dataesr/scientific-paragraphs-categorization huggingface dataset:
The dataset with the MK0677-GHSR-Gi complex was collected with a pixel size of 0. 826 Å and a defocus range of -0. 8 to -1. 6 μm. Movies were recorded during a 3 s exposure with 50 ms subframes (60 total frames) at a dose rate of 1. 35 e/Å 2 /frame. Both datasets were processed in cryoSPARC  46  using a similar strategy (Extended Data Fig. 1c  and d ). Briefly, super-resolution image stacks were gain-normalized, binned by 2 with Fourier cropping, and corrected for beam-induced motion using MotionCor2  47. Contrast transfer function (CTF) parameters were estimated from motion-corrected images using GCTF  48. Following multiple rounds of 2D classification, ab initio reconstruction was performed in cryoSPARC asking for four classes, which resulted in one good class and three "trash" classes. Then multiple rounds of heterogenous refinement were performed against the four ab initio models to remove bad particles.
Following the CTF refinement and non-uniform (NU) refinement, one last round of heterogenous refinement was carried out using three identical initial models (output of NU refinement). The heterogeneous refinement yielded two slightly different conformations, which were further refined by NU refinement. Resolutions were estimated using the 'gold standard' criterion (FSC=0. 143), and local resolution was calculated in cryoSPARC. The coordinates of NTSR, Gi and scFv16 from the structure of the neurotensin-NTSR-Gi-scFv16 complex (PDB ID 6OS9) were used as initial models to dock into the cryo-EM maps using Chimera  49. The structures of GHSR-Gi with both agonists were then built by iterative manual building and adjustment in Coot 50 and real-space refinement in Phenix  51. The final models were validated by Molprobity  52.
2% pluronicacid and 1% FBS was added to the GHSR-expressing cells and incubated for 1 h at 37 °C. Cells were then washed twice with HBSS buffer and left at room temperature in 50 μL HBBS. Fluo-4 fluorescence intensity (excitation 480 nm, emission 520 nm) was measured as an indicator of calcium release in a multimode reader (Spark 20 M, TECAN). For the concentration-dependent responses of ghrelin or MK-0677, 50 μL HBSS buffer containing different concentrations of ligand was injected to each well, and fluorescence was measured constantly in real time for 90 s. The data were analyzed in GraphPad Prism 8 using the one site dose-response stimulation method. Results were presented as Mean ± S. D. from 3 independent experiments.
Acknowledgement We thank members of the  Cryo-electron Microscopy and Tomography Center of St Jude Children's Research Hospital  for help with cryo-EM data collection. J. S. is supported by the  National Institutes of Health (NIH ,  HL143037 ) and  American Lebanese Syrian Associated Charities (ALSAC). C. Z. is supported by  NIH  grants  R35GM128641  and  R03TR003306.
Data availability The 3D cryo-EM density maps have been deposited in the Electron Microscopy Data Bank under the accession numbers EMD-24267 for the ghrelin-GHSR-Gi complex and EMD-24268 for the ibutamoren-GHSR-Gi complex. Atomic coordinates for the atomic models of the ghrelin-GHSR-Gi and ibutamoren-GHSR-Gi complexes have been deposited in the Protein Data Bank under the accession numbers 7NA7 and 7NA8, respectively.
, 2013; Shin et al. , 2014; Newhouse et al. , 2017). Symptomatic OSA has clearly emerged in adults as an independent risk factor for insulin resistance and onset of T2DM  (Anothaisintawee et al. , 2016). Clinical studies suggest that insulin resistance assessed by the homeostastic model assessment (HOMA-IR) and glucose intolerance are positively associated with OSA, independent of the degree of obesity  (Archontogeorgis et al. , 2017). Two large recent prospective cohort studies, one from Canada and another from patients recruited in the Atherosclerosis Risk in Communities Study (ARIC) and/or in the Sleep Heart Health Study (SHHS), have shown an increase in T2DM prevalence that is related to OSA severity. Few comparable data are available, however, among older patients with OSA. The main objective of the present study was to evaluate the impact of unrecognized OSA in a nondiabetic community population on the occurrence of T2DM or the rate of insulin resistance in subjects over 65 years of age after a period of 7 years.
