As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.

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Cancer metastasis accounts for the major cause of cancer-related deaths. How disseminated cancer cells cope with hostile microenvironments in secondary site for full-blown metastasis is largely unknown. Here, we show that AMPK (AMP-activated protein kinase), activated in mouse metastasis models, drives pyruvate dehydrogenase complex (PDHc) activation to maintain TCA cycle (tricarboxylic acid cycle) and promotes cancer metastasis by adapting cancer cells to metabolic and oxidative stresses. This AMPK-PDHc axis is activated in advanced breast cancer and predicts poor metastasis-free survival. Mechanistically, AMPK localizes in the mitochondrial matrix and phosphorylates the catalytic alpha subunit of PDHc (PDHA) on two residues S295 and S314, which activates the enzymatic activity of PDHc and alleviates an inhibitory phosphorylation by PDHKs, respectively. Importantly, these phosphorylation events mediate PDHc function in cancer metastasis. Our study reveals that AMPK-mediated PDHA phosphorylation drives PDHc activation and TCA cycle to empower cancer cells adaptation to metastatic microenvironments for metastasis.

Although deletion of certain autophagy-related genes has been associated with defects in hematopoiesis, it remains unclear whether hyperactivated mitophagy affects the maintenance and differentiation of hematopoietic stem cells (HSCs) and committed progenitor cells. Here we report that targeted deletion of the gene encoding the AAA+-ATPase Atad3a hyperactivated mitophagy in mouse hematopoietic cells. Affected mice showed reduced survival, severely decreased bone-marrow cellularity, erythroid anemia and B cell lymphopenia. Those phenotypes were associated with skewed differentiation of stem and progenitor cells and an enlarged HSC pool. Mechanistically, Atad3a interacted with the mitochondrial channel components Tom40 and Tim23 and served as a bridging factor to facilitate appropriate transportation and processing of the mitophagy protein Pink1. Loss of Atad3a caused accumulation of Pink1 and activated mitophagy. Notably, deletion of Pink1 in Atad3a-deficient mice significantly 'rescued' the mitophagy defect, which resulted in restoration of the progenitor and HSC pools. Our data indicate that Atad3a suppresses Pink1-dependent mitophagy and thereby serves a key role in hematopoietic homeostasis.