VR. The second half investigates scenarios that required some form of transformation into VR, such as scaling down extreme values and representing abstract measures. While we describe, show pictures, and include video of these examples, one needs to experience them in a VR setup to assess the data visceralization experience. Therefore, we made these available in the supplementary material and on a public GitHub repository 1.
, 2019) , even more so when the sample size increases  (Marchini et al. , 2004). This led GLMs to be replaced by linear mixed models (LMMs)  (Price, Zaitlen, Reich, & Patterson, 2010; Yang, Zaitlen, Goddard, Visscher, & Price, 2014; Zhang et al. , 2019)  which co-varies out all features by fitting them as random effects. LMMs have been shown to better control the inflation of false positive associations arising from LD or correlation between probes and to minimise the occurrence of false positives in both GWAS and MWAS  (Nabais et al. , 2020; Yang et al. , 2014; Zhang et al. , 2019). Here, we sought to evaluate whether the inflation of false positives observed with omics dataset is also present in neuroimaging data. In the first part of the analysis, we performed extensive phenotype simulations from real grey-matter data in order to quantify false positive rate as well as statistical power, mapping precision and resulting prediction accuracy achieved from mass-univariate analyses results.
Image processing We processed the T1w and T2 FLAIR images together to enhance the tissue segmentation in FreeSurfer 6. 0  (Fischl, 2012) , which should result in a more precise skull stripping and pial surfaces definition. When the T2 FLAIR was not acquired or not usable, we processed the T1w image alone, though a recent report showed this results in systematic differences in cortical thickness  (Lindroth et al. , 2019). This may represent a source of noise in the data, albeit it was limited in term of number of individuals (see quality control). We extracted vertex-wise data mapping cortical surface area and thickness, using the maximal resolution allowed by the FreeSurfer software (fsaverage -unsmoothed). We previously showed that this cortical processing maximised the morphometricity for a wide range of phenotypes  (Couvy-Duchesne et al.
, 2019). In other words, this cortical processing maximised the information retained by the processed MRI images. In addition, we applied the ENIGMA-shape processing (Boris A. Gutman, Madsen, Toga, & Thompson, 2013; B. A. Gutman, Wang, Rajagopalan, Toga, & Thompson, 2012)  to extract radial thickness and log Jacobian determinant (analogous to a surface area  (Roshchupkin et al. , 2016 )) of the hippocampus, putamen, amygdala, thalamus, caudate, pallidum and accumbens. In short, segmented subcortical volume from FreeSurfer are projected onto atlases in order to measure their vertex-wise radial thickness and surface area (Boris A. Gutman et al. , 2013; B. A. Gutman et al. , 2012). Overall, the imaging data used in the analyses comprised 652,283 vertex measurements per individual: 299,009 for cortical thickness, another 299,034 for cortical surface area, 27,120 for subcortical thickness and 27,120 for subcortical surface area.
Sample description -simulation and discovery sample We considered the first 10,103 participants of the UK Biobank (UKB) imaging wave. None of the participants withdrew consent after the data were collected. We excluded 231 participants due to T1 images labelled unusable by the UKB or because the FreeSurfer processing failed or did not complete within 48 hours. Our final sample comprised 9,890 adults with complete cortical and subcortical data, aged 62. 5 on average (SD=7. 5, range 44. 6-79. 6) with slightly more (52. 4%) female participants. To note, 341 participants did not have an exploitable T2 image. We performed a stringent quality control (QC) of the vertex-wise data in order to exclude participants who may bias the LMM estimates, though there may be a more optimal QC threshold that could maximise the sample size in future studies.
Independent samples for prediction and replication Our first independent sample included an additional 4,942 participants of the UKB with a T1w image (downloaded in May 2018, most participants also had an exploitable T2w). The final sample (N=4,160 after processing and QC) was on average 63. 1 years old (SD=7. 46, range 46. 1-80. 3) with 52. 1% of females. In addition, we used the OASIS3 (Open Access Series of Imaging Studies) sample  (LaMontagne et al. , 2019)  to evaluate the generalizability of the prediction. The OASIS3 dataset gathers several longitudinal MRI studies conducted in the Washington University Knight Alzheimer Disease Research Center over the past 15 years. Our final sample included 1,006 unique participants after processing based on T1w images and QC. When several visits were available for a participant, we selected the one with the most phenotypic information.
Simulation of phenotypic traits from real grey-matter data We simulated phenotypic traits from the UKB processed (standardised) grey-matter data. To do so, we randomly selected a set of associated vertices and drew their relative effects from a normal distribution. We then calculated the simulated phenotypes as a linear combination of the individuals' vertex values and noise (see  (Zhang et al. , 2019) for formulas). We considered three scenarios that differ in term of number of associated vertices and total association with the phenotype. This global association between grey-matter measurements and a trait has been coined morphometricity  (Couvy-Duchesne et al. , 2019; Sabuncu et al. , 2016)  and may be expressed as the proportion of the trait variance (R 2 ) captured by the vertex-wise measurement.
We defined significance using a Bonferroni significance threshold of 0. 05/(652283*5)=1. 5e-8, which accounts for the total number of tests performed. For those phenotypes, the true pattern of association is unknown which prevents evaluation of the false positive rate (or power) of the different approaches. However, false positives or redundant associations should not improve prediction accuracy, which we therefore evaluated for each GLM or LMM model in the UKB replication sample, as well as in the OASIS3 dataset. We used linear predictors as in section 2. 8. 5.
