dataesr/scientific-paragraphs-categorization huggingface dataset:
We find that 36% of genes dysregulated in TSC2 -/-neurons 79 are also differentially expressed in our KPTN -/-NPCs (301 out of 834 expressed in NPCs, 1. 35 fold over-enriched, hypergeometric test p=4. 3x10 -10 ), thus identifying a common signal in neural cell types driven by convergent disease mechanisms affecting the mTOR pathway. The Connectivity Map 80 (CMAP), a large-scale collection of transcriptional response profiles after compound exposure (20,000 different compounds and drugs) or gene knockdown across numerous cell lines, uses negative correlation between drug-disease profile pairs to identify potential therapeutics to reverse disease states. We queried the CMAP database with the transcriptional response signature of our KPTN -/-NPCs. We find that torin-2  81  , a known mTOR inhibitor, scores as the most strongly negatively correlated profile (global correlation score of -96.
Mouse behavioural paradigms In all assays, the mice were tracked using over-head infrared video cameras and a userindependent automated video tracking software Ethovision XT 8. 5 (Noldus Information Technology, The Netherlands). All experiments were carried out on mice aged 10-22 weeks. The mice were handled for 2-3 days before the onset of testing. All mice were habituated to the behavioural room in their home cages for ≥ 30 minutes under the same light condition as the test. Male mice were tested unless stated otherwise.
Microcomputed X-ray tomography Skulls of male mice (n=5 per genotype) were collected after transcardial perfusion and stored in formalin solution at 4C until they were ready to be scanned. High-resolution images were acquired using the 1172 Bruker microCT scanner. The scanning parameters were set to: 2000 x 1332 object position, pixel size 9. 89, Medium camera size. Prior to acquisition, we ensured the average intensity value (av=xx) was in the acceptable range of 40-60% (no more than 70%) and the 'min' value was ≥10%. DICOM files generated by the Bruker software were loaded into 3DimViewer (V3. 0, http://www. 3dim-laboratory. cz) for surface model creation using default thresholding (1482/7000) and saved as an. STL file. The STL files were loaded individually into Meshlab software 114 (V2016.
iPSC RNA extraction and RNA sequencing RNA extraction and sequencing of iPSC derived materials was performed using manufacturer's protocols for the RNeasy QIAcube kit on a QIAcube automated system (Qiagen). RNA sequencing libraries were prepared using established protocols: library construction (poly(A) pulldown, fragmentation, 1st and 2nd strand synthesis, end prep and ligation) was performed using the NEB Ultra II RNA custom kit (New England Biolabs) on an Agilent Bravo automated system. Indexed multiplexed sequencing was performed on the Novaseq 6000 system (S4 flow cell, Xp workflow; Illumina), collecting approximately 30 million paired-end reads per sample with 100 base read length. The sequencing data were de-multiplexed into separate CRAM files for each library in a lane. Adapters that had been hard-clipped prior to alignment were reinserted as soft-clipped post alignment, and duplicated fragments were marked in the CRAM files.
The data pre-processing, including sequences QC, and STAR alignments were made with a custom Nextflow pipeline, which is publicly available at https://github. com/wtsihgi/nextflow-pipelines/blob/rna_seq_5607/pipelines/rna_seq. nf, including the specific aligner parameters. We assessed the sequence data quality using FastQC v0. 11. 8. Reads were aligned to the GRCh38 human reference genome (Ensembl GTF annotation v91). We used STAR  115  version STAR_2. 6. 1d with the --twopassMode Basic parameter. The STAR index was built against GRCh38 Ensembl GTF v91 using the option -sjdbOverhang 75. We then used featureCounts version 1. 6. 4  116 to obtain a readcount matrix. Genes with no count or only a single count across all samples were filtered out. The counts were normalised using DESEQ2's median of ratios method 117.
Library preparation was performed by Wellcome Sanger Institute DNA Pipelines, as described above for iPSCs. Samples were sequenced using 75 bp paired-end sequencing reads (reverse stranded) on a Illumina-HTP HiSeq 4000 system. The sequencing data were de-multiplexed into separate CRAM files for each library in a lane. Adapters that had been hard-clipped prior to alignment were reinserted as soft-clipped post alignment, and duplicated fragments were marked in the CRAM files. The data pre-processing, including sequences QC, and STAR alignments were made with a Nextflow pipeline, which is publicly available at https://github. com/wtsi-hgi/nextflowpipelines/blob/rna_seq_mouse/pipelines/rna_seq. nf, including the specific aligner parameters. We assessed the sequence data quality using FastQC v0.
