Membrane-based cells are the fundamental structural and functional units of organisms, while evidences demonstrate that liquid–liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation (PS), but these studies lacked an effective tool to identify the sequence segments that critical for LLPS. In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the PS-driving regions. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted PS-driving regions enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted PS-driving regions enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS-related research.
Scaffold materials, neurotrophic factors, and seed cells are three elements of neural tissue engineering. As well-known self-assembling peptide-based hydrogels, RADA16-I and modified peptides are attractive matrices for neural tissue engineering. In addition to its neuroprotective effects, cerebral dopamine neurotrophic factor (CDNF) has been reported to promote the proliferation, migration, and differentiation of neural stem cells (NSCs). However, the role of RADA16-I combined with CDNF on NSCs remains unknown. First, the effect of RADA16-I hydrogel and CDNF on the proliferation and differentiation of cultured NSCs was investigated. Next, RADA16-I hydrogel and CDNF were microinjected into the lateral ventricle (LV) of middle cerebral artery occlusion (MCAO) rats to activate endogenous NSCs. CDNF promoted the proliferation of NSCs, while RADA16-I induced the neural differentiation of NSCs in vitro. Importantly, both RADA16-I and CDNF promoted the proliferation, migration, and differentiation of endogenous NSCs by activating the ERK1/2 and STAT3 pathways, and CDNF exerted an obvious neuroprotective effect on brain ischemia-reperfusion injury. These findings provide new information regarding the application of the scaffold material RADA16-I hydrogel and the neurotrophic factor CDNF in neural tissue engineering and suggest that RADA16-I hydrogel and CDNF microinjection may represent a novel therapeutic strategy for the treatment of stroke.
PurposeThis study aims to investigate the rule‐based decentralised control framework for a swarm of UAVs carrying out a cooperative ground target engagement mission scenario.Design/methodology/approachThis study is to investigate the rule‐based decentralised control framework for missions which require high‐level cooperation between team members. The design of the authors’ control strategy is based on agent‐level interactions. Different to a centralized task assignment algorithm, the cooperation of the agents is entirely implicit. The behaviour of the UAVs is governed by rule sets which ultimately lead to cooperation at a system level. The information theoretic measures are adopted to estimate the value of possible future actions. The prediction model is further considered to enhance the team performance in the scenario where there are tight coupled task constraints.FindingsThe simulation study evaluates the performance of the decentralised controller and compares it with a centralised controller quantitatively. The results show that the proposed approach leads to a highly cooperative performance of the group without the need for a centralised control authority. The performance of the decentralised control depends on the complexity of the coupled task constraints. It can be improved by using a prediction model to provide information such as the intentions of the neighbours that is not available locally.Originality/valueThe achievable performance of the decentralised control was considered to be low due to the absence of communication and little global coordinating information. This study demonstrated that the decentralised control can achieve highly cooperative performance. The achievable performance is related to the complexity of the coupled constraints and the accuracy of the prediction model.
Interest in self-organized (SO), multi-robotic systems is increasing because of their flexibility, robustness, and scalability in performing complex tasks. This paper describes a decentralized task allocation model based on both task stimulus intensity and a responding threshold. The response threshold method was developed through observations of social insects. It allows a swarm of insects with a relatively low-level of intelligence to perform complex tasks. In this work, an agent based simulation environment is developed incorporating these ideas. The mission scenario simulated in this study is a wide area search and destroy mission in an initially unknown environment. The mission objectives are to effectively allocate a UAV swarm to both optimize coverage of the search space and attack a target. Rule based behaviours were used to create UAV formations. Two sets of simulations with different swarm size and target numbers were performed. The simulation results show that with task stimulus intensity and a responding threshold, the UAV swarm demonstrates emergent behaviour and individual vehicles respond adaptively to the changing environment.
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