dataesr/scientific-paragraphs-categorization huggingface dataset:
Qualtrics (Qualtrics, Provo, UT) will be used for data collection to improve data protection as responses to questionnaires will only be accessible to the investigator. Collected data will be password protected. Personal identifiable and clinical trial data will be separated, with the latter receiving a unique participant ID. Access to informed consents, personal identifiable data and the link with the participant ID will be restricted to the researchers and stored separately from the trial data. Eventual further dissemination of data will only occur in a pseudonymised or aggregated way. The Vrije Universiteit Brussel supports the FOSB metadata standard (= dataset metadata schema defined by the Flemish Open Science Board) which can be mapped to the international DataCite metadata schema. At the project level, the general information (title, investigators, aim, objectives, concepts, hypotheses, funder), protocol, sampling procedure, instruments, hardware and software used to collect data, data handling log, accessibility of the data and data manipulations will be provided in research plans and publications.
Statistical methods for primary and secondary outcomes {20a} Considering the longitudinal data, a generalised estimating equation will be constructed to evaluate and compare therapy effects. The primary outcome will be analysed as the proportion of clinical holistic responders from baseline to 6 months (taking into account the longitudinal nature of the trial) between both groups. Baseline variables will be used as covariates. For secondary outcome measurements, a similar strategy will be applied with longitudinal mixed models. This longitudinal data analysis approach will also be applied for 12 months data. Statistical as well as clinical significant differences will be defined at alpha < 0. 05. Furthermore, based on the baseline data, we will determine predictive factors and which subgroup of patients will benefit the most of the treatment.
Methods for additional analyses (e. g. subgroup analyses) {20b} Baseline data will provide cross-sectional results on work status, pain intensity, quality of life, disability, medication use and self-management for the complete PSPS-T2 group and comparisons between possible subgroups. Furthermore, correlation analyses will be performed in order to unravel correlations between the different outcome measures in patients with PSPS-T2, eligible for SCS. Correlation analysis will be performed with Pearson correlation coefficients if the assumption of a linear relation between two variables is met; otherwise, Spearman correlation coefficients will be calculated and tested at alpha < 0. 05. A within trial economic evaluation will be conducted. All costs of all patients will be considered, for the time horizon starting from start of the allocated intervention until the 6-month follow-up period.
Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c} Both intention-to-treat as well as per protocol analyses will be conducted to determine whether both definitions of the population will point towards similar results, which could improve robustness of the results. At first, analysis will be performed on data as observed. Hence, intermittent missing is not expected to substantially bias the results. To address possible informative drop-outs, a sensitivity analysis will be performed using multiple imputation, including all available information on background characteristics and outcome. All analysis will be performed in SAS or R.
Frequency and plans for auditing trial conduct {23} The steering committee will submit a summary of the progress of the trial to the central ethics committee once a year. Information will be provided on the date of inclusion of the first patient, numbers of patients included and numbers of patients that have completed the trial, serious adverse events/serious adverse reactions, other problems and amendments. There is no planned on-site auditing of the trial. However, to ensure compliance with relevant regulations, an independent quality assurance representative may review this study. This implies that auditors will have the right to audit the site(s) at any time during and/or after completion of the study and will have access to the data generated during the clinical investigation, source documents and patient's files.
Background: About half of cancer deaths in Brazil occur among individuals of working-age (under 65 years for men, under 60 for women), resulting in a substantial economic impact for the country. We aimed to estimate the years of potential productive life lost (YPPLL) and value the productivity lost due to premature deaths from cancer between 2001 and 2015 and the projected to 2030. Methods: We used the Human Capital Approach to estimate the productivity losses corresponding to YPPLL for cancer deaths in working age people (15-64 years). Mortality data were obtained from the Mortality Information System from 2001 to 2015 and projected between 2016 and 2030. Economic data were obtained from the Continuous National Household Sample Survey and forecasted to 2030. Productivity lost was calculated as the monetary value arising from YPPLL in Int$(2016).
