Dexing Zhong

CarSPred: A Computational Tool for Predicting Carbonylation Sites of Human Proteins

Protein carbonylation is one of the most pervasive oxidative stress-induced post-translational modifications (PTMs), which plays a significant role in the etiology and progression of several human diseases. It has been regarded as a biomarker of oxidative stress due to its relatively early formation and stability compared with other oxidative PTMs. Only a subset of proteins is prone to carbonylation and most carbonyl groups are formed from lysine (K), arginine (R), threonine (T) and proline (P) residues. Recent advancements in analysis of the PTM by mass spectrometry provided new insights into the mechanisms of protein carbonylation, such as protein susceptibility and exact modification sites. However, the experimental approaches to identifying carbonylation sites are costly, time-consuming and capable of processing a limited number of proteins, and there is no bioinformatics method or tool devoted to predicting carbonylation sites of human proteins so far. In the paper, a computational method is proposed to identify carbonylation sites of human proteins. The method extracted four kinds of features and combined the minimum Redundancy Maximum Relevance (mRMR) feature selection criterion with weighted support vector machine (WSVM) to achieve total accuracies of 85.72%, 85.95%, 83.92% and 85.72% for K, R, T and P carbonylation site predictions respectively using 10-fold cross-validation. The final optimal feature sets were analysed, the position-specific composition and hydrophobicity environment of flanking residues of modification sites were discussed. In addition, a software tool named CarSPred has been developed to facilitate the application of the method. Datasets and the software involved in the paper are available at https://sourceforge.net/projects/hqlstudio/files/CarSPred-1.0/.

Fingerprint Identification Using SIFT-Based Minutia Descriptors and Improved All Descriptor-Pair Matching

The performance of conventional minutiae-based fingerprint authentication algorithms degrades significantly when dealing with low quality fingerprints with lots of cuts or scratches. A similar degradation of the minutiae-based algorithms is observed when small overlapping areas appear because of the quite narrow width of the sensors. Based on the detection of minutiae, Scale Invariant Feature Transformation (SIFT) descriptors are employed to fulfill verification tasks in the above difficult scenarios. However, the original SIFT algorithm is not suitable for fingerprint because of: (1) the similar patterns of parallel ridges; and (2) high computational resource consumption. To enhance the efficiency and effectiveness of the algorithm for fingerprint verification, we propose a SIFT-based Minutia Descriptor (SMD) to improve the SIFT algorithm through image processing, descriptor extraction and matcher. A two-step fast matcher, named improved All Descriptor-Pair Matching (iADM), is also proposed to implement the 1:N verifications in real-time. Fingerprint Identification using SMD and iADM (FISiA) achieved a significant improvement with respect to accuracy in representative databases compared with the conventional minutiae-based method. The speed of FISiA also can meet real-time requirements.

An improved routing algorithm of Zigbee wireless sensor network for smart home system

Smart home is a networked intelligent living environment which employs a house as the platform to integrate automatic control system, computer network system and communication technology all-in-one. In order to avoid the constraints of cabling, moving and aesthetics in the traditional smart home system using wired network, a design of smart home network based on Zigbee wireless sensor network technology is demonstrated in this paper. An improved routing algorithm combining Cluster-Tree and AODVjr is proposed for Zigbee wireless network. Therefore a more convenient and reliable wireless communication environment is achieved in the smart home system.

A novel computational method for the identification of plant alternative splice sites

Alternative splicing (AS) increases protein diversity by generating multiple transcript isoforms from a single gene in higher eukaryotes. Up to 48% of plant genes exhibit alternative splicing, which has proven to be involved in some important plant functions such as the stress response. A hybrid feature extraction approach which combing the position weight matrix (PWM) with the increment of diversity (ID) was proposed to represent the base conservative level (BCL) near splice sites and the similarity level of two datasets, respectively. Using the extracted features, the support vector machine (SVM) was applied to classify alternative and constitutive splice sites. By the proposed algorithm, 80.8% of donor sites and 85.4% of acceptor sites were correctly classified. It is anticipated that the novel computational method is promising for the identification of AS sites in plants.

A practical application combining wireless sensor networks and Internet of Things: Safety Management System for Tower Crane Groups.

The so-called Internet of Things (IoT) has attracted increasing attention in the field of computer and information science. In this paper, a specific application of IoT, named Safety Management System for Tower Crane Groups (SMS-TC), is proposed for use in the construction industry field. The operating status of each tower crane was detected by a set of customized sensors, including horizontal and vertical position sensors for the trolley, angle sensors for the jib and load, tilt and wind speed sensors for the tower body. The sensor data is collected and processed by the Tower Crane Safety Terminal Equipment (TC-STE) installed in the drivers operating room. Wireless communication between each TC-STE and the Local Monitoring Terminal (LMT) at the ground worksite were fulfilled through a Zigbee wireless network. LMT can share the status information of the whole group with each TC-STE, while the LMT records the real-time data and reports it to the Remote Supervision Platform (RSP) through General Packet Radio Service (GPRS). Based on the global status data of the whole group, an anti-collision algorithm was executed in each TC-STE to ensure the safety of each tower crane during construction. Remote supervision can be fulfilled using our client software installed on a personal computer (PC) or smartphone. SMS-TC could be considered as a promising practical application that combines a Wireless Sensor Network with the Internet of Things.

