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Multisensory integration refers to sensory inputs from different sensory modalities being processed simultaneously to produce a unitary output. Surrounded by stimuli from multiple modalities, animals utilize multisensory integration to form a coherent and robust representation of the complex environment. Even though multisensory integration is fundamentally essential for animal life, our understanding of the underlying mechanisms, especially at the molecular, synaptic and circuit levels, remains poorly understood. The study of sensory perception in

With the large-scale application of electronic equipment, the reliability and safety of the system have become the focus of attention. Testability is a design characteristic to maintain reliability, which provides a timely and accurate fault diagnosis capacity. The testability verification test (TVT) is a valid technique to validate the fault diagnosis capacity of electronic equipment. In order to clarify the development tendency and improve the efficiency of TVT, we firstly discuss the definition of testability and analyze the development process of TVT. Then, we study the related approaches of TVT and give a review on test scheme and fault injection, in which we introduce the principles and the engineering experience. Finally, based on the existing researches, we give several suggestions on TVT improvement.

Intermittent fault of electronic equipment is an essential problem in the industry, and its diagnosis is paid more attention. The data-driven approach is commonly utilized, but it focuses on the mapping relation more than the inner data structure characteristics. In this paper, a method for intermittent fault diagnosis based on SOM. SOM topology is obtained based on unsupervised learning, and it is labeled using the majority voting strategy in the associated samples of every neuro. Inputs are judged as states excluding intermittent fault are processed again using SVM, and two new features are proposed for the SVM training. The “one-versus-one” is utilized to establish multi-classification SVM. Voltage conversion board is taken as an example, in which the presented method is applied and compared with ANN and SVM. The result shows that the proposed method is effective.

The inner collection between process reliability and stable process of space product is analyzed, and the model of controlling-detecting process parameters is built by multiple linear regression technology. Then the extremum and mean value of multivariable controlling parameters’ expected value, which meet the requirement of process detecting parameters’ stability, are used to predict each mean response and new response, and also prediction ellipsoid and joint confidence intervals of detecting parameters are obtained. Furthermore, three optimization problems based on genetic algorithm, which focused on controlling parameters’ fluctuation interval, are studied. Finally, taking the coating process of space product ferrite phase shifter as an example, the method is analyzed and verified. Therefore, the significant guarantee is provided for improving space products’ quality.

Chondroitin sulfate proteoglycans (CSPGs), up-regulated in and around the glial scar after mammalian spinal cord injury, have been suggested to be key inhibitory molecules for functional recovery by impeding axonal regrowth/sprouting and synaptic rearrangements. CSPG-mediated inhibition is mainly associated with the glycosaminoglycan chains of CSPGs, and chondroitin-4-sulfate (C4S) is the predominant sulfated structure that regulates axonal guidance and growth in the adult nervous system. With the aim to find molecules that neutralize the inhibitory functions of C4S, we screened a phage display library for peptides binding to C4S. From the phage clones binding to C4S we selected three peptides for further analysis. We observed that these peptides bind to C4S, but not chondroitin-6-sulfate, heparin sulfate or dermatan sulfate, in a concentration-dependent and saturable manner, whereas the scrambled peptides showed highly reduced or no binding to C4S. The C4S-binding peptides, but not their scrambled counterparts, when added to cultures of mouse cerebellar neurons and human neuroblastoma cells, neutralized the inhibitory functions of the C4S- and CSPG-coated substrate on cell adhesion, neuronal migration and neurite outgrowth. These results indicate that the C4S-binding peptides neutralize several inhibitory functions of CSPGs, suggesting that they may be beneficial in repairing mammalian nervous system injuries.

Equipment maintenance support is a very importance and efficient method, which can maintain, restore and improve the force of Equipment. Maintenance decision scheme is the structural framework of maintenance support system, as a guiding document, which involves in maintenance resources, inventory, task, management of equipment system. This paper aimed at the preventative maintenance capability constraints of the users, this paper constructs a mathematical model to solve for a set of preventative maintenance decision scheme that lowers maintenance costs while maintaining the task sustainability probability of foreign trade Equipment. An application case is carried out for robustness check of this method.

Malignant gliomas are the most aggressive forms of brain tumors, whose metastasis and recurrence contribute to high rates of morbidity and mortality. Glioma stem cell-like cells are a subpopulation of tumor-initiating cells responsible for glioma tumorigenesis, metastasis, recurrence and resistance to therapy. Epidermal growth factor receptor (EGFR) has been reported to be dysregulated in most cancers, including gliomas and its functions are closely linked to initiating tumor metastasis and a very poor prognosis. In search for compounds that may reduce the tumorigenic potential of gliomas/glioblastomas honokiol attracted our attention. Honokiol, purified from the bark of traditional Chinese herbal medicine Magnolia species, is beneficial in vitro and in animal models via a variety of pharmacological effects, including anti-inflammatory, anti-angiogenetic, anti-arrhythmic and antioxidant activities, as well as anti-proliferative and proapoptotic effects in a wide range of human cancer cells. However, its effects on glioma cells are unknown. Here, we used different concentrations of honokiol in treating U251 and U-87 MG human glioma/glioblastoma cells in cell culture. Results showed that honokiol inhibited glioma cell viability and colony formation and promoted apoptosis. It also inhibited glioma cell migration/proliferation and invasion. In addition, honokiol promoted apoptosis and reduced Bcl-2 expression, accompanied by increase in Bax expression. Honokiol reduced expression of EGFR, CD133 and Nestin. Moreover, honokiol inhibited the activation of both AKT and ERK signaling pathways, increased active caspase-3 level and reduced phosphorylation of STAT3. U-87 MG xenografts in nude mice and in immunotolerant zebrafish yolk sac showed that honokiol inhibits tumor growth and metastasis. Altogether, results indicate that honokiol reduces tumorigenic potentials, suggesting hopes for honokiol to be useful in the clinical management of glioma/glioblastoma.

As an important marketing tool, recommendation systems for e-commerce offer an opportunity for merchants to discovery potential consumption tendency. This paper puts forward a novel recommendation algorithm to make the recommendation system more accurate, personalized and intelligent. Firstly, we use intelligent mining on purchase information, and regress consumer preference rating on click behavior. Secondly, we use Bipartite Network Recommendation model based on resource allocation and improved collaborative filtering model; the former abstracts products and consumers into nodes in the graph, and finds the correlation of products that recommend to others using alternative relation; and the latter solves the problem, caused by sparse data, by compressing rating matrix and predicting null values. Finally, according to Alibaba e-commerce customers purchase data, we verify that Hybrid Recommendation Model optimizes the accuracy and coverage of the recommendation results.