This also indicates that the higher prediction accuracy in the UKB replication sample for GLM is likely to be driven by the same confounding factor shared between UKB data sets. The comparable performance of GLM and LMM seen on OASIS3 for BMI aligns with our simulations. For age and sex prediction, prediction accuracy of LMMs was sometimes inferior to that achieved from GLMs, in particular those from the simplest models ("no Covariates" and "age, sex, ICV"). However, prediction from LMM generalised well (comparable accuracy in in UKB and OASIS3), while the GLMs often displayed heterogeneous performances across the test samples (in particular for the GLMs with PCs, which may suffer from PCs being different between samples). Regarding fluid IQ and smoking status, no LMM predictor was available, and the different GLMs resulted in comparable prediction accuracy, albeit limited in both the UKB and OASIS3 sample.
As in the simulations, the LMMs identified fewer vertices and clusters than the GLMs (Figure  4 , STable 2). The LMM with multiple random-effect components was the most stringent (a single cluster of association), consistent with simulations which showed it had the lowest FWER and statistical power. In contrast, the LMM with a single random-effect component identified several cortical and subcortical associations with BMI, age and sex (STable 3). Most (12/19) of the top vertices (in each cluster) replicated in the UKB left out sample (STable 3), even though we cannot rule out that the same confounders might act similarly on the two UKB data sets. Overall, replication may be warranted to conclude about an association in future studies, considering the inflation of false positives (even when using LMMs, Figure  2 ).
Nonetheless, prediction from LMMs generalised better in another independent sample (Figure  4 ), the OASIS3 dataset  (LaMontagne et al. , 2019). This suggests that LMMs result in a more robust estimator of the association between trait and vertices. In the past years, many studies have been published on the association between greymatter structure and our phenotypes of interest (see STable 4-8 for a selective review of publications). Our simulation and empirical results suggest that some of these studies could report a substantial number of false positive or redundant associations. Nevertheless, due to the limitations outlined below, it is unclear which of these studies suffer from this issue and to which extent. Firstly, it has been shown in the omics literature, that power of LMM may be reduced for phenotypes strongly associated with the covariation between features  (Lloyd-Jones et al.
Funding statement This research was supported by the  Australian National Health and Medical Research Council  ( 1078037 ,  1078901 ,  1113400 ,  1161356  and  1107258 ), the  Australian Research Council  ( FT180100186  and  FL180100072 ), the  Sylvia & Charles Viertel Charitable Foundation , the program " Investissements d'avenir "  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche - 10-IA Institut Hospitalo-Universitaire-6 ) and reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ), the  European Union   H2020 program  (project  EuroPOND , grant number  666992 , the joint  NSF/NIH/ANR program  " Collaborative Research in Computational Neuroscience"  (project  HIPLAY7 , grant number  ANR-16-NEUC-0001-01 ), the  ICM Big Brain Theory Program  (project  DYNAMO , project  PredictICD ), and the  Abeona Foundation  (project  Brain@Scale ).
Background Data from neuro-imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as "how old the brain looks", and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility. Objectives To investigate the relationship between brain age and information processing speed in MS. Methods A ridge-regression model was trained to predict age from brain MRI volumetric features and sex in a healthy control dataset (HC_train, n=1690). This model was used to predict brain age in two test sets: HC_test (n=50) and MS_test (n=201). Brain-Predicted Age Difference (BPAD) was calculated as BPAD=brain age minus chronological age. Information processing speed was assessed with the Symbol Digit Modalities Test (SDMT).
Results Brain age was significantly related to SDMT scores in the MS_test dataset (r=-0. 44, p<. 001), and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r=-0. 21, p=0. 003) and a significant weight (-0. 21, p=0. 011) in a multivariate regression equation with age. Conclusions Brain age is a candidate biomarker for information processing speed in MS and an easy to grasp metric for brain health.
Although the SDMT is a brief screening test  (8) , it is prone to practice effects  (9). An objective biomarker to diagnose IPS deficits might circumvent the aforementioned problem. Currently, predominantly structural brain characteristics, extracted from brain imaging techniques such as MRI, were found to be related to IPS  (10). Yet more information can be extracted from an MRI than meets the eye. By using large datasets of brain images of healthy individuals, a machine learning model can be trained to estimate the age of a given brain. For a new brain image, the model will output the best guess of the age of that person's brain, i. e. the "brain age", which can look older or younger than the actual, chronological age of that person. The elegance of brain age is in its interpretable nature; it is easily graspable as how old a brain appears to be.
Methods A. Data description. Data are described by subdividing them in HC_train, HC_test and MS_test, used to train and test the brain age decoding model as shown in figure  1. HC_train was constructed from a large sample of 1690 healthy control (HC) subjects from online open-source repositories. We refer to supplementary table S1 and figure  S1  for a more detailed description. HC_test was collected as part of a study  (15, 16)  on understanding the neural origins of cognitive disturbances in MS, and for which both HC (HC_test, n = 50) and MS (n = 97) subjects were assessed. The MS subjects of this study were recruited at the National Multiple Sclerosis Center (NMSC) of Melsbroek, and were included in the MS_test dataset, which was further enriched with data from the Universitätsmedizin Greifswald (n = 104).