11. 8. Reads were aligned to the GRCm38 mouse reference genome (Mus_musculus. GRCm38. dna. primary_assembly. fa, Ensembl GTF annotation v99). We used STAR version 2. 7. 3a 115 with the --twopassMode Basic parameter. The STAR index was built against Mus_musculus GRCm38 v99 Ensembl GTF using the option -sjdbOverhang 75. We then used featureCounts version 2. 0. 0 116 to obtain a readcount matrix. The count data was used as input for differential gene expression analysis using DESeq2 package  117  with SVA correction  118. The default DESeq2 cut-off of BH-adjusted p-value<0. 1 was used for the mouse RNA-Seq analyses. For the identification of functionally enriched terms in the differentially expressed genes, Gene ontology (GO) and KEGG pathway enrichment analyses was performed using the grpofiler online suite 72 (http://biit.
Acknowledgements First and foremost, we are grateful to the patients and their families for participating in this study. In particular we would like to thank the KPTN alliance and the  Amish community  for their support of the Windows of  Hope  project. This work was supported by core  Wellcome  funding to the  Wellcome Sanger Institute  (Grant reference numbers:  206194  and  108413/A/15/D ), and  Open Targets  project  OTAR2053. B. Y. is supported by funding from the  Jerôme Lejeune Foundation , the  French National Research Agency (ANR)  and the  National Institute of Health and Medical Research (INSERM). PBC, ELB and AHC are supported by a Javits Award  ( NINDS R37-NS125632 ). LER, ELB and AHC were also supported by the  Newlife Foundation  ( 16-17/12 ). We would like to thank all the staff of the  Wellcome Sanger Research Support Facility  for mouse husbandry and their contributions to rapamycin injection sessions,  Hannah Wardle-Jones  for expert colony management, and members of the  Wellcome Sanger Mouse Genetics Programme  for the initial mouse model observations.
Data availability The European Nucleotide Archive accession numbers for the RNA-seq sequences reported in this paper are as follows: Mouse-derived brain RNA-Seq samples: ERA941410-ERA941421, ERA941432-ERA941465, ERA2115013-ERA2115023, ERA2115035-ERA2115045 iPSC-derived NPC samples: ERS8443331, ERS8443330, ERS8443332, ERS8443333, ERS8443322, ERS8443323, ERS8443326, ERS8443327, ERS8443328, ERS8443329.
1. Data deposition: If your manuscript contains data that requires submission (or will be submitted) to a public database, please include the accession number in the manuscript. For more details see our Information for Authors at https://nam02. safelinks. protection. outlook. com/?url=http%3A%2F%2Fwww. cell. com %2Fajhg%2Fauthors&amp;data=04%7C01%7Cpduggal%40jhu. edu%7Cc908fefb2b014 79a89da08d9b694bd80%7C9fa4f438b1e6473b803f86f8aedf0dec%7C0%7C0%7C63774 1573442450642%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIj oiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&amp;sdata=ir9vJcNcgL xv04oFaEsWRcvuI0WhNDInNhBe4axgZb8%3D&amp;reserved=0 This manuscript didn't generate new genotype data. Genotypes of the analyzed individuals are already deposited in the dbGaP databases. The accession numbers for these datasets, and the accession number for other used resources, are provided in the "Data and Code Availability" section as described below.
Data and Code Availability The datasets used in this study are available at dbGaP accession numbers phs000454. v1. p1 (https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs000454. v1. p1) and phs000248. v1. p1 (https://www. ncbi. nlm. nih. gov/projects/gap/cgibin/study. cgi?study_id=phs000248. v1. p1). The reference panel derived from the "Consortium on Asthma among African-ancestry Populations in the Americas" is available at dbGaP accession number phs001123. v1. p1 (https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs001123. v1. p1) The HCV polyprotein sequence is available at GenBank: AFE48416. 1. https://www. ncbi. nlm. nih. gov/protein/AFE48416. 1) 2. Accept all changes, remove line numbers, and turn off the 'Track changes while editing' feature of MS Word.
