Starting around December 2019, an epidemic of pneumonia, which was named COVID-19 by the World Health Organization, broke out in Wuhan, China, and is spreading throughout the world. A new coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the Coronavirus Study Group of the International Committee on Taxonomy of Viruses was soon found to be the cause. At present, the sensitivity of clinical nucleic acid detection is limited, and it is still unclear whether it is related to genetic variation. In this
A recent line of research on deep learning focuses on the extremely over-parameterized setting, and shows that when the network width is larger than a high degree polynomial of the training sample size n and the inverse of the target accuracy ´1, deep neural networks learned by (stochastic) gradient descent enjoy nice optimization and generalization guarantees. Very recently, it is shown that under certain margin assumption on the training data, a polylogarithmic width condition suffices for two-layer ReLU networks to converge and generalize (Ji and Telgarsky, 2019b). However, how much over-parameterization is sufficient to guarantee optimization and generalization for deep neural networks still remains an open question. In this work, we establish sharp optimization and generalization guarantees for deep ReLU networks. Under various assumptions made in previous work, our optimization and generalization guarantees hold with network width polylogarithmic in n and ´1. Our results push the study of over-parameterized deep neural networks towards more practical settings.
PRKN/parkin activation through phosphorylation of its ubiquitin and ubiquitin-like domain by PINK1 is critical in mitophagy induction for eliminating the damaged mitochondria. Deubiquitinating enzymes (DUBs) functionally reversing PRKN ubiquitination are critical in controlling the magnitude of PRKNmediated mitophagy process. However, potential DUBs that directly target PRKN and antagonize its promitophagy effect remains to be identified and characterized. Here, we demonstrated that USP33/VDU1 is localized at the outer membrane of mitochondria and serves as a PRKN DUB through their interaction. Cellular and in vitro assays illustrated that USP33 deubiquitinates PRKN in a DUB activity-dependent manner. USP33 prefers to remove K6, K11, K48 and K63-linked ubiquitin conjugates from PRKN, and deubiquitinates PRKN mainly at Lys435. Mutation of this site leads to a significantly decreased level of K63-, but not K48-linked PRKN ubiquitination. USP33 deficiency enhanced both K48-and K63-linked PRKN ubiquitination, but only K63-linked PRKN ubiquitination was significantly increased under mitochondrial depolarization. Further, USP33 knockdown increased both PRKN protein stabilization and its translocation to depolarized mitochondria leading to the enhancement of mitophagy. Moreover, USP33 silencing protects SH-SY5Y human neuroblastoma cells from the neurotoxin MPTP-induced apoptotic cell death. Our findings convincingly demonstrate that USP33 is a novel PRKN deubiquitinase antagonizing its regulatory roles in mitophagy and SH-SY5Y neuron-like cell survival. Thus, USP33 inhibition may represents an attractive new therapeutic strategy for PD patients.
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