OVERRIDING CISPLATIN RESISTANCE IN LUNG SQUAMOUS CELL CARCINOMA: INSIGHTS FROM COMBINATION WITH HDAC INHIBITOR

Ph.DDOCTOR OF PHILOSOPH

Superluminal group velocity in an anisotropic metamaterial

Based on boundary condition and dispersion relation, the superluminal group velocity in an anisotropic metamaterial (AMM) is investigated. The superluminal propagation is induced by the hyperbolic dispersion relation associated with the AMM. It is shown that a modulated Gaussian beam exhibits a superluminal group velocity which depends on the choice of incident angles and optical axis angles. The superluminal propagation does not violate the theory of special relativity because the group velocity is the velocity of the peak of the localized wave packet which does not carry information. It is proposed that a triglycine sulfate (TGS) crystal can be designed and the superluminal group velocity can be measured experimentally.Comment: 9 pages, 3 figure

Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training

Recently, sparse training has emerged as a promising paradigm for efficient deep learning on edge devices. The current research mainly devotes efforts to reducing training costs by further increasing model sparsity. However, increasing sparsity is not always ideal since it will inevitably introduce severe accuracy degradation at an extremely high sparsity level. This paper intends to explore other possible directions to effectively and efficiently reduce sparse training costs while preserving accuracy. To this end, we investigate two techniques, namely, layer freezing and data sieving. First, the layer freezing approach has shown its success in dense model training and fine-tuning, yet it has never been adopted in the sparse training domain. Nevertheless, the unique characteristics of sparse training may hinder the incorporation of layer freezing techniques. Therefore, we analyze the feasibility and potentiality of using the layer freezing technique in sparse training and find it has the potential to save considerable training costs. Second, we propose a data sieving method for dataset-efficient training, which further reduces training costs by ensuring only a partial dataset is used throughout the entire training process. We show that both techniques can be well incorporated into the sparse training algorithm to form a generic framework, which we dub SpFDE. Our extensive experiments demonstrate that SpFDE can significantly reduce training costs while preserving accuracy from three dimensions: weight sparsity, layer freezing, and dataset sieving.Comment: Published in 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Targeting Metabolism in Cancer Cells and the Tumour Microenvironment for Cancer Therapy.

Targeting altered tumour metabolism is an emerging therapeutic strategy for cancer treatment. The metabolic reprogramming that accompanies the development of malignancy creates targetable differences between cancer cells and normal cells, which may be exploited for therapy. There is also emerging evidence regarding the role of stromal components, creating an intricate metabolic network consisting of cancer cells, cancer-associated fibroblasts, endothelial cells, immune cells, and cancer stem cells. This metabolic rewiring and crosstalk with the tumour microenvironment play a key role in cell proliferation, metastasis, and the development of treatment resistance. In this review, we will discuss therapeutic opportunities, which arise from dysregulated metabolism and metabolic crosstalk, highlighting strategies that may aid in the precision targeting of altered tumour metabolism with a focus on combinatorial therapeutic strategies

M2-like macrophages in the fibrotic liver protect mice against lethal insults through conferring apoptosis resistance to hepatocytes.

Acute injury in the setting of liver fibrosis is an interesting and still unsettled issue. Most recently, several prominent studies have indicated the favourable effects of liver fibrosis against acute insults. Nevertheless, the underlying mechanisms governing this hepatoprotection remain obscure. In the present study, we hypothesized that macrophages and their M1/M2 activation critically involve in the hepatoprotection conferred by liver fibrosis. Our findings demonstrated that liver fibrosis manifested a beneficial role for host survival and apoptosis resistance. Hepatoprotection in the fibrotic liver was tightly related to innate immune tolerance. Macrophages undertook crucial but divergent roles in homeostasis and fibrosis: depleting macrophages in control mice protected from acute insult; conversely, depleting macrophages in fibrotic liver weakened the hepatoprotection and gave rise to exacerbated liver injury upon insult. The contradictory effects of macrophages can be ascribed, to a great extent, to the heterogeneity in macrophage activation. Macrophages in fibrotic mice exhibited M2-preponderant activation, which was not the case in acutely injured liver. Adoptive transfer of M2-like macrophages conferred control mice conspicuous protection against insult. In vitro, M2-polarized macrophages protected hepatocytes against apoptosis. Together, M2-like macrophages in fibrotic liver exert the protective effects against lethal insults through conferring apoptosis resistance to hepatocytes