dataesr/scientific-paragraphs-categorization huggingface dataset:
Incomplete hippocampal inversion (IHI), also called hippocampal malrotation, is an atypical presentation of the hippocampus present in about 20% of healthy individuals. Here we conducted the first genome-wide association study (GWAS) in IHI to elucidate the genetic underpinnings that may contribute to the incomplete inversion during brain development. A total of 1381 subjects contributed to the discovery cohort obtained from the IMAGEN database. The incidence rate of IHI was 26. 1%. Loci with P<1e-5 were followed up in a validation cohort comprising 161 subjects from the PING study. Summary statistics from the discovery cohort were used to compute IHI heritability as well as genetic correlations with other traits. A locus on 18q11. 2 (rs9952569; OR = 1. 999; Z = 5. 502; P = 3. 755e-8) showed a significant association with the presence of IHI.
Subjects Subjects were investigated from two cohorts: IMAGEN  [26]  and PING  [27]. The IMAGEN cohort comprises >2000 subjects collected at eight sites across Europe  [26] , and local ethics committee approved the study (see at the end of the paper for details and study  [26] ). At the time of baseline data collection and study inclusion all participants were 14 years of age. The second cohort was obtained from the Pediatric Imaging Neurocognition and Genetics (PING) Study database (http://www. chd. ucsd. edu/research/ping-study. html). PING was launched in 2009 by the National Institute on Drug Abuse (NIDA) and the Eunice Kennedy Shriver National Institute Of Child Health & Human Development (NICHD) as a 2-year project of the American Recovery and Reinvestment Act. The primary goal of PING has been to create a data resource of highly standardized and carefully curated magnetic resonance imaging (MRI) data, comprehensive genotyping data, and developmental and neuropsychological assessments for a large cohort of developing children aged 3 to 20 years.
The scientific aim of the project is, by openly sharing these data, to amplify the power and productivity of investigations of healthy and disordered development in children, and to increase understanding of the origins of variation in neurobehavioral phenotypes. Access to the dataset was granted through a Federal Wide Assurance (FWA). For up-to-date information, see http://www. chd. ucsd. edu/research/ping-study. html and  [27]. All methods were performed in accordance with relevant guidelines and regulations.
Image data processing and IHI scoring The procedure for scoring IHI  [15] , which has been previously described in detail and shown a good intra-and inter-reproducibility  [15] , was applied to the subjects used in this study (from IMAGEN and PING). Inter-and intra-rater variability were assessed in a previous publication  [15]. This was studied on 42 participants from the discovery cohort using the kappa statistic. In all cases, intra-and inter-rater agreements were beyond substantial (κ�0. 64). Very strong agreements (κ�0. 8) were observed in the majority of comparisons (14/20). Rating on the validation cohort was conducted after by a single rater (CC), thus the rater was not blinded to whether subjects were from the discovery or validation cohort. In brief, the IHI score is composed of four different criteria: (1) assessing the roundness of the hippocampal body; (2) evaluating the verticality of the collateral sulcus which is located between the 4 th and the 5 th temporal lobe convolution (Fig 2  );  (3) the mediality of the hippocampal body; and (4) the depth of the fusiform gyrus, separating the 4th and the 3rd convolution of the temporal lobe (Fig  2 ).
Each criterion is assessed from a coronal point of view after registering the subjects' T1 weighted MRI into the standard MNI space using the FSL's affine transformation FLIRT  [28, 29]. Evaluation was carried out using an inhouse Java interface (https://github. com/ cclairec/viewerIHI_java). During scoring each criterion received a score between 0. 0 and 2. 0. The first three criteria have a step size of 0. 5, the fourth criterion is binary (0 or 2), and the 5th criterion, assessed between 0 and 2, has a step size of 1. 0. The sum of those criteria forms the overall IHI score ranging from 0. 0 to 10. 0. This is a semi-continuous score (with a step of 0. 5), where an IHI score of 0. 0 indicates the total absence of IHI, and a score of 8. 0 represents a very pronounced presentation of IHI. In their previous study  [15]  established an optimal cut-off (at 3.
