Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

High accuracy video label prediction (classification) models are attributed to large scale data. These data could be frame feature sequences extracted by a pre-trained convolutional-neural-network, which promote the efficiency for creating models. Unsupervised solutions such as feature average pooling, as a simple label-independent parameter-free based method, has limited ability to represent the video. While the supervised methods, like RNN, can greatly improve the recognition accuracy. However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased. In this paper, we proposes a novel video classification method based on a deep convolutional graph neural network(DCGN). The proposed method utilize the characteristics of the hierarchical structure of the video, and performed multi-level feature extraction on the video frame sequence through the graph network, obtained a video representation re ecting the event semantics hierarchically. We test our model on YouTube-8M Large-Scale Video Understanding dataset, and the result outperforms RNN based benchmarks.Comment: ECCV 201

A chalcone derivative reactivates latent HIV-1 transcription through activating P-TEFb and promoting Tat-SEC interaction on viral promoter.

The principal barrier to the eradication of HIV/AIDS is the existence of latent viral reservoirs. One strategy to overcome this barrier is to use latency-reversing agents (LRAs) to reactivate the latent proviruses, which can then be eliminated by effective anti-retroviral therapy. Although a number of LRAs have been found to reactivate latent HIV, they have not been used clinically due to high toxicity and poor efficacy. In this study, we report the identification of a chalcone analogue called Amt-87 that can significantly reactivate the transcription of latent HIV provirses and act synergistically with known LRAs such as prostratin and JQ1 to reverse latency. Amt-87 works by activating the human transcriptional elongation factor P-TEFb, a CDK9-cyclin T1 heterodimer that is part of the super elongation complex (SEC) used by the viral encoded Tat protein to activate HIV transcription. Amt-87 does so by promoting the phosphorylation of CDK9 at the T-loop, liberating P-TEFb from the inactive 7SK snRNP, and inducing the formation of the Tat-SEC complex at the viral promoter. Together, our data reveal chalcones as a promising category of compounds that should be further explored to identify effective LRAs for targeted reversal of HIV latency

Prevalence and Associated Factors of Elder Mistreatment in a Rural Community in People's Republic of China: A Cross-Sectional Study

Background: Current knowledge about elder mistreatment is mainly derived from studies done in Western countries, which indicate that this problem is related to risk factors such as a shared living situation, social isolation, disease burden, and caregiver strain. We know little about prevalence and risk factors for elder mistreatment and mistreatment subtypes in rural China where the elder population is the most vulnerable. Methods: In 2010, we conducted a cross-sectional survey among older adults aged 60 or older in three rural communities in Macheng, a city in Hubei province, China. Of 2245 people initially identified, 2039 were available for interview and this was completed in 2000. A structured questionnaire was used to collect data regarding mistreatment and covariates. Logistic regression analysis was used to identify factors related to elder mistreatment and subtypes of mistreatment. Results: Elder mistreatment was reported by 36.2 % (95 % CI: 34.1%–38.3%) of the participants. Prevalence rates of psychological mistreatment, caregiver neglect, physical mistreatment, and financial mistreatment were 27.3 % (95 % CI