dataesr/scientific-paragraphs-categorization huggingface dataset:
SIOPE is committed to converting QUARTET into a long-term, sustainable programme, and the SIOPE-ROWG encourages use of QUARTET for clinical trials recruiting children and adolescents across SIOPE-affiliated ECTGs. QUARTET will continue to collaborate closely with trial sponsors to ensure data completeness, disseminate findings and share its experience with the paediatric radiation oncology community, and invest in education activities to support institutions in delivering optimal radiotherapy. SIOPE is exploring further initiatives to support imaging and surgical QA. Any professionals with an interest in paediatric radiation oncology, or indeed quality assurance in clinical trials, are welcome to contribute and encouraged to contact QUARTET and also join the SIOPE-ROWG, especially those from countries without current representation.
Acknowledgements The authors would like to thank the wider QUARTET group and all supporting collaborators for their past and future contributions to this initiative; this includes all consenting patients or parents for their participation in clinical trials. The authors also thank the international trial sponsors who have integrated QUARTET:  Associazione Italiana Ematologia Oncologia Pediatrica, German Paediatric Oncology Group, Gustave Roussy, Instituto de Investigacion Sanitaria La Fe de Valencia, Universita ¨tsklinikum des Saarlandes, UMC Utrecht , and the  University of Birmingham ; as well as the European Clinical Trial Groups supporting trial and research activities:  SIOPE   Brain Tumour Group, European Paediatric Soft Tissue Sarcoma Study Group, SIOP Renal Tumour Study Group ,  SIOPE-Neuroblastoma , and the  Euro Ewing Consortium.
Funding QUARTET and the work of  Sarah Kelly  and  Dr Rachel Effeney  as fellows at EORTC Headquarters are funded by  Fondatioun Kriibskrank Kanner ,  Luxembourg. Dr Mark N. Gaze  is supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre  and by the  Radiation Research Unit at the Cancer Research UK City of London Centre Award  [ C7893/A28990 ]. Dr Rudolf Schwarz  is supported by  Deutsche Kinderkrebsstiftung.
In the presence of non-proportionality, stratification on the dependent variable was applied. Incident AD and DVC were further analyzed as distinct end-points. Additional sensitivity analyses were performed to ensure the robustness of the results and to address possible reverse causality: (1) adjustment for hypnotic use, (2) exclusion of participants using hypnotics, (3) imputation of missing data on covariates in 539 participants using Multiple Imputation Chained Equation (7% of missing data, 10 imputation templates were generated), and (4) exclusion of dementia cases at a 2-year follow-up. In supplementary analyses, time-dependent Cox models were performed to take into account the use of hypnotics during the follow-up. The use of hypnotic was considered to be a time-dependent variable, with data for hypnotics use defined throughout follow-up as: no user (no hypnotic during the follow-up), intermittent users (cumulative proportion of hypnotic use ≤ 50% over the entire follow-up), and persistent users (cumulative proportion of hypnotic use > 50% over the entire follow-up).
Interactions between significant sleep complaints, hypnotic use, and several factors describing population at higher risk for dementia (i. e. , age, sex, depressive status, APOE-ɛ4, history of cardiovascular, and metabolic diseases) on dementia risk were tested using the Wald-χ 2 test for all-cause dementia, AD, and DVC. In a subsample, Cox models were performed to study the association between plasma Aβ levels and incident dementia. Linear regressions were used to study the association between sleep complaints, hypnotics intake, and β-amyloid levels (taken as a continuous variable or categorized into tertiles). Significance level was set at a two-sided p < 0. 05. Statistical analyses were performed using SAS 9. 4 (SAS Institute Inc. ) and R 3. 6. 3 statistical software.
Subjects characteristics As shown in the study diagram (Fig. 1 ), the study sample included 6851 participants free of dementia (60% women) with a mean age of 73. 7 years (SD = 5. 3), with completed sleep questionnaires, with follow-up, and no missing data on covariates. Subjects excluded from the study were more frequently female, older, had a lower education level, living alone, and were more often confined, and present more metabolic and cardiovascular diseases, depressive status, and sleep complaints. Regarding baseline sleep characteristics, the presence of EDS, DIS, DMS, and EMA was reported by 18. 1%, 35. 3%, 63. 7%, and 36. 8% of the participants respectively, while the presence of a poor SQ was reported by 52. 6% of the participants. Around a quarter (23%) reported no sleep complaints, 25% one complaint, 50% two to four complaints, and only 2% had the five complaints.
