Experimental and numerical studies on pattern formation in electrochemical deposition

An experimental and theoretical investigation on pattern formation in electrochemical deposition from copper and zinc binary sulfate electrolyte in two dimensional cells was carried out in this study. Aggregates were produced by electrochemical deposition. An interferometric setup was developed to measure the concentration boundary layer around the aggregates produced during growth. Convection is observed during electrodeposition from binary sulfate solution of zinc and copper with the concentration of 0.04M and higher in horizontal cells. The theoretical and experimental investigation indicates that natural convection is much stronger in a horizontal cell than that in a vertical cell. Channel growth is observed in our experiment for both zinc and copper deposition from binary sulfate solution. Zinc channel growth is produced under conditions where natural convection is suppressed by deposition in a vertical configuration; however, zinc dendrites are observed in a horizontal cell under the same experimental conditions. In contrast to zinc deposition, channel growth from copper deposition is produced in either the horizontal or vertical configuration. Therefore, the role of natural convection is of primary importance in morphological selection for deposition from ZnSO\sb4. However, no effect of natural convection is found on pattern selection for deposition from CuSO\sb4. Electrokinetic streaming was identified as a morphology determining process. The preliminary theoretical results show that the electric force acting at the double layer close to the tip of copper aggregates is much larger than is the case with zinc aggregates. The dependence of morphology selection for zinc and copper deposition on the vertical or horizontal configuration of cells is due to the interaction of natural convection and electrokinetic effects. Theoretical models of velocity selection developed in solidification were translated to the systems of electrochemical deposition. However, numerical simulations based on this theory for both diffusion controlled and the ohmic controlled growth are not consistent with the experimental results. An adiabatic cell model was developed to quantify the effect of ohmic heating in the deposition experiments. The predicted temperature increases during electrodeposition in two dimensional cells are much higher than the experimental values, indicating that the cells efficiently shed the heat generated by the passage of current. Analysis based on this adiabatic model shows that the errors for the interferometric concentration measurement due to temperature rises are negligible for electrochemical deposition in two dimensional cells at moderate applied potential

Intelligent Data Mining using Kernel Functions and Information Criteria

Radial Basis Function (RBF) Neural Networks and Support Vector Machines (SVM) are two powerful kernel related intelligent data mining techniques. The current major problems with these methods are over-fitting and the existence of too many free parameters. The way to select the parameters can directly affect the generalization performance(test error) of theses models. Current practice in how to choose the model parameters is an art, rather than a science in this research area. Often, some parameters are predetermined, or randomly chosen. Other parameters are selected through repeated experiments that are time consuming, costly, and computationally very intensive. In this dissertation, we provide a two-stage analytical hybrid-training algorithm by building a bridge among regression tree, EM algorithm, and Radial Basis Function Neural Networks together. Information Complexity (ICOMP) criterion of Bozdogan along with other information based criteria are introduced and applied to control the model complexity, and to decide the optimal number of kernel functions. In the first stage of the hybrid, regression tree and EM algorithm are used to determine the kernel function parameters. In the second stage of the hybrid, the weights (coefficients) are calculated and information criteria are scored. Kernel Principal Component Analysis (KPCA) using EM algorithm for feature selection and data preprocessing is also introduced and studied. Adaptive Support Vector Machines (ASVM) and some efficient algorithms are given to deal with massive data sets in support vector classifications. Versatility and efficiency of the new proposed approaches are studied on real data sets and via Monte Carlo sim- ulation experiments

Scalable Sparse Cox's Regression for Large-Scale Survival Data via Broken Adaptive Ridge

This paper develops a new scalable sparse Cox regression tool for sparse high-dimensional massive sample size (sHDMSS) survival data. The method is a local $L_0$-penalized Cox regression via repeatedly performing reweighted $L_2$-penalized Cox regression. We show that the resulting estimator enjoys the best of $L_0$- and $L_2$-penalized Cox regressions while overcoming their limitations. Specifically, the estimator is selection consistent, oracle for parameter estimation, and possesses a grouping property for highly correlated covariates. Simulation results suggest that when the sample size is large, the proposed method with pre-specified tuning parameters has a comparable or better performance than some popular penalized regression methods. More importantly, because the method naturally enables adaptation of efficient algorithms for massive $L_2$-penalized optimization and does not require costly data driven tuning parameter selection, it has a significant computational advantage for sHDMSS data, offering an average of 5-fold speedup over its closest competitor in empirical studies

