Expanded Expression Landscape and Prioritization of Circular RNAs in Mammals

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Proposing a Radial Basis Function and CSDM Indices to Predict the Traumatic Brain Injury Risk

BackgroundMany head injury indices and finite element (FE) head models have been proposed to predict traumatic brain injury (TBI). Although FE head models are suitable methods with high accuracy, they are computationally intensive. Head motion-based brain injury criteria are usually fast tools with lower accuracy. So, the objective of this study is to propose new criteria along with an artificial neural network model to predict TBI risks, which can be fast and accurate.MethodsFor this purpose, 250 FE head simulations have been carried out at 5 magnitudes and 50 rotational impact directions using the SIMon model. The effects of directions and magnitudes of rotational impacts were assessed for cumulative strain damage measure (CSDM) values. Next, statistical analysis and neural network were applied to predict CSDM values.ResultsThe results of the present research showed that the direction of rotation in the sagittal and frontal planes had a considerable effect on the CSDM values. Furthermore, new brain injury indices and a radial basis function neural network have been proposed to predict CSDM values which having high correlation coefficients with SIMon responses.ConclusionsThe results of this research demonstrated that rotational impact directions should be used to develop new head injury criteria being able to predict CSDM values. However, findings of present research proved that head motion-based brain injury criteria and RBF network can be used to predict FE head model responses with high speed and accuracy.     

A Persistence Detector for Metabolic Network Rewiring in an Animal

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Research on WNN Greenhouse Temperature Prediction Method Based on GA

Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data to model greenhouses, but there is a widening gap in the application of different neural networks. This paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm (GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis function, wavelet neural network (WNN) improved model training speed and accuracy of prediction results compared with back propagation neural networks (BPNN), which was conducive to the prediction and control of short-term greenhouse temperature fluctuations. At the same time, the genetic algorithm (GA) was introduced to globally optimize the initial weights of the original model, which improved the insensitivity of the model to the initial weights and thresholds, and improved the training speed and stability of the model. Finally, simulation results for the greenhouse showed that the model training speed, prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.     

神经嵴细胞和神经嵴病及其致病机制的研究进展

神经嵴细胞(neural crest cells,NCCs)是一类脊椎动物特有的可迁移的多能干细胞,其可分化为软骨细胞、神经元和黑色素细胞等多种类型细胞。NCCs的形成、迁移和分化受到严格调控,任何扰乱NCCs发育的因素都可导致胚胎发育畸形。由神经嵴细胞发育异常所导致的一系列疾病统称为神经嵴病(neurocristopathies,NCPs)。NCPs种类繁多且表型复杂,可累及人体多个部位(颅面部、心脏、肠胃和皮肤等),严重危害患者的身体机能和心理健康。NCPs占所有出生缺陷患儿的1/3,遗传因素是导致NCPs的主要风险因素,但环境风险因子以及基因–环境交互作用异常也可导致NCPs。本文对神经嵴细胞和神经嵴病及其致病机制进行综述,为系统认知神经嵴细胞发育以及神经嵴病提供参考,为了解神经嵴病的病因以及开展有效防控提供科学支撑。     

Multiscale kinetic analysis of proteins

The stochasticity of molecular motion results in the existence of multiple kinetically relevant pathways in many biomolecular mechanisms. Because it is highly demanding to characterize them for complex systems, mechanisms are often described with a single-pathway perspective. However, kinetic network analysis and sub-ensemble experimental insight are increasingly demonstrating not only the existence of competing pathways but also the importance of kinetic selection in biology. This review focuses on advances in multiscale kinetic analysis of proteins, which connects molecular level information from simulations to macroscopic data to characterize mechanistic reaction networks and the reactive flux through them. We describe a range of methods used and highlight several examples where kinetic modeling has revealed functional importance of pathway heterogeneity.