SNP genotyping and pre-processing IMAGEN subjects were genotyped from blood samples on 610-Quad SNP and 660-Quad SNP arrays from Illumina. Genetic data was available for 1,841 subjects. In a first round of quality control (QC) we performed subject-level QC by removing subjects with mismatching selfreported sex and genotype inferred sex (N = 10) or where more than 10% of SNPs were missing (N = 0). Next, we performed ancestry matching based on the HapMap3 data  [30]. Population outliers were defined as subjects exhibiting more than five standard deviations distance from the CEU and TSI population in any of the first five principal components. Based on these criteria, 220 subjects were excluded from further analysis (S1 Fig). For the remaining subjects the genetic relationship matrix (GRM) was computed on common SNPs (minor allele frequency [MAF] >5%) after LD pruning using GCTA  [31].
Another 18 subjects were removed due to relatedness (i. e. , PIHAT > 0. 05) leaving a total of 1593 subjects for the analysis. The raw genotyping data were prepared for imputation using a series of scripts (http://www. well. ox. ac. uk/~wrayner/tools/). Haplotype reference consortium (HRC) v1. 1  [32]  SNPs were imputed on the Sanger imputation server (https://imputation. sanger. ac. uk) using EAGLE2  [33]  for prephasing and PBWT  [34]  for imputation. Data from the two different genotyping chips were imputed independently. Genotypes were hard called based on the maximal genotype posterior probability with a threshold of 0. 9. That is, if none of the three genotypes reached a posterior probability of at least 0. 9, then the SNP was set to missing in the corresponding subject. Finally, an additional round of QC was conducted on SNP level based on imputation quality (INFO score > 0.
3), missingness (< 5%), minor allele frequency (MAF>1%) and deviation from Hardy-Weinberg-Equilibrium (p<1e-6) leaving 6,742,645 SNPs across the autosomes for the association analysis. PING subjects were genotyped from saliva samples on Human660W-Quad arrays from Illumina. After QC, genetic data for 1,391 participants was suitable for analysis. Individual SNPs of the PING dataset were accessed through the PING data portal (ping-dataportal. ucsd. edu). Ancestry and admixture proportions in the PING participants were based on the ADMIXTURE software  [35]  and downloaded through the data portal (for details see  [27] ). We restricted the validation cohort to participants of at least 12 years of age and of European ancestry (minimum 90% European ancestry as per ADMIXTURE; N = 197).
6 in the 1000 Genomes Phase 3 EUR panel) and with a minimum MAF of 1%. Positional mapping was performed by linking lead SNPs to genes in a 50kb window. Mapping based on eQTL was performed by using only SNP-gene pairs significant at FDR<0. 05 in all tissues/cell types from 4 data repositories (GTEx  [40] , the Westra blood eQTL dataset  [41] , the BIOS QTL browser  [42]  and BRAINEAC  [43] ); the available data only covers cis-eQTLs with up to 1 Mb distance between SNP and gene. Chromatin interaction mapping was also performed to take into account potential long-range interactions between risk loci and genes due to chromatin folding. We based mapping on interactions significant at FDR<1e-6 in 14 tissue types and seven cell lines from  [44]. We also based mapping on tissue/cell type specific enhancer or promoter regions annotated in 111 epigenomes from the Roadmap Epigenomics Project  [45].
Heritability analysis and genetic correlation We used LD score regression  [49]  in order to estimate IHI heritability from the GWAS summary statistics data. Next, we computed partitioned heritability estimates using the LD score method described in  [50]  and  [51]. Heritability estimates were partitioned into 53 overlapping functional categories, derived from 24 main annotations, from  [50]. Stratified LD score regression was also used to test for heritability enrichment in genes specifically expressed in a number of tissues of cell types. For this analysis, we used the specifically expressed gene lists compiled by  [51]  for the following datasets: expression levels from RNA-seq experiments in the 53 GTEx tissues and cell types, as well as only the 13 GTEx brain regions; the Cahoy dataset, comprising microarray expression data from three cell types (astrocyte, neuron, oligodendrocyte) in the mouse brain  [52] ; the Franke dataset, comprising microarray expression data in 152 tissues and cell types from human, mouse and rat  [53] ; and the Immunological Genome Project Consortium dataset, comprising microarray expression data for 292 immune cell types in the mouse  [54].
