dataesr/scientific-paragraphs-categorization huggingface dataset:
Animals Three adult male monkeys (Macaca mulatta, M1, M2 and M3), purpose-bred within the United Kingdom, were used in the study. The monkeys were 7-8 years old and weighed 10-13 kg at the time of data collection. All monkeys lived in large communal rooms (but with separate housing areas) with several other macaques with whom they could visually interact. Monkeys M1 and M2 were pair-housed, whereas M3 was singly housed. All monkeys were kept on a 12 h light/dark cycle and were given free access to water on nontesting days, and at least 14 h access to water on testing days. Veterinary staff and animal technicians performed regular health and welfare assessments, including formalized behavioral monitoring. Before fixation training, all monkeys were implanted with MR-compatible polyether ether ketone head-posts (Rogue Research) and ceramic screws (Thomas Recordings) under aseptic conditions (for further details, see  Bell et al.
Stimuli and data collection We used four different 220 s video sequences (total video length 880 s) presented in separate runs. There were between three and four runs per daily session for each sequence. Each video sequence consisted of 16 clips of 5, 10, or 20 s long, interspersed with 20 s blank sections. A fixation cue (red circle, 0. 3 degrees) was visible during both video clips and blank periods of the sequences (see Fig. 1A ). Throughout the sequences, monkeys were rewarded for maintaining their gaze within the boundaries of the videos (largest dimension set to 13 degrees). Clips used in the video sequences depicted either conspecifics or the Bonnet macaque (Macaca radiata), a closely related species within the Macaca genus. The clips were obtained from three sources: two nature documentaries and a set of clips filmed within a local breeding center.
Although most video clips depicted M. mulatta, the minority of videos depicting M. radiata (19% of the videos containing monkeys) were included to provide a greater range of behaviors. M. mulatta and M. radiata sometimes form mixed species social groups  (Fooden, 2000). We observed no difference in the behavior of the 3 subjects when they viewed scenes containing either species, and so clips from both species were treated equally in all subsequent data analysis. Clips featuring monkeys had either 1 or 21 actors engaging in different social behaviors. They were classified into three different categories: aggressive, affiliative, and ambiguous (Movies 1, 2, 3). The videos obtained from the local breeding center were initially classified by staff at that facility and later confirmed by the authors.
MRI data acquisition Imaging data were collected using a 3T MR scanner and a four-channel phased-array receive coil in conjunction with a radial transmission coil (Windmiller Kolster Scientific). Both fMRI images and proton-densityweighted reference images were collected while awake animals were head-fixed in a sphinx position in an MR-compatible chair (Rogue Research). fMRI data were acquired using a gradient-echo T2* EPI sequence with 1. 5 Â 1. 5 Â 1. 5 mm resolution, 32 ascending slices, TR = 2 s, TE = 29 ms, flip angle = 78. Proton-density weighted images using a gradient-refocused echo sequence (TR = 10 ms, TE= 2. 52 ms, flip angle= 25) were acquired as reference for body motion artifact correction during preprocessing. T1-weighted MP-RAGE images (0. 5 Â 0. 5 Â 0. 5 mm resolution, TR = 2500 ms, TE = 4.
01 ms, 3-5 sequences per image) were acquired from each of the 3 monkeys in separate scanning sessions and were collected under general anesthesia (for further details of anesthesia protocols and T1 image acquisition, see  Mitchell et al. , 2016; Ainsworth et al. , 2018). Data analysis fMRI preprocessing. Initial fMRI data preprocessing was conducted on a run-by-run basis using Matlab toolboxes developed to correct for common artefacts in monkey functional imaging (Offline Sense and Align EPI toolboxes, Windmiller Kolster Scientific, Fresno, USA). Data were first reconstructed offline from raw image files using SENSE reconstruction to reduce Nyquist/ghost artefacts  (Kolster et al. , 2009). Nonlinear motion artefacts in the data were corrected on a slice-by-slice basis using a third order polynomial to align all volumes within a run to an ideal EPI reference image  (Kolster et al.
