Outlier detection using difference-based variance estimators in multiple regression

<p>In this article, we propose an outlier detection approach in a multiple regression model using the properties of a difference-based variance estimator. This type of a difference-based variance estimator was originally used to estimate error variance in a non parametric regression model without estimating a non parametric function. This article first employed a difference-based error variance estimator to study the outlier detection problem in a multiple regression model. Our approach uses the leave-one-out type method based on difference-based error variance. The existing outlier detection approaches using the leave-one-out approach are highly affected by other outliers, while ours is not because our approach does not use the regression coefficient estimator. We compared our approach with several existing methods using a simulation study, suggesting the outperformance of our approach. The advantages of our approach are demonstrated using a real data application. Our approach can be extended to the non parametric regression model for outlier detection.</p

Semiparametric Bayesian hierarchical models for heterogeneous population in nonlinear mixed effect model: application to gastric emptying studies

<div><p>Gastric emptying studies are frequently used in medical research, both human and animal, when evaluating the effectiveness and determining the unintended side-effects of new and existing medications, diets, and procedures or interventions. It is essential that gastric emptying data be appropriately summarized before making comparisons between study groups of interest and to allow study the comparisons. Since gastric emptying data have a nonlinear emptying curve and are longitudinal data, nonlinear mixed effect (NLME) models can accommodate both the variation among measurements within individuals and the individual-to-individual variation. However, the NLME model requires strong assumptions that are often not satisfied in real applications that involve a relatively small number of subjects, have heterogeneous measurement errors, or have large variation among subjects. Therefore, we propose three semiparametric Bayesian NLMEs constructed with Dirichlet process priors, which automatically cluster sub-populations and estimate heterogeneous measurement errors. To compare three semiparametric models with the parametric model we propose a penalized posterior Bayes factor. We compare the performance of our semiparametric hierarchical Bayesian approaches with that of the parametric Bayesian hierarchical approach. Simulation results suggest that our semiparametric approaches are more robust and flexible. Our gastric emptying studies from equine medicine are used to demonstrate the advantage of our approaches.</p></div

Colloidal Wurtzite Cu₂SnS₃ (CTS) Nanocrystals and Their Applications in Solar Cells

In the development of low-cost, efficient, and environmentally friendly thin-film solar cells (TFSCs), the search continues for a suitable inorganic colloidal nanocrystal (NC) ink that can be easily used in scalable coating/printing processes. In this work, we first report on the colloidal synthesis of pure wurtzite (WZ) Cu₂SnS₃ (CTS) NCs using a polyol-mediated hot injection route, which is a nontoxic synthesis method. The synthesized material exhibits a random distribution of CTS nanoflakes with an average lateral dimension of ∼94 ± 15 nm. We also demonstrate that CTS NC ink can be used to fabricate low-cost and environmentally friendly TFSCs through an ethanol-based ink process. The annealing of as-deposited CTS films was performed under different S vapor pressures in a graphite box (volume; 12.3 cm³), at 580 °C for 10 min using a rapid thermal annealing (RTA) process. A comparative study on the performances of the solar cells with CTS absorber layers annealed under different S vapor pressures was conducted. The device derived from the CTS absorber annealed at 350 Torr of S vapor pressure showed the best conversion efficiency 2.77%, which is the first notable efficiency for an CTS NCs ink-based TFSC. In addition, CTS TFSC’s performance degraded only slightly after 50 days in air atmosphere and under damp heating at 90 °C for 50 h, indicating their good stability. These results confirm that WZ CTS NCs may be very attractive and interesting light-absorbing materials for fabricating efficient solar-harvesting devices

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification

A high-throughput multiconstriction microfluidic channels device can distinguish human breast cancer cell lines (MDA-MB-231, HCC-1806, MCF-7) from immortalized breast cells (MCF-10A) with a confidence level of ∼81–85% at a rate of 50–70 cells/min based on velocity increment differences through multiconstriction channels aligned in series. The results are likely related to the deformability differences between nonmalignant and malignant breast cells. The data were analyzed by the methods/algorithms of Ridge, nonnegative garrote on kernel machine (NGK), and Lasso using high-dimensional variables, including the cell sizes, velocities, and velocity increments. In kernel learning based methods, the prediction values of 10-fold cross-validations are used to represent the difference between two groups of data, where a value of 100% indicates the two groups are completely distinct and identifiable. The prediction value is used to represent the difference between two groups using the established algorithm classifier from high-dimensional variables. These methods were applied to heterogeneous cell populations prepared using primary tumor and adjacent normal tissue obtained from two patients. Primary breast cancer cells were distinguished from patient-matched adjacent normal cells with a prediction ratio of 70.07%–75.96% by the NGK method. Thus, this high-throughput multiconstriction microfluidic device together with the kernel learning method can be used to perturb and analyze the biomechanical status of cells obtained from small primary tumor biopsy samples. The resultant biomechanical velocity signatures identify malignancy and provide a new marker for evaluation in risk assessment

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification

A high-throughput multiconstriction microfluidic channels device can distinguish human breast cancer cell lines (MDA-MB-231, HCC-1806, MCF-7) from immortalized breast cells (MCF-10A) with a confidence level of ∼81–85% at a rate of 50–70 cells/min based on velocity increment differences through multiconstriction channels aligned in series. The results are likely related to the deformability differences between nonmalignant and malignant breast cells. The data were analyzed by the methods/algorithms of Ridge, nonnegative garrote on kernel machine (NGK), and Lasso using high-dimensional variables, including the cell sizes, velocities, and velocity increments. In kernel learning based methods, the prediction values of 10-fold cross-validations are used to represent the difference between two groups of data, where a value of 100% indicates the two groups are completely distinct and identifiable. The prediction value is used to represent the difference between two groups using the established algorithm classifier from high-dimensional variables. These methods were applied to heterogeneous cell populations prepared using primary tumor and adjacent normal tissue obtained from two patients. Primary breast cancer cells were distinguished from patient-matched adjacent normal cells with a prediction ratio of 70.07%–75.96% by the NGK method. Thus, this high-throughput multiconstriction microfluidic device together with the kernel learning method can be used to perturb and analyze the biomechanical status of cells obtained from small primary tumor biopsy samples. The resultant biomechanical velocity signatures identify malignancy and provide a new marker for evaluation in risk assessment