Notes

Immunoproteasome problems by means of β5i/LMP7-deletion results in sustained pancreatic damage coming from experimental pancreatitis.
Finally, the results of extensive experiments for sparse signal recovery and sparse image reconstruction on benchmark problems are elaborated to substantiate the effectiveness and superiority of the proposed approach in terms of computational time and estimation error.In deep reinforcement learning, off-policy data help reduce on-policy interaction with the environment, and the trust region policy optimization (TRPO) method is efficient to stabilize the policy optimization procedure. In this article, we propose an off-policy TRPO method, off-policy TRPO, which exploits both on- and off-policy data and guarantees the monotonic improvement of policies. A surrogate objective function is developed to use both on- and off-policy data and keep the monotonic improvement of policies. We then optimize this surrogate objective function by approximately solving a constrained optimization problem under arbitrary parameterization and finite samples. We conduct experiments on representative continuous control tasks from OpenAI Gym and MuJoCo. The results show that the proposed off-policy TRPO achieves better performance in the majority of continuous control tasks compared with other trust region policy-based methods using off-policy data.Sleep posture, as a crucial index for sleep quality assessment, has been widely studied in sleep analysis. In this paper, an unobtrusive smart mat system based on a dense flexible sensor array and printed electrodes along with an algorithmic framework for sleep posture recognition is proposed. With the dense flexible sensor array, the system offers a comfortable and high-resolution solution for long-term pressure sensing. Meanwhile, compared to other methods, it reduces production costs and computational complexity with a smaller area of the mat and improves portability with fewer sensors. To distinguish the sleep posture, the algorithmic framework that includes preprocessing and Deep Residual Networks (ResNet) is developed. With the ResNet, the proposed system can omit the complex hand-crafted feature extraction process and provide compelling performance. The feasibility and reliability of the proposed system were evaluated on seventeen subjects. Experimental results exhibit that the accuracy of the short-term test is up to 95.08% and the overnight sleep study is up to 86.35% for four categories (supine, prone, right, and left) classification, which outperform the most of state-of-the-art studies. With the promising results, the proposed system showed great potential in applications like sleep studies, prevention of pressure ulcers, etc.DNase I hypersensitive sites (DHSs) have proven to be tightly associated with cis-regulatory elements, commonly indicating specific function on the chromatin structure. read more Thus, identifying DHSs plays a fundamental role in decoding gene regulatory behavior. While traditional experimental methods turn to be time-consuming and expensive for genome-wide exploration, computational techniques promise to be a practical approach to discovering and analyzing regulatory factors. In this study, we applied an efficient model that could take care of both performance and speed. Our predictor, CEPZ, greatly improved a Matthews correlation coefficient and accuracy of 0.7740 and 0.9113 respectively, far more competitive than any predictor ever. This result suggests that it may become a useful tool for DHSs research in the human and other complex genomes. Our research was anchored on the properties of dinucleotides and we identified several dinucleotides with significant differences in the distribution of DHS and non-DHS samples, which are likely to have a special meaning in the chromatin structure. The datasets, feature sets and the relevant algorithm are available at https//github.com/YanZheng-16/CEPZ_DHS/.An enhancer is a short region of DNA with the ability to recruit transcription factors and their complexes, thus increasing the likelihood of the transcription possibility. Considering the importance of enhancers, the enhancer identification was popular in computational biology. In this paper, we propose a two-layer enhancer predictor, called iEnhancer-KL. Kullback-Leibler (KL) divergence is taken into consideration to improve feature extraction method PSTNP. Furthermore, LASSO is used to reduce the dimension of features to get better prediction performance. Finally, the selected features are tested on several machine learning models to find the best model with great performance. The rigorous cross-validations have indicated that our proposed predictor is remarkably superior to the existing state-of-the-art methods with an accuracy of 84.23% and the MCC of 0.6849 for identifying enhancer. Our code and results can be freely download from https//github.com/Not-so-middle/iEnhancer-KL.git.Natural language moment localization aims at localizing video clips according to a natural language description. The key to this challenging task lies in modeling the relationship between verbal descriptions and visual contents. Existing approaches often sample a number of clips from the video, and individually determine how each of them is related to the query sentence. However, this strategy can fail dramatically, in particular when the query sentence refers to some visual elements that appear outside of, or even are distant from, the target clip. In this paper, we address this issue by designing an Interaction-Integrated Network (I2N), which contains a few Interaction-Integrated Cells (I2Cs). The idea lies in the observation that the query sentence not only provides a description to the video clip, but also contains semantic cues on the structure of the entire video. Based on this, I2Cs go one step beyond modeling short-term contexts in the time domain by encoding long-term video content into every frame feature. By stacking a few I2Cs, the obtained network, I2N, enjoys an improved ability of inference, brought by both (I) multi-level correspondence between vision and language and (II) more accurate cross-modal alignment. When evaluated on a challenging video moment localization dataset named DiDeMo, I2N outperforms the state-of-the-art approach by a clear margin of 1.98%. On other two challenging datasets, Charades-STA and TACoS, I2N also reports competitive performance.
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