Study design and data sources Mortality at working-age was defined as the occurrence of cancer death at an economically productive age, that is, before retirement. We undertook a population-based study to calculate the YPPLL and productivity lost from cancer deaths in individuals between 15 years old and, for women, 60 years old and, for men, 65 years old. All analyses were carried out for men and women separately as cancer patterns vary considerably by sex, and by dividing the study period 2001-2030 into quinquennia. Information on premature cancer deaths which occurred between 2000 and 2016 was obtained from the Mortality Information System (SIM) of the Department of Informatics of the Unified Health System (DATASUS)  [5]. Premature deaths from cancer at the following sites (as per the 10th edition of the International Classification of Diseases (ICD-10)) were enumerated: lip, oral cavity and pharynx (C00-14); esophagus (C15); stomach (C16); colorectal (C18-21); liver (C22); gallbladder (C23-24); pancreas (C25); larynx (C32); lung with trachea (C33-34); melanoma skin (C43); breast (C50); cervix (C53); uterus body (C54); ovary (C56); prostate (C61); testis (C62); kidneys (C64-66); bladder (C67); brain and central nervous system (CNS) (C70-72); thyroid (C73); Hodgkin's lymphoma (C81); Non-Hodgkin's lymphoma (C82-85, 96); multiple myeloma (C88, 90); leukemia (C91-95) and all neoplasms combined (C00-96), excluding non-melanoma skin cancers.
Population data for 2010 were obtained from the demographic census carried out by the Brazilian Institute of Geography and Statistics (IBGE); for the other years, the IBGE inter census population estimates were used  [6]. Data on average wages, labor force participation rates (formal and informal) and unemployment rates were obtained from the Continuous National Household Sample Survey (Continuous PNAD) carried out by IBGE (Supplementary material Table  1 )  [7]. These data were extracted according to sex and age group for 2001-2015. The labor force participation and unemployment rates (by sex and age-group) used in subsequent calculations were based on the median of the values for the period 2001-2015.
Calculation of mortality projections Deaths from ill-defined or unspecified causes were redistributed pro rata  [8]. In order to avoid overestimation, we attributed 50% of the calculated weight as corresponding to neoplasms  [9]. Deaths from unspecified uterine cancer (ICD C55) were proportionally redistributed between cervix (C53) and corpus uterus (C54)  [10]. Deaths classified as neoplasms of other digestive organs and ill-defined locations in the digestive tract (C26) were proportionally redistributed among neoplasms of digestive organs (C15-25)  [11]. Deaths for the period 2016-2030 were projected using the ageperiod-cohort (APC) model from the NordPred package in the software R, version 3. 6. 0  [12]. NordPred is widely used to estimate long-term projections of cancer incidence and mortality.
The model requires at least fifteen years of consecutive data (three five-year periods) and provides projections for a maximum of four periods. The model assumption is that current incidence rate trends will continue into the future. This modified version of the age-period-cohort model is used to avoid exponential growth overtime which, can give unrealistic predictions for certain cancer types when the exponential function of the APC model is employed. To curb this exponential growth, the power model uses the functional form x^5 instead of exp(x) when considering age, period, and cohort as explanatory factors  [13]. Projections were calculated for each combination of cancer topography, sex, and period. Crude mortality rates were calculated using IBGE population data [6]. Truncated age-standardized mortality rates (15-64 for men and 15-59 for women) were calculated by the direct method, using the world population proposed by Segi and adapted by Doll et al.
We highlight as strengths of this study the fact that while most studies evaluate the most common cancers, we evaluated all neoplasms, showing that less commonly diagnosed cancers -such as testicular cancer and Hodgkin's lymphoma -have a significant impact on productivity lost, since that these neoplasms occur in young adults of working age  [20, 38]. Furthermore, previous studies are predominantly in Europe and the USA, locations with distinct economic and sociodemographic characteristics from those of middle-income countries. Regarding methods, we used the HCA, which is widely used to assess the productivity lost due to premature death from cancer. Economic and mortality data came from high detailed quality, national sources, which allowed us to include income from formal and informal (not officially registered) jobs.
This agnostic genomic approach has aided gene discovery for other brain and cerebrovascular malformations  [33] [34] [35] [36]  , congenital heart disease  37  , congenital hydrocephalus  38, 39  , as well as heterogeneous neurodevelopmental disorders like autism, craniosynostosis, and epilepsy  [40] [41] [42] [43] [44] [45]. In addition, recent single-cell RNA sequencing (scRNA-seq) studies have begun to systematically catalog cell types of the developing and mature human cerebrovasculature, expanding knowledge about perivascular cell diversity and defining endothelial cell molecular signatures correlated with arteriovenous segmentation  46, 47. Integration of WES findings with other large-scale -omics datasets can help elucidate the cellular and molecular mechanisms of disease genes by defining their spatiotemporal expression patterns and associated transcriptional and protein-protein interaction networks  36, 39, 48.