4. For publication, we require a "Data and Code Availability" section. The section should describe the availability of datasets and/or code associated with the paper, including any conditions for access of datasets and/or code not publicly available. This section should also include any accession numbers, DOIs or unique identifiers, or web links to deposited datasets. Please see https://nam02. safelinks. protection. outlook. com/?url=https%3A%2F%2Fwww. cell. co m%2Fajhg%2Fauthors&amp;data=04%7C01%7Cpduggal%40jhu. edu%7Cc908fefb2b01 479a89da08d9b694bd80%7C9fa4f438b1e6473b803f86f8aedf0dec%7C0%7C0%7C6377 41573442450642%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQI joiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&amp;sdata=3%2BtAoj vjeNlse3shmTxzDjljAyDIBBLm%2FzRFiRoyAl4%3D&amp;reserved=0 for more information.
Genotyping, Imputation of SNPs, classical HLA alleles and amino acids Our initial dataset consisted of genotypes obtained using the Illumina Omni1-Quad BeadChip array (Illumina, San Diego, CA)  5, 6  and processed using standard genome wide association study protocols for quality control  6  (dbGaP accession numbers phs000454. v1. p1 and phs000248. v1. p1). We extracted genotyped markers from a 2Mb segment of the MHC corresponding to the extended class II region  31  delimited by Chr6:32,000,000-34,000,000 (coordinates based on the Genome Reference Consortium Human Build 37, GRCh37/hg19). SNP imputation was done using the Minimac3 32 software through the publicly available Michigan Imputation Server 6. As part of the quality control, we excluded SNPs with imputation r 2 < 0. 3 and minor allele frequency (MAF) < 1%.
In each ancestry group, we used the method implemented in HIBAG (HLA Imputation using attribute BAGging)  33  to impute classical HLA alleles from genotyped/imputed SNPs for HLA-DPB1, HLA-DQA1, HLA-DQB1 and HLA-DRB1 loci producing genotypes at two fieldresolution for each individual. This method takes advantage of the extended haplotype structure within the MHC and makes predictions of classical HLA alleles by averaging HLA-type posterior probabilities over an ensemble of classifiers built on bootstrap samples. Classifiers are trained from large databases of individuals with known HLA and SNP genotypes within the MHC region  33. In order to capture the appropriate genetic background, we used a paired ancestry group of classifiers for each target population. For European ancestry we used pre-built classifiers computed from individuals of European ancestry from the HLARES database  33  included with the software, and for individuals of African ancestry we built classifiers based on the HLA types and genotypes from the "Consortium on Asthma among Africanancestry Populations in the Americas" (CAAPA-dbGaP Study Accession: phs001123.
v1. p1)  34, 35  (Supplementary Material). For the imputation of classical HLA class II alleles and amino acids, we used markers that intersected between our target populations and those included in the classifiers for each ancestry group, corresponding to 3,569 SNPs for European ancestry and 991 for African ancestry. For the association analysis, we excluded classical HLA genotypes imputed at two-field resolution with a posterior probability lower than 0. 5  36, 37  , and alleles with MAF lower than 1%. Details of HLA imputation performance of classical alleles for the different HLA genes are presented in Table  S1. Translation of classical HLA alleles to amino acid residues in the antigen binding domain of the HLA proteins was performed based on the classical HLA alleles imputed by HIBAG and the peptide sequence of alleles available in the HLA database using the HIBAG software  33.
Association analyses and meta-analysis of each type of variant For each type of variant that met quality control thresholds, we tested for association with HCV spontaneous clearance using an additive logistic regression model adjusting for HIV infection status and the first two principal components using PLINK 2. 0 alpha version  38, 39  for SNPs, and HIBAG  33  for HLA classical allele and amino acid residue in the proteins (Supplementary Material). Population specific PCs were calculated using the smartpca program in EIGENSOFT  33, 40  separately in the European and the African ancestry populations (excluding 1000 Genomes reference populations) using 16,142 genomic independent markers. In each ancestry group, individuals grouped in a tight cluster, and PC1 and PC2 explained most of the variance in the data (Figure  S1 ).
