dataesr/scientific-paragraphs-categorization huggingface dataset:
MRI Data Analysis After scanning, the T1-weighted images were stored in Bruker format and were converted to ANALYZE format using Bruker2Analyze Paravision Converter 1 after the experiments. Registration, smoothing, and statistical analysis were performed using SPM8 (Wellcome Department of Cognitive Neurology, London, United Kingdom) running on MATLAB R2014a (MathWorks, Sherborn, MA, United States). The images were registered to the mouse template images, which were created by averaging 20 mouse brain images and co-registered to the Franklin and Paxinos mouse brain atlas  (Franklin and Paxinos, 2008). The signal intensities of all images were normalized to the simultaneously measured signal from the reference solution of 0. 1 mM MnCl 2. The images were smoothed with a Gaussian kernel of 0. 2 mm full-width at half-maximum.
Using a voxel-by-voxel t-test for statistical analysis, smoothed images were compared between formalin-and saline-injected animals (i. e. , [2 h Formalin] vs. [2 h Saline] and [6 h Formalin] vs. [6 h Saline]), between 2-and 6-h injection animals (i. e. , [2 h Saline] vs. [6 h Saline] and [2 h Formalin] vs. [6 h Formalin]), and between [hM4D] and  [EGFP]. The comparisons in this study were always made between two datasets with only single difference in the experimental conditions. We did not make any multiple (≥3) group comparisons because it makes the hypothesis to be tested meaningless. The areas showing a significant difference in the manganese signal (P < 0. 05, corrected for multiple comparisons using the false discovery rate (FDR) procedure at cluster level) were analyzed to construct the T1-weighted images.
FUNDING This research was supported by the  AMED  under Grant Number  16ek0610002h0003  and funding from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) - Supported Program  for the  Strategic Research Foundation at Private Universities  (No. S1311009 ) to FK,  Grants-in-Aid for Scientific Research  (B) to FK (Nos. 25293136  and  18H02722 ), a  Grantin-Aid for Young Scientists (B) and Grant-in-Aid for Scientific Research (C)  to YT (Nos. 15K19194  and  17K09042 ), and a  Grant-in-Aid for Early-Career Scientists  to YS (No. 19K16932 ).
Methodological overview We quantified DNA methylation across the genome using the Illumina Infinium HumanMethylation450 BeadChip ('450K array') in 223 post-mortem tissue samples comprising PFC, TC and CB dissected from 43 donors with ASD (including 7 patients with dup15q syndrome and 36 patients with iASD) and 38 nonpsychiatric control subjects. After implementing a stringent quality control (QC) pipeline (see Materials and Methods), we obtained high-quality DNA methylation data from 76 PFC samples (n = 36 iASD patients, n = 7 dup15q patients, n = 33 controls), 77 TC samples (n = 33 iASD patients, n = 6 dup15q patients, n = 38 controls) and 70 CB samples (n = 34 iASD patients, n = 7 dup15q patients, n = 29 controls) (Supplementary Material, Table  S1 ). Our primary analyses focused on identifying differentially methylated positions (DMPs) and differentially methylated regions (DMRs) associated with iASD and dup15q, controlling for cellular heterogeneity and other potential confounds, exploring the extent to which signals were shared across idiopathic and syndromic autism cases.
Cortical co-methylation modules associated with ASD are enriched for immune, synaptic and neuronal processes We next used WGCNA  (45)  to characterize systems-level differences in DNA methylation associated with ASD. We built co-methylation networks using all 'variable' DNA methylation sites (defined as those where the range of DNA methylation values for the middle 80% of individuals was greater than 5%; N = 251 311) using cross-cortex (PFC and TC) data from all donors (see Materials and Methods). WGCNA identified 61 co-methylation modules (Supplementary Material, Table  S16 ), and we used the 'module eigengene' (i. e. the first principal component) for each module to explore differences between controls (n = 29), individuals with iASD (n = 30), individuals with dup15q (n = 6) and a combined ASD group (n = 36).