Data Monitoring Three 2-year interval examinations were initially scheduled for the 7 years of  follow-up (2000-2007). Participants received an invitation by mail for each evaluation. Missing information was obtained from hospital records and questionnaires sent to the attending physician. After the first polygraphic evaluation, the results were sent to their primary care physician, who decided whether to initiate or not continuous positive airway pressure therapy or treatment with a mandibular advance device. Blood samples were taken by a fasting technician or nurse in the laboratory in the morning within a week of the respiratory polygraphic study. Presence of T2DM and of insulin resistance were assessed in the fourth assessment. T2DM was identified by a diagnostic test defined by fasting glucose ≥ 1.
Statistical Analyses Statistical analysis was performed using Statview 5. 1 software. The results were expressed in terms of absolute or percentage values, and adjusted odds ratios with corresponding 95% confidence intervals (CIs). A value of p < 0. 05 was considered statistically significant. The use of one-factor ANOVA analysis for continuous variables and of contingency tables compared "T2DM" and "nondiabetic" groups as well as "insulin resistant" and "non-insulin resistant" groups. Logistic regression analysis assessed the role of clinical, anthropometric, biological, and polygraphic features at baseline that may increase the rate of T2DM and insulin resistance. We built and compared a series of models. Model 1 included gender and BMI ≥ 30 kg/m 2. Model 2 additionally adjusted for whole body densitometry (fat mass) data.
This prevents us from concluding on the incidence rate of this carbohydrate control metabolism disorder according to the presence of OSA 7 years earlier. In fact, in this cohort we only have data on fasting blood glucose levels at P1. As another limitation of the study, we should remark that participants were recruited as part of a cross-sectional survey conducted within a homogeneous group of seniors fulfilling very stringent criteria which could introduce sampling bias. It should also be noted that the initial blood samples obtained during the study precluded evaluation of insulin resistance in this population. Furthermore, all OSA patients treated during the 7-years follow-up period were also excluded from the analysis. Therefore, we cannot rule out the possibility that our overall "healthy" or "super healthy" cohort of people may not be representative of the general population, in which case our results cannot be extrapolated to OSA patients of the same age and seen in routine clinical practice.
The study is a sub-study of the PROOF cohort and the data presented were obtained from a population for which it was not possible to calculate a priori the number of participants required. In fact, the PROOF study's enrollment calculation indicated that 1,000 subjects were needed to assess whether or not progressive alteration of cardiac autonomic activity increased the risk of occurrence of a cardiovascular or cerebrovascular event, atrial fibrillation, or incident heart failure. Secondly, our cohort included 65-year-old subjects who could be considered "young" seniors, excluding the application of our results to "very old" subjects. Thirdly, we used an ambulatory polygraph system for the study of sleep disorders, which obviates the opportunity to assess the structure of sleep or the relationship between respiratory events and the stages of sleep that would have allowed for a more detailed analysis.
Here, we combine new and existing individual level-genotype data in the largest GWAS of ALS to date. We present a comprehensive screen for pathogenic rare variants and short tandem repeat (STR) expansions as well as regulatory effects observed in brain cortex-derived RNA-seq and methylation datasets to prioritize causal genes within ALS risk loci. Furthermore, we reveal similarities and differences between ALS and other neurodegenerative diseases as well as the biological processes in disease-relevant tissues and cell-types that affect ALS risk.
Rare variant association analyses in ALS. To assess a general pattern of underlying architectures that link associated SNPs to causal genes, we first tested for annotation specific enrichment using stratified linkage disequilibrium score regression (LDSC). This revealed that 5' UTR regions as well as coding regions in the genome and those annotated as conserved were most enriched for ALS-associated SNPs (Supplementary figure  18 ). Subsequently we investigated how rare, coding variants contributed to ALS risk generating a whole-genome sequencing dataset of ALS patients (N = 6,538) and controls (N = 2,415). The exome-wide association analysis included transcript-level rare-variant burden testing for different models of allele-frequency thresholds and variant annotations (Online methods). This identified NEK1 as the strongest associated gene (minimal P = 4.
9 × 10 -8 for disruptive and damaging variants at minor allele frequency < 0. 005), which was the only gene to pass the exome-wide significance thresholds (0. 05/17,994 = 2. 8 × 10 -6 and 0. 05/58,058 = 8. 6 × 10 -7 for number of genes and protein-coding transcripts, respectively, Supplementary figures 19-32). This association is independent from the previously reported increased rare variant burden in familial ALS patients  17  that were not included in this study. Gene prioritization shows locus-specific underlying architectures. To assess whether rare variant associations could drive the common variant signals at the 15 genome-wide significant loci, we combined the common and rare variants analyses to prioritize genes within these loci. The SNP effects on gene expression were assessed through summary-based Mendelian Randomization (SMR) in blood (eQTLGen  19  ) and a new brain cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain  20  ).