Gene expression in the developing human brain In order to explore the transcription pattern of two candidate genes, we downloaded their expression values from BrainSpan through the web interface (http://www. brainspan. org/ rnaseq/search/index. html). The data comprises post-mortem gene expression data of 42 subjects at ages spanning from prenatal development (eight post conception weeks) till adulthood (40 years). Brains were sampled across 26 brain structures. Gene expression was measured using RNA-sequencing and expression levels for each gene were provided as reads per kilobase of exon model per million mapped reads (RPKM). We analysed this data using a linear mixed effects model implemented in the lme4 package in R. In these analyses, the gene expression level was the target variable, subject ID and structure ID were random effects, while an indicator variable for age less than 25 weeks post conception was the fixed effect.
Genome wide association study and functional annotation We tested each of the 6. 7mio SNPs for an association with the presence of IHI. In the discovery dataset comprising subjects from the IMAGEN study, 17 loci passed the threshold for suggestive association (Fig  3 ; Table  2 ). One locus on chromosome 18 reached genome-wide significance (top SNP: rs9952569; OR = 1. 999; Z = 5. 502; P = 3. 755e-8; Fig  4 ). There was no inflation.
Larger validation cohorts are needed to confirm the association of the identified locus with IHI: e. g. , to validate the top variant with 80% at least N = 500 subjects are required. Although there are growing imaging and genetic datasets, e. g. , the UKBB that aims at 100,000 participants with genetics and brain imaging data, few studies focus on healthy younger subjects (children, adolescents or young adults), which is beneficial for the validation in order to exclude confounding by disease processes or age-related atrophy. One such option is the Philadelphia Neurodevelopmental Cohort (PNC)  [70]. However, it is important to keep in mind that the evaluation of IHI is not restricted to adolescents. IHI can be observed in children and adults too, without extra difficulties. The study here focus on adolescents, since the discovery cohort is the dataset used for the reference study  [15].
Still, in older patients, even though IHI still exist, their detection may be more difficult and less reliable due to the confounding effect of hippocampal atrophy to ageing, hippocampal sclerosis or neurodegeneration. Also scoring bigger databases (such as UKBB) will be feasible only after automatic methods for IHI scoring have been developed. We estimated heritability of IHI based on the state-of-the-art LD score regression method that operates on the GWAS summary statistics. The inferred heritability was substantial with h 2 = 0. 54; the estimate was subject to high uncertainty as reflected by the high standard deviation of 0. 3, which is likely a direct reflection of the low sample size of the discovery cohort. Analysis on twin data, such as the healthy young adult twins participating in the Queensland Twin IMaging (QTIM) study  [71] , can be used to confirm this preliminary heritability estimate, but would require a significant effort in manually scoring IHI in these large cohorts.
Paris: CPP IDF VII (Comitede protection des personnes Ile de France), ID RCB: 2007-A00778-45 September 24th 2007. Dublin: TCD School of Psychology REC. Berlin: ethics committee of the Faculty of Psychology. And Mannheim's ethics committee approved the whole study. All informed consents have been obtained from a parent and/or legal guardian. Written informed assent and consent were obtained, respectively, from all adolescents and their parents after complete description of the study. For the PING dataset, written parental informed consent was obtained for all PING subjects below the age of 18, and child assent was also obtained for all participants between the ages of 7 and 17. Written informed consent was obtained directly from all participants aged 18 years or older.
(RDoCdb). RDoCdb is a collaborative informatics system created by the  National Institute of Mental Health  to store and share data resulting from grants funded through the  Research Domain Criteria (RDoC)  project. Dataset identifier(s): # 2607. Data collection and sharing for this project was funded by the  Pediatric Imaging, Neurocognition and Genetics Study Funding: MAS  acknowledges financial support by the  EPSRC -funded  UCL Centre for Doctoral Training in Medical Imaging  ( EP/L016478/1 ). AA holds an  MRC eMedLab Medical Bioinformatics Career Development Fellowship. This work was supported by the  Medical Research Council  [grant number  MR/L016311/1 ]. The research leading to these results has received funding from the program " Investissements d'avenir "  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche - 10-IA Institut Hospitalo-Universitaire-6 ) and from the  "Contrat d'Interface Local" program  (to OC) from  Assistance Publique-Hopitaux de Paris (AP-HP ).