We extended the analysis to consider HIV status and applied the same regression model and meta-analysis to those variants in individuals with and without HIV independently in each population. In the European ancestry population, we tested associations with 22,259 genotyped/imputed SNPs; 54 classical alleles in the genes HLA-DRB1, HLA-DQA1, HLA-DQB1 and HLA-DPB1 and 205 residues (and combinations of residues) in 99 amino acid positions in these genes. For the African ancestry population, we evaluated associations with 25,565 SNPs, 33 classical HLA alleles in the genes HLA-DRB1 and HLA-DQB1 and 155 residues and their combinations. Classical HLA alleles and amino acids in HLA-DQA1 and HLA-DPB1 were not imputed in the African ancestry population since data for those genes was not available in the reference panel used for imputation (Table  S1 ).
Identification of amino acids accounting for independent signals We performed conditional analyses with customized scripts in R  42  and PLINK 2. 0 alpha version  38, 39  , to determine the amino acid residues accounting for the independently associated SNPs and classical HLA alleles by using iterative additive logistic regression models until the association of the top SNPs (or classical HLA alleles) were diminished and no other SNP (or classical HLA allele) was significantly associated in the region (Figure  2 ) at the conditional P valueMeta threshold. In the conditional models, we included every associated amino acid as a covariate as well as HIV infection status and two principal components  43. We prioritized the amino acids with the strongest associations and those identified as independently associated and their combinations.
Epitope prediction of associated classical HLA class II alleles containing causal amino acids We selected the classical class II HLA alleles positively (clearance) and negatively (persistence) associated with HCV spontaneous clearance that represented the associated amino acids of interest. To investigate the capabilities of associated alleles to bind epitopes derived from the entire HCV polyprotein sequence (Supplementary Material), we then performed T cell epitope prediction analyses. Using tools available in IEDB-Analysis Resources (www. tools. iedb. org)  44 , we analyzed the HCV polyprotein sequence (GenBank: AFE48416. 1) in combination with the associated clearance and persistence HLA alleles. Predictions were performed by applying the NetMHCIIpan 3. 2 algorithm  45  and extracting all the predicted IC50 values for each possible 15-mer spanning the entire HCV proteome (Supplementary Material).
Location of amino acid residues on HLA proteins To delineate the physical location of the causal amino acids in the tertiary structure of the HLA proteins, we used X-ray structures from the Protein Data Bank (PDB)  47. This provides structural visualization of HLA-DQα1/DQβ1 heterodimer localizing the amino acids that account for the signal in MHC class II region. The accession code for the selected structure was PDB 1jk8  48  for HLA-DQα1/DQβ1 and was visualized using PyMOL v. 2. 3. 2 (The PyMOL Molecular Graphics System, Version 2. 0 Schrödinger, LLC).
This study was limited by the lack of HCV genotypes, which is an additional factor associated with HCV spontaneous clearance, and lack of imputation of the HLA-DQA alleles and binding affinity assays in the African populations restricting the generalization of the associations. As with other complex diseases, the penetrance of these HLA alleles is not 100%, other genes also play a role  63, 64  and HCV outcome may be mediated or interact with age and initial HCV viral load. We used HIBAG  33  , a robust imputation program that references large databases with high genetic diversity and has demonstrated sucess in multi-ethnic benchmarking studies  34, [65] [66] [67]. Moreover, we confirmed that the allelic frequency of the HIBAG imputed alleles is in concordance with that from other populations reported in large databases such as the Allele Net Frequency Database  68  and Immuno Polymorphism Database  69  , which supports the accuracy of the imputation.
Data and Code Availability The datasets used in this study are available at dbGaP accession numbers phs000454. v1. p1 (https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs000454. v1. p1) and phs000248. v1. p1 (https://www. ncbi. nlm. nih. gov/projects/gap/cgibin/study. cgi?study_id=phs000248. v1. p1). The reference panel derived from the "Consortium on Asthma among African-ancestry Populations in the Americas" is avalaible at dbGaP accession number phs001123. v1. p1 (https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs001123. v1. p1) The HCV polyprotein sequence is available at GenBank: AFE48416. 1. https://www. ncbi. nlm. nih. gov/protein/AFE48416. 1).
