Background: Integrin β1, as a member of the adhesion molecule family, is widely distributed in many kinds of cells and participates in multiple biological functions of the nervous system, including cytoskeleton reorganization, axon growth, and inflammatory injury. Summary: After nervous system injury, integrin β1 expressed by microglia is mainly involved in promoting inflammatory damage; integrin β1 expressed by astrocytes plays an important role in axon regeneration; integrin β1 expressed by endothelial cells mainly participates in vascular remodeling. We concluded that the function of integrin β1 depends on the location of the receptor cells. The mechanism of integrin β1, which is involved in the inflammatory response of immune regulatory cells and affects the axonal regeneration of neuronal cells, is the key to explore the repair after nervous system injury. The development of drugs targeting integrin β1 is expected to bring a breakthrough in the treatment of nervous system injury. Key Messages: This paper expounds the important role of integrin β1 in neurons of the nervous system and emphasizes the central role of integrin β1 in regulating non-neuronal cells after nervous system damage.

Since the commodity and financial attributes of crude oil will have a long-term or short-term impact on crude oil prices, we propose a de-dimension machine learning model approach to forecast the international crude oil prices. First, we use principal component analysis (PCA), multidimensional scale (MDS), and locally linear embedding (LLE) methods to reduce the dimensions of the data. Then, based on the recurrent neural network (RNN) and long-term and short-term memory (LSTM) models, we build eight models for predicting the future and spot prices of international crude oil. From the analysis and comparison of the prediction results, we find that reducing the dimension of the data can improve the accuracy of the model and the applicability of RNN and LSTM models. In addition, the LLE-RNN/LSTM models can most successfully capture the nonlinear characteristics of crude oil prices. When the moving window size is twenty, that is, when crude oil price data are lagging by almost a month, each model can minimize its error, and the LLE-RNN /LSTM models have the best robustness.

In many society and natural science fields, some stochastic orders have been established in the literature to compare the variability of two random variables. For a stochastic order, if an individual (or a unit) has some property, sometimes we need to infer that the population (or a system) also has the same property. Then, we say this order has closed property. Reversely, we say this order has reversed closure. This kind of symmetry or anti-symmetry is constructive to uncertainty management. In this paper, we obtain a quantile version of DCPE, termed as the dynamic cumulative past quantile entropy (DCPQE). On the basis of the DCPQE function, we introduce two new nonparametric classes of life distributions and a new stochastic order, the dynamic cumulative past quantile entropy (DCPQE) order. Some characterization results of the new order are investigated, some closure and reversed closure properties of the DCPQE order are obtained. As applications of one of the main results, we also deal with the preservation of the DCPQE order in several stochastic models.

We extend the mental accounting perspective to build models on product quality matching strategy, which is effective in resolving the conflict between traditional and online channels. We consider that perceived values of high-quality and low-quality products belong to different mental accounts, and construct three product quality matching strategies for a dual-channel supply chain. Comparing the profit of each member, we find that (1) the strategy where the traditional channel distributes premium products, while the e-tailer sells low-quality products, is most conducive to the manufacturer; (2) the profit elasticity of the e-tailer is larger than that of the traditional retailer; (3) traditional retailers should sell more price elastic products, and e-tailers should sell the product with lower price elasticity; and (4) the manufacturer has more monopoly power if consumers have a higher degree of acceptance for the online channel that distributes premium products.

In this paper, we establish two new stochastic orders, DMTFR (decreasing mean time to failure or replaced) and GDMTFR (generalized decreasing mean time to failure or replaced), and mainly investigate properties of the GDMTFR order. Some characterizations of the GDMTFR order are given. The implication relationships between the DMTFR and the GDMTFR orders are considered. Also, closure and reversed closure properties of the new order GDMTFR are investigated. Meanwhile, several illustrative examples that meet the GDMTFR order are shown as well.

To compare the variability of two random variables, we can use a partial order relation defined on a distribution class, which contains the anti-symmetry. Recently, Nair et al. studied the properties of total time on test (TTT) transforms of order n and examined their applications in reliability analysis. Based on the TTT transform functions of order n, they proposed a new stochastic order, the TTT transform ordering of order n (TTT-n), and discussed the implications of order TTT-n. The aim of the present study is to consider the closure and reversed closure of the TTT-n ordering. We examine some characterizations of the TTT-n ordering, and obtain the closure and reversed closure properties of this new stochastic order under several reliability operations. Preservation results of this order in several stochastic models are investigated. The closure and reversed closure properties of the TTT-n ordering for coherent systems with dependent and identically distributed components are also obtained.