, 2014). Further preprocessing of the reconstructed and motion-corrected data was conducted using functions from both AFNI  (Cox, 1996)  and FSL (fMRI of the Brain Software Library)  (Jenkinson et al. , 2012). Individual runs were concatenated to yield a single 4D data file for each session and the resultant data were skull-stripped and signal outliers were removed (using 3dDespike from the AFNI package)  (Cox, 1996). Remaining volumes that were contaminated by excessive motion were identified based on the volume-to-volume variance  (Power et al. , 2012)  (performed with fsl_motion_outliers using the dvars option). For each session, individual volumes with variance greater than the session mean 1 2. 5 times the session SD were identified as outliers and modeled in further analysis as nuisance regressors.
Total motion between video frames was calculated using a block matching method with video frames divided into 25 Â 25 pixel blocks (Block Matching function, Computer Vision System Toolbox, MATLAB 2014a, The MathWorks). These two additional regressors were treated as nuisance regressors to remove the effect of these low-level features on the data. Finally, three binary regressors were created based on the number of monkeys visible on each frame (no monkeys, 1 monkey, and 2 or more monkeys). Video content was manually scored on a frame-by-frame basis and assigned to the appropriate regressor. All regressors were downsampled to 0. 5 Hz to match the 2 s TR of the fMRI sequence. Before convolution with a g function (SD 1. 5 s, mean lag 3 s), all regressors were modified such that volumes in which the monkey failed to fixate for.
Dynamic functional connectivity within a social network The above data show that activation of frontal areas was more prominent when monkeys viewed scenes containing multiple monkeys. We therefore examined the functional interactions, in the form of dynamic functional connectivity, between frontal, temporal, and subcortical regions corresponding to instances where monkeys viewed the different social behaviors. We did so using a progressive four-stage analysis approach, and we present the results of each stage to clearly illustrate how the final result was achieved. Briefly, we assessed the suitability of this approach for identifying how social behaviors affect global functional connectivity within the network by averaging connectivity measures across all ROIs and social behaviors (Stage 1).
In the right hemisphere, four ROIs were identified in the temporal lobe; this included an ROI in V4 as well as three ROIs located along the STS. In addition, a single ROI was located in the parietal cortex of the right hemisphere (the inferior aspect of area 7a). Finally, three ROIs were located in the right frontal cortex. These included one premotor ROI (area 6DR), one in the cingulate gyrus (24a/b), and one ROI in frontopolar area 10 (Fig. 4A ; see Extended Data Fig. 4-1 ). We calculated the dynamic functional connectivity (see Materials and Methods) between all possible pairings of ROIs in this network over an entire session. After preprocessing and concatenating the individual BOLD time-series for each run, the time-series were averaged over the sets of videos that were repeated within a session.
supported by  Medical Research Council Intramural Program   SUAG/051 G101400  and Research Grants  MC-A060-5PR20 ,  WT101092MA , and  MR/P024955/1 ;  Wellcome Trust  Project Grant  203139/Z/ 16/Z ; and  Henry Dale Fellowship   105651/Z/14/Z. We thank the veterinary staff and animal technicians of the  Biological Sciences Building  for the high standards of animal care and husbandry. The authors declare no competing financial interests.
Methods We searched the Pubmed and Web of Science databases for EEG studies of reward processing on 8/10/2019. To be included, studies had to (1) include a group of healthy subjects; (2) involve the delivery of monetary rewards; (3) report statistical analyses of brain activity related to the anticipation and/or receipt of rewards. Based on these criteria, 359 articles (out of 2469 retrieved records) were considered for our survey. Detailed information about the selection process as well as a flow chart are provided in the Supplementary Materials. The following information was extracted from the abstracts or method sections of these articles: (1) nature of the cognitive processes investigated, i. e. reward anticipation and/or reward outcome processing; (2) methods used to collect and process the EEG data, i.
e. technique used (e. g. , EEG, EEG-fMRI…), and presence of ERP, time-frequency and/or source analyses. We chose to extract this information regarding data processing because it captures the use of the three axes for discriminative power of EEG. Indeed, while both ERPs and time-frequency analyses make use of the temporal dimension, only time-frequency analyses exploit the spectral dimension. By source analyses, we here refer to the methods that can contribute to enhanced spatial information, in particular source separation and/or localization methods.
