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Human digital twin (HDT) is an emerging paradigm that bridges physical twins (PTs) with powerful virtual twins (VTs) for assisting complex task executions in human-centric services. In this paper, we study a two-timescale online optimization for building HDT under an end-edge-cloud collaborative framework. As a unique feature of HDT, we consider that PTs' corresponding VTs are deployed on edge servers, consisting of not only generic models placed by downloading experiential knowledge from the cloud but also customized models updated by collecting personalized data from end devices. To maximize task execution accuracy with stringent energy and delay constraints, and by taking into account HDT's inherent mobility and status variation uncertainties, we jointly and dynamically optimize VTs' construction and PTs' task offloading, along with communication and computation resource allocations. Observing that decision variables are asynchronous with different triggers, we propose a novel two-timescale accuracy-aware online optimization approach (TACO). Specifically, TACO utilizes an improved Lyapunov method to decompose the problem into multiple instant ones, and then leverages piecewise McCormick envelopes and block coordinate descent based algorithms, addressing two timescales alternately. Theoretical analyses and simulations show that the proposed approach can reach asymptotic optimum within a polynomial-time complexity, and demonstrate its superiority over counterparts.

Long Chen,1,2,* Huixin Yang,1,* Yanbin Xiao,3,* Xiaoxia Tang,4 Yuqian Li,1 Qiaoqiao Han,1 Junping Fu,3 Yuye Yang,1 Yuechun Zhu1 1Department of Biochemistry and Molecular Biology, Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 2PET/CT Center, Yunan Tumor Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 3Department of Orthopaedics, Yunan Tumor Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 4Department of Pharmacy, the Second Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, Yunnan, People’s Republic of China *These authors contributed equally to this work Abstract: The present study intended to demonstrate the effects of long noncoding RNA growth arrest-specific transcript 5 (GAS5) on the migration and invasion of melanoma cells. We first detected the expression of GAS5 among four kinds of melanoma cell lines, followed by constructing GAS5-knocked down and overexpressed stable cells. Next, we evaluated the effects of GAS5 on cell migration and invasion using wound healing and gelatin zymography assays. Finally, melanoma cells with different GAS5 expression were injected into nude mice, and the tumor volumes were recorded and tumor tissues were analyzed after sacrificing the mice. This study systematically examined the function of GAS5 in mediating melanoma metastasis and revealed that GAS5 plays an anticancer role in melanoma via regulating gelatinase A and B, both in vitro and in vivo. Keywords: lncRNA, GAS5, melanoma, metastasis, gelatinase