Similarly, we analyzed methylation-QTL (mQTL) through SMR in blood and brain-derived mQTL datasets  [21] [22] [23]. Finally, we leveraged the genome-wide signature of ALS associated gene features in a new gene prioritization method to calculate a polygenic priority score (PoPS)  24. Through these multi-layered gene prioritization strategies we classified each locus into one of four classes of most likely underlying genetic architecture to prioritize the causal gene (Supplementary figures 33-47). First, in three GWAS loci the strongest associated SNP was a low-frequency coding variant which was nominated as the causal variant. This is the case for rs80265967 (SOD1, p. D90A, Supplementary Figure  46 ) and rs113247976 (KIF5A p. P986L, Supplementary Figure  40 ) which are coding variants in known ALS risk genes.
Enrichment of glutamatergic neurons indicate cell-autonomous processes in ALS susceptibility. To find tissues and cell-types which gene expression profiles are enriched for genes within ALS risk loci, we first combined gene-based association statistics calculated using MAGMA 31 with gene expression patterns from GTEx (v8) in a gene-set enrichment analysis using FUMA 32. We observed a significant enrichment in genes expressed in brain tissues, specifically the cerebellum, basal ganglia (caudate nucleus, accumbens, and putamen), and cortex, but not peripheral nervous tissue or muscle. Whereas this pattern roughly resembles the enrichments observed in PD, it is strikingly different from that observed in AD where blood, lung and spleen were mostly enriched (Figure  3a ). We subsequently queried single-cell RNA sequencing datasets of human-derived brain samples to further specify brainspecific enriched cell-types using the cell-type analysis module in FUMA  33.
Brain-specific co-expression networks improve detection of ALS-relevant pathways. To assess which processes were mostly enriched in ALS, we performed enrichment analyses that combined gene-based association statistics with gene co-expression patterns obtained from either multi-tissue transcriptome datasets 34 or RNA-seq data from brain cortex samples (MetaBrain 20 ). To validate this approach, we first tested for enrichment of Human Phenotype Ontology (HPO) terms that are linked to well-established disease genes in the Online Mendelian Inheritance in Man (OMIM) and Orphanet catalogues. Using the multi-tissue co-expression matrix, we found no enriched HPO terms after Bonferroni correction for multiple testing. Using the brain-specific co-expression matrix however, we found a strong enrichment of HPO terms that are related to ALS or neurodegenerative diseases in general, including Cerebral cortical atrophy (P = 1.
8 × 10 -8 ), Abnormal nervous system electrophysiology (P = 4. 1 × 10 -7 ) and Distal amyotrophy (P = 8. 6 × 10 -7 , full-list in Supplementary table  21 ). In general, HPO terms in the neurological branch (Abnormality of the nervous system) showed an increase in enrichment statistics in ALS when using the brain-specific co-expression matrix compared to the multitissue dataset (Supplementary Figure  53 ), which illustrates the benefit of the brain-specific coexpression matrix for ALS-specific enrichment analyses. Subsequently, we tested for enriched biological processes using Reactome and Gene Ontology terms. Again, using the multi-tissue expression profiles, we found no Reactome annotations to be enriched. Leveraging the brain-specific co-expression networks we identified Vesicle Mediated Transport ("Membrane Trafficking" P = 4.
All combinations of allele frequency threshold and variant annotations were used to test the genic burden on a transcript level in a Firth logistic regression framework where burden was defined as the number of variants per individual. Sex and the first 20 principal components were included as covariates. All ENSEMBL protein coding transcripts for which at least five individuals had a non-zero burden were included in the analysis. Conditional genic burden analysis. We selected for each gene the protein coding transcripts that were strongest associated with ALS across all different combinations of MAF and variant impact thresholds that exhibited the strongest association with ALS. For these transcripts and variants, we applied Firth logistic regression on individuals overlapping the GWAS and WGS dataset (5,158 cases and 2,167 controls).