g. , FASTICA, EFICA) or minimization of mutual information (e. g. , INFOMAX, ExtINFOMAX). These algorithms decompose the EEG signal obtained from a specific number of electrodes into the same number of statistically maximally independent components. They are robust to Gaussian noise in the dataset but temporal information is lost. Second order statistics algorithms (SOS; e. g. , SOBI, UWSOBI, WASOBI, AJDC;  Sutherland and Tang, 2006 ; Tang et al. , 2006 Tang et al. , , 2005 ) )  are interesting alternatives. They use time-frequency information to perform the decomposition, with spectral diversity as the main separation criterion. Indeed, they allow applying BSS on shorter time intervals and are more robust with respect to outliers  ( Congedo et al. , 2008 ; Lio and Boulinguez, 2013 ; Tang, 2010 ).
g. , those reflected in the evoked delta and theta band activities) or missed (e. g. , those reflected in the induced beta band activity) in classical time-domain analyses. Paying more attention to reward anticipation and employing more recent methodological advances as discussed here will undoubtedly help to build a more comprehensive picture of the neurocognitive mechanisms underlying reward processing. We believe that it can help answer outstanding questions on reward processing ( Box 3 ). We want to highlight that, even though the analyses we describe here are more difficult to apply than classical ERP analyses, and may also require more resources (e. g. , time, or data storage capacity), they are now greatly facilitated by available software (e. g. , Fieldtrip, EEGlab; see  Michel and Brunet, 2019  for a list of packages including source localization tools).
Funding This work was supported by the  Agence Nationale de la Recherche  [grant number  ANR-16-CE37-0007-03  ], the  Fondation NRJ-Institut de France  and the  University of Lyon  [grant number  ANR-16-IDEX-0005  ], the  Spanish Ministry of Science, Innovation and Universities  [grant number  PGC2018-098032-B-I00  ] and the  ICREA academia program.
Potential biomarkers for disease progression and therapy efficacy We developed a strategy to identify potential biomarkers, combining RNAseq, RT-qPCR and western blotting in muscle with mass spectrometry and ELISA assays in blood, supported by database and literature mining. The validity of these biomarkers for monitoring disease progression and therapy efficacy has to be confirmed in human samples. This will require muscle and blood sampling of untreated and treated patients with different CNM forms together with adequate aged-matched controls. We found that Mstn RNA level is strongly decreased in Mtm1 /y and Dnm2 SL/+ mice and to a lesser extent in Bin1 mck- /-(Log2FC=-0. 6), and was normalized upon modulation of MTM1, BIN1 and Dnm2 (Figures S5, S9, S10, S11)  46. In agreement, MSTN was recently found decreased in plasma from MTM1-and DNM2-CNM patients and responded to ASO Dnm2 treatment in Mtm1 /y mice 69.
In addition, MSTN plasma level was also normalized upon BIN1 overexpression (Figure  7c ). ANXA2 is a calcium-dependent phospholipidbinding protein that has a role in muscle repair  70  , and was validated here as a muscle biomarker for all the CNM forms that we have tested. ANXA2 was detected in plasma, increased in the Bin1 mck-/-mouse model, and normalized upon ASO Dnm2 injection. Moreover, several reports cited ANXA2 to be a valuable biomarker in different cancers  71, 72. In particular, these potential biomarkers could be used in clinical trials to monitor the progression/reversion of the disease or/and efficacy of the therapy. Currently, there are two clinical trials ongoing to treat CNM. Firstly, the clinical trial (NCT03199469) for X-linked myotubular/centronuclear myopathy in patient under 5 years old based on MTM1 gene replacement using AAV.
Perinatal data included obstetric and neonatal information: gestational age (GA), calculated with date of last menstruation and first trimester ultrasound; birth weight; gender; single or multiple pregnancy; mode of delivery; antenatal corticosteroids; small for gestational age (SGA) defined by a birth weight less than the 10th centile of the weight expected for their gestational age, evaluated by Fenton growth chart  (13) ; premature rupture of membranes (PRM); 1-min and 5-min Apgar score; umbilical cord pH; early onset sepsis; ventilator settings; timing and dosage of the first and second dose of surfactant; severe RDS was defined by a grade greater than or equal to grade 3 radiologically. We also recorded all additional respiratory treatments: Doxapram, caffeine, inhaled NO, and corticosteroids.
