The Rbm38-p63 feedback loop is critical for tumor suppression and longevity.

The RNA-binding protein Rbm38 is a target of p63 tumor suppressor and can in-turn repress p63 expression via mRNA stability. Thus, Rbm38 and p63 form a negative feedback loop. To investigate the biological significance of the Rbm38-p63 loop in vivo, a cohort of WT, Rbm38-/-, TAp63+/-, and Rbm38-/-;TAp63+/- mice were generated and monitored throughout their lifespan. While mice deficient in Rbm38 or TAp63 alone died mostly from spontaneous tumors, compound Rbm38-/-;TAp63+/- mice had an extended lifespan along with reduced tumor incidence. We also found that loss-of-Rbm38 markedly decreased the percentage of liver steatosis in TAp63+/- mice. Moreover, we found that Rbm38 deficiency extends the lifespan of tumor-free TAp63+/- mice along with reduced expression of senescence-associated biomarkers. Consistent with this, Rbm38-/-;TAp63+/- MEFs were resistant, whereas Rbm38-/- or TAp63+/- MEFs were prone, to cellular senescence. Importantly, we showed that the levels of inflammatory cytokines (IL17D and Tnfsf15) were significantly reduced by Rbm38 deficiency in senescence-resistant Rbm38-/-;TAp63+/- mouse livers and MEFs. Together, our data suggest that Rbm38 and p63 function as intergenic suppressors in aging and tumorigenesis and that the Rbm38-p63 loop may be explored for enhancing longevity and cancer management

Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data

A recent study finds that existing few-shot learning methods, trained on the source domain, fail to generalize to the novel target domain when a domain gap is observed. This motivates the task of Cross-Domain Few-Shot Learning (CD-FSL). In this paper, we realize that the labeled target data in CD-FSL has not been leveraged in any way to help the learning process. Thus, we advocate utilizing few labeled target data to guide the model learning. Technically, a novel meta-FDMixup network is proposed. We tackle this problem mainly from two aspects. Firstly, to utilize the source and the newly introduced target data of two different class sets, a mixup module is re-proposed and integrated into the meta-learning mechanism. Secondly, a novel disentangle module together with a domain classifier is proposed to extract the disentangled domain-irrelevant and domain-specific features. These two modules together enable our model to narrow the domain gap thus generalizing well to the target datasets. Additionally, a detailed feasibility and pilot study is conducted to reflect the intuitive understanding of CD-FSL under our new setting. Experimental results show the effectiveness of our new setting and the proposed method. Codes and models are available at https://github.com/lovelyqian/Meta-FDMixup.Comment: Accepted by ACM Multimedia 202

Deep R-Learning for Continual Area Sweeping

This publication is by UT affiliates that was featured in the October Good Systems Network Digest in 2020.Office of the VP for Researc

Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition

Deep learning techniques have shown their superior performance in dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty of incorporating the useful dermatologist clinical knowledge into the learning process. In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically, to exploit the knowledge of morphological expressions of the lesion region and also the periphery region for melanoma identification, a lesion-based pooling and shape extraction (LPSE) scheme is designed, which transfers the structure information obtained from skin lesion segmentation into melanoma recognition. Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma recognition to skin lesion segmentation, an effective diagnosis guided feature fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual learning mechanism that further promotes the inter-task cooperation, and thus iteratively improves the joint learning capability of the model for both skin lesion segmentation and melanoma recognition. Experimental results on two publicly available skin lesion datasets show the effectiveness of the proposed method for melanoma analysis.Comment: Pattern Recognitio