Short tandem repeat screen For all individuals that were sequenced on the HiSeqX dataset (5,392 cases, 1,795 controls) we screened all loci harboring SNPs associated with ALS meeting genome-wide significance for expansions of known and new short tandem repeats (STRs) using ExpansionHunter  73  and ExpansionHunter Denovo  74. First we used ExpansionHunter (v4. 0) to screen for expansions of known STRs located within 1 MB of the top ALS-associated SNP. For this we used the STR catalogue of the ExpansionHunter software which is based on STRs identified from indels in 18 high quality genomes and the gangSTR STR catalogue based on STR annotations in the reference genome  75. From these catalogues, we excluded all homopolymers. Repeat length was subsequently regressed on case-control status using Firth logistic regression including the first 20 principal components as covariates, recoding the STR size to a biallelic variant using a sliding window over all observed repeat lengths.
To correct for multiple testing across all possible thresholds, we applied Benjamini Hochberg correction per STR. methylation and SNP data from 522 individuals from the Brains for Dementia Research cohort  22  and included 4,623,966 cis mQTLs (distance between QTL SNP and DNAm site ≤ 500 kb) between 1,744,102 SNPs and 43,337 DNA methylation sites. The whole blood mQTL dataset was generated using DNAm and SNP data from 2,082 individuals  78  and included 30,432,023 cis mQTLs between 4,030,902 SNPs and 167,854 DNA methylation sites. mQTLs reaching the significance threshold P ≤ 1 × 10 -10 were taken forward for SMR analysis as described by Hannon and colleagues  78. To map CpG sites to their putative target genes we used the expression quantitative trait methylation (eQTM) results from a paired methylation and gene expression (RNA-seq) study in blood  79.
For CpG sites where no eQTM were present in this dataset, we used positional mapping based on the basal regulatory domains and extended regulatory domains as defined in the Genomic Regions Enrichment of Annotations Tool (GREAT)  80  which is applied in thecpg_to_gene` function in the CpGtools toolkit  81. Polygenic Priority Score (PoPS) We used the polygenic priority score (PoPS 24 v0. 1) to rank genes according to the gene features that were enriched in ALS. For this we applied MAGMA in the European ancestries GWAS since it depends on an LD reference panel (1000 Genomes Project, EUR population) to obtain gene-wise association statistics. We used the default 57,543 gene features that were based on expression data, proteinprotein interaction networks and pathway membership. Genes were ranked based on the Polygenic Priority Score.
Genetic correlation We first assessed residual confounding through estimating the LD Score regression  60   Genomes project for high-quality HapMap3 SNPs were used (eur_w_ld_chr). A free intercept was modelled to allow for potential sample overlap. samples  84  ), the Allen Brain Atlas Cell-type  85  for the human-derived major neuronal subtypes and the DropViz 86 dataset for mouse-derived brain cell-types across all brain regions.
88 ) as a reference. In the Downstreamer method, putative core genes are defined as those that are coexpressed with disease-associated genes and can therefore be implicated in disease. Co-expression networks are based on either a large, multi-tissue transcriptome dataset including 56,435 genes and 31,499 individuals, or brain-specific RNA-sequencing data obtained in the MetaBrain resource. The gene-based association statistics, co-expression matrix and gene Z-scores per pathway or HPO term are then combined in a generalized least squares regression model to obtain enrichment statistics. 20 ichment analyses were performed for Reactome, Gene Ontology and Human Phenotype Ontology (HPO) terms using the multi-tissue or brain-specific transcriptome datasets to calculate the coexpression matrix. The distribution of enrichment Z-score statistics were compared between the analyses using the multitissue or the brain-specific co-expression matrices.
Acknowledgments W. v. R. is supported by funding provided by the  Dutch Research Council (NWO)  [ VENI scheme grant 09150161810018] and Prinses Beatrix Spierfond (neuromuscular fellowship  grant W. F19-03 ). J. J. F. A. v. V. is funded by Projectnumber W. OR20-08  (The "Repeatome" as a basis for new treatments of  ALS ) of the  Prinses Beatrix Spierfonds. K. P. K. is supported by funding provided by the  Dutch Research Council (NWO)  [VIDI grant 91719350]. G. S. was supported by a PhD studentship from the  Alzheimer's Society. E. H. and J. M. were supported by  Medical Research Council (MRC)  grant  K013807  (awarded to J. M). mQTL SMR data analysis was undertaken using high-performance computing supported by a  Medical Research Council (MRC) Clinical Infrastructure  award  M008924  (awarded to J.
