Multi-Task Offloading via Graph Neural Networks in Heterogeneous Multi-access Edge Computing

In the rapidly evolving field of Heterogeneous Multi-access Edge Computing (HMEC), efficient task offloading plays a pivotal role in optimizing system throughput and resource utilization. However, existing task offloading methods often fall short of adequately modeling the dependency topology relationships between offloaded tasks, which limits their effectiveness in capturing the complex interdependencies of task features. To address this limitation, we propose a task offloading mechanism based on Graph Neural Networks (GNN). Our modeling approach takes into account factors such as task characteristics, network conditions, and available resources at the edge, and embeds these captured features into the graph structure. By utilizing GNNs, our mechanism can capture and analyze the intricate relationships between task features, enabling a more comprehensive understanding of the underlying dependency topology. Through extensive evaluations in heterogeneous networks, our proposed algorithm improves 18.6\%-53.8\% over greedy and approximate algorithms in optimizing system throughput and resource utilization. Our experiments showcase the advantage of considering the intricate interplay of task features using GNN-based modeling

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

IFITM3, TLR3, and CD55 gene SNPs and cumulative genetic risks for severe outcomes in Chinese patients with H7N9/H1N1pdm09 influenza

Background. We examined associations between single-nucleotide polymorphisms (SNPs) of IFITM3, TLR3, and CD55 genes and influenza clinical outcomes in Chinese. Methods. A multicenter study was conducted on 275 adult cases of avian (H7N9) and pandemic (H1N1pdm09) influenza. Host DNA was extracted from diagnostic respiratory samples; IFITM3 rs12252, TLR3 rs5743313, CD55 rs2564978, and TLR4 rs4986790/4986791 were targeted for genotyping (Sanger sequencing). The primary outcome analyzed was death. Results. IFITM3 and TLR3 SNPs were in Hardy–Weinberg equilibrium; their allele frequencies (IFITM3/C-allele 0.56, TLR3/C-allele 0.88) were comparable to 1000 Genomes Han Chinese data. We found over-representation of homozygous IFITM3 CC (54.5% vs 33.2%; P = .02) and TLR3 CC (93.3% vs 76.9%; P = .04) genotypes among fatal cases. Recessive genetic models showed their significant independent associations with higher death risks (adjusted hazard ratio [aHR] 2.78, 95% confidence interval [CI] 1.29–6.02, and aHR 4.85, 95% CI 1.11−21.06, respectively). Cumulative effects were found (aHR 3.53, 95% CI 1.64−7.59 per risk genotype; aHR 9.99, 95% CI 1.27−78.59 with both). Results were consistent for each influenza subtype and other severity indicators. The CD55 TT genotype was linked to severity. TLR4 was nonpolymorphic. Conclusions. Host genetic factors may influence clinical outcomes of avian and pandemic influenza infections. Such findings have important implications on disease burden and patient care in at-risk populations.</p

IFITM3, TLR3, and CD55 gene SNPs and cumulative genetic risks for severe outcomes in Chinese patients with H7N9/H1N1pdm09 influenza

Background. We examined associations between single-nucleotide polymorphisms (SNPs) of IFITM3, TLR3, and CD55 genes and influenza clinical outcomes in Chinese. Methods. A multicenter study was conducted on 275 adult cases of avian (H7N9) and pandemic (H1N1pdm09) influenza. Host DNA was extracted from diagnostic respiratory samples; IFITM3 rs12252, TLR3 rs5743313, CD55 rs2564978, and TLR4 rs4986790/4986791 were targeted for genotyping (Sanger sequencing). The primary outcome analyzed was death. Results. IFITM3 and TLR3 SNPs were in Hardy–Weinberg equilibrium; their allele frequencies (IFITM3/C-allele 0.56, TLR3/C-allele 0.88) were comparable to 1000 Genomes Han Chinese data. We found over-representation of homozygous IFITM3 CC (54.5% vs 33.2%; P = .02) and TLR3 CC (93.3% vs 76.9%; P = .04) genotypes among fatal cases. Recessive genetic models showed their significant independent associations with higher death risks (adjusted hazard ratio [aHR] 2.78, 95% confidence interval [CI] 1.29–6.02, and aHR 4.85, 95% CI 1.11−21.06, respectively). Cumulative effects were found (aHR 3.53, 95% CI 1.64−7.59 per risk genotype; aHR 9.99, 95% CI 1.27−78.59 with both). Results were consistent for each influenza subtype and other severity indicators. The CD55 TT genotype was linked to severity. TLR4 was nonpolymorphic. Conclusions. Host genetic factors may influence clinical outcomes of avian and pandemic influenza infections. Such findings have important implications on disease burden and patient care in at-risk populations