Data and/or research tools used in the preparation of this manuscript were obtained and analyzed from the controlled access datasets distributed from the  NIMH -supported research Domain Criteria Database (RDoCdb). RDoCdb is a collaborative informatics system created by the  National Institute of Mental Health  to store and share data resulting from grants funded through the  Research Domain Criteria (RDoC)  project. Dataset identifier(s): # 2607. Data collection and sharing for this project was funded by the  Pediatric Imaging, Neurocognition and Genetics Study (PING)  ( National Institutes of Health  Grant  RC2DA029475 ). IMAGEN was supported by the  European Union -funded  FP6  ( LSHM-CT-2007-037286. The funders had no role in study design, data collection (except for PING and IMAGEN grants) and analysis, decision to publish, or preparation of the manuscript.
Data Availability Statement The 15 April 2018 reconnection event was discovered in a database created for the International Space Science Institute Team 442, "Study of the physical processes in magnetopause and magnetosheath current sheets using a large MMS database. " The entire MMS data set is available online (at https://lasp. colorado. edu/mms/sdc/public/links/). Fully calibrated data are placed online at this site within 30 days of their transmission to the MMS Science Operations Center. The data are archived in the NASA Common Data Format (CDF) and so can be plotted using a number of different data display software packages that can use CDF files. A very comprehensive system called the Space Physics Environment Data Analysis System (SPEDAS) is available by downloading (http://themis.
Comparative modelling of the LRRK2 domain The comparative modelling of the LRRK2 domain was done using the hhsearch suite  [23]  to identify a possible 3D template of the Protein Data Bank  [24]  3D modelling was performed using as template the 3DPT PDB entry and the Tito software  [25]  to refine the hhsearch alignment, and identify preserved regions of the template. Loops were then built using the DaR-eUS-loop approach  [26].
Quantification of cellular internalization Human cell line MDA-MB231 was seeded in 24 well plate (1x10 5 cells/well) and treated with different concentrations of FITC-labelled peptides (GL-Biochem) or for different periods of time. After treatment, cells were harvested and washed twice with PBS to remove the extracellular unbound peptide and resuspended in 200 μL of PBS. FITC fluorescence intensity of internalized peptides was measured by flow cytometry on a FACSCanto II as previously described  [19]  (Beckton Dickinson). Data were analysed with FACSDiva 6. 1. 3 software (DB Biosciences). Untreated cells were used as control. For PMBC from HD or CLL patients, cells were maintained in RPMI culture medium.
Analysis of peptide integrity on human serum Peptides were incubated at 37˚C in 250 μl of human serum (Sigma Aldrich) for different periods of time. Samples were collected and peptide degradation stopped by freezing. Peptides were extracted from samples using the Proteo Miner Protein Enrichment System (Bio-Rad). Percentage of intact peptide was estimated by mass spectrometry (MS) using MALDI-TOFF as described previously  [19]  (Bruker Autoflex II) following their protocol. Measurements were performed in triplicate. MS data were analysed using the software Cliprot tools, Felx analysis, Bruker.
In addition, a set of 4 spots is also observed in the upper part of the membrane. As depicted in Fig  2A , this LRRK2 region (1701 to 1718) is exposed to solvent, and adopts anα helical conformation, consistant with its ability to mediate binding to PP1. Such helical conformation is also predicted for the peptide in isolation (Fig  2B ) by the PEP-FOLD software. A chimeric peptide was synthesized containing the newly identified PP1-interacting sequence of LRRK2 fused to an optimized cell penetrating peptide, Mut3DPT-Sh1 (VKKKKIKAEIKI)m an optimized peptide derived from DPT-Sh1 (  [27]. The chimeric peptide, named Mut3DPT-LRRK2-Long, was then used for functional analysis. To minimize the entropic cost upon peptide binding we analysed whether the original interacting sequence could be shortened.
As shown on Fig 2B, although the peptide is predicted to adopt a helical conformation, its N-and C-terminal regions appeared less structured. A shorter peptide (Mut3DPT-LRRK2-Short) lacking the first 2 and the last 3 amino acids was designed using PEP-FOLD prediction software. A search for sequence variants in modelled mammalian sequences was performed using the Uniprot server with the proposed default parameters and an e-value less than 0. 00001. Over 129 matches were identified, revealing a strong conservation at most positions of the interfering fragment (Fig  2C ), 8 out 18 residues being strictly conserved among the vertebrate sequences  [28]. The other positions showed only limited variation, suggesting possible functional constraints for this fragment.