Statistical Analysis Fifty-two patients received 2 doses in the cohort of 400 very premature infants born during the study period. Two controls per patient, paired for sex and gestational age, were chosen for the analysis. Thus, 156 patients were evaluated in this study. Normally distributed data are presented as mean values with SD; non-normally distributed data are presented as medians with range or Inter Quartile Range (IQR). A Chi2 test or Fisher exact test was used when appropriate for categorical variables. For continuous variables not normally distributed, we used the Mann-Whitney U-test in comparisons. As the FiO2 values after the first dose of surfactant have non-normal distributions and as our aim was more to identify subgroups of subjects than to establish an equation for the whole sample, we did not use multivariate regression models because of the instability that would have affected some coefficients.
We chose a strategy allowing us to hierarchize variables and identify subgroups of subjects with respect to the SGA, Maturation, Outborn and FiO2 1h after the first dose of surfactant. We used a Chi-squared automatic interaction detector (CHAID) method. The IBM SPSS statistic software has extended CHAID algorithms to handle target or predictor variables even if they are categorical, ordinal or continuous. An alpha level of 0. 05 was chosen as a significant difference. All analyses were performed with SPSS IBM Statistics V25.
We use the systems biology markup language (SBML) model 'e-coli-core' from the BIGG database (bigg. ucsd. edu) and extract the stoichiometric matrix S. The adjacency matrix of the metabolite-centric metabolic network shown in figure  1  is then obtained by mapping all non-zero entries in SS T to 1, where S T is the transpose of S. Social graph: includes 77 nodes (people) and 875 edges  [34]. It is an undirected graph and an edge between two nodes is created, if one's knowledge about the skills of others within the company exceeds a threshold equal to 5. 0 (see electronic supplementary material for more details about the real-life networks).
The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions' phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes.
g. motion) across both studies. Asymmetry in ASD has also been under scrutiny. An ENIGMA study of 54 datasets reported cortical thickness asymmetries involving mainly the superior frontal gyrus (Cohen's d = 0. 13), the medial frontal, orbitofrontal, inferior temporal, and cingulate regions, that were reduced in ASD compared to controls. 54 ikewise, functional connectivity has been investigated in ASD. Resting-state functional MRI is particularly appropriate to study psychiatric paediatric population because it enables data acquisition on functional connectivity without patient participation (contrary to task-based functional MRI) and limits excessive motion during scanning. Several analytical methods applied to a large aggregate dataset showed a widespread decrease of connectivity in ASD compared to controls across all datasets.
62, 63 As an example, the largest resting-state functional MRI study in ASD identified across four datasets reproducible patterns of underconnectivity within sensorimotor networks and overconnectivity within the frontoparietal networks (Fig. 3  and Table  1 ) across datasets (0. 2-0. 6 Cohen's d). 64 However, these results were not found by previous studies, likely due to different analytical strategies. The 'gradient' analysis of human functional networks provides an additional coordinate system. 65 It has been studied in the general population, and more recently in ASD. In normative/typically developing studies, this framework identifies a smooth transition along a gradient from unimodal areas of function (sensory, auditory, motor, visual) to higher-order transmodal areas (e. g. DMN). Studies showed that both extremes of the rostrocaudal gradient were decreased in ASD.
25). A widespread decrease in cortical thickness (Cohen's d = -0. 52) was also reported. Adjusting for mean cortical thickness showed thinner cortex in fusiform, parahippocampal, and inferior temporal gyri, and thicker cortex in the precuneus, and superior parietal cortex (Cohen's d = 0. 25). CT differences were greater in the group of individuals treated with antipsychotic medication and were correlated with illness duration. 35 Of note, treatment may play a larger role in neuroimaging studies in schizophrenia compared to ASD due to the lower frequency of medication in the latter group. Functional imaging studies in schizophrenia show reduced mean connectivity but in the absence of large functional MRI datasets in schizophrenia, results should be interpreted with caution. 32, 67 This is predominantly observed within the DMN, ventral attention, frontoparietal, and somatomotor networks (Fig.