WES of the largest trio-based VOGM cohort to date We ascertained a total of 114 probands with radiographically confirmed VOGM (see Methods) treated by endovascular therapy, including 90 proband-parent trios (each with a single affected offspring), 13 duo cases, and 11 singleton cases (Supplementary Data 1). These included 55 previously described VOGM probands  26. Among 114 VOGM probands, 34. 2% were female; 78. 9% were self-reported Europeans; 60% of probands were diagnosed prenatally or within 1 month after birth; only 3. 8% were diagnosed after age 2. Salient features at diagnosis included developmental delay (54%), macrocephaly (48%), hydrocephalus (48%), prominent face and/or scalp vasculature (45%), cutaneous vascular lesions (22%), and congestive heart failure (40%). Also present were structural heart defects (7%), including partial anomalous pulmonary venous return, patent ductus arteriosus, and pulmonary valve stenosis.
Supplementary Table  1  summarizes the demographics and clinical features of our VOGM cohort. Supplementary Fig. 1  depicts representative angiographic images of VOGM probands. DNA was isolated and WES was performed (see Methods)  26. In parallel, WES of 1,798 control trios comprising parents and unaffected siblings of autism probands was analyzed  49, 50  by our published informatics pipeline  51. 92. 7% or more of targeted bases had ≥8 independent reads in both cases and controls, and 87. 8% or more had ≥15 independent reads (see Supplementary Table  2  for exome sequence metrics). Variant calling was performed utilizing a combination of the Genome Analysis Toolkit (GATK) HaplotypeCaller  52, 53  and Freebayes  54. Population allele frequencies were annotated by the Genome Aggregation Database (gnomAD v.
2. 1. 1) and the BRAVO databases  55. De novo variant (DNV) identification was performed by TrioDeNovo  56. MetaSVM and MPC algorithms were used to infer the effect of missense variants  57, 58. Missense variants were considered damaging (D-mis) when predicted as deleterious by MetaSVM or with MPC-score ≥ 2. Inferred loss-of-function (LoF) variants, including stop-gains, stoplosses, frameshift insertions, deletions, and canonical splice-site variants were considered damaging. Variants in genes of interest were validated by PCR amplification and Sanger sequencing (Supplementary Fig. 2  and 4 ).
VOGM-associated genes are enriched in developing cerebrovascular endothelial cells To gain insight into the developmental periods, cell types, and molecular pathways involved in VOGM biology, we performed a spatiotemporal consensus Weighted Gene Co-expression Network Analysis (WGCNA)  99  , which leverages a large bulk RNAseq data set encompassing multiple human brain regions across development and into early childhood 100. We constructed 88 modules characterized by genes that share highly similar expression patterns during brain development across different cortical regions and therefore likely to be involved in similar functions  99. Each module was assessed for relative enrichment of high-confidence and probable (p) VOGM genes, brain AVM genes, AVM+VOGM genes together (since each is a high-flow arterio-venous communication disorder), cerebral cavernous malformation (CCM) genes, moyamoya disease (MMD genes, and human height genes (as a negative control)), using multivariate logistic regression (see Supplementary Table  13  and Methods for description of gene lists).
Examples include the excitatory embryonic neuron (cluster "ExN6b", p = 4. 75 × 10 -4 ) and the inhibitory neurons (cluster "InN4b", p = 3. 18 × 10 -4 ) (Fig. 5d ). Control human height genes were unenriched in any of the 42 cell subtypes. The second sc-RNAseq transcriptomic atlas included 181,388 total cells from normal and diseased human cerebrovasculature, including both endothelial and perivascular cell subtypes  46. Corroborating results from the STAB dataset, VOGM genes showed specific enrichment in ECs (p = 6. 43 x 10 -6 ). VOGM, AVM, and AVM+VOGM genes were enriched in arterial and venous EC subtypes, but not in perivascular subtypes such as smooth muscle cells, fibroblasts, fibromyocytes, pericytes, or other perivascular cell types (Fig. 5e ). In contrast, CCM and MMD genes were unenriched in arterial and venous EC subtypes.