Single nucleotide variants (SNVs) occurring in a protein coding gene may disrupt its function in multiple ways. Predicting this disruption has been recognized as an important problem in bioinformatics research. Many tools, hereafter p-tools, have been designed to perform these predictions and many of them are now of common use in scientific research, even in clinical applications. This highlights the importance of understanding the semantics of their outputs. To shed light on this issue, two questions are formulated, (i) do p-tools provide similar predictions? (inner consistency), and (ii) are these predictions consistent with the literature? (outer consistency). To answer these, six p-tools are evaluated with exhaustive SNV datasets from the BRCA1 gene. Two indices, called K all and K strong , are proposed to quantify the inner consistency of pairs of p-tools while the outer consistency is quantified by standard information retrieval metrics.
In this case, the SNV involves a non-synonymous consistency of the selected p-tools were evaluated against two particular datasets involving the breast cancer type one susceptibility protein encoded by the BRCA1 gene. Both datasets comprise in vitro experiments allowing the exhaustive screening of BRCA1 mutation effects  [14]. The first dataset comprises roughly 4000 SNVs on 1792 nucleotide positions generated by means of the saturation genome editing (SGE) technique  [15]  relying on the CRISPR-Cas9 technology. The second dataset comprises 1056 amino acid mutations in the first 191 residues of the BRCA1 protein generated by means of site-saturation mutagenesis were the authors  [16]  perform a multiplex homology-directed DNA repair assay designed to test whether homology-directed repair (HDR)  [17]  of double-strand DNA breaks occurs in BRCA1 mutant cells.
Due to its CRISPR-Cas9 foundation, the SGE technique may induce multiple genetic mutations beyond the desired one. These undesired mutations may compromise the viability of cells beyond the effect of the SNVs under study. As a result, conclusions concerning the pathogenecity of BRCA1 SNVs drawn from SGE could, in principle, be biased. Fortunately, this does not appear to be the case and the results reported in  [15]  are in good agreement with those reported in  [16] , confirming the value of the SGE technique for performing high throughput studies into the effect of SNVs. From a computational point of view, the SGE technique provides exhaustive and unbiased SNV datasets as every gene position can be tested for all possible mutations. In addition, site-saturation mutagenesis allows the generation of exhaustive and unbiased single-amino acid mutagenesis datasets for the BRCA1 protein.
Although only a fraction of these mutations are accessible by SNVs relevant to human disease, the information content of the whole dataset is definitively higher and thus better for evaluation studies of p-tools. On the whole, the availability of exhaustive and unbiased datasets of SNVs or mutated amino acids remarkably simplifies and normalizes the evaluation of p-tools. To the best of our knowledge, the public availability of SGE datasets is currently limited to the BRCA1 gene. This gene belongs to the 'first wave' of susceptibility genes for common types of cancer  [18]. Therefore, the identification of carriers of pathogenic mutations in this gene is expected to be more impactful for cancer control.
P-Tools The effect of SNVs on the functionality of the BRCA1 gene was assessed by means of the PolyPhen2  [10] , the Provean  [19] , the Align GVGD  [20] , the Strum  [9] , the Cupsat  [21] , and the Panther  [8]  prediction tools. In all cases, except for PolyPhen2, in which we used the HumVar classification model (advanced options), which was better suited for this study, their online version configured with default parameters were used. For Cupsat predictions, the Protein Data Bank (PDB) file of BRCA1 was provided. Further details about the selected p-tools can be found in the Appendix A.
Datasets BRCA1-SGE dataset. The authors  [15]  studied the ability to grow haploid human cells in cell cultures. Cells were edited by means of the CRISPR-Cas9 technology with a focus on every nucleotide (saturation genome editing) of the BRCA1 gene in a region spanning 13 different exons known to encode critical functional domains. The original study comprises nearly 4000 mutations belonging to exons 2-5 and 15-23, including some adjacent intron sequence. Cultured cells that managed to survive to gene editing were considered to hold a functional BRCA1 protein. The original dataset was filtered to remove misleading SNVs classified as "Likely Benign" missense mutations. As a result, the final dataset comprises 387 "pathogenic" missense SNVs (positive examples) and 1405 "benign" missense ones (negative examples).