Cloud computing can be considered as one of the leading-edge technological advances in the current IT industry. Cloud computing or simply cloud is attributed to the Service Oriented Architecture. ...Every organization is trying to utilize the benefit of cloud not only to reduce the cost overhead in infrastructure, network, hardware, software, etc., but also to provide seamless service to end users with the benefit of scalability. The concept of multitenancy assists cloud service providers to leverage the costs by providing services to multiple users/companies at the same time via shared resource. There are several cloud service providers currently in the market and they are rapidly changing and reorienting themselves as per market demand. In order to gain market share, the cloud service providers are trying to provide the latest technology to end users/customers with the reduction of costs. In such scenario, it becomes extremely difficult for cloud customers to select the best service provider as per their requirement. It is also becoming difficult to decide upon the deployment model to choose among the existing ones. The deployment models are suitable for different companies. There exist divergent criteria for different deployment models which are not tailor made for an organization. As a cloud customer, it is difficult to decide on the model and determine the appropriate service provider. The multicriteria decision making method is applied to find out the best suitable service provider among the top existing four companies and choose the deployment model as per requirement.
The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults comprising 14 established US prospective cohort studies. Starting as early as 1971, ...investigators in the C4R cohort studies have collected data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R links this pre–coronavirus disease 2019 (COVID-19) phenotyping to information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and acute and postacute COVID-related illness. C4R is largely population-based, has an age range of 18–108 years, and reflects the racial, ethnic, socioeconomic, and geographic diversity of the United States. C4R ascertains SARS-CoV-2 infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey conducted via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations and high-quality event surveillance. Extensive prepandemic data minimize referral, survival, and recall bias. Data are harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these data will be pooled and shared widely to expedite collaboration and scientific findings. This resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including postacute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term health trajectories.
Estimating the moisture content of insulation paper is important in transformer condition evaluation, the results of which are affected by thermal aging. This study investigates the independent ...effects of aged oil and aged paper on moisture equilibrium charts to determine which of the two primarily causes the inaccuracy in moisture estimation of aged transformers. A thermal aging experiment was conducted. At five aging degrees, four kinds of oil-paper insulation systems were formed: new oil-new paper, aged oil-new paper, new oil-aged paper, and aged oil-aged paper. Moisture equilibrium curves were drawn for each insulation system. The relative moisture content of oil and that of paper were calculated. Results showed that the moisture content of insulation paper decreased with aging, which led to the migration of the moisture equilibrium curves and further influenced the moisture estimation results. A comparative analysis showed that the migration of the moisture equilibrium curves was mainly caused by aged paper at the early and middle aging stages and by the synergistic effects of aged paper and aged oil at the late aging stage. The effects of aging on moisture distribution were verified using a retired transformer.
The aim of this study was to identify hub genes associated with metastasis and prognosis in melanoma. Weighted gene coexpression network analysis (WGCNA) was performed to screen and identify hub ...genes. ROC and K-M analyses were used to verify the hub genes in the internal and external data sets. The risk score model and nomogram model were constructed based on the IHC result. Through WGCNA, the three hub genes, SNRPD2, SNRPD3, and EIF4A3, were identified. In the external data set, the hub genes identified were associated with the worse prognosis (TCGA, SNRPD2, P≤0.02; SNRPD3, P=0.12; EIF4A3, P=0.11; GSE65904, SNRPD2, P=0.04; SNRPD3, P=0.10; EIF4A3, P<0.01; GSE19234, SNRPD2, P<0.01; SNRPD3, P<0.01; EIF4A3, P<0.01). In the GSE8401, we found that the hub genes were highly expressed in the metastasis compared with the nonmetastasis group (SNRPD2, 988.5±47.83 vs. 738.4±35.35, P<0.01; SNRPD3, 502.7±25.7 vs. 416.4±23.88, P=0.02; EIF4A3, 567.6±19.56 vs. 495.2±21.1, P=0.01). Moreover, the hub genes were identified by the IHC in our data set. The result was similar with the external data set. The hub genes could predict the metastasis and prognosis in the Chinese MM patients. Finally, the GSEA and Pearson analysis demonstrated that the SNRPD2 was associated with the immunotherapy. The three hub genes were identified and validated in MM patients in external and internal data sets. The risk factor model was constructed and verified as a powerful model to predict metastasis and prognosis in MM patients.
Background. Pyroptosis has been confirmed as a type of inflammatory programmed cell death in recent years. However, the prognostic role of pyroptosis in colon cancer (CC) remains unclear. Methods. ...Dataset TCGA-COAD which came from the TCGA portal was taken as the training cohort. GSE17538 from the GEO database was treated as validation cohorts. Differential expression genes (DEGs) between normal and tumor tissues were confirmed. Patients were classified into two subgroups according to the expression characteristics of pyroptosis-related DEGs. The LASSO regression analysis was used to build the best prognostic signature, and its reliability was validated using Kaplan–Meier, ROC, PCA, and t-SNE analyses. And a nomogram based on the multivariate Cox analysis was developed. The enrichment analysis was performed in the GO and KEGG to investigate the potential mechanism. In addition, we explored the difference in the abundance of infiltrating immune cells and immune microenvironment between high- and low-risk groups. And we also predicted the association of common immune checkpoints with risk scores. Finally, we verified the expression of the pyroptosis-related hub gene at the protein level by immunohistochemistry. Results. A total of 23 pyroptosis-related DEGs were identified in the TCGA cohort. Patients were classified into two molecular clusters (MC) based on DEGs. Kaplan–Meier survival analysis indicated that patients with MC1 represented significantly poorer OS than patients with MC2. 13 overall survival- (OS-) related DEGs in MCs were used to construct the prognostic signature. Patients in the high-risk group exhibited poorer OS compared to those in the low-risk group. Combined with the clinical features, the risk score was found to be an independent prognostic factor of CC patients. The above results are verified in the external dataset GSE17538. A nomogram was established and showed excellent performance. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the varied prognostic performance between high- and low-risk groups may be related to the immune response mediated by local inflammation. Further analysis showed that the high-risk group has stronger immune cell infiltration and lower tumor purity than the low-risk group. Through the correlation between risk score and immune checkpoint expression, T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) was predicted as a potential therapeutic target for the high-risk group. Conclusion. The 13-gene signature was associated with OS, immune cells, tumor purity, and immune checkpoints in CC patients, and it could provide the basis for immunotherapy and predicting prognosis and help clinicians make decisions for individualized treatment.