1B ). After supracoeliac and abdominal aortic cross clamping, the tumour was resected en bloc (Fig. 1C  and D ). Blood loss was 1000 mL during the surgery, and the patient received six units of packed red blood cells (pRBC) and three units of fresh frozen plasma (FFP). The following is/are the supplementary data related to this article:Supplementary Video S1This video shows the bypass originating from the infraclavicular left axillary artery with a 7 mm diameter polyethylene terephthalate graft (PET; LeMaitre Vascular, Inc, Burlington, MA, USA) with three PET branches allowing anastomoses to the target vessels: the coeliac trunk, the superior mesenteric artery, and the right and left renal arteries. Blood pressure was assessed clinically by a good graft pulse. Supplementary video related to this article can be found at doi:10.
EXPERIMENTS To compare the behaviour of the evaluated measures and to test the Wasserstein distance (WD), two sets of experiments, one on simulated transformations and another one on real data, were performed. In the first set of experiments, the 5 evaluation measures (Dice, correlation, VC, VS and WD) were computed on 4 controlled transformations cases, often found at least partially in real cases. The data used here is the Cortico-Spinal Tract (CST) of 5 subjects of the Human connectome Project (HCP). This ground truth data was obtained by Wasserthal et al. [22]  by performing fiber tracking using Mrtrix and multiple times filtered, first by ROIs, then by an expert. The 4 experiments were designed as follows: Translation The reference track is translated along the X axis from 0 to 12. 5 mm, in step of 0.
Degradation The track is degraded by randomly removing fibers from 0 to 100 percent of the reference fiber bundle For each of these simulated datasets, cross-correlation coefficients were further computed between each pair of measure curves computed. This allowed to quantify the complementarity between these measures (the smaller the correlation the more complementary information). The second set of experiments is based on data from the HCP and more particularly on 3 bundles, the CST, the Optic radiation (OR) and the first Superior longitudinal fascicle (SLF) tracked with the parallel transport frame algorithm  [15] , the Mrtrix3 iFOD2  [13]  and the DIPY Probabilistic PFT algorithm  [14]. For the 3 algorithms, only default parameters were used and 10000 streamlines were generated by running a whole brain tractography, then by filtering the results with the start and end ROIs, segmented with TractSeg.
Phenome-wide association studies and MR-PheWAS We performed a Phenome-Wide Association Study (PheWAS) and a subsequent MR-Phenome-Wide Association Study (MR-PheWAS) to explore associations between our aortic traits of interest and clinical diagnoses in the whole UK Biobank population for which relevant clinical data were available (n = up to 406,827), controlling for age, sex, and for genotype array and four principal components of genotype for the MR-PheWAS analysis. Initial PheWAS identified significant phenotypic associations between aortic traits and multiple hypertension-related clinical codes. Aortic areas showed a positive phenotypic association with hypertension (for example, AAmin β = 0. 001; p = 2. 5 × 10 -21 ; see Supplementary Data 17 for full results). Aortic distensibility was negatively associated with hypertension (β = -0.
For the positional mapping of SNPs to genes, a maximal distance of 10kB was set. eQTL mapping was performed based on the aorta tissue samples in GTEx v8 (using only gene pairs with significant SNPs with the default settings of FDR < 0. 05 or p value <1e -3 ). 3D chromatin interaction mapping was based on HiC aorta data (GSE87112) within a promotor region window of 250 bp upstream and 500 bp downstream from the transcriptional start site and a threshold for significant loops of FDR < 1e -6. Enhancer and promotor regions were annotated using the aorta epigenome (E065) from the Roadmap Epigenome Project (http://www. roadmapepigenomics. org/). MAGMA (Multi-marker Analysis of GenoMic Annotation) was employed to obtain the significance of individual genes with the specificity of tissue expression based on 54 types in GTEx v8 and the association with 10,678 gene sets from MsigDB v6.