95 Larger amygdala and hippocampus volumes were associated with higher scores on the 'Reading the Mind in the Eyes Test'. 95 t the functional level, studies showed that underconnectivity in the medial prefrontal cortex, anterior and posterior cingulate cortex, as well as the precuneus, were altered along a psychosis spectrum (i. e. bipolar disorder and schizophrenia). 67, 96, 97 A large Figure  3  Correspondence between brain regions and functional networks. What constitutes a core functional network is not clear, and no universal taxonomy has been adopted yet. 57 Networks have been defined at several levels of resolution including the commonly used 7network parcellation (top right  58  ) compared to the 12-network MIST parcellation (bottom right 59 ) (https://simexp. github. io/multiscale_dash board/index.
These exciting attempts to bridge macro-and micro-scale observations are initiating fruitful collaborations between genomics, neurobiology, computational and evolutionary neuroscience. Functional dimensions disturbed across psychiatric conditions may also be distributed through these modules of co-expression and functional gradients. 150 Such properties might be related to emerging properties of the genome and the recent evolution of the human brain. 151, 152 nclusion: what have we learned and what are the next steps? Early neuroimaging genomic studies in psychiatry were plagued by small sample sizes and inappropriate candidate gene strategies. Studies of psychiatric disorders were performed on the assumption of relative specificity (Box 1). With access to larger datasets in the past years, both top-down and bottom-up neuroimaging-genomics studies have gained traction with increased reproducibility of nature and effect-size of the alterations.
161 These collaborative initiatives, among others, facilitate advances in psychiatry by providing large brain imaging and genomics datasets to the research community worldwide. Human Genome Project (HGP)  162  ; Neuroimaging Tools and Resources Collaboratory (NITRC platform); Psychiatric Genomics Consortium (PGC)  159  ; Alzheimer's Disease Neuroimaging Initiative (ADNI)  163  ; 1000 Genomes 164 ; ADHD-200  165  ; Open fMRI 166 ; Human Brain Project (HBP)  167  ; ENIGMA Consortium = Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA Consortium) 160 ; = Autism Brain Imaging Data Exchange (ABIDE-1)  55  ; NeuroVault 168 ; UK Biobank 161 ; HCP = Human Connectome Project (HCP)  169  ; PING = Pediatric Imaging; Neurocognition; and Genetics (PING)  170  ; SchizConnect 171 ; EU-Aims  172  ; Adolescent Brain Cognitive Development (ABCD).
] [157] [158]  Only a few datasets currently allow neuroimaging genomic studies (Fig. 6 ): UKBB  161  and ABCD  173  are large population cohorts with great potential to study genomic variation and neuroimaging phenotypes, but they include few individuals with mental illnesses and behavioural deficits. EU-AIMS is among the few psychiatric cohorts integrating genomics, neuroimaging and cognitive data, in 250 individuals with autism. 172 Given our assumptions on the mechanistic heterogeneity in ASD, one would expect that a neuroimaging genomic dataset of several thousand individuals with autism would be required to provide the power to investigate brain-molecular dimensions. Of note, there are currently no neuroimaging genomic cohorts in schizophrenia that are available to the community. The ENIGMA consortium  160  has also been instrumental in moving the field and has provided well-powered metaanalytic studies.
to unambiguously identify in EM studies without complete reconstructions to connect them to identifiable somata. Modern computational volume EM methods offer new opportunities to increase our understanding of brain ultrastructure  [9] [10] [11] [12]  , particularly for morphologically complex, highly dynamic glial cells like OPCs. Here, we used two densely segmented and reconstructed datasets of mouse visual cortex, ages P36 (Fig. 1a ) and P49, to perform a detailed morphometric analysis and quantification of the anatomical features of somata, processes, and organelles of OPCs at this highly dynamic phase of neocortical maturation to help define their roles in the developing brain.