The benefit of adding social determinants of health (SDOH) when estimating atherosclerotic cardiovascular disease (ASCVD) risk is unclear.
To examine the association of SDOH at both individual and ...area levels with ASCVD risks, and to assess if adding individual- and area-level SDOH to the pooled cohort equations (PCEs) or the Predicting Risk of CVD Events (PREVENT) equations improves the accuracy of risk estimates.
This cohort study included participants data from 4 large US cohort studies. Eligible participants were aged 40 to 79 years without a history of ASCVD. Baseline data were collected from 1995 to 2007; median (IQR) follow-up was 13.0 (9.3-15.0) years. Data were analyzed from September 2023 to February 2024.
Individual- and area-level education, income, and employment status.
ASCVD was defined as the composite outcome of nonfatal myocardial infarction, death from coronary heart disease, and fatal or nonfatal stroke.
A total of 26 316 participants were included (mean SD age, 61.0 9.1 years; 15 494 women 58.9%; 11 365 Black 43.2%, 703 Chinese American 2.7%, 1278 Hispanic 4.9%, and 12 970 White 49.3%); 11 764 individuals (44.7%) had at least 1 adverse individual-level SDOH and 10 908 (41.5%) had at least 1 adverse area-level SDOH. A total of 2673 ASCVD events occurred during follow-up. SDOH were associated with increased risk of ASCVD at both the individual and area levels, including for low education (individual: hazard ratio HR, 1.39 95% CI, 1.25-1.55; area: HR, 1.31 95% CI, 1.20-1.42), low income (individual: 1.35 95% CI, 1.25-1.47; area: HR, 1.28 95% CI, 1.17-1.40), and unemployment (individual: HR, 1.61 95% CI, 1.24-2.10; area: HR, 1.25 95% CI, 1.14-1.37). Adding area-level SDOH alone to the PCEs did not change model discrimination but modestly improved calibration. Furthermore, adding both individual- and area-level SDOH to the PCEs led to a modest improvement in both discrimination and calibration in non-Hispanic Black individuals (change in C index, 0.0051 95% CI, 0.0011 to 0.0126; change in scaled integrated Brier score IBS, 0.396% 95% CI, 0.221% to 0.802%), and improvement in calibration in White individuals (change in scaled IBS, 0.274% 95% CI, 0.095% to 0.665%). Adding individual-level SDOH to the PREVENT plus area-level social deprivation index (SDI) equations did not improve discrimination but modestly improved calibration in White participants (change in scaled IBS, 0.182% 95% CI, 0.040% to 0.496%), Black participants (0.187% 95% CI, 0.039% to 0.501%), and women (0.289% 95% CI, 0.115% to 0.574%).
In this cohort study, both individual- and area-level SDOH were associated with ASCVD risk; adding both individual- and area-level SDOH to the PCEs modestly improved discrimination and calibration for estimating ASCVD risk for Black individuals, and adding individual-level SDOH to PREVENT plus SDI also modestly improved calibration. These findings suggest that both individual- and area-level SDOH may be considered in future development of ASCVD risk assessment tools, particularly among Black individuals.
To investigate the association between regular coffee consumption and the prevalence of coronary artery calcium (CAC) in a large sample of young and middle-aged asymptomatic men and women.
This ...cross-sectional study included 25 138 men and women (mean age 41.3 years) without clinically evident cardiovascular disease who underwent a health screening examination that included a validated food frequency questionnaire and a multidetector CT to determine CAC scores. We used robust Tobit regression analyses to estimate the CAC score ratios associated with different levels of coffee consumption compared with no coffee consumption and adjusted for potential confounders.
The prevalence of detectable CAC (CAC score >0) was 13.4% (n=3364), including 11.3% prevalence for CAC scores 1-100 (n=2832), and 2.1% prevalence for CAC scores >100 (n=532). The mean ±SD consumption of coffee was 1.8±1.5 cups/day. The multivariate-adjusted CAC score ratios (95% CIs) comparing coffee drinkers of <1, 1-<3, 3-<5, and ≥5 cups/day to non-coffee drinkers were 0.77 (0.49 to 1.19), 0.66 (0.43 to 1.02), 0.59 (0.38 to 0.93), and 0.81 (0.46 to 1.43), respectively (p for quadratic trend=0.02). The association was similar in subgroups defined by age, sex, smoking status, alcohol consumption, status of obesity, diabetes, hypertension, and hypercholesterolaemia.
In this large sample of men and women apparently free of clinically evident cardiovascular disease, moderate coffee consumption was associated with a lower prevalence of subclinical coronary atherosclerosis.
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