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Background Stroke is a leading cause of death worldwide, with a lack of effective treatments for improving the prognosis. The aim of the present study was to identify novel therapeutic targets for functional outcome after ischemic stroke . Methods and Results Cis‐expression quantitative trait loci data for druggable genes were used as instrumental variables. The primary outcome was the modified Rankin Scale score at 3 months after ischemic stroke, evaluated as a dichotomous variable (3–6 versus 0–2) and also as an ordinal variable. Drug target Mendelian randomization, Steiger filtering analysis, and colocalization analysis were performed. Additionally, phenome‐wide Mendelian randomization analysis was performed to identify the safety of the drug target genes at the genetic level. Among >2600 druggable genes, genetically predicted expression of 16 genes (ABCC2, ATRAID, BLK, CD93, CHST13, NR1H3, NRBP1, PI3, RIPK4, SEMG1, SLC22A4, SLC22A5, SLCO3A1, TEK, TLR4, and WNT10B) demonstrated the causal associations with ordinal modified Rankin Scale (P

Abstract Deploying microservices in container‐based cloud environments increases the agility of resource scaling. However, the delay in autoscaling for microservices caused by container cold start results in response time service‐level objectives (SLO) violations under burst workloads. This paper proposes Cushion, a proactive resource provisioning method for containerized microservices to mitigate SLO violations caused by burst workloads, which promptly schedules workloads to reserved container instances when workloads suddenly increase and meanwhile steadily scales the container instances according to the SLOs of the microservices. Cushion was evaluated through prototype‐based experiments in a containerized testbed using a benchmark microservice and four web workload traces by comparing it against existing methods. The experimental results show 9.12x times lower SLO violations, 23.75% higher throughput with 24.35% more CPU usage, and 4.85% more memory usage compared to the baseline methods.

Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge features or node similarity have been proposed to solve the link prediction problem, many technical challenges still exist due to the unique structural properties of networks, especially when the networks are sparse. From the graph mining perspective, we first give empirical evidence of the inconsistency between heuristic and learned edge features. Then, we propose a novel link prediction framework, AdaSim, by introducing an Adaptive Similarity function using features obtained from network embedding based on random walks. The node feature representations are obtained by optimizing a graph-based objective function. Instead of generating edge features using binary operators, we perform link prediction solely leveraging the node features of the network. We define a flexible similarity function with one tunable parameter, which serves as a penalty of the original similarity measure. The optimal value is learned through supervised learning and thus is adaptive to data distribution. To evaluate the performance of our proposed algorithm, we conduct extensive experiments on eleven disparate networks of the real world. Experimental results show that AdaSim achieves better performance than state-of-the-art algorithms and is robust to different sparsities of the networks.