Zhejiang Textile and Fashion College

Ningbo, China

Zhejiang Textile and Fashion College

Ningbo, China
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Xiao X.,Jing de Zhen Ceramic Institute | Xiao X.,ZheJiang Textile and Fashion College | Xiao X.,Gordon Life Science Institute | Min J.-L.,Jing de Zhen Ceramic Institute | And 3 more authors.
Current Topics in Medicinal Chemistry | Year: 2013

Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment. © 2013 Bentham Science Publishers.

Xiao X.,Jing de Zhen Ceramic Institute | Xiao X.,ZheJiang Textile and Fashion College | Xiao X.,Gordon Life Science Institute | Lin W.-Z.,Jing de Zhen Ceramic Institute | And 2 more authors.
Current Topics in Medicinal Chemistry | Year: 2013

With the explosion of protein sequences generated in the postgenomic era, the gap between the number of attribute- known proteins and that of uncharacterized ones has become increasingly large. Knowing the key attributes of proteins is a shortcut for prioritizing drug targets and developing novel new drugs. Unfortunately, it is both time-consuming and costly to acquire these kinds of information by purely conducting biological experiments. Therefore, it is highly desired to develop various computational tools for fast and effectively classifying proteins according to their sequence information alone. The process of developing these high throughput tools is generally involved with the following procedures: (1) constructing benchmark datasets; (2) representing a protein sequence with a discrete numerical model; (3) developing or introducing a powerful algorithm or machine learning operator to conduct the prediction; (4) estimating the anticipated accuracy with a proper and objective test method; and (5) establishing a user-friendly web-server accessible to the public. This minireview is focused on the recent progresses in identifying the types of G-protein coupled receptors (GPCRs), subcellular localization of proteins, DNA-binding proteins and their binding sites. All these identification tools may provide very useful informations for in-depth study of drug metabolism. © 2013 Bentham Science Publishers.

Xu L.L.,Zhejiang Textile and Fashion College
Applied Mechanics and Materials | Year: 2014

The development of sportswear is closely related to the development of scientific and technological,the improvement of the community's civilization.As the professional sportswear design and production are close to new material,high-tech sportswear will not only help the athletes improve performance and raise campaign skills,but also more advantageous in protecting athlete's safety. © (2014) Trans Tech Publications, Switzerland.

Qiu W.-R.,Jing de Zhen Ceramic Institute | Xiao X.,Jing de Zhen Ceramic Institute | Xiao X.,ZheJiang Textile and Fashion College | Xiao X.,Gordon Life Science Institute | And 2 more authors.
International Journal of Molecular Sciences | Year: 2014

Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called "hotspots", while with lower probability in so-called "coldspots". With the ever-increasing amount of genome sequence data in the postgenomic era, computational methods for effectively identifying the hotspots and coldspots have become urgent as they can timely provide us with useful insights into the mechanism of meiotic recombination and the process of genome evolution as well. To meet the need, we developed a new predictor called "iRSpot-TNCPseAAC", in which a DNA sample was formulated by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample according to its genetic codes. The former was used to incorporate its local or short-rage sequence order information; while the latter, its global and long-range one. Compared with the best existing predictor in this area, iRSpot-TNCPseAAC achieved higher rates in accuracy, Mathew's correlation coefficient, and sensitivity, indicating that the new predictor may become a useful tool for identifying the recombination hotspots and coldspots, or, at least, become a complementary tool to the existing methods. It has not escaped our notice that the aforementioned novel approach to incorporate the DNA sequence order information into a discrete model may also be used for many other genome analysis problems. The web-server for iRSpot-TNCPseAAC is available at http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to obtain their desired result without the need to follow the complicated mathematical equations. © 2014 by the authors; licensee MDPI, Basel, Switzerland.