1 was set (following the recommendations on the DEPICT website). Note that DEPICT excludes any SNPs in the HLA region, on a sex chromosome, or not found in the 1000 Genomes Project data. Comparisons between our data and those reported by Pirruccello et al. 30  and Tcheandjieu et al. 31  were made using the lead SNPs and corresponding beta-values. The overlaps are reported in Supplementary Data 11. To assess the degree of convergence of their studies with our results, lead SNPs were assigned to a genomic locus found in our study if they were within the locus or a 250 kb distance interval. Replication data: Study of Health in Pomerania (SHIP) cohort Cohort characteristics and recruitment criteria have previously been reported  33  and details of the study population, image acquisition protocols, genotyping and statistical analysis are reported in Supplementary Information.
Ethics approval was given by the medical ethics committee of the University of Greifswald. In brief, 2787 individuals recruited as part of two separate cohorts within this population study (SHIP and SHIP-Trend; see Supplementary Information for details), had baseline whole-body CMR scans. Aortic areas were manually measured from the axial images at the level of the pulmonary bifurcation. Genotyping was performed using Affymetrix Genome-Wide Human SNP Array 6. 0 and Illumina Human Omni 2. 5 array and imputation was performed in the Michigan Imputation Server using the HRC v 1. 1 reference panel. Genome-wide linear regression analyses were performed in each cohort separately using EPACTS-3. 2. 9 (https://github. com/statgen/EPACTS) adjusted for sex, age, mean arterial pressure, body height, body weight, array type (SHIP-Trend only), and the first two genetic principal components.
LD Score regression We performed LD Score regression using LDSC (LD SCore) v1. 0. 1 (https://github. com/bulik/ldsc)  94, 99 to assess the heritability of the imaging phenotypes. The genetic correlation between imaging phenotypes and blood pressure by LD score was computed using 1000 Genomes European data  94. We used the GWAS summary statistics for SBP, DBP and pulse pressure (PP) from the International Consortium for Blood Pressure (ICBP)  43  for the corresponding analyses for genetic correlation with blood pressure.
Co-expression analysis Co-expression networks for aortic phenotypes were derived using a single cell RNA-seq (scRNA-seq) data set for primate arteries  42. The data were obtained from Gene Expression Omnibus (accession number GSE117715) and included read counts for over 9000 single cells from aortas and coronary arteries of 16 Macaca fascicularis. Low abundance genes were removed if they had read counts of less than 5% of the cells, leaving a total of 9903 genes for further analysis. The Bioconductor package scater (v1. 14. 6) was applied in R (v3. 6. 0) to compute log-transformed normalised expression values from the count matrix  100. Subsequently, correlation of expression and its significance was derived using the correlatePairs function of the Bioconductor scran (v1. 14. 6) package, which calculated modified Spearman correlation coefficients and derived their significance using a permutation approach  101.
A basal co-expression network was constructed using gene pairs with significant correlation (false discovery rate; FDR < 0. 01) and a minimum Spearman correlation coefficient rho of 0. 1 or 0. 2. Subsequently, sub-networks for imaging phenotypes were derived by retrieving gene pairs with at least one gene associated by MAGMA or FUMA with the specific phenotype. Functional enrichment of genes connected with variants identified on GWAS was carried out using overrepresentation enrichment analysis implemented in the Bioconductor clusterProfiler (v3. 14. 3) package  102. The background gene set (or universe) was defined by the genes covered by the scRNA-seq data after exclusion of low abundance genes.
Potential horizontal pleiotropic effects were investigated using MR-Egger  106. Outlier SNPs identified by MR-PRESSO were excluded from the analyses  107. In an additional analysis, we tested the validity of the genetic variants as instrumental variables using MR-ConMix  103. We accounted for multiple comparisons of four aortic imaging phenotypes, three blood pressure traits, and two directions using Bonferroni correction with a P value threshold of 0. 05/(4*3*2)=0. 002. MR analysis was also used to investigate the potential causal relationships between different aortic traits with WMH. In addition to IVW, WM, and MR-Egger, we implemented R package RadialMR (v1. 0, available through CRAN repositories)  108. A p value < 0. 01 correcting for 6 tests (for the 6 aortic traits) was considered significant.
Multivariable MR We also conducted MVMR using the MVMR package in R 110 to estimate the direct effect of aortic traits on the cerebral small vessel disease (cSVD) outcome (WMH) after accounting for potential confounding with blood pressure traits, by conditioning on every other explanatory variable included in the model. Different combinations of explanatory variables were considered and the F TS conditional on the other variables was calculated as a measure of instrument strength  111. Briefly, MVMR by regressing a given instrumental variable on all the remaining variables as controls generates a predicted value for the instrumental variable that is not correlated with other variables in the model thus accounting for possible pleiotropic effects.