DNA methylation profiling and data QC All samples were randomized with respect to phenotypic status, age, sex and brain bank to avoid batch effects throughout all experimental procedures. Genomic DNA (500 ng) from each sample was treated in duplicate with sodium bisulfite using the Zymo EZ DNA Methylation-Lightning Kit TM to minimize potential bisulfite-related biases (Zymo Research, Irvine, CA, USA). Genome-wide DNA methylation was quantified using the pooled bisulfite-converted DNA samples and the Illumina Infinium HumanMethylation450 BeadChip ('450K array') (Illumina, San Diego, CA, USA), scanned on an Illumina HiScan System. Illumina GenomeStudio software was used to extract signal intensities for each probe, generating a final report that was imported in to the R statistical environment 3.
0. 2 (www. r-project. org) (62) using the methylumi (63) package. Data QC and pre-processing were performed using the wateRmelon package as described previously  (64). Multidimensional scaling plots of sex chromosome probes were used to check that the predicted sex corresponded with the reported sex for each individual, and comparison of 65 single nucleotide polymorphism (SNP) probes on the array confirmed that matched tissues were sourced from the same individual. Stringent filtering of the pre-normalized Illumina 450K data was performed. First, we removed the 65 SNP probes, cross-reactive probes and probes overlapping polymorphic CpGs containing an SNP with minor allele frequency of >5% within 10 bp of the single base extension position as detailed in the Illumina annotation file and identified in recent publications  (65, 66).
Second, CpG sites with a detection P-value of >0. 05 in 1% of samples identified by the pfilter function within the wateRmelon R package were removed. Third, polymorphic SNP control probes (n = 65) located on the array were used to confirm that matched cortex and CB tissues were sourced from the same individual. The final data set consists of a total of 417 460 probes from 76 PFC samples (n = 36 iASD patients, n = 7 dup15q patients, n = 33 controls), 77 TC samples (n = 33 iASD patients, n = 6 dup15q patients, n = 38 controls) and 70 CB samples (n = 34 iASD patients, n = 7 dup15q patients, n = 29 controls) (Supplementary Material, Table  S1 ) was normalized with the dasen function of the wateRmelon R package and then batch-corrected with the ComBat function of the ComBat R package  (67). For the dup15q samples, their duplication status and breakpoints were confirmed by genotyping  (11)  and further validated by CNV calling from the 450K DNA methylation data using R package ChAMP  (68).
Identification of autism-associated differential methylation All statistical analyses were conducted using R statistical package (version 3. 1. 1). Analyses were performed to test for DMPs and DMRs associated with disease status for each brain region. The R package Cell EpigenoType Specific (CETS) mapper (69) designed for the quantification and normalization of differing neuronal proportions in genome-wide DNA methylation data sets was used to estimate brain cellular heterogeneity in cortex (both PFC and TC) samples. CETS-based neuronal cell composition estimate was not applied on the CB samples given the known high proportion of non-NeuN-expressing neurons in this brain region  (41, 69). To model the effect of sample-specific variables, we performed linear regression for each probe using age, gender, brain bank, CETS (for PFC and TC but not CB) and diagnosis as independent variables.
Weighted gene correlation network analysis For the co-methylation network analyses, modules of comethylated probes were identified using the WGCNA package in R  (45) , and a network was constructed for cross-cortex samples. For each network, the input probe set was pruned to remove probes with minimal variability across samples. We required that each probe have a minimum range of methylation values of 5% within the middle 80% of samples, resulting in 251 311 probes for cortex. In the cross-cortex network, to enrich for modules relating to diagnosis, we regressed out the effect of CET score and age after fitting a linear model for each probe with diagnosis, age, sex, cortical region, CET score and brain bank as independent variables. A signed network was constructed using the biweight mid-correlations between probes with a soft power of 7.
This network was generated blockwise, using the WGCNA function blockwiseModules with the following parameters: maxBlockSize = 15 000, mergeCutHeight = 0. 1, deepSplit = 4 and minModuleSize = 100. For each module, a linear mixed effects model was fit between its eigengene and diagnosis, age, sex, cortical region, batch, CET score and brain bank as fixed effects with individual ID as a random effect. This LME model was fit for three subsets of samples: (iASD + dup15q) versus control, iASD versus control and dup15q versus control.
