Biomedical subjects

Kuang-Yu Liu

Publications and source records attributed to Kuang-Yu Liu.

11 recordsLinked to original sources

PubMed2006

Thermal treatment of the fly ash from municipal solid waste incinerator with rotary kiln.

Reuse of the fly ash from the municipal solid waste incinerator (MSWI) is a policy of Taiwan EPA. However, the fly ash is often classified as a hazardous waste and cannot be reused directly because the concentrations of heavy metals exceed the TCLP regulations. The main objective of this study is to investigate the continuous sintering behavior of fly ash with a rotary kiln and seek a solution to reduce the concentrations of heavy metal to an acceptable value. The partitions of the heavy metals in the process are also considered. The results of TCLP showed that among the metals of Cr, Cd, Cu and Pb, only the concentrations of Pb in raw fly ash exceeded the regulation. At sintering temperatures of 700, 800 and 900 degrees C, the concentration of Pb decreased in sintering products, however, the concentration of Pb still exceeded the limitation at 700 and 800 degrees C. Additionally, the water-washing was used to pre-treat the fly ash before sintering process. The washing treatment effectively reduced the leaching concentrations of Pb to agree the regulations. Therefore, water-washing followed by a sintering treatment is an available process for detoxifying the fly ash of MSWI.

Carbon↗

PubMed2006

Mutual information-based feature selection in studying perturbation of dendritic structure caused by TSC2 inactivation.

In this study, the effect of protein Tuberous sclerosis 2 (TSC2) on the dendritic spine density and length was demonstrated by using TSC2-RNAinactivation. In addition, the role of rapamycin, an antagonist of the molecular target of rapamycin, in the morphological changes of spine caused by TSC2 silencing was investigated. The features were extracted from highresolution three-dimensional image stacks collected by two-photon laser scanning microscopy of green fluorescing pyramidal cells expressing TSC2-RNA interference (RNAi), or TSC2-RNAi and rapamycin treatment in rat hippocampal slice cultures. We proposed to apply the lognormal distribution method for feature extraction. The extracted features of three cases under investigation, namely, (1) green-fluorescent protein GFP vs TSC2-RNAi, (2) GFP vs TSC2-RNAi and rapamycin, and (3) TSC2-RNAi vs TSC2-RNAi and rapamycin, were analyzed by mutual information-based feature selection and evaluated by three classifiers, K-nearest neighbor, Perceptron, and two-layer neural networks. The results showed that both the spine density and length have significant morphological changes after TSC2-RNAi treatment. However, rapamycin treatment could reverse the effect of TSC2-RNAi on spine length but not on spine density. These results are consistent with the results reported in the scientific literature. Finally, we explored the application of pattern recognition method in a small sample with richer feature properties, namely bootstrap mutual information estimation and a mutual information- based feature selection method.

Algorithms↗

PubMed2006

Bayesian variable selection for gene expression modeling with regulatory motif binding sites in neuroinflammatory events.

Multiple transcription factors (TFs) coordinately control transcriptional regulation of genes in eukaryotes. Although numerous computational methods focus on the identification of individual TF-binding sites (TFBSs), very few consider the interdependence among these sites. In this article, we studied the relationship between TFBSs and microarray gene expression levels using both family-wise and memberspecific motifs, under various combination of regression models with Bayesian variable selection, as well as motif scoring and sharing conditions, in order to account for the coordination complexity of transcription regulation. We proposed a three-step approach to model the relationship. In the first step, we preprocessed microarray data and used p-values and expression ratios to preselect upregulated and downregulated genes. The second step aimed to identify and score individual TFBSs within DNA sequence of each gene. A method based on the degree of similarity and the number of TFBSs was employed to calculate the score of each TFBS in each gene sequence. In the last step, linear regression and probit regression were used to build a predictive model of gene expression outcomes using these TFBSs as predictors. Given a certain number of predictors to be used, a full search of all possible predictor sets is usually combinatorially prohibitive. Therefore, this article considered the Bayesian variable selection for prediction using either of the regression models. The Bayesian variable selection has been applied in the context of gene selection, missing value estimation, and regulatory motif identification. In our modeling, the regressor was approximated as a linear combination of the TFBSs and a Gibbs sampler was employed to find the strongest TFBSs. We applied these regression models with the Bayesian variable selection on spinal cord injury gene expression data set. These TFs demonstrated intricate regulatory roles either as a family or as individual members in neuroinflammatory events. Our analysis can be applied to create plausible hypotheses for combinatorial regulation by TFBSs and avoiding false-positive candidates in the modeling process at the same time. Such a systematic approach provides the possibility to dissect transcription regulation, from a more comprehensive perspective, through which phenotypical events at cellular and tissue levels are moved forward by molecular events at gene transcription and translation levels.

Amino Acid Motifs↗

PubMed2005

Boosting alternating decision trees modeling of disease trait information.

