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Biomedical subjects

W J Zhang

Publications and source records attributed to W J Zhang.

At least 19 recordsLinked to original sources
PubMed2005

Mapping the RNA-binding domain on the DpCPV VP4.

The RNA-binding properties of VP4 protein of Dendrolimus punctatus cytoplasmic polyhedrosis virus (DpCPV) VP4 were analyzed. VP4 was expressed in E. coli and assayed for RNA binding activity by gel mobility shift assay. VP4 was found to bind RNA (ssRNA and dsRNA) in a sequence-independent manner, but did not interact with DNA. To identify the domain(s) of the protein important for RNA binding, a number of deletions were made and tested by gel mobility shift assays and northwestern blot. The central region of VP4 from amino acid residues 77 to 155 was found to contain the RNA binding domain.

Animals↗
PubMed2005

Characterization of the RNA-binding domain in the Dendrolimus punctatus cytoplasmic polyhedrosis virus nonstructural protein p44.

Dendrolimus punctatus cytoplasmic polyhedrosis virus (DpCPV-1) belongs to the Cypovirus genus in the Reoviridae family. The ORF of genome segment 8 (S8) of DpCPV-1 was cloned into vector pMAL-c2X and used to express a 44kDa protein (p44) in E. coli, which was detected by Western blotting. The gel mobility shift assays showed that p44 had ssRNA-binding activity. Competitive assay indicated that this protein only bind to ssRNA and could not interact with DNA and dsRNA. The binding of p44 to ssRNA is sequence non-specific. To identify the domain(s) important for RNA binding of the protein, a number of deletions were made. These truncated proteins were expressed in E. coli and purified. The affinity of each truncated protein towards ssRNA was then assayed by electrophoretic mobility shift assays and northwestern blot. The results indicated that glutamic acid-rich domain in the central region of p44 from residues 104 to 201 was the ssRNA-binding domain.

Animals↗
PubMed2005

Dynamic model-based clustering for time-course gene expression data.

Microarray technology has produced a huge body of time-course gene expression data. Such gene expression data has proved useful in genomic disease diagnosis and genomic drug design. The challenge is how to uncover useful information in such data. Cluster analysis has played an important role in analyzing gene expression data. Many distance/correlation- and static model-based clustering techniques have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterize the data and that should be considered in cluster analysis so as to obtain high quality clustering. This paper proposes a dynamic model-based clustering method for time-course gene expression data. The proposed method regards a time-course gene expression dataset as a set of time series, generated by a number of stochastic processes. Each stochastic process defines a cluster and is described by an autoregressive model. A relocation-iteration algorithm is proposed to identity the model parameters and posterior probabilities are employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. Computational experiments are performed on a synthetic and three real time-course gene expression datasets to investigate the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g. k-means) for time-course gene expression data, and thus it is a useful and powerful tool for analyzing time-course gene expression data.

Algorithms↗
PubMed2004

Residues of hexachlorocyclohexane isomers and their distribution characteristics in soils in the Tianjin area, China.

Hexachlorocyclohexane (HCH) has a history of use in China. This paper presents the results of an investigation of HCH residue isomers and their distribution characteristics in soils near Tianjin, China. One hundred eighty-eight soil samples were collected from the Tianjin area. Four HCH isomers-alpha-HCH, beta-HCH, gamma-HCH, and delta-HCH-were detected using gas chromatography for all samples. Concentrations of the sum HCH ranged from 1.3 to 1095 ng g(-1), among which beta-HCH accounted for 52.5%. In addition, residues of HCH within Tianjin's urban areas were found to be higher. No significant differences were found between the residues of HCH in soils from waste irrigation areas and those in other areas. Total organic carbon content was determined to impact the residue levels of HCH in soils, while pH value and clay content were not related to concentrations of HCH. In general, all HCH isomers in soil samples had abnormally high residue levels, possibly the result of continuous use of HCH in this area.

China↗
PubMed2004

Level and distribution of DDT in surface soils from Tianjin, China.

One hundred and eighty eight surface soil samples were collected from the Tianjin area to study the contamination of DDT and its metabolites. Measurements were taken for p,p'-DDE, p,p'-DDD, p,p'-DDT, o,p'-DDE, o,p'-DDD and o,p'-DDT for all samples. The results indicated that p,p'-DDT and p,p'-DDE were the predominant contaminant compounds in the surface soil samples, with mean concentrations of 27.5 and 18.8 ng g(-1) respectively. No significant differences in DDT concentrations were found between the soils from wastewater treated irrigated areas and other areas, suggesting that wastewater irrigation is not an important source of DDT in the area. However, the spatial distribution of soil DDTs levels in the area did correlate well with early direct application rates of pesticides. In addition, both pH level and organic carbon content are also known factors affecting the level of DDT and its metabolites. Although it was assumed that the use of these chemicals was banned in the early 1980s, the current concentration levels appear to be too high to be mere residuals after 20 years degradation.

Agriculture↗
PubMed2004

The survival and value of liver transplantation for liver carcinoma: a single-center experience.

UNLABELLED: Liver transplantation for liver carcinoma with cirrhosis is a treatment still in dispute. The objectives were to summarize the survival and cost of 50 liver transplant cases performed for liver carcinoma over nearly 3 years. METHODS: We performed 138 liver transplants from January 1999 to February 2002. There were 50 cases (36.2%) of liver carcinoma with HBV cirrhosis, which were divided into three stages based on the tumor pathology: Stage 1 cases showed a single mass (< or = 5 cm), 4 cases; Stage 2, a single mass > 5 cm or intrahepatic multiple masses without PV cancer embolus, 32 cases; and Stage 3: tumor invasion of the PV or perihepatic lymph nodes or organs, 14 cases. All patients received three to six courses of chemotherapy postoperatively. RESULTS: All four cases of stage 1 survived > 1 year; one of them is at 3 years with good liver function and tumor free. The mean half-year medical cost was $27.100 +/- 108 in stage 1. The half-year survival and medical costs were 62.5% and $31,500 +/- 260 in stage 2 and 15.0% and $35,500 +/- 134 in stage 3. CONCLUSION: Liver transplantation is an effective treatment for early-stage liver carcinoma, that achieves good medical and economic results, but should be limited to advanced liver cancer.

Analysis of Variance↗
PubMed2004

Modeling gene expression from microarray expression data with state-space equations.

We describe a new method to model gene expression from time-course gene expression data. The modelling is in terms of state-space descriptions of linear systems. A cell can be considered to be a system where the behaviours (responses) of the cell depend completely on the current internal state plus any external inputs. The gene expression levels in the cell provide information about the behaviours of the cell. In previously proposed methods, genes were viewed as internal state variables of a cellular system and their expression levels were the values of the intemal state variables. This viewpoint has suffered from the underestimation of the model parameters. Instead, we view genes as the observation variables, whose expression values depend on the current intemal state variables and any external input. Factor analysis is used to identify the internal state variables, and Bayesian Information Criterion (BIC) is used to determine the number of the internal state variables. By building dynamic equations of the internal state variables and the relationships between the internal state variables and the observation variables (gene expression profiles), we get state-space descriptions of gene expression model. In the present method, model parameters may be unambiguously identified from time-course gene expression data. We apply the method to two time-course gene expression datasets to illustrate it.

Algorithms↗