Objective The association of platelet-to-lymphocyte ratio (PLR) with the clinicopathological features and prognosis in patients with breast cancer was evaluated. Method Related studies were searched from PubMed, Embase, Cochrane Library, and Web of Science up to July 1, 2021. Then, basic characteristic and prognostic data were extracted from the included studies. We synthesized and compared primary outcomes such as overall survival. Subgroups analyses in pathology, geographical area, follow-up time, and sample size were conducted. The pooled hazard ratio (HR), odds ratio (OR), and 95% confidence interval (CI) served as measures to assess the relationship of PLR with prognosis and clinicopathological features of breast cancer patients. After literature retrieval and selection, 20 studies with 7484 patients were included in this meta-analysis. Results High PLR was significantly related to poor overall survival (HR = 1.88; 95% CI 1.61, 2.19; P < 0.001) in breast cancer patients. Also, high PLR was associated with lymph node metastasis (LNM) (OR = 1.82; 95% CI 1.32, 2.52; P < 0.001), advanced tumor-node-metastasis (TNM) stage (OR = 1.89; 95% CI 1.25, 2.87; P = 0.003), and distant metastasis (OR = 1.76; 95% CI 1.14, 2.72; P = 0.01) in breast cancer. The stability and reliability of results in this meta-analysis were confirmed by sensitivity analysis. Conclusion Elevated PLR is related to a poor prognosis and a higher risk of LNM, advanced TNM stage, and distant metastasis in breast cancer patients. Therefore, PLR can be identified as a biomarker with potential prognostic value in breast cancer.

Cloud computing, as an emerging computing model, is attracting more and more attention of industry and academia. Though it is of significant importance to carry out studies on such programming models, so far, there are few programming models for cloud computing. Because various kinds of cloud platforms are developed independently, it is necessary to have the support of cloud platform in making cloud programming model be able to deploy application to the cloud. This paper takes the cloud platform as its center. The cloud computing model in this paper is proposed as the Cloud platform, which is a kind of distributed structure without central management node. Each cloud node has equal status and manages its local resources respectively. In addition, this paper proposes mechanisms for resource management and allocation as well as the organizational structure of the user virtual machine.

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Satisfiability algorithms have become one of the most practical and successful approaches for solving a variety of real-world problems, including hardware verification, experimental design, planning and diagnosis problems. The main reason for the success is due to highly optimized algorithms for SAT based on resolution. The most successful of these is clause learning, a DPLL scheme based on caching intermediate clauses that are "learned" throughout the backtrack search procedure. The main bottleneck to this approach is space, and thus there has been a tremendous amount of research aimed at identifying good heuristics for deciding what information to cache. Haken first suggested a formal approach to this issue, and Ben-Sasson [3] posed the question of whether there is a time/space tradeoff for resolution. Our main result is an optimal time/space tradeoff for resolution. Namely, we present an infinite family of propositional formulas whose minimal space proofs all have exponential time, but if just three extra units of storage are allowed, then the formulas can be proved in linear time. We also prove another related theorem. Given an unsatisfiable formula F and an integer k, the resolution space problem is to determine if F has a resolution proof which can be verified using space k. We prove that this problem is PSPACE complete. ¡

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