MR phenome-wide association studies (MR-PheWAS) and PheWAS We performed an MR-PheWAS using data from UK Biobank participants who did not undergo aortic imaging to assess the effects of aortic traits on clinical disease classifications. Using the PheWAS package (https://github. com/PheWAS/PheWAS), we mapped 1157 phecodes with more than 200 cases from the International Classification of Diseases, 9th Revision, Clinical Modification and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM)  112. For each aortic imaging phenotype, we selected SNPs with a p value < 5 × 10 -8 and minor allele frequency >0. 05 from the single-trait GWAS for AAmax, AAmin, DAmax, and DAmin, and from the multi-trait GWAS for AAdis and DAdis. Given that we considered a large number of phecodes as the outcome in the MR-PheWAS analysis with the number of cases ranging from 201 to 116,879 (median = 1353), we used a more stringent LD threshold for independent SNPs (r 2 < 0.
01) to achieve a better-stablised model. The PheWAS model was adjusted for age, sex, genotype array, and its four principal components for population stratification. MR estimates were then obtained for each pair of aortic traits and phecode by combining the SNP-specific associations using IVW, WM, and MR-Egger. We accounted for multiple comparisons of 1157 phecodes using Bonferroni correction with p value < 0. 05/ 1157 = 4. 3 × 10 -5. Standard (non-MR) PheWAS was also performed, using Python (v3. 8. 10) with the statsmodels (v0. 12. 2) package. Logistic regression was performed for each disease code against each imaging phenotype, adjusting for sex, age and BMI. The regression coefficient and two-tailed p value of the imaging phenotype were reported. Analyses of associations of white matter hyperintensities, aortic and cervical artery dissection LD score regression (LDSR) method was applied to test genetic correlation at the genome-wide scale for the different aortic traits with the most common MRI feature of cSVD, WMH and with CeAD.
Acknowledgements C. F. acknowledges generous support from the  NHLI and Imperial College , and support from the  BHF Imperial Centre of Research Excellence   RE/18/4/34215  and  BHF Clinical Research Fellowship   FS/15/81/31817. P. M. M. acknowledges generous personal and research support from the  Edmond J Safra Foundation and Lily Safra , a  National Institute for Health Research (NIHR) Senior Investigator Award , the  UK Dementia Research Institute  and the  NIHR Biomedical Research Centre at Imperial College London. This research has been conducted using the UK Biobank Resource under Application Number  18545. J. S. W. has received support from the  Medical Research Council (UK) ,  British Heart Foundation  [ RE/18/4/34215 ],  NIHR Imperial College Biomedical Research Centre ,  Sir Jules Thorn Charitable Trust  [21/JTA].
P. E. is the director of the  Medical Research Council (MRC) Centre for Environment and Health  and acknowledges support from the  MRC  ( MR/L01341X/1 ;  MR/ S019669/1 ). P. E. is director of the  Health Protection Research Unit in Chemical and Radiation Threats and Hazards , funded by the  NIHR. P. E. also acknowledges support from the  NIHR Imperial Biomedical Research Centre , the  Imperial College British Heart Foundation Centre for Research Excellence  ( RE/18/4/34215 ), and the  UK Dementia Research Institute at Imperial College London  ( MC_PC_17114 ), and Health Data Research UK for London. S. D. is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the' Investment for the Future '  Programme   ANR-18-RHUS-002  and by the  ERC  and the  EU H2020  under grant agreements  640643 ,  667375 , and  754517.
SHIP is part of the  Community Medicine Research net of the University of Greifswald, Germany , which is funded by the  Federal Ministry of Education and Research  (grants no. 01ZZ9603 ,  01ZZ0103 , and  01ZZ0403 ), the  Ministry of Cultural Affairs  as well as the  Social Ministry of the Federal State of Mecklenburg-West Pomerania , and the  network 'Greifswald Approach to Individualised Medicine (GANI_MED) ' funded by the  Federal Ministry of Education and Research  (grant  03IS2061A ). Whole-body MR imaging was supported by a joint grant from  Siemens Healthineers, Erlangen, Germany  and the  Federal State of Mecklenburg-West Pomerania. The  University of Greifswald  is a member of the  Caché Campus programme of InterSystems GmbH. CADISP list of investigators:  Belgium: Departments of Neurology, Erasmus University Hospital, Brussels and Laboratory of Experimental Neurology, ULB, Brussels  ( Shérine Abboud ,  Massimo Pandolfo );  Department of Neurology, Leuven University Hospial (Vincent Thijs ).