We applied the alternating decision trees (ADTrees) method to the last 3 replicates from the Aipotu, Danacca, Karangar, and NYC populations in the Problem 2 simulated Genetic Analysis Workshop dataset. Using information from the 12 binary phenotypes and sex as input and Kofendrerd Personality Disorder disease status as the outcome of ADTrees-based classifiers, we obtained a new quantitative trait based on average prediction scores, which was then used for genome-wide quantitative trait linkage (QTL) analysis. ADTrees are machine learning methods that combine boosting and decision trees algorithms to generate smaller and easier-to-interpret classification rules. In this application, we compared four modeling strategies from the combinations of two boosting iterations (log or exponential loss functions) coupled with two choices of tree generation types (a full alternating decision tree or a classic boosting decision tree). These four different strategies were applied to the founders in each population to construct four classifiers, which were then applied to each study participant. To compute average prediction score for each subject with a specific trait profile, such a process was repeated with 10 runs of 10-fold cross validation, and standardized prediction scores obtained from the 10 runs were averaged and used in subsequent expectation-maximization Haseman-Elston QTL analyses (implemented in GENEHUNTER) with the approximate 900 SNPs in Hardy-Weinberg equilibrium provided for each population. Our QTL analyses on the basis of four models (a full alternating decision tree and a classic boosting decision tree paired with either log or exponential loss function) detected evidence for linkage (Z >or= 1.96, p < 0.01) on chromosomes 1, 3, 5, and 9. Moreover, using average iteration and abundance scores for the 12 phenotypes and sex as their relevancy measurements, we found all relevant phenotypes for all four populations except phenotype b for the Karangar population, with suggested subgroup structure consistent with latent traits used in the model. In conclusion, our findings suggest that the ADTrees method may offer a more accurate representation of the disease status that allows for better detection of linkage evidence.

Algorithms↗

PubMed2005

Linkage and association analyses of microsatellites and single-nucleotide polymorphisms in nuclear families.

Several simulation studies have suggested that a high-density single-nucleotide polymorphisms (SNPs) marker set may be as useful as a traditional microsatellites (MS) marker set in performing whole-genome linkage analysis. However, very few studies have directly tested the SNPs-based genome-wide scan. In the present study, we compared the linkage results from the SNPs-based scan with a map density of 3-cM spacing with those from the MS scan using a 10-cM marker set among 300 nuclear families each from the Aipotu (AI), Danacaa (DA), and Karangar (KA) populations from the simulated Genetic Analysis Workshop 14 Problem 2 data. We found that information contents obtained from the SNPs scan were somewhat lower than those from the MS scan. However, the linkage results obtained from the two scans showed a high degree of similarity. Both scans identified a similar number of chromosomal regions attaining nominal significance (p < 0.05). Specifically, both scans detected confirmed evidence for linkage (NPL >or= 4.07, p = 2 x 10(-5)) to chromosome 1 in the AI families, chromosomes 1 and 3 in the DA families, and chromosomes 3, 5, and 9 in the KA families. An additional confirmed linkage to chromosome 5 in the AI families was detected only by the MS scan. We also observed slightly wider 1-LOD intervals for more of the SNP peaks than for the MS peaks, which is likely due to lower information contents for the SNPs. Subsequent fine-mapping association analysis further identified 2 to 3 markers significantly associated with disease status in each population; B03T3056, B03T3058, and B05T4139 in the AI population, B03T3056 and B03T3058 in the KA population, and B03T3056, B03T3057, and B03T3058 in the DA population. Among the four markers, three were chosen based on results obtained from the two scans, but one was solely from the SNP scan. In summary, our finding suggests that the SNP-based genome scan has the potential to be as powerful as the traditional MS-based scan and offers good identification of peak location for further fine-mapped association analysis.

Alleles↗

PubMed2005

The effect of ash and filter media characteristics on particle filtration efficiency in fluidized bed.

The phenomenon of filtering particles by a fluidized bed is complex and the parameters that affect the control efficiency of filtration have not yet been clarified. The major objective of the study focuses on the effect of characteristics of ash and filter media on filtration efficiency in a fluidized bed. The performance of the fluidized bed for removal of particles in flue gas at various fluidized operating conditions, and then the mechanisms of collecting particles were studied. The evaluated parameters included (1) various ashes (coal ash and incinerator ash); (2) bed material size; (3) operating gas velocity; and (4) bed temperature. The results indicate that the removal efficiency of coal ash increases initially with gas velocity, then decreases gradually as velocity exceeds some specific value. Furthermore, the removal of coal ash enhance with silica sand size decreasing. When the fluidized bed is operated at high temperature, diffusion is a more important mechanism than at room temperature especially for small particles. Although the inertial impaction is the main collection mechanism, the "bounce off" effect when the particles collide with the bed material could reduce the removal efficiency significantly. Because of layer inversion in fluidized bed, the removal efficiency of incinerator ash is decreased with increasing of gas velocity.