Human height genes were not enriched in any of the 18 cell subtypes. We again constructed modules from the cerebrovascular dataset comprising co-expressed genes. These were then assessed for the enrichment of VOGM and other disease genes (see Methods). Of the 25 defined modules, VOGM genes were most highly enriched in Modules 11 (p = 3. 23 × 10 -4 ) and 12 (p = 8. 22 × 10 -5 ), overlapping with AVM genes (Fig. 5f ). In contrast, CCM and MMD genes were most enriched in Module 6 defined by mitochondrial processes. Human height genes were not significantly enriched in any of the modules (Fig. 5f ). Module 11 genes were most highly enriched for pathways related to VEGFA-VEGFR2 signaling (WP3888; 1. 97-fold enrichment; p = 4. 51 x 10 -5 ), vascular transport (GO:0010232; 5. 0-fold enrichment; p = 7.
Our data in mice supports this genetic mechanism, as heterozygous Ephb4 F867L mice expressing a VOGM-specific EPHB4 kinase-domain missense variant exhibited severe vascular abnormalities, but only when carrying a second variant allele, i. e. Ephb4 F867L or a cre-disrupted Ephb4 allele. Exome sequencing of ultra-rare human VOGM lesion tissue could test this two-hit hypothesis. Integration of our WES findings with scRNA-seq datasets of the developing human cerebrovasculature helped define developing endothelial cells as a potential key spatio-temporal locus of VOGM pathophysiology (see Fig. 5 ). Pathway analysis showed VOGM genes are enriched in growth-factor-regulated, tyrosine receptor kinaseassociated signaling, which regulates vasculogenesis, angiogenesis, and arterio-venous specification  1.
clinicaltrialsreg-ister. eu; unique identifier: 2019-003573-26)). In contrast to systemic vascular lesions, the narrow window of gestational weeks 6-11 during which the primitive choroidal arteries and the MPV are usually present, and therefore during which VOGMs are speculated to develop  10  , may pose a challenge to improved early therapeutic strategies for VOGM. Thus, attempted diagnosis with intention to treat must proceed before the safe gestational age threshold for amniocentesis  126. These data highlight the need for continued genetic research on VOGM, as well as investigation into the cellular and molecular mechanisms of newly discovered VOGM-associated variants in mammalian model systems.
Exome sequencing and variant calling Exome capture was performed on genomic DNA samples derived from saliva or blood using Roche SeqCap EZ MedExome Target Enrichment kit or IDT xGen target capture kit followed by 99 base paired-end sequencing on the Illumina sequencing platform. Sequence reads were aligned to the human reference genome GRCh37/hg19 using BWA-MEM  127  and further processed to call variants following the GATK Best Practices workflow  52  and Freebayes  54. Variants were annotated with ANNOVAR 128 , gnomAD (v. 2. 1. 1) 129 , and BRAVO  55  databases. We used MetaSVM and MPC algorithms to predict the deleteriousness of missense variants (D-Mis, defined as MetaSVM-deleterious or MPC-score ≥ 2)  57, 130. Inferred LoF variants consist of stop-gain, stop-loss, frameshift insertions/deletions, canonical splice sites, and start-loss.
LoF and D-Mis variants were considered 'damaging. ' DNVs were called using TrioDeNovo  56. Candidate DNVs were further filtered based on the following criteria: (1) exonic or splice-site variants; (2) a minimum read depth (DP) of 10 in the proband and both parents; (3) minimum proband alternative read depth of 5; (4) proband alternative allele ratio ≥ 28% if having <10 alternative reads or ≥20% if having ≥10 alternative reads; (5) alternative allele ratio in both parents ≤ 3. 5%; (6)  global MAF ≤4 x 10 -4 in the Exome Aggregation Consortium database. For recessive variant analysis, we filtered for rare (MAF ≤ 1 × 10 -3 in BRAVO and in-cohort MAF ≤ 1 × 10 -2 ) homozygous and compound heterozygous variants that exhibited high-quality sequence reads (pass GATK variant quality score recalibration, ≥ 8 total reads for proband, genotype quality (GQ) score ≥ 20).
