Alteration Detection of Tensor Dependence Structure via Sparsity-Exploited Reranking Algorithm

Tensor-valued data arise frequently from a wide variety of scientific applications, and many among them can be translated into an alteration detection problem of tensor dependence structures. In this article, we formulate the problem under the popularly adopted tensor-normal distributions and aim at two-sample correlation/partial correlation comparisons of tensor-valued observations. Through decorrelation and centralization, a separable covariance structure is employed to pool sample information from different tensor modes to enhance the power of the test. Additionally, we propose a novel Sparsity-Exploited Reranking Algorithm (SERA) to further improve the multiple testing efficiency. The algorithm is approached through reranking of the p-values derived from the primary test statistics, by incorporating a carefully constructed auxiliary tensor sequence. Besides the tensor framework, SERA is also generally applicable to a wide range of two-sample large-scale inference problems with sparsity structures, and is of independent interest. The asymptotic properties of the proposed test are derived and the algorithm is shown to control the false discovery at the pre-specified level. We demonstrate the efficacy of the proposed method through intensive simulations and two scientific applications

A temperature independent driver for Mach-Zehnder modulators

This paper describes a fully differential inductorlesstemperature independent laser driver for a Mach-Zehndermodulator (MZM). The laser driver in this work exhibits robustnessin bandwidth and gain across the temperature range27oC to 135oC. This driver (fabricated using a 130nm CMOSprocess) can achieve a differential output voltage swing of up to4Vpp when driving a 50 Ohm Load at speeds of up to 12.5Gb/sacross a temperature range 27oC to 135oC

Natural products target glycolysis in liver disease

Mitochondrial dysfunction plays an important role in the occurrence and development of different liver diseases. Oxidative phosphorylation (OXPHOS) dysfunction and production of reactive oxygen species are closely related to mitochondrial dysfunction, forcing glycolysis to become the main source of energy metabolism of liver cells. Moreover, glycolysis is also enhanced to varying degrees in different liver diseases, especially in liver cancer. Therefore, targeting the glycolytic signaling pathway provides a new strategy for the treatment of non-alcoholic fatty liver disease (NAFLD) and liver fibrosis associated with liver cancer. Natural products regulate many steps of glycolysis, and targeting glycolysis with natural products is a promising cancer treatment. In this review, we have mainly illustrated the relationship between glycolysis and liver disease, natural products can work by targeting key enzymes in glycolysis and their associated proteins, so understanding how natural products regulate glycolysis can help clarify the therapeutic mechanisms these drugs use to inhibit liver disease