HIV-1 capsid proteins (CAs) assemble into a capsid that encloses the viral RNA. The binding between a pair of C-terminal domains (CTDs) constitutes a major interface in both the CA dimers and the large CA assemblies. Here we attempt to use a general residue-level coarse-grained model to describe the interaction between two isolated CTDs in Monte Carlo simulations. With the standard parameters that depend only on the residue types, the model predicts a much weaker binding in comparison to the experiments. Detailed analysis reveals that some Lennard−Jones parameters are not compatible with the experimental CTD dimer structure, thus resulting in an unfavorable interaction energy. To improve the model for the CTD binding, we introduce ad hoc modifications to a small number of Lennard−Jones parameters for some specific pairs of residues at the binding interface. Through a series of extensive Monte Carlo simulations, we identify the optimal parameters for the CTD-CTD interactions. With the refined model parameters, both the

Modeling temporal event sequences on the vertices of a network is an important problem with widespread applications; examples include modeling influences in social networks, preventing crimes by modeling their space-time occurrences, and forecasting earthquakes. Existing solutions for this problem use a parametric approach, whose applicability is limited to event sequences following some well-known distributions, which is not true for many real life event datasets. To overcome this limitation, in this work, we propose a composite recurrent neural network model for learning events occurring in the vertices of a network over time. Our proposed model combines two long short-term memory units to capture base intensity and conditional intensity of an event sequence. We also introduce a second-order statistic loss that penalizes higher divergence between the generated and the target sequence's distribution of hop count distance of consecutive events. Given a sequence of vertices of a network in which an event has occurred, the proposed model predicts the vertex where the next event would most likely occur. Experimental results on synthetic and real-world datasets validate the superiority of our proposed model in comparison to various baseline methods.

Clinical trials for the treatment of coronavirus disease 2019 (COVID-19): A rapid response to urgent need SCIENCE CHINA Life Sciences 63, 774 (2020); Interpretation of the 7th edition of the "diagnosis and treatment guidelines of coronavirus disease 2019 in China": Progress and challenges Chronic Diseases and Translational Medicine 6, 75 (2020); Chinese experts' consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19) Clinical eHealth 3, 7 (2020); Mesenchymal stem cells for treatment of steroid-resistant acute rejection after liver transplantation

Detecting the source of an outbreak cluster during a pandemic like COVID-19 can provide insights into the transmission process, associated risk factors, and help contain the spread.In this work we study the problem of source detection from multiple snapshots of spreading on an arbitrary network structure. We use a spatial temporal graph convolutional network based model (SD-STGCN) to produce a source probability distribution, by fusing information from temporal and topological spaces. We perform extensive experiments using popular compartmental simulation models over synthetic networks and empirical contact networks. We also demonstrate the applicability of our approach with real COVID-19 case data.