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Background The liquid–liquid phase separation (LLPS) of biomolecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular function. Dysregulation of LLPS is implicated in a number of diseases. Although the LLPS of biomolecules has been investigated intensively in recent years, the knowledge of the prevalence and distribution of phase separation proteins (PSPs) is still lag behind. Development of computational methods to predict PSPs is therefore of great importance for comprehensive understanding of the biological function of LLPS. Results Based on the PSPs collected in LLPSDB, we developed a sequence-based prediction tool for LLPS proteins (PSPredictor), which is an attempt at general purpose of PSP prediction that does not depend on specific protein types. Our method combines the componential and sequential information during the protein embedding stage, and, adopts the machine learning algorithm for final predicting. The proposed method achieves a tenfold cross-validation accuracy of 94.71%, and outperforms previously reported PSPs prediction tools. For further applications, we built a user-friendly PSPredictor web server (http://www.pkumdl.cn/PSPredictor), which is accessible for prediction of potential PSPs. Conclusions PSPredictor could identifie novel scaffold proteins for stress granules and predict PSPs candidates in the human genome for further study. For further applications, we built a user-friendly PSPredictor web server (http://www.pkumdl.cn/PSPredictor), which provides valuable information for potential PSPs recognition.

Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document. Obviously, document knowledge plays a critical role in Document Grounded Conversations, while existing dialogue models do not exploit this kind of knowledge effectively enough. In this paper, we propose a novel Transformerbased architecture for multi-turn document grounded conversations. In particular, we devise an Incremental Transformer to encode multi-turn utterances along with knowledge in related documents. Motivated by the human cognitive process, we design a two-pass decoder (Deliberation Decoder) to improve context coherence and knowledge correctness. Our empirical study on a real-world Document Grounded Dataset proves that responses generated by our model significantly outperform competitive baselines on both context coherence and knowledge relevance. * * Fandong Meng is the corresponding author of the paper. This work was done when Zekang Li was interning at Pattern Recognition Center, WeChat AI, Tencent.

20-HETE, an arachidonic acid metabolite synthesized by cytochrome P450 4A, plays an important role in acute brain damage from ischemic stroke or subarachnoid hemorrhage. We tested the hypothesis that 20-HETE inhibition has a protective effect after intracerebral hemorrhage (ICH) and then investigated its effect on angiogenesis. We exposed hippocampal slice cultures to hemoglobin and induced ICH in mouse brains by intrastriatal collagenase injection to investigate the protective effect of 20-HETE synthesis inhibitor N-hydroxy-N'-(4- n-butyl-2-methylphenyl)-formamidine (HET0016). Hemoglobin-induced neuronal death was assessed by propidium iodide after 18 h in vitro. Lesion volume, neurologic deficits, cell death, reactive oxygen species (ROS), neuroinflammation, and angiogenesis were evaluated at different time points after ICH. In cultured mouse hippocampal slices, HET0016 attenuated hemoglobin-induced neuronal death and decreased levels of proinflammatory cytokines and ROS. In vivo, HET0016 reduced brain lesion volume and neurologic deficits, and decreased neuronal death, ROS production, gelatinolytic activity, and the inflammatory response at three days after ICH. However, HET0016 did not inhibit angiogenesis, as levels of CD31, VEGF, and VEGFR2 were unchanged on day 28. We conclude that 20-HETE is involved in ICH-induced brain damage. Inhibition of 20-HETE synthesis may provide a viable means to mitigate ICH injury without inhibition of angiogenesis.