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

Health-status outcomes with invasive or conservative care in coronary disease

BACKGROUND In the ISCHEMIA trial, an invasive strategy with angiographic assessment and revascularization did not reduce clinical events among patients with stable ischemic heart disease and moderate or severe ischemia. A secondary objective of the trial was to assess angina-related health status among these patients. METHODS We assessed angina-related symptoms, function, and quality of life with the Seattle Angina Questionnaire (SAQ) at randomization, at months 1.5, 3, and 6, and every 6 months thereafter in participants who had been randomly assigned to an invasive treatment strategy (2295 participants) or a conservative strategy (2322). Mixed-effects cumulative probability models within a Bayesian framework were used to estimate differences between the treatment groups. The primary outcome of this health-status analysis was the SAQ summary score (scores range from 0 to 100, with higher scores indicating better health status). All analyses were performed in the overall population and according to baseline angina frequency. RESULTS At baseline, 35% of patients reported having no angina in the previous month. SAQ summary scores increased in both treatment groups, with increases at 3, 12, and 36 months that were 4.1 points (95% credible interval, 3.2 to 5.0), 4.2 points (95% credible interval, 3.3 to 5.1), and 2.9 points (95% credible interval, 2.2 to 3.7) higher with the invasive strategy than with the conservative strategy. Differences were larger among participants who had more frequent angina at baseline (8.5 vs. 0.1 points at 3 months and 5.3 vs. 1.2 points at 36 months among participants with daily or weekly angina as compared with no angina). CONCLUSIONS In the overall trial population with moderate or severe ischemia, which included 35% of participants without angina at baseline, patients randomly assigned to the invasive strategy had greater improvement in angina-related health status than those assigned to the conservative strategy. The modest mean differences favoring the invasive strategy in the overall group reflected minimal differences among asymptomatic patients and larger differences among patients who had had angina at baseline

Initial invasive or conservative strategy for stable coronary disease

BACKGROUND Among patients with stable coronary disease and moderate or severe ischemia, whether clinical outcomes are better in those who receive an invasive intervention plus medical therapy than in those who receive medical therapy alone is uncertain. METHODS We randomly assigned 5179 patients with moderate or severe ischemia to an initial invasive strategy (angiography and revascularization when feasible) and medical therapy or to an initial conservative strategy of medical therapy alone and angiography if medical therapy failed. The primary outcome was a composite of death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. A key secondary outcome was death from cardiovascular causes or myocardial infarction. RESULTS Over a median of 3.2 years, 318 primary outcome events occurred in the invasive-strategy group and 352 occurred in the conservative-strategy group. At 6 months, the cumulative event rate was 5.3% in the invasive-strategy group and 3.4% in the conservative-strategy group (difference, 1.9 percentage points; 95% confidence interval [CI], 0.8 to 3.0); at 5 years, the cumulative event rate was 16.4% and 18.2%, respectively (difference, 121.8 percentage points; 95% CI, 124.7 to 1.0). Results were similar with respect to the key secondary outcome. The incidence of the primary outcome was sensitive to the definition of myocardial infarction; a secondary analysis yielded more procedural myocardial infarctions of uncertain clinical importance. There were 145 deaths in the invasive-strategy group and 144 deaths in the conservative-strategy group (hazard ratio, 1.05; 95% CI, 0.83 to 1.32). CONCLUSIONS Among patients with stable coronary disease and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of ischemic cardiovascular events or death from any cause over a median of 3.2 years. The trial findings were sensitive to the definition of myocardial infarction that was used