MicroRNA-485-5p reduces keratinocyte proliferation and migration by regulating ITGA5 expression in skin wound healing

Purpose: To determine the effect of miR-485-5p on keratinocyte proliferation and migration.Methods: Human primary keratinocytes (HaCaT cells) were treated with different concentrations of transforming growth factor-β1 (TGF)-β1. miR-485-5p expression levels were determined using quantitative reverse transcription-polymerase chain reaction (qRT-PCR). MTT (3-[4,5-dimethylthiazol-2- yl]-2,5 diphenyl tetrazolium bromide) and wound healing assays were performed to investigate the regulatory effects of miR-485-5p on cell viability and migration of HaCaT cells. Downstream target gene expression of miR-485-5p was determined using a luciferase activity assay.Results: In HaCaT cells, miR-485-5p was time- and dose-dependently downregulated by TGF-β1 treatment (p < 0.05). Forced expression of miR-485-5p decreased cell viability and migration of HaCaT cells (p < 0.05). Knockdown of miR-485-5p enhanced HaCaT cell viability and migration. Integrin subunit alpha-5 (ITGA5) was predicted and verified to be a downstream target of miR-485-5p in HaCaT cells. Overexpression of ITGA5 attenuated the miR-485-5p-induced decrease of HaCaT cell viability and migration (p < 0.05).Conclusion: MiR-485-5p reduces cell proliferation and migration of keratinocytes through the regulation of ITGA5. This mechanism provides a potential therapeutic strategy for skin wound healing. Keywords: ITGA5, Keratinocyte, Cell migration, MiR-485-5p, Cell proliferation, Wound healin

Private Model Compression via Knowledge Distillation

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices' capacity. What is worse, app service providers need to collect and utilize a large volume of users' data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful $(9.83,10^{-6})$-differential privacy is guaranteed, the compact model trained by RONA can obtain 20$\times$ compression ratio and 19$\times$ speed-up with merely 0.97% accuracy loss.Comment: Conference version accepted by AAAI'1

Adversarial Attack and Defense on Graph Data: A Survey

Deep neural networks (DNNs) have been widely applied to various applications including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works studying adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph structure data due to its representation challenges. Given the importance of graph analysis, an increasing number of works start to analyze the robustness of machine learning models on graph data. Nevertheless, current studies considering adversarial behaviors on graph data usually focus on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation which makes the comparison among different methods difficult. Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph. Moreover, we also compare different attacks and defenses on graph data and discuss their corresponding contributions and limitations. In this work, we systemically organize the considered works based on the features of each topic. This survey not only serves as a reference for the research community, but also brings a clear image researchers outside this research domain. Besides, we also create an online resource and keep updating the relevant papers during the last two years. More details of the comparisons of various studies based on this survey are open-sourced at https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date information, please check our Github repository: https://github.com/YingtongDou/graph-adversarial-learning-literatur

Isolation and functional characterization of CE1 binding proteins

<p>Abstract</p> <p>Background</p> <p>Abscisic acid (ABA) is a plant hormone that controls seed germination, protective responses to various abiotic stresses and seed maturation. The ABA-dependent processes entail changes in gene expression. Numerous genes are regulated by ABA, and promoter analyses of the genes revealed that <it>cis</it>-elements sharing the ACGTGGC consensus sequence are ubiquitous among ABA-regulated gene promoters. The importance of the core sequence, which is generally known as ABA response element (ABRE), has been demonstrated by various experiments, and its cognate transcription factors known as ABFs/AREBs have been identified. Although necessary, ABRE alone is not sufficient, and another <it>cis</it>-element known as "coupling element (CE)" is required for full range ABA-regulation of gene expression. Several CEs are known. However, despite their importance, the cognate transcription factors mediating ABA response via CEs have not been reported to date. Here, we report the isolation of transcription factors that bind one of the coupling elements, CE1.</p> <p>Results</p> <p>To isolate CE1 binding proteins, we carried out yeast one-hybrid screens. Reporter genes containing a trimer of the CE1 element were prepared and introduced into a yeast strain. The yeast was transformed with library DNA that represents RNA isolated from ABA-treated Arabidopsis seedlings. From the screen of 3.6 million yeast transformants, we isolated 78 positive clones. Analysis of the clones revealed that a group of AP2/ERF domain proteins binds the CE1 element. We investigated their expression patterns and analyzed their overexpression lines to investigate the <it>in vivo </it>functions of the CE element binding factors (CEBFs). Here, we show that one of the CEBFs, AtERF13, confers ABA hypersensitivity in Arabidopsis, whereas two other CEBFs enhance sugar sensitivity.</p> <p>Conclusions</p> <p>Our results indicate that a group of AP2/ERF superfamily proteins interacts with CE1. Several CEBFs are known to mediate defense or abiotic stress response, but the physiological functions of other CEBFs remain to be determined. Our <it>in vivo </it>functional analysis of several CEBFs suggests that they are likely to be involved in ABA and/or sugar response. Together with previous results reported by others, our current data raise an interesting possibility that the coupling element CE1 may function not only as an ABRE but also as an element mediating biotic and abiotic stress responses.</p