Evaluation of Genetic Diversity and Divergence Genetic diversity was assessed among Icelandic strains and in comparison to a data set of cts sequences from 933 reference strains that included those from our previous work  [5]  and from open access sources representing a dozen habitat types (wild and cultivated plants, surface freshwaters, irrigation water, ground water, lithic biofilms, leaf litter, cloud water, rain and snowfall, snowpack and soil) and 27 countries of isolation in the Northern and Southern hemispheres. The names, origins and associated cts sequences for these 933 reference strains are in Supplementary Table  S1. Sequences were aligned and cut and then Neighbor-joining trees were constructed with MEGA 10. 2. 6 (https:// www. megasoftware. net/; accessed on 26 June 2021) based on 1000 bootstrap replications as described previously  [5].
The T-Model: A Coupled Thermomechanical Surface Process Model We use a lithosphere-scale thermomechanical surface process model to investigate continental rifting  (Wolf et al. , 2021;  see Figure  2a ). The model consists of the 2D thermo-mechanical code Fantom  (Thieulot, 2011) , coupled to the 2D landscape evolution code FastScape  (Braun & Willett, 2013; Yuan et al. , 2019). FastScape and Fantom are tightly coupled in a T-manner  (Beaumont et al. , 1992; Wolf et al. , 2021) , so that uplift and subsidence arising from the thermomechanical model provide a cylindrical uplift signal to FastScape and average erosion and deposition feed back to the thermomechanical computation. The coupled model captures the temporally and spatially transient evolution of continental rifts subject to the interaction between surface processes and tectonics.
Data Availability Statement As earth surface process model, we use the freely available, open source code FastScape (https://fastscape. org/), as outlined in the methods section. As thermo-mechanical model, we use the code Fantom  (Thieulot, 2011) , using the equations and procedure as outlined in the methods section. These models are used in a coupled manner to create the model output, and the data used for the analyses of the paper are available as animations at https://doi. org/10. 6084/m9. figshare. 18039509. v1 (Wolf et al. , 2022).
Computer-based cognitive training The training will be done for each participant over a period of 4 months on the PC using the Happyneuron Pro software (https://www. scientificbraintrainingpro. fr). Patients will complete the training as described in Design section, page 5. At the beginning of each session, the SLT will ask the participant to report any event that have occurred during the week that may, in any way, disturb his/her participation in the training. These events will be reported in the electronic observation notebooks (EON). The rational for a 4-month training period is that we wish to evaluate the benefits of a relatively short period of time Open access that would be less prone to drop-out and that is of sufficient duration, according to the literature, to produce benefits. 41 42 We choose the training tool, Happyneuron Pro (Happyneuron Pro is a product developed by Scientific Brain Training), because it is a well-known cognitive remediation product frequently used by the SLTs in France, and in particular by the SLTs participating in our study.
Equipment and programming The SLT's office and patient's personal computers are the only equipment used to run our protocol. All questionnaires and neuropsychological tests (except TMT and figure from MMSE) were digitalised on Typeform. The experimental tasks were designed and programmed on the Open Sesame free access software (V. 3. 2. 5). This software was therefore installed on the SLT's computers. The training sessions were programmed on Happyneuron Pro Platform https://www. scientificbraintrainingpro. fr/.
The workspace on Happyneuron Pro platform is used to create the training area for each included patient and to specify the weekly frequency and the days of training sessions, depending on the training group. Once the patient's space is created and the sessions scheduled, the patient receives a link by email on the scheduled days and all he /she has to do to access the training, is to click on the link. The study is monitored by the Clinical Research and Innovation Department of the Hospices Civils of Lyon (HCL's identification code for the study 69HCL18_0881). A designed clinical research assistant is in charge of the monitoring which includes: ► A study start-up visit to the coordinating centre and the inclusion centres. ► A mid-term visit. ► A closing visit. At the mid-term and closing visits, the consent forms and EON will be checked.
Data management and storage The performance on the neuropsychological tests performed via Typeform is automatically recorded. When completed, an email containing the patients' scores is automatically sent to the investigator and the SLT, and patients' scores can be extracted from Typeform into Excel. Finally, the SLT enters the scores of interest into the patient's EON. Performances on the experimental tasks are recorded on the SLT's office computer and the scores of interests are entered into the patient's EON. The training results for each session are automatically stored on a secure server hosted by a health data host. There is no transit between the servers, nor is there any storage of data on the patient's computer. SLTs have the option of monitoring the trainings remotely: this is possible by accessing the patient's space, which allows the SLTs to check whether the training has been carried out regularly and to monitor patients' progress.