Analysis of interleukin-8 gene variants reveals their relative importance as genetic susceptibility factors for chronic periodontitis in the Han population

Interleukin (IL)-8, an important chemokine that regulates the inflammatory response, plays an important role in periodontitis. Previous studies indicate that certain IL-8 gene polymorphisms are associated with periodontitis susceptibility in some populations. However, the literature is somewhat contradictory, and not all IL-8 polymorphisms have been examined, particularly in Han Chinese individuals. The aim of this study was to investigate the association of every IL-8 SNP with chronic periodontitis in Han Chinese individuals. We analyzed 23 SNPs with minor allele frequency (MAF)≥0.01, which were selected from 219 SNPs in the NCBI dbSNP and preliminary HapMap data analyses from a cohort of 400 cases and 750 controls from genetically independent Han Chinese individuals. Single SNP, haplotype and gender-specific associations were performed. We found that rs4073 and rs2227307 were significantly associated with chronic periodontitis. Further haplotype analysis indicated that a haplotype block (rs4073-rs2227307-rs2227306) that spans the promoter and exon1 of IL-8 was highly associated with chronic periodontitis. Additionally, the ATC haplotype in this block was increased 1.5-fold in these cases. However, when analyzing the samples by gender, no significant gender-specific associations in IL-8 were observed, similar to the results of haplotype association analyses in female and male subgroups. Our results provide further evidence that IL-8 is associated with chronic periodontitis in Han Chinese individuals. Furthermore, our results confirm previous reports suggesting the intriguing possibilities that IL-8 plays a role in the pathogenesis of chronic periodontitis and that this gene may be involved in the etiology of this condition.

An Improved Multihop Distance Estimation for DV-Hop Localization Algorithm in Wireless Sensor Networks

Range-free, distributed localization method is an important and challenging issue in wireless sensor networks. Typical hop-count-based method always assumes that the average hop size for different hop count is the same. In this paper, we analyze the hop progress for different hop counts, and modify the assumption as the hop progress is the same for different hop counts except the 1-hop. We propose two improved DV-Hop estimation distance methods based on the new assumption. simulation result shows that the proposed methods obtain more accurate estimation distance and get better performance of localization.

Neighborhood discriminant embedding in face recognition

We present a novel feature extraction method for face recog- nition called neighborhood discriminant embedding NDE, which incor- porates graph embedding and Fishers criterion and includes an indi- vidual discriminative factor IDF. Graph embedding is able to reveal the representative and discriminative features from the underlying nonlinear face data structure. Fishers criterion is recognized as an effective tech- nique for discriminative feature extraction. IDF is proposed as an indi- vidual property of each sample to describe the contribution to classifica- tion. NDE can remain the local structure of the nearest neighbors of each data point during the dimensionality reduction as well as gather the within-class points and separate the between-class points in the low- dimensional projected space. Utilizing Fishers criterion and taking into account IDF, the discriminative capability of NDE is further enhanced. Comprehensive experiments are conducted using the Olivetti Research Laboratory ORL and Facial Recognition Technology FERET face da- tabases to demonstrate the effectiveness of our methods.

Improved localisation method based on multi-hop distance unbiased estimation

Range-free, distributed localisation method is a challenging issue in wireless sensor networks (WSN). Typical hop count-based localisation methods assume that the Hopsize for different hop count is the same. However, many observations in WSN indicate that this assumption does not always correspond to the practical situation. In this study, the authors propose an approximate distribution of k-hop node distance and derive the expectation of k-hop node distance under the above distribution. Furthermore, the authors propose a novel range-free localisation method based on the multi-hop distance unbiased estimation (MDUE), and the simulation results demonstrate that more accurate distance and localisation can be obtained by MDUE than the state-of-the-art methods.

Genome-wide identification and predictive modeling of lincRNAs polyadenylation in cancer genome

Long noncoding RNAs (lncRNAs) play essential regulatory roles in the human cancer genome. Many identified lncRNAs are transcribed by RNA polymerase II in which they are polyadenylated, whereby the long intervening noncoding RNAs (lincRNAs) have been widely used for the researches of lncRNAs. To date, the mechanism of lincRNAs polyadenylation related to cancer is rarely fully understood yet. In this paper, first we reported a comprehensive map of global lincRNAs polyadenylation sites (PASs) in five human cancer genomes; second we proposed a grouping method based on the pattern of genes expression and the manner of alternative polyadenylation (APA); third we investigated the distribution of motifs surrounding PASs. Our analysis reveals that about 70% of PASs are located in the sense strand of lincRNAs. Also more than 90% PASs in the antisense strand of lincRNAs are located in the intron regions. In addition, around 40% of lincRNA genes with PASs has APA sites. Four obvious motifs i.e., AATAAA, TTTTTTTT, CCAGSCTGG, and RGYRYRGTGG were detected in the sequences surrounding PASs in the normal and cancer tissues. Furthermore, a novel algorithm was proposed to recognize the lincRNAs PASs of tumor tissues based on support vector machine (SVM). The algorithm can achieve the accuracies up to 96.55% and 89.48% for identification the tumor lincRNAs PASs from the non-polyadenylation sites and the non-lincRNA PASs, respectively.