Ying Y.,Zhejiang Textile and Fashion College
Lecture Notes in Electrical Engineering | Year: 2011

Everyday there are masses of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. Personalized recommendation is the process to alleviative the problem. Collaborative filtering is one of the most popular technologies in the personal recommendation system. As the user rating matrix becoming extremely sparsity, traditional collaborative filtering recommendation algorithm calculates similarity between items using the rating data, and it does not consider the semantic relationship between different items, thus recommendation quality is very poor. To solve this problem, the paper combines the item semantic similarity and the item rating similarity, which takes into account the influence of item semantic and user rating to enhance the item-based collaborative filtering. The personalized collaborative filtering recommendation algorithm combining the item semantic similarity and item rating similarity can mitigate the sparsity problem in the electronic commerce recommender systems. © 2011 Springer-Verlag Berlin Heidelberg.

Ye H.,Zhejiang Textile and Fashion College
Journal of Software | Year: 2011

With the development of the Internet, the problem of information overload is becoming increasing serious. People all have experienced the feeling of being overwhelmed by the number of new books, articles, and proceedings coming out each year. Many researchers pay more attention on building a proper tool which can help users obtain personalized resources. Personalized recommendation systems are one such software tool used to help users obtain recommendations for unseen items based on their preferences. The commonly used personalized recommendation system methods are content-based filtering, collaborative filtering, and association rules mining. Unfortunately, each method has its drawbacks. This paper presented a personalized collaborative filtering recommendation method combining the association rules mining and self-organizing map. It used the association rules mining to fill the vacant where necessary. Then, it employs clustering function of self-organizing map to form nearest neighbors of the target item and it produces prediction of the target user to the target item using itembased collaborative filtering. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems. © 2011 ACADEMY PUBLISHER.

Zhang J.F.,Zhejiang Textile and Fashion College
Advanced Materials Research | Year: 2013

Due to fast-paced society and fierce competition, more and more attentions are paid to consumers' way of life and their psychological needs in fashion design. Color, a major element of fashion design, is a certain psychological feeling stimulated from vision, a combination of perception and ration. It should be designed and applied from the aspect of color psychology. Based on the cases and survey, this study aims to explore the application of psychological effect of color in fashion design. Fashion designers should consider the fashion color, focus on consumers' preference for color, shade, style, patterns and the psychological effect, meet psychological needs of the target and potential consumers, and express psychological suggestion effect of color. Designers should also develop products at the level of "matter" and "spirit", select fashion color to consumers' mind, and finally apply color psychological effect in fashion planning and designing, products developing and marketing. The results of this study may play a guiding role in the strategy of modern fashion design. © (2013) Trans Tech Publications, Switzerland.

Ju H.,Zhejiang Textile and Fashion College
Applied Mechanics and Materials | Year: 2012

The shortage of transmission link bandwidth limits the application of cloud computing. In this contribution a multilink dynamic aggregation model is suggested. Through effective integration of link flow monitoring units, link aggregation control unit and the gateway, more effective aggregation of transmission multilink and rational utilization of network bandwidth in cloud computing network were realized. In addition, dynamically access load flow problems were solved based on the actual load of different transmission link. The model is costly, stable and highly efficient.

Ju H.,Zhejiang Textile and Fashion College
International Journal of Sensor Networks | Year: 2014

The disaster recovery structure and mechanism for cloud computing networks are the key factors that determine the disaster recovery ability and efficiency of networks. Through studies on technologies, including intelligent monitoring, load scheduling, hybrid gateways, clustering and data mirroring, we construct a multi-level disaster recovery network framework with remote intelligent monitoring mechanism by integrating remote dynamic monitoring, round robin algorithm, address mapping, load balancing and virtual private network technology. The principle of the framework is also elucidated. The proposed framework solves intelligent remote disaster recovery problem of cloud computing networks. Safe inter-regional services and fast response are realised. The structure also increases the disaster recovery ability and the security level of cloud computing networks. Copyright © 2014 Inderscience Enterprises Ltd.

Hua Q.,Zhejiang Textile and Fashion College
Advances in Information Sciences and Service Sciences | Year: 2011

The recommendation model of textile products used by family, which is based on adaptive resonance theory(ART) of artifical neural network, is researched. The first, data collection of web pages, which is browsed, is introduced. Then data refining procedure is analysed. In order to solve the recommendation model, we study the ART, which analyses clustering items and processes, implements self-adaptive recommendation services. Experimental results show that the system is stable and effective to predict users interest preferences and capture users excursion of interests. All these results demonstrate that the recommendation algorithm performed well.

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