MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods.

Gradient-type distributed optimization methods have blossomed into one of the most important tools for solving a minimization learning task over a networked agent system. However, only one gradient update per iteration makes it difficult to achieve a substantive acceleration of convergence. In this article, we propose an accelerated framework named multiupdates single-combination (MUSIC) allowing each agent to perform multiple local updates and a single combination in each iteration. More importantly, we equip inexact and exact distributed optimization methods into this framework, thereby developing two new algorithms that exhibit accelerated linear convergence and high communication efficiency. Our rigorous convergence analysis reveals the sources of steady-state errors arising from inexact policies and offers effective solutions. Numerical results based on synthetic and real datasets demonstrate both our theoretical motivations and analysis, as well as performance advantages.

High spindle and kinetochore-associated complex subunit-3 expression predicts poor prognosis and correlates with adverse immune infiltration in hepatocellular carcinoma.

BACKGROUND: Spindle and kinetochore-associated complex subunit 3 (SKA3) is a malignancy-associated gene that plays a critical role in the regulation of chromosome separation and cell division. However, the molecular mechanism through which SKA3 regulates tumor cell proliferation in hepatocellular carcinoma (HCC) has not been fully elucidated. AIM: To investigate the molecular mechanisms underlying the role of SKA3 in HCC. METHODS: SKA3 expression, clinicopathological, and survival analyses were performed using multiple public database platforms, and the results were verified by Western blot and immunohistochemistry staining using collected clinical samples. Functional enrichment analyses were performed to evaluate the biological functions and molecular mechanisms of SKA3 in HCC. Furthermore, the Tumor Immune Estimation Resource and single-sample Gene Set Enrichment Analysis (ssGSEA) algorithms were utilized to investigate the abundance of tumor-infiltrating immune cells in HCC. The response to chemotherapeutic drugs was evaluated by the R package "pRRophetic". RESULTS: We found that upregulated SKA3 expression was significantly correlated with poor prognosis in patients with HCC. Multivariable Cox regression analysis indicated that SKA3 was an independent risk factor for survival. GSEA revealed that SKA3 expression may facilitate proliferation and migratory processes by regulating the cell cycle and DNA repair. Moreover, patients with high SKA3 expression had significantly decreased ratios of CD8+ T cells, natural killer cells, and dendritic cells. Drug sensitivity analysis showed that the high SKA3 group was more sensitive to sorafenib, sunitinib, paclitaxel, doxorubicin, gemcitabine, and vx-680. CONCLUSION: High SKA3 expression led to poor prognosis in patients with HCC by enhancing HCC proliferation and repressing immune cell infiltration surrounding HCC. SKA3 may be used as a biomarker for poor prognosis and as a therapeutic target in HCC.

The HealthChain Blockchain for Electronic Health Records: Development Study.

BACKGROUND: Health care professionals are required to maintain accurate health records of patients. Furthermore, these records should be shared across different health care organizations for professionals to have a complete review of medical history and avoid missing important information. Nowadays, health care providers use electronic health records (EHRs) as a key to the implementation of these goals and delivery of quality care. However, there are technical and legal hurdles that prevent the adoption of these systems, such as concerns about performance and privacy issues. OBJECTIVE: This study aimed to build and evaluate an experimental blockchain for EHRs, named HealthChain, which overcomes the disadvantages of traditional EHR systems. METHODS: HealthChain is built based on consortium blockchain technology. Specifically, three organizations, namely hospitals, insurance providers, and governmental agencies, form a consortium that operates under a governance model, which enforces the business logic agreed by all participants. Every peer node hosts an instance of the distributed ledger consisting of EHRs and an instance of chaincode regulating the permissions of participants. Designated orderers establish consensus on the order of EHRs and then disseminate blocks to peers. RESULTS: HealthChain achieves functional and nonfunctional requirements. It can store EHRs in a distributed ledger and share them among different participants. Moreover, it demonstrates superior features, such as privacy preservation, security, and high throughput. These are the main reasons why HealthChain is proposed. CONCLUSIONS: Consortium blockchain technology can help to build new EHR systems and solve the problems that prevent the adoption of traditional systems.

Edge Computing and Blockchain for Quick Fake News Detection in IoV.

The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks. QcFND consists of two tiers: edge and vehicles. The edge is composed of Software-Defined Road Side Units (SDRSUs), which is extended from traditional Road Side Units (RSUs) and hosts virtual machines such as SDN controllers and blockchain servers. The SDN controllers help to implement the load balancing on IoV. The blockchain servers accommodate the reports submitted by vehicles and calculate the probability of the presence of a traffic event, providing time-sensitive services to the passing vehicles. Specifically, we exploit Bayesian Network to infer whether to trust the received traffic reports. We test the performance of QcFND with three platforms, i.e., Veins, Hyperledger Fabric, and Netica. Extensive simulations and experiments show that QcFND achieves good performance compared with other solutions.

(t, n) Threshold d-Level Quantum Secret Sharing.

Most of Quantum Secret Sharing(QSS) are (n, n) threshold 2-level schemes, in which the 2-level secret cannot be reconstructed until all n shares are collected. In this paper, we propose a (t, n) threshold d-level QSS scheme, in which the d-level secret can be reconstructed only if at least t shares are collected. Compared with (n, n) threshold 2-level QSS, the proposed QSS provides better universality, flexibility, and practicability. Moreover, in this scheme, any one of the participants does not know the other participants' shares, even the trusted reconstructor Bob 1 is no exception. The transformation of the particles includes some simple operations such as d-level CNOT, Quantum Fourier Transform(QFT), Inverse Quantum Fourier Transform(IQFT), and generalized Pauli operator. The transformed particles need not to be transmitted from one participant to another in the quantum channel. Security analysis shows that the proposed scheme can resist intercept-resend attack, entangle-measure attack, collusion attack, and forgery attack. Performance comparison shows that it has lower computation and communication costs than other similar schemes when 2 < t < n - 1.