Acknowledgements We are grateful to the patients and families who participate in the tissue programs from which our samples were obtained. Human tissue was obtained from the  Autism BrainNet  (sponsored by the  Simons Foundation  and  Autism Speaks , formerly the  Autism Tissue Program)  and the  University of Maryland Brain  and  Tissue Bank , which is a component of the  NIH NeuroBioBank  and the  Oxford Brain Bank.
e. , head and tail) of the hippocampus. After this step, the working group will focus on the entorhinal, perirhinal, and parahippocampal cortices (i. e. MTL cortical regions). The workflow comprises five iterative steps (see Fig. 1B ). First, several neuroanatomists (K. A. , O. K. , J. C. A. , S-L. D. , and R. I. ) label ex vivo data sets, the results of which are serving as an additional reference set for boundary definition. This step has been completed for the hippocampal body and is in progress in the head (details in Table  1 ). This new HSG postmortem data set allows us to better account for interindividual and across-laboratory variability than previously possible. Second, the HSG working groups, through meetings and conference calls, regularly discuss and establish guidelines for subfield boundaries based on reference atlases, neuroanatomical landmarks visible on MRI, and geometric heuristics.
This difference is particularly problematic in anatomically complex regions such as the hippocampal head. To overcome the outlined limitations inherent to the extant references, the HSG developed a novel data set corresponding to the oblique-coronal plane used in MRI research (i. e. perpendicular to the long axis of the hippocampus; Table  1 ). The data set includes delineations of subfields in histological sections obtained from multiple specimens, each labeled by multiple neuroanatomists). This HSG histology data set thus enables us to develop a harmonized protocol that better addresses discrepancies across neuroanatomy laboratories and individual differences in regional delineation than previously possible. In creating this data set, as noted previously, the HSG includes MTL neuroanatomy experts who actively participate in this harmonization effort (see Table  1 ).
Moreover, to address limitations of our HSG histology data set regarding sample size, gender distribution, and inclusion of subjects with neurodegenerative disease, we also have access to a newly developed postmortem data set from the University of Pennsylvania containing 0. 2 ! 0. 2 ! 0. 2 mm 3 resolution MRI scans of 31 hippocampal specimens (25 individuals; 12 male; 16 with and 9 without dementia), nine of which have densely sampled serial histology sections, and two of which also have ante-mortem imaging  [39]. The University of Pennsylvania data set will continue to grow with additional cases over time.
Milestone #1: Histological data sets of the hippocampal body and head created for the HSG Histologically stained slices of the hippocampal body originated from three research sites: Research Centre J€ ulich, Massachusetts General Hospital, and the University of Pennsylvania, and were prepared using the methods customary for each laboratory (sample characteristics in Table  1 ). Three neuroanatomy laboratories labeled the hippocampal subfields, on all samples, enabling the evaluation of acrosslaboratory differences in boundary definitions (see Fig. 3 ). The HSG is developing a similar novel histological data set to guide harmonization using samples from the hippocampal head (Table  1 ). Three samples were obtained from two research sites (the University of Pennsylvania and the Allen Institute) and labeled by the HSG neuroanatomists.
Discussion Segmentation of the MTL is important for the study of development, neurodegeneration, and the neural basis of memory and other cognitive functions. The integration of knowledge and replication of results across laboratories has been hindered by variable methodology. Although a harmonization process that requires time and resources from scientists worldwide is challenging, the HSG is committed to the effort. We are encouraged by the success of the Delphi procedure in obtaining consensus on the outer boundaries of the hippocampal body (Milestone 4), and we anticipate that this procedure, although time-intensive, will result in a protocol that is more readily adopted by the community. This project addresses the "interoperability" and "reusability" terms in the "FAIR" guiding principles for reproducible science (FAIR is an abbreviation for: findability, accessibility, interoperability, and reusability)  [45] , and is part of a larger movement in the field to provide greater access to neuroimaging data sets and standardized research procedures to pool data and increase reliability of individual studies  [46, 47].
1. Systematic review: Imaging of medial temporal lobe subregions, including the hippocampal subfields, has become increasingly popular due to the improvement of neuroimaging techniques. The Hippocampal Subfield Group (HSG), which includes more than 200 international scientists with various fields of expertise, was formed in 2013 to develop a harmonized, valid, and reliable protocol for in vivo medial temporal lobe segmentation. 2. Interpretation: Our group has gathered a unique comprehensive histology dataset to support the anatomical validity of the proposed harmonized segmentation protocol and achieved community consensus on an initial set of boundary definitions using online questionnaires. Adoption of the finalized harmonized protocol will facilitate aggregation of data across research sites and across-lab replications.