De novo expectation model As case trios were captured by two different reagents (MedExome and IDT), we took the union of all bases covered by different capture reagents. We generated a Browser Extensible Data file representing a unified capture for all trios. We used bedtools (v. 2. 27. 1) to extract sequences from the Browser Extensible Data file. We then applied a sequence context-based method to calculate the probability of observing a DNV for each base in the coding region, adjusting for sequencing depth in each gene  133. Briefly, for each base in the exome, the probability of observing every trinucleotide mutating to other trinucleotides was determined. ANNOVAR (v2015Mar22) was used to annotate the consequence of each possible substitution. RefSeq was used to annotate variants (based on the file 'hg19_refGene.
Estimation of the expected number of rare, transmitted variants We implemented a one-sided binomial test to quantify enrichment of rare damaging or LoF heterozygous variants in each gene by comparing observed and expected counts estimated by mutability  39. Because the number of rare damaging or LoF heterozygous variants in a gene was inversely correlated with the constraint score (i. e. , pLI score) obtained from the gnomAD database, we stratified genes into five subsets by pLI quartiles to control for the potential confounding effect: (1) the first quartile with pLI <6. 4 × 10 -8 ; (2) the second quartile with pLI score 6. 4 × 10 -8 -1. 9 × 10 -3 ; (3) the third quartile with pLI 1. 9 x 10 -3 -0. 48; (4) the fourth quartile with pLI 0. 48-1. 0; between third quantile and 1; and (5) those without a pLI score.
Gene lists for specific diseases Gene lists for VOGM, AVM, CCM, MMD, and human height were curated from the publications referenced and genes were considered disease-causing based on guidelines from the Centers for Mendelian Genomics  23, 26, 36, 113, [135] [136] [137] [138] [139] [140] [141] [142] [143] [144] [145] [146] [147] [148]. Genes were classified as "possible" VOGM risk genes if they are established VOGM genes, or if they harbored ≥ 1 damaging DNV in a gene with pLI score ≥0. 9 in the gnomAD database.
Weighted Gene Co-expression Network Analysis (WGCNA) Robust consensus WGCNA was conducted by applying a processed bulk transcriptome sequencing data set encompassing sixteen human brain regions across human brain development 100. To increase resolution of the primary neurodevelopmental time periods, analysis was limited to timepoints between gestational week 9 and postnatal year 3. We removed samples >3 standard deviations above the mean network connectivity as outliers. Network analysis was performed with WGCNA  99  , assigning genes to specific modules based on bi-weight mid-correlations among genes. Soft threshold power of 10 was chosen to achieve scale-free topology (r 2 > 0. 9). A signed co-expression network was then generated. The topological overlap matrix was clustered hierarchically using average linkage hierarchical clustering (using '1 -TOM' as a dis-similarity measure).
Module enrichment analysis Module gene lists were obtained via WGCNA as described above. In a background set of all genes categorized in co-expression modules, logistic regression was used for an indicator-based enrichment: is. disease ~is. module + gene covariates (GC content, gene length, and mean expression in bulk RNA-seq atlas) 101. Of the 88 WGCNA modules, the gray module, by WGCNA convention 100 , contains all genes that do not co-express and are consequently unassigned to a co-expression network. Thus, the gray module was excluded from enrichment testing, and enrichment significance was defined at the Bonferroni multiple-testing cutoff (α = 5. 68 × 10 -04 ).
Acknowledgements This work is supported by the  Yale-National Institutes of Health (NIH) Center for Mendelian Genomics  ( 5U54HG006504 );  R01 NS111029-01A1 ,  R01 NS109358 ,  K12 228168 , and the  Rudi Schulte Research Institute  (K. T. K. );  R01 117609  (K. K. and T. J. B. );  NIH   Medical Scientist Training Program  ( NIH/National Institute of General Medical Sciences  Grant  T32GM007205 );  NIH Clinical and   Translational Science Award  from the  National Center for Advancing Translational Science  ( TL1 TR001864 ); the  K99/R00   Pathway to Independence Award  ( K99HL143036  and  R00HL143036-02  to S. C. J. ), the  Vernon W. Lippard Research Fellowship , the  Hydrocephalus Foundation Innovator Award  (K. T. K. and S. C. J. ), the  March of Dimes , the  Howard Hughes Medical Institute ,  R01 HL146352  and  2R01 HL120888  (both P.