Carbon↗

PubMed2005

Dynamic purification of coal ash by a gas-solid fluidized bed.

Fluidized bed filtration classified as granular bed filtration is a solution for removing particles from flue gas. Fine particles are captured by colliding with bed material. Binary mixtures including fines exist in the fluidized bed; the situation becomes more complex because the fines increases with time as particles enter continuously. Coal ash was put into the inlet gas of a gas-solid fluidized bed with silica sand and the variations in concentration with time was monitored, to elucidate the dynamic purification of fines by bed material and the interaction therewith. Water was injected into the inlet gas also to study the influence of water content on the removal of particles. Experimental results showed that the particle concentration rapidly increased with time until equilibrium was achieved. The outlet concentrations exceeded the inlet concentration (such that the removal efficiency was negative) at operating time between 26 and 30 min, yielding an efficiency as low as -35%. The removal efficiency increased with the relative humidity from 30% to 70%. The maximum cumulative collection ratios of particles (by mass) were 20%, 22% and 27% at humidities of 30%, 50% and 70%, declining to 7%, 13%, and 19% at the end.

Adsorption↗

PubMed2005

Data mining.

Group 14 used data-mining strategies to evaluate a number of issues, including appropriate diagnosis, haplotype estimation, genetic linkage and association studies, and type I error. Methods ranged from exploratory analyses, to machine learning strategies (neural networks, supervised learning, and tree-based methods), to false discovery rate control of type I errors. The general motivations were to find the "story" in the data and to summarize information from a multitude of measures. Several methods illustrated strategies for better trait definition, using summarization of related traits. In the few studies that sought to identify genes for alcoholism, there was little agreement among the different strategies, likely reflecting the complexities of the disease. Nevertheless, Group 14 found that these methods offered strategies to gain a better understanding of the complex pathways by which disease develops.

Alcoholism↗

PubMed2005

Filtration of fly ash using a fluidized-bed filter.

This study focuses on the control of particulates with a fluidized-bed filter in exhaust gas stream. The fluidized-bed filter classified in the granular bed filtration technology was employed to demonstrate the performance for removal of fly ash at indicated operating velocities, fixed bed heights, and bed temperatures; then the collecting mechanisms of particulates by fluidized-bed filter were studied. The size distribution of fly ash passing through the fluidized-bed filter was also analyzed. The results indicate that at higher operating velocities and fixed bed heights, the removal of fly ash is more efficient and inertial impaction is the main mechanism when the fluidized-bed is operated at room temperature (25 degrees C). While operating at higher temperatures (200 degrees C), efficiency of 93.2% to 99.4% can be achieved for submicron particles. It is supposed to be the diffusion mechanism that is responsible for collecting such small particles, and high temperature is a favorable condition because of diffusion.

Air Pollution↗

PubMed2004

Cancer classification and prediction using logistic regression with Bayesian gene selection.

In microarray-based cancer classification and prediction, gene selection is an important research problem owing to the large number of genes and the small number of experimental conditions. In this paper, we propose a Bayesian approach to gene selection and classification using the logistic regression model. The basic idea of our approach is in conjunction with a logistic regression model to relate the gene expression with the class labels. We use Gibbs sampling and Markov chain Monte Carlo (MCMC) methods to discover important genes. To implement Gibbs Sampler and MCMC search, we derive a posterior distribution of selected genes given the observed data. After the important genes are identified, the same logistic regression model is then used for cancer classification and prediction. Issues for efficient implementation for the proposed method are discussed. The proposed method is evaluated against several large microarray data sets, including hereditary breast cancer, small round blue-cell tumors, and acute leukemia. The results show that the method can effectively identify important genes consistent with the known biological findings while the accuracy of the classification is also high. Finally, the robustness and sensitivity properties of the proposed method are also investigated.

Algorithms↗

PubMed2002

Replication of linkage of familial hypobetalipoproteinemia to chromosome 3p in six kindreds.

Familial hypobetalipoproteinemia (FHBL) is a genetically heterogeneous condition characterized by very low apolipoprotein B (apoB) concentrations in plasma and/or low levels of LDL-cholesterol (LDL-C) with a propensity to developing fatty liver. In a minority of cases, truncation-specifying mutations of the apoB gene (APOB) are etiologic, but the genetic basis of most cases is unknown. We previously reported linkage of FHBL to a 10 cM region on 3p21.1-22 in one kindred. The objectives of the current study were to identify other FHBL families with linkage to 3p and to narrow the FHBL susceptibility region on 3p. Six additional FHBL kindreds unlinked to the APOB region on chromosome 2 were genotyped with polymorphic markers spanning a region of approximately 13 cM on chromosome 3. Quantitative linkage analyses indicated that the FHBL in these families was linked to 3p21.1-22. Haplotype analysis identified several meiotic crossover events, allowing us to narrow the critical region from 10 cM to 2.0 cM, between markers D3S2407 and D3S1767.

Adult↗