Acknowledgments This work is the product of an  EU   Joint Programme-Neurodegenerative Disease Research (JPND)  project. The project is supported by  France ,  Agence National de la Recherch  e, under the aegis of JPND-www. jpnd. eu. R. K. O. also acknowledges funding support from the  Alzheimer Society of Canada, Natural Sciences and Engineering Research Council of Canada (NSERC) , and  Canadian Institutes of Health Research (CIHR). The work on this article was also supported by  NIH  grant  R01 AG-011230  to  Naftali Raz ,  NIH  grant  R01 AG-055121  to  Lei Wang ,  NIH  grant  R01 AG-056014  to  Paul Yushkevich , and  NIH  grant  R01 AG-034613  to  Craig Stark. M. M. C. receives salary support from the  Fonds du Recherches Sant es Qu  ebec and also acknowledges support from  NSERC ,  Weston Brain Institute , and  CIHR.
AbsTrACT Social position is known to play a role in the quality of ageing, notably through the stimulation/dysregulation of key physiological systems in response to external stresses. Using data from one wave of Understanding Society including 9088 participants, we defined, as an extension of the allostatic load, a synthetic Biological Health Score (BHS) capturing the wear-and-tear of four physiological systems (endocrine, inflammatory, cardiovascular and metabolic systems) and two organs (liver and kidney). We used 16 established blood-derived biomarkers of these systems to calculate the BHS and explored the relative contribution of socioeconomic position to the BHS and its main components across age groups. We identified a systematic decreasing educationrelated gradient of the BHS (p<0. 001) leading to lower biological risk in participants with longer education.
Search Strategy Two high-profile databases (PubMed and Web of Science) were used to perform the computer-based research on the 25 January 2019. The string used to carry out the search (Title/Abstract for PubMed and Topic for Web of Science) was as follows: ("active" OR "enactment") AND ("spatial memory" OR "spatial knowledge" OR "episodic memory") AND ("virtual reality" OR "environment*). The search resulted in 647 articles for Web of Science and 94 for PubMed (total of 741). We made a first selection by reading titles and abstracts after removing duplicates. Four papers were identified through other sources. A total of 35 manuscripts were chosen for full-text screening. This procedure resulted in 31 experimental studies. See the flow diagram (Figure  1 ) for the paper selection procedure.
Search Strategy Two high-profile databases (PubMed and Web of Science) were used to perform the computerbased research on the 25 January 2019. The string used to carry out the search (Title/Abstract for PubMed and Topic for Web of Science) was as follows: ("active" OR "enactment") AND ("spatial memory" OR "spatial knowledge" OR "episodic memory") AND ("virtual reality" OR "environment*). The search resulted in 647 articles for Web of Science and 94 for PubMed (total of 741). We made a first selection by reading titles and abstracts after removing duplicates. Four papers were identified through other sources. A total of 35 manuscripts were chosen for full-text screening. This procedure resulted in 31 experimental studies. See the flow diagram (Figure  1 ) for the paper selection procedure.
g. source localization problem) call for alternative approaches. -6-Addressing this challenge, we here studied patterns of oscillatory activity induced by single pulses of intracranial electrical stimulation through intracranial EEG (iEEG) recordings in a cohort of epilepsy patients implanted with depth multi-electrodes  17. Datasets were acquired during invasive mapping sessions using single electrical pulses to causally localize and probe epileptogenic regions, prior to neurosurgical removal. The impact of electric stimulation was analyzed in the frequency domain by recording iEEG activity from multi-electrode contacts monitoring non-epileptogenic regions. The spectral signatures induced by electrical stimulation were integrated in a common human brain atlas presented in normalized MRI space.
