BackgroundRNA structure is the crucial basis for RNA function in various cellular processes. Over the last decade, high throughput structure profiling (SP) experiments have brought enormous insight into RNA secondary structure.ResultsIn this review, we first provide an overview of approaches for RNA secondary structure prediction, including free energy‐based algorithms and comparative sequence analysis. Then we introduce SP technologies, databases to document SP data, and pipelines/algorithms to normalize and interpret SP data. Computational frameworks that incorporate SP data in RNA secondary structure prediction are also presented.ConclusionsWe finally discuss potential directions for improvement in the prediction and differential analysis of RNA secondary structure.
This study aimed to detect the difference in resting cerebral activities between ischemic stroke patients and healthy participants, define the abnormal site, and provide new evidence for pathological mechanisms, clinical diagnosis, prognosis prediction and efficacy evaluation of ischemic stroke. At present, the majority of functional magnetic resonance imaging studies focus on the motor dysfunction and the acute stage of ischemic stroke. This study recruited 15 right-handed ischemic stroke patients at subacute stage (15 days to 11.5 weeks) and 15 age-matched healthy participants. A resting-state functional magnetic resonance imaging scan was performed on each subject to detect cerebral activity. Regional homogeneity analysis was used to investigate the difference in cerebral activities between ischemic stroke patients and healthy participants. The results showed that the ischemic stroke patients had lower regional homogeneity in anterior cingulate and left cerebrum and higher regional homogeneity in cerebellum, left precuneus and left frontal lobe, compared with healthy participants. The experimental findings demonstrate that the areas in which regional homogeneity was different between ischemic stroke patients and healthy participants are in the cerebellum, left precuneus, left triangle inferior frontal gyrus, left inferior temporal gyrus and anterior cingulate. These locations, related to the motor, sensory and emotion areas, are likely potential targets for the neural regeneration of subacute ischemic stroke patients.
Abstract-Real-time multi-channel neuronal signal recording has spawned broad applications in neuro-prostheses and neurorehabilitation. Detecting and discriminating neuronal spikes from multiple spike trains in real-time require significant computational efforts and present major challenges for hardware design in terms of hardware area and power consumption. This paper presents a Hebbian eigenfilter spike sorting algorithm, in which principal components analysis (PCA) is conducted through Hebbian learning. The eigenfilter eliminates the need of computationally expensive covariance analysis and eigenvalue decomposition in traditional PCA algorithms and, most importantly, is amenable to low cost hardware implementation. Scalable and efficient hardware architectures for real-time multi-channel spike sorting are also presented. In addition, folding techniques for hardware sharing are proposed for better utilization of computing resources among multiple channels. The throughput, accuracy and power consumption of our Hebbian eigenfilter are thoroughly evaluated through synthetic and real spike trains. The proposed Hebbian eigenfilter technique enables real-time multi-channel spike sorting, and leads the way towards the next generation of motor and cognitive neuro-prosthetic devices.Index Terms-Brain-machine interface, Hebbian learning, spike sorting, FPGAs, hardware architecture design.
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