Constructing Tumor Progression Pathways and Biomarker Discovery with Fuzzy Kernel Kmeans and DNA Methylation Data

Constructing pathways of tumor progression and discovering the biomarkers associated with cancer is critical for understanding the molecular basis of the disease and for the establishment of novel chemotherapeutic approaches and in turn improving the clinical efficiency of the drugs. It has recently received a lot of attention from bioinformatics researchers. However, relatively few methods are available for constructing pathways. This article develops a novel entropy kernel based kernel clustering and fuzzy kernel clustering algorithms to construct the tumor progression pathways using CpG island methylation data. The methylation data which come from tumor tissues diagnosed at different stages can be used to distinguish epigenotype and phenotypes the describe the molecular events of different phases. Using kernel and fuzzy kernel kmeans, we built tumor progression trees to describe the pathways of tumor progression and find the possible biomarkers associated with cancer. Our results indicate that the proposed algorithms together with methylation profiles can predict the tumor progression stages and discover the biomarkers efficiently. Software is available upon request

Regularized F-Measure Maximization for Feature Selection and Classification

Receiver Operating Characteristic (ROC) analysis is a common tool for assessing the performance of various classifications. It gained much popularity in medical and other fields including biological markers and, diagnostic test. This is particularly due to the fact that in real-world problems misclassification costs are not known, and thus, ROC curve and related utility functions such as F-measure can be more meaningful performance measures. F-measure combines recall and precision into a global measure. In this paper, we propose a novel method through regularized F-measure maximization. The proposed method assigns different costs to positive and negative samples and does simultaneous feature selection and prediction with L1 penalty. This method is useful especially when data set is highly unbalanced, or the labels for negative (positive) samples are missing. Our experiments with the benchmark, methylation, and high dimensional microarray data show that the performance of proposed algorithm is better or equivalent compared with the other popular classifiers in limited experiments

Selecting Genes by Test Statistics

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets

Antagonizing CD105 enhances radiation sensitivity in prostate cancer.

Radiation therapy is the primary intervention for nearly half of the patients with localized advanced prostate cancer and standard of care for recurrent disease following surgery. The development of radiation-resistant disease is an obstacle for nearly 30-50% of patients undergoing radiotherapy. A better understanding of mechanisms that lead to radiation resistance could aid in the development of sensitizing agents to improve outcome. Here we identified a radiation-resistance pathway mediated by CD105, downstream of BMP and TGF-β signaling. Antagonizing CD105-dependent BMP signaling with a partially humanized monoclonal antibody, TRC105, resulted in a significant reduction in clonogenicity when combined with irradiation. In trying to better understand the mechanism for the radio-sensitization, we found that radiation-induced CD105/BMP signaling was sufficient and necessary for the upregulation of sirtuin 1 (SIRT1) in contributing to p53 stabilization and PGC-1α activation. Combining TRC105 with irradiation delayed DNA damage repair compared to irradiation alone. However, in the absence of p53 function, combining TRC105 and radiation resulted in no reduction in clonogenicity compared to radiation alone, despite similar reduction of DNA damage repair observed in p53-intact cells. This suggested DNA damage repair was not the sole determinant of CD105 radio-resistance. As cancer cells undergo an energy deficit following irradiation, due to the demands of DNA and organelle repair, we examined SIRT1's role on p53 and PGC-1α with respect to glycolysis and mitochondrial biogenesis, respectively. Consequently, blocking the CD105-SIRT1 axis was found to deplete the ATP stores of irradiated cells and cause G2 cell cycle arrest. Xenograft models supported these findings that combining TRC105 with irradiation significantly reduces tumor size over irradiation alone (p value = 10-9). We identified a novel synthetic lethality strategy of combining radiation and CD105 targeting to address the DNA repair and metabolic addiction induced by irradiation in p53-functional prostate cancers