Data availability The results of stage 1 GWAS and MTAG generated in this study have been deposited in the Imperial College data repository at https://doi. org/10. 14469/hpc/10653 and are freely available for download. Raw data from the UKBB participants can be requested from the UKBB Access Management System (https://bbams. ndph. ox. ac. uk). Other publicly available data used for annotation and analysis are available as follows: eQTL data used in this study from aortic tissue are available at the GTEx portal (v8) (https://www. gtexportal. org/home/ tissue/Artery_Aorta). HiC aorta data used for 3D chromatin interaction mapping was based on HiC aorta data (accession number GSE87112). Co-expression data used in this study are available from Gene Expression Omnibus (accession number GSE117715).
Data: main cohort The UK Biobank CMR imaging was conducted using a rigorously controlled acquisition protocol  88. The cohort has been well-described previously and ethics approval for UK Biobank has been given by the North West Multi-Centre Research Ethics Committee (MREC). These analyses were conducted under Application number 18545. The mean age at the time of CMR was 64 ± 8 years (range 45-82, 49% of participants were male [see Supplementary Data 1]). Exclusion criteria for imaging included a range of relative contraindications to magnetic resonance imaging scanning as well as childhood-onset disease and pregnancy. Aortic cine images were acquired using transverse bSSFP sequence at the level of the pulmonary trunk and right pulmonary artery on clinical wide bore 1. 5 Tesla scanners (MAGNETOM Aera, Syngo Platform VD13A, Siemens Healthcare).
3) External quality control Two control vitality slides are prepared by accreditation laboratories and are assessed every six months for external QC. The designated technologist scores at least 200 spermatozoa and records results on the data sheet as percent viable. Results should be within ±2 standard deviation of mean for all laboratories as reported by the accreditation agency. Corrective action must be undertaken if the results are not within the specified range. The technologist will have to be retrained in the test and then instructed to re-score the proficiency slides  [15].
Methodology The American Center for Reproductive Medicine (ACRM) designed and conducted an online survey to investigate the importance, use and clinical implication of sperm vitality testing in male infertile patients. No ethical approval was required for this study. Researchers and clinicians from ACRM drafted the questions (AA, RKS, RF, SG, NP), which were subsequently revised by a pool of experienced andrologists, urologists, and attending physicians with expertise in male infertility selected from all over the world. English was the primary language of the survey and questions were carefully revised to make them understandable for non-native English speakers. The survey consisted of 15 questions, in 2 different sections: the first section (6 questions) aimed to collect the demographic data of the participants, while the questions in the second section targeted the clinical practice pattern of sperm vitality testing (Supplement File 2).
We used SelectSurvey (https://www. classapps. com/product_ssv5. aspx; ClassApps Inc. , Kansas City, MO, USA), a tool approved by the Information Technology Department, Cleveland Clinic, to securely populate the survey. A total of 106 international experts in male infertility received the survey link by email on June 19, 2021, and they were asked to complete the survey by June 28, 2021. SelectSurvey automatically.
RESULTS AND DISCUSSION In order to test the effect of the detector sensitivity on the performance in UH-DPP irradiation conditions, six prototypes were fabricated, nominally identical to a mD dosimeter, with the only exception being a systematic variation of the active area diameter, from 1. 0 up to 2. 5 mm. The detector with a 2. 2 mm diameter is thus a standard mD detector. The sensitivity of the prototypes was evaluated at the PTW calibration laboratory, under 60 Co source irradiation. In Figure  3 , the obtained sensitivities are reported as a function of the active area. A linear trend is confirmed by the experimental data and an overall change by a factor of 7 was achieved, with sensitivities ranging from 0. 228 to 1. 610 nC/Gy. The data scattering in Figure  3  is to be attributed to unintentional relatively small differences in the active layer thickness of the detector prototypes, affecting their sensitivities.