A Survey on Graph Database Management Techniques for Huge Unstructured Data

Data analysis, data management, and big data play a major role in both social and business perspective, in the last decade. Nowadays, the graph database is the hottest and trending research topic. A graph database is preferred to deal with the dynamic and complex relationships in connected data and offer better results. Every data element is represented as a node. For example, in social media site, a person is represented as a node, and its properties name, age, likes, and dislikes, etc and the nodes are connected with the relationships via edges. Use of graph database is expected to be beneficial in business, and social networking sites that generate huge unstructured data as that Big Data requires proper and efficient computational techniques to handle with. This paper reviews the existing graph data computational techniques and the research work, to offer the future research line up in graph database management

Best Practices for Reliable and Robust Spacecraft Structures

A study was undertaken to capture the best practices for the development of reliable and robust spacecraft structures for NASA s next generation cargo and crewed launch vehicles. In this study, the NASA heritage programs such as Mercury, Gemini, Apollo, and the Space Shuttle program were examined. A series of lessons learned during the NASA and DoD heritage programs are captured. The processes that "make the right structural system" are examined along with the processes to "make the structural system right". The impact of technology advancements in materials and analysis and testing methods on reliability and robustness of spacecraft structures is studied. The best practices and lessons learned are extracted from these studies. Since the first human space flight, the best practices for reliable and robust spacecraft structures appear to be well established, understood, and articulated by each generation of designers and engineers. However, these best practices apparently have not always been followed. When the best practices are ignored or short cuts are taken, risks accumulate, and reliability suffers. Thus program managers need to be vigilant of circumstances and situations that tend to violate best practices. Adherence to the best practices may help develop spacecraft systems with high reliability and robustness against certain anomalies and unforeseen events

Prescribing in type 2 diabetes patients with and without cardiovascular disease history: A descriptive analysis in the UK CPRD

PURPOSE: Some classes of glucose-lowering medications, including sodium-glucose co-transporter 2 inhibitors (SGLT2is) and glucagon-like peptide 1-receptor agonists (GLP1-RAs) have cardio-protective benefit, but it is unclear whether this influences prescribing in the United Kingdom (UK). This study aims to describe class-level prescribing in adults with type 2 diabetes mellitus (T2DM) by cardiovascular disease (CVD) history using the Clinical Practice Research Datalink (CPRD). METHODS: Four cross-sections of people with T2DM aged 18-90 and registered with their general practice for >1 year on 1st January 2017 (n = 166,012), 1st January 2018 (n = 155,290), 1st January 2019 (n = 152,602) and 31st December 2019 (n = 143,373) were identified. Age-standardised proportions for class use through time were calculated separately in those with and without CVD history and by total number of medications prescribed (one, two, three, four+). An analysis by UK country was also performed. FINDINGS: Around 31% of patients had CVD history at each cross-section. Metformin was the most common treatment (>70% of those with and without CVD had prescriptions across all treatment lines). Overall use of SGLT2is and GLP1-RAs was low, with slightly less use in patients with CVD (SGLT2i: 9.8% and 13.8% in those with and without CVD respectively; GLP1-RA: 4.3% and 4.9%, December 2019). Use of SGLT2is as part of dual therapy was low but rose throughout the study. In January 2017, estimated use was 8.0% (95% CI 6.9-9.1%) and 8.9% (8.6-9.3%) in those with and without CVD. By December 2019 this reached 18.3% (17.0-19.5%) and 21.2% (20.6-21.7%) for those with and without CVD respectively. SGLT2i use as triple therapy increased: 22.7% (21.0-24.4%) and 25.9% (25.2-26.6%) in January 2017 to 41.3% (39.5-43.0%) and 45.5% (44.7-46.3%) in December 2019. GLP1-RA use also increased, but observed usage remained lower than SGLT2 inhibitors. Insulin use remained stable throughout, with higher use observed in those with CVD (16% vs 9.7% Dec 2019). Time trends in England, Wales, Scotland and Northern Ireland were similar, although class prevalence varied. IMPLICATIONS: Although use of SGLT2is and GLP1-RAs has increased, overall usage remains low with slightly lower use in those with CVD history, suggesting there is opportunity to optimise use of these medicines in T2DM patients to manage CVD risk. Insulin use was substantially more prevalent in those with CVD despite no evidence of CVD benefit. Further investigation of factors influencing this finding may highlight strategies to improve patient access to the most appropriate treatments, including those with evidence of cardiovascular benefit

Cost-effectiveness of 10-kHz Spinal Cord Stimulation Therapy Compared With Conventional Medical Management Over the First 12 Months of Therapy for Patients With Nonsurgical Back Pain: Randomized Controlled Trial

Objective: This analysis evaluated if spinal cord stimulation (SCS) at 10 kHz plus conventional medical management (CMM) is cost-effective compared with CMM alone for the treatment of nonsurgical refractory back pain (NSRBP). Methods: NSRBP subjects were randomized 1:1 into the 10-kHz SCS (n = 83) or CMM (n = 76) group. Outcomes assessed at 6 months included EQ-5D 5-level (EQ-5D-5L), medication usage, and healthcare utilization (HCU). There was an optional crossover at 6 months and follow-up to 12 months. The incremental cost-effectiveness ratio (ICER) was calculated with cost including all HCU and medications except for the initial device and implant procedure, and cost-effectiveness was analyzed based on a willingness-to-pay threshold of \u3c $50,000 per quality-adjusted life-year. Results: Treatment with 10-kHz SCS resulted in a significant improvement in quality of life (QOL) over CMM (EQ-5D-5L index score change of 0.201 vs -0.042, p \u3c 0.001) at a lower cost, based on reduced frequency of HCU resulting in an ICER of -$4964 at 12 months. The ICER was -$8620 comparing the 6 months on CMM with postcrossover on 10-kHz SCS. Conclusions: Treatment with 10-kHz SCS provides higher QOL at a lower average cost per patient compared with CMM. Assuming an average reimbursement for device and procedure, 10-kHz SCS therapy is predicted to be cost-effective for the treatment of NSRBP compared with CMM within 2.1 years

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

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

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

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

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