Multivariate Relations Aggregation Learning in Social Networks

Multivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, and academic networks. Due to the principle of ternary closures and the trend of group formation, the multivariate relationships in social networks are complex and rich. Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important. Existing graph learning methods are based on the neighborhood information diffusion mechanism, which often leads to partial omission or even lack of multivariate relationship information, and ultimately affects the accuracy and execution efficiency of the task. To address these challenges, this paper proposes the multivariate relationship aggregation learning (MORE) method, which can effectively capture the multivariate relationship information in the network environment. By aggregating node attribute features and structural features, MORE achieves higher accuracy and faster convergence speed. We conducted experiments on one citation network and five social networks. The experimental results show that the MORE model has higher accuracy than the GCN (Graph Convolutional Network) model in node classification tasks, and can significantly reduce time cost.Comment: 11 pages, 6 figure

Tracking and predicting the treatment adherence of patients under rehabilitation: a three-wave longitudinal validation study for the Rehabilitation Adherence Inventory

This study aimed to develop and validate a new measurement tool, the Rehabilitation Adherence Inventory (RAI), to measure patients’ rehabilitation adherence. We recruited 236 patients with anterior cruciate ligament (ACL) ruptures from the United Kingdom (Mage = 33.58 ± 10.03, range = 18 to 59; female = 46.2%). Participants completed a survey, that measured their rehabilitation adherence, rehabilitation volume, psychological needs support, autonomous motivation, and intention at baseline, and at the 2nd and 4th month. Factorial, convergent, discriminant, concurrent, predictive, ecological validity and test–retest reliability of the RAI were tested via exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modelling (SEM). All the EFAs, CFAs, and SEMs yielded acceptable to excellent goodness-of-fit, χ2 = 10.51 to 224.12, df = 9 to 161, CFI > 0.95, TLI > 0.95, RMSEA <0.09 [90%C I < 0.06 to 0.12], SRMR <0.04. Results fully supported the RAI’s factorial, convergent, discriminant, and ecological validity, and test–retest reliability. The concurrent and predictive validity of the RAI was only partially supported because the RAI scores at baseline was positively associated with rehabilitation frequency at all time points (r = 0.34 to 0.38, p < 0.001), but its corresponding associations with rehabilitation duration were not statistically significant (p = 0.07 to 0.93). Overall, our findings suggest that this six-item RAI is a reliable and valid tool for evaluating patients’ rehabilitation adherence

Analysis of meiotic recombination in 22q11.2, a region that frequently undergoes deletions and duplications

BACKGROUND: The 22q11.2 deletion syndrome is the most frequent genomic disorder with an estimated frequency of 1/4000 live births. The majority of patients (90%) have the same deletion of 3 Mb (Typically Deleted Region, TDR) that results from aberrant recombination at meiosis between region specific low-copy repeats (LCRs). METHODS: As a first step towards the characterization of recombination rates and breakpoints within the 22q11.2 region we have constructed a high resolution recombination breakpoint map based on pedigree analysis and a population-based historical recombination map based on LD analysis. RESULTS: Our pedigree map allows the location of recombination breakpoints with a high resolution (potential recombination hotspots), and this approach has led to the identification of 5 breakpoint segments of 50 kb or less (8.6 kb the smallest), that coincide with historical hotspots. It has been suggested that aberrant recombination leading to deletion (and duplication) is caused by low rates of Allelic Homologous Recombination (AHR) within the affected region. However, recombination rate estimates for 22q11.2 region show that neither average recombination rates in the 22q11.2 region or within LCR22-2 (the LCR implicated in most deletions and duplications), are significantly below chromosome 22 averages. Furthermore, LCR22-2, the repeat most frequently implicated in rearrangements, is also the LCR22 with the highest levels of AHR. In addition, we find recombination events in the 22q11.2 region to cluster within families. Within this context, the same chromosome recombines twice in one family; first by AHR and in the next generation by NAHR resulting in an individual affected with the del22q11.2 syndrome. CONCLUSION: We show in the context of a first high resolution pedigree map of the 22q11.2 region that NAHR within LCR22 leading to duplications and deletions cannot be explained exclusively under a hypothesis of low AHR rates. In addition, we find that AHR recombination events cluster within families. If normal and aberrant recombination are mechanistically related, the fact that LCR22s undergo frequent AHR and that we find familial differences in recombination rates within the 22q11.2 region would have obvious health-related implications

Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers

Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. Methods: We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]-positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. Results: The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P = 8.2 x 10(53)). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P = 7.2 x 10(-20)). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Conclusions: BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genetic counselling and testing in pulmonary arterial hypertension:a consensus statement on behalf of the International Consortium for Genetic Studies in PAH

Pulmonary arterial hypertension (PAH) is a rare disease that can be caused by (likely) pathogenic germline genomic variants. In addition to the most prevalent disease gene, BMPR2 (bone morphogenetic protein receptor 2), several genes, some belonging to distinct functional classes, are also now known to predispose to the development of PAH. As a consequence, specialist and non-specialist clinicians and healthcare professionals are increasingly faced with a range of questions regarding the need for, approaches to and benefits/risks of genetic testing for PAH patients and/or related family members. We provide a consensus-based approach to recommendations for genetic counselling and assessment of current best practice for disease gene testing. We provide a framework and the type of information to be provided to patients and relatives through the process of genetic counselling, and describe the presently known disease causal genes to be analysed. Benefits of including molecular genetic testing within the management protocol of patients with PAH include the identification of individuals misclassified by other diagnostic approaches, the optimisation of phenotypic characterisation for aggregation of outcome data, including in clinical trials, and importantly through cascade screening, the detection of healthy causal variant carriers, to whom regular assessment should be offered.</p

Assessing associations between the AURKAHMMR-TPX2-TUBG1 functional module and breast cancer risk in BRCA1/2 mutation carriers

While interplay between BRCA1 and AURKA-RHAMM-TPX2-TUBG1 regulates mammary epithelial polarization, common genetic variation in HMMR (gene product RHAMM) may be associated with risk of breast cancer in BRCA1 mutation carriers. Following on these observations, we further assessed the link between the AURKA-HMMR-TPX2-TUBG1 functional module and risk of breast cancer in BRCA1 or BRCA2 mutation carriers. Forty-one single nucleotide polymorphisms (SNPs) were genotyped in 15,252 BRCA1 and 8,211 BRCA2 mutation carriers and subsequently analyzed using a retrospective likelihood appr