Biomedical subjects

Yaning Yang

Publications and source records attributed to Yaning Yang.

6 recordsLinked to original sources

PubMed2003

Enhanced pesticide sorption by soils containing particulate matter from crop residue burns.

Lack of proper techniques to isolate black carbon (BC) from soils has hindered the understanding of their roles in the sorption and environmental fate of organic contaminants in soils and sediments. The burning of crop residues may be the primary source of BC in agricultural soils. In this study, wheat (Triticum aestivum L.) and rice (Oryza sativa L.) residues were burned, and the resulting particulate matter (ashes) along with a soil were used to sorb diuron from water. Calculations indicated that the burning of crop residues may result in an appreciable level of ashes in soils. The diuron sorption isotherms on ashes were curvilinear Langmuir type, suggestive of surface adsorption and similar to that with activated carbon. Ashes were 400-2500 times more effective than soil in sorbing diuron over the concentration range of 0-6 mg/L. Sorption by wheat ash-amended soils and the degree of isotherm nonlinearity increased with increasing ash content from 0% to 1% (weight), indicating the significant contribution of wheat ash to the sorption. Calculations show that wheat ash and soil independently contributed to the sorption. Above the wheat ash content of 0.05%, the sorption was largely controlled by the ash. Density-based fractionation and repeated HCI-HF washing of wheat ash yielded carbon-enriched fractions and enhanced diuron sorption by these fractions. BC appeared primarily responsible for the high adsorptivity of ashes. Ashes arising from the burning of crop residues may be an important determinant of pesticide immobilization and environmental fate in soils.

Adsorption↗

PubMed2003

Pesticide adsorptivity of aged particulate matter arising from crop residue burns.

Particulates (ashes) arising from the burning of crop residues are potentially effective adsorbents for pesticides in agricultural soils. To determine the long-term adsorptive sustainability of ashes, a wheat (Triticum aestivum L.) ash was aged under environmentally relevant conditions (in CaCl(2) solution at room temperature and pH 7) in soil extract for 1 month and in a soil (1% ash) for a period of up to 12 months. The aged ash and ash-amended soil were used to sorb diuron from water. The diuron sorption was also measured in the presence of atrazine as a competing pesticide. There was no observed microbial impact on the stability of the wheat ash in soil. All isotherms with the ash were nonlinear type-I curves, suggestive of the surface adsorption. On a unit mass basis, the ash in soil extract was 600-10000 times more effective than the soil in sorbing diuron. Adsorption of dissolved soil organic matter (DOM) during aging on the ash surfaces reduced the diuron adsorption by 50-60%. Surface competition from the atrazine adsorption also reduced the ash adsorption of diuron by 10-30%. A total of 55-67% reduction in diuron sorption by the ash-amended soil was observed. Due to its high initial adsorptivity, the ash fraction of the aged ash-amended soil contributed >50% to the total diuron sorption. Thus, the wheat ash aged in the soil remained highly effective in adsorbing diuron. As crop residues are frequently burned in the field, pesticides in agricultural soils may be highly immobilized due to the presence of ashes.

Adsorption↗

PubMed2003

Efficiency of single-nucleotide polymorphism haplotype estimation from pooled DNA.

The efficiency of single-nucleotide polymorphism haplotype analysis may be increased by DNA pooling, which can dramatically reduce the number of genotyping assays. We develop a method for obtaining maximum likelihood estimates of haplotype frequencies for different pool sizes, assess the accuracy of these estimates, and show that pooling DNA samples is efficient in estimating haplotype frequencies. Although pooling K individuals increases ambiguities, at least for small pool size K and small numbers of loci, the uncertainty of estimation increases <K times that of unpooled DNA. We also develop the asymptotic variance-covariance of maximum likelihood estimates and evaluate the accuracy of variance estimates by Monte Carlo methods. When the sample size of pools is moderately large, the asymptotic variance estimates are rather accurate. Completely or partially missing genotyping information is allowed for in our analysis. Finally, our methods are applied to single-nucleotide polymorphisms in the angiotensinogen gene.

Algorithms↗

PubMed2003

Statistical methods for analyzing microarray feature data with replications.

Expression levels in oligonucleotide microarray experiments depend on a potentially large number of factors, for example, treatment conditions, different probes, different arrays, and so on. To dissect the effects of these factors on expression levels, fixed-effects ANOVA methods have previously been proposed. Because we are not necessarily interested in estimating the specific effects of different probes and arrays, we propose to treat these as random effects. Then we only need to estimate their means and variances but not the effect of each of their levels; that is, we can work with a much reduced number of parameters and, consequently, higher precision for estimating expression levels. Thus, we developed a mixed-effects ANOVA model with some random and some fixed effects. It automatically accounts for local normalization between different arrays and for background correction. The method was applied to each of the 6,584 genes investigated in a microarray experiment on two mouse cell lines, PA6/S and PA6/8, where PA6/S enhances proliferation of Pre B cells in vitro but PA6/8 does not. To detect a set of differentially expressed genes (multiple testing problem), we applied the method of controlling the false discovery rate (FDR), which successfully identified 207 genes with significantly different expression levels.

Algorithms↗

PubMed2003

Microarray expression profiling: analysis and applications.

Recent advances in the technology of massive parallel gene expression profiling using microarrays are revolutionizing biomedical research and pharmacological discovery. These new and powerful tools allow researchers to examine the dynamics of a whole biological system by simultaneously interrogating the expression of tens of thousands of genes. This article will review the various bioinformatics and statistical methods commonly used to analyze the enormous amounts of data generated by this technology. The objective of these analyses is to reliably extract important biological information from carefully designed and controlled microarray experiments, and to organize the information in ways that reveal structured and coordinated physiological processes and key components controlling the biological responses studied. The major applications of the microarray technology, recently used to address a variety of biological questions of interest to scientists and clinicians in academia, healthcare and the pharmaceutical industry, will also be presented.

Databases, Genetic↗

PubMed2002

Zipf's law in importance of genes for cancer classification using microarray data.

Using a measure of how differentially expressed a gene is in two biochemically/phenotypically different conditions, we can rank all genes in a microarray dataset. We have shown that the falling-off of this measure (normalized maximum likelihood in a classification model such as logistic regression) as a function of the rank is typically a power-law function. This power-law function in other similar ranked plots are known as the Zipf's law, observed in many natural and social phenomena. The presence of this power-law function prevents an intrinsic cutoff point between the "important" genes and "irrelevant" genes. We have shown that similar power-law functions are also present in permuted dataset, and provide an explanation from the well-known chi(2) distribution of likelihood ratios. We discuss the implication of this Zipf's law on gene selection in a microarray data analysis, as well as other characterizations of the ranked likelihood plots such as the rate of fall-off of the likelihood.

Diagnosis, Differential↗