M. ). French ALS patients of the  Pitié-Salpêtrière hospital (Paris)  have been collected with ARSla funding support. D. B. and T. R. G. received funding from  Biogen  and  UK Medical Research Council (MRC Epidemiology Unit ,  MC_UU_00011/1  and  MC_UU_00011/4 ) for this project. G. D. S. works in the  Medical Research Council Integrative Epidemiology Unit at the University of Bristol   MC_UU_00011/1. D. B. ,  E. Tsai  and H. R. are employees of  Biogen. J. P. R. is funded by the  Canadian Institutes of Health Research  ( FRN 159279 ). A. A. K is supported by The  Motor Neurone Disease Association (MNDA)  and  NIHR Maudsley Biomedical Research Centre. R. J. P. is supported by the  Gravitation program  of the  Dutch Ministry of Education, Culture , and Science and the  Netherlands Organization for Scientific Research (NWO; BRAINSCAPES).
G. A. R is supported by the  Canadian Institutes of Health. Research Australia including its Ice Bucket Challenge Grant. We acknowledge funding from the  National Health and Medical Research Council (NHMRC)  ( 1078901 ,  1083187 ,  1113400 ,  1095215 ,  1121962 ,  1173790 , Enabling Grant # 402703 ). The  Older Australian Twins Study  (OATS, used for controls) acknowledges funding from the  NHMRC/Australian Research Council   Strategic Award  ( 401162 ) and  NHMRC  ( 1405325 ,  1024224 ,  1025243 ,  1045325 ,  1085606 ,  568969 ,  1093083 ). OATS was facilitated through access to  Twins Research Australia , a national resource supported by a  NHMRC Centre of Research Excellence  Grant  50  ( 1079102 ). The  Sydney Memory and Ageing Study (Sydney MAS , used for controls) has been funded by three  NHMRC Program Grants  ( 350833 ,  568969 , and  1093083 ).
The project is supported through the following funding organisations under the aegis of JPND -www. jpnd. eu( United Kingdom ,  Medical Research Council  ( MR/L501529/1 ;  MR/R024804/1 ) and  Economic and Social Research Council  ( ES/L008238/1 )) and through the  Motor Neurone Disease Association. This study represents independent research part funded by the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. A. A-C is supported by an  NIHR   Senior Investigator Award. Samples used in this research were in part obtained from the  UK National DNA Bank for MND Research , funded by the  MND Association  and the  Wellcome Trust. We would like to thank people with  MND  and their families for their participation in this project.
GWAS Data description We obtained individual genotype level data for all individuals in the previously published GWAS in ALS in European ancestries  7, 9  and publicly available control datasets including 120,971 controls genotyped on Illumina platforms. Additionally 6,374 cases and 22,526 controls were genotyped on the IlluminaOmniExpress and Illumina GSA array. Details for each cohort are provided in Supplementary Table  1. For ALS cases, both cases with and without a family-history for ALS and/or dementia were included. Cases were not pre-screened for specific ALS related mutations. Given the late onset and relatively low life-time risk of ALS, controls were not screened for (subclinical) signs of ALS. A detailed description of the newly genotyped cases and controls is provided in the Supplementary Information.
Summary-based Mendelian randomization We used multi-SNP SMR  76, 77  to infer the effect of gene expression variation on ALS using eQTLs (the association of a SNP with expression of a gene) on ALS risk. MetaBrain is a harmonized set of 8,727 RNA-seq samples from 7 regions of the central nervous system from 15 datasets, and we selected eQTLs derived from the cortex region of the brain in samples of European ancestry (MetaBrain Cortex-EUR eQTLs) as our instrument variable  20. The European-only ALS summary statistics were used as the outcome. To supplement this analysis, we also used eQTLs in blood from the eQTLGen consortium, as this is the largest eQTL resource available. European-ancestry samples in the Health and Retirement study (HRS, cohort 65 of this GWAS) were used as LD reference panel.
SNP with MAF ≥ 1% in HRS were included. Further SMR settings were left as default, meaning probes with at least one eQTL with P ≤ 5 × 10 -8 were included. We subsequently performed SMR using DNA methylation QTL (mQTL) data and European-only ALS summary statistics. Human prefrontal cortex and whole blood DNA mQTLs were generated as part of ongoing analyses by the Complex Disease Epigenomics Group at the University of Exeter (www. epigenomicslab. com) using the Illumina EPIC HumanMethylation array that quantifies DNAm at >850,000 sites across the genome  21. The prefrontal cortex mQTL dataset was generated using DNA.