Patient populations and intracranial iEEG electrode montage We studied intracranial EEG datasets from 19 medication-resistant epilepsy patients (11 females, 8 males) between 18 and 41 years old (mean age 24) implanted with intracranial deep electrodes (Figure  1 ) undergoing a thorough clinical evaluation of epileptic foci prior to consider their neurosurgical resection (   Each patient was implanted with an average of 9±2 depth electrodes (Adtech, Racine, Wisconsin, USA) and 63±12 contacts (~7 contacts per electrode). Implantation sites were selected exclusively on clinical criteria, unrelated to the aims of analyses planned for the present study. patients were medication-free at the time intracranial stimulation and iEEG recordings were performed. The implantation procedure was guided with a Leksell stereotactic frame (Elekta, Stackholhom, Sweden) using T1 magnetic resonance imaging sequences (3T, General Electrics, Fairfield, Conneticut, USA) performed prior to the implantation procedure (Figure  1 ).
For each patient, a post-implantation CT scan was co-registered with the normalized T1 MRI sequence in MNI space. The MNI coordinates of each contact were recovered automatically using in-house custom-made The coordinates of each multielectrode contact were associated to a brain region of the Automated Anatomical Labeling (AAL) human brain atlas  20. This identification was performed by overlaying MNI normalized contact coordinates to the AAL atlas template using the MRIcron software  21. Our analyses included iEEG datasets from electrodes implanted in 66 different anatomical areas, vastly covering left and right frontal, temporal, parietal, occipital regions, as well as the hippocampus (Figure  2 ). Signals from contacts implanted in the cerebellum, the ventricular system or in structures such as, the skull, meningeal spaces and coverings were not included in our analyses.
Intracranial stimulation and intracranial EEG acquisition procedures Raw EEG data were recorded continuously from each implanted contact using a 16 bits Micromed Amplifier system (Micromed, Mogliano Veneto, Italy). The sampling rate was set at 1024 Hz and signals were bandpass filtered at 0. 15-350 Hz. An external electrode located on the scalp FCz position (10/20 system) was used as a reference for iEEG recordings. During this session, electrical stimulation was performed as a part of the routine clinical protocol put in place to identify the localization of epileptogenic areas  22  with a programmable clinical Micromed stimulator. Patients laid comfortably on a bed and were asked to keep their gaze on a fixation cross displayed on the center of a computer screen placed in front of them during the delivery of each stimulation burst.
During stimulation sessions leading to our dataset, pulses were delivered at increasing intensities (from low-to-high intensity) ranging from 0. 5 mA to a maximum of 5 mA, totaling an overall number (across patients) of 3475 pulses (225 pulses at 0. 5 mA, 815 pulses at 1mA, 1270 pulses at 2mA, 770 pulses at 3mA, 280 pulses at 4mA, 115 pulses at 5mA). Each subject received an average of 200 pulses (a minimum of 40 and a maximum of 340 pulses). The mean duration of a stimulation session was ~4 h, including all the resting periods that each patient needed during the session.
Intracranial EEG data pre-processing Intracranial EEG responses to single-pulse electrical stimulation were recorded by multielectrode contacts of the implantation scheme not involved in delivering electrical current. For each stimulation pulse we extracted 1400 ms of iEEG data (500 ms prior and 900 ms following the onset of each electrical pulse). Data were pre-processed with a pipeline based on in-house programmed software (Matlab, Mathworks, MA, USA) including an artifact removal procedure to eliminate stimulation artifacts  16  and a Laplacian data transformation to estimate local field potentials induced by each electrical pulse  23. Importantly, only single pulse iEEG data from contacts corresponding to areas classified by an expert epileptologist as non-epileptogenic (i. e. , documented as 'healthy' in a clinical report employed later to discuss and plan neurosurgical approaches) were included in our analyses.
Additionally, trace-by-trace and subject-by-subject visual inspection with ad hoc help from an expert epileptologist allowed further verification that the selected iEEG dataset were clean of interictal or post-pulse epileptogenic activity and free of any additional sort of artifact (e. g. , malfunctioning contacts, 50 Hz noise, contacts placed in non-neural tissue such as subdural spaces or air) which could eventually affect analyses and bias findings. Dataset obtained from sites classified as epileptogenic and eventually considered as potential candidates for neurosurgical resection were left aside and remained unexamined. Electrical currents injected by the stimulation induced a stereotyped artifact characterized by highamplitude waveforms, which affected recordings for at least 7 milliseconds  24.