Kernel based methods for accelerated failure time model with ultra-high dimensional data

<p>Abstract</p> <p>Background</p> <p>Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with <it>L</it>₁and <it>L_p</it>penalty have been extensively studied in survival analysis with high-dimensional genomic data. However, when the sample size <it>n </it>≪ <it>m </it>(the number of genes), directly identifying a small subset of genes from ultra-high (<it>m </it>> 10, 000) dimensional data is time-consuming and not computationally efficient. In current microarray analysis, what people really do is select a couple of thousands (or hundreds) of genes using univariate analysis or statistical tests, and then apply the LASSO-type penalty to further reduce the number of disease associated genes. This two-step procedure may introduce bias and inaccuracy and lead us to miss biologically important genes.</p> <p>Results</p> <p>The accelerated failure time (AFT) model is a linear regression model and a useful alternative to the Cox model for survival analysis. In this paper, we propose a nonlinear kernel based AFT model and an efficient variable selection method with adaptive kernel ridge regression. Our proposed variable selection method is based on the kernel matrix and dual problem with a much smaller <it>n </it>× <it>n </it>matrix. It is very efficient when the number of unknown variables (genes) is much larger than the number of samples. Moreover, the primal variables are explicitly updated and the sparsity in the solution is exploited.</p> <p>Conclusions</p> <p>Our proposed methods can simultaneously identify survival associated prognostic factors and predict survival outcomes with ultra-high dimensional genomic data. We have demonstrated the performance of our methods with both simulation and real data. The proposed method performs superbly with limited computational studies.</p

Strength of Hydrogen Bond Network Takes Crucial Roles in the Dissociation Process of Inhibitors from the HIV-1 Protease Binding Pocket

To understand the underlying mechanisms of significant differences in dissociation rate constant among different inhibitors for HIV-1 protease, we performed steered molecular dynamics (SMD) simulations to analyze the entire dissociation processes of inhibitors from the binding pocket of protease at atomistic details. We found that the strength of hydrogen bond network between inhibitor and the protease takes crucial roles in the dissociation process. We showed that the hydrogen bond network in the cyclic urea inhibitors AHA001/XK263 is less stable than that of the approved inhibitor ABT538 because of their large differences in the structures of the networks. In the cyclic urea inhibitor bound complex, the hydrogen bonds often distribute at the flap tips and the active site. In contrast, there are additional accessorial hydrogen bonds formed at the lateral sides of the flaps and the active site in the ABT538 bound complex, which take crucial roles in stabilizing the hydrogen bond network. In addition, the water molecule W301 also plays important roles in stabilizing the hydrogen bond network through its flexible movement by acting as a collision buffer and helping the rebinding of hydrogen bonds at the flap tips. Because of its high stability, the hydrogen bond network of ABT538 complex can work together with the hydrophobic clusters to resist the dissociation, resulting in much lower dissociation rate constant than those of cyclic urea inhibitor complexes. This study may provide useful guidelines for design of novel potent inhibitors with optimized interactions

Improving business incubator service performance in China: The role of networking resources and capabilities

This research analyses the relationships between resources and capabilities in Chinese business incubators to determine the relative importance in enhancing the service performance of incubators. A mixed-method design is used consisting of an in-depth case study and structural equation modelling based on survey data. Incubator managers are advised to invest in infrastructural and external resources and networking capabilities, which are positively correlated with performance. We find that resources relating to government policy, such as funding, may have a negative impact on incubator performance while other integrated service capabilities have little correlation with improved performance

A Compliant Ionic Adhesive Electrode with Ultralow Bioelectronic Impedance

Simultaneous implementation of high signal-to-noise ratio (SNR) but low crosstalk is of great importance for weak surface electromyography (sEMG) signals when precisely driving a prosthesis to perform sophisticated activities. However, due to gaps with the curved skin during muscle contraction, many electrodes have poor compliance with skin and suffer from high bioelectrical impedance. This causes serious noise and error in the signals, especially the signals from low-level muscle contractions. Here, the design of a compliant electrode based on an adhesive hydrogel, alginate-polyacrylamide (Alg-PAAm) is reported, which eliminates those large gaps through the strong electrostatic interaction and abundant hydrogen bond with the skin. The obtained compliant electrode, having an ultralow bioelectrical impedance of ≈20 kΩ, can monitor even 2.1% maximal voluntary contraction (MVC) of muscle. Furthermore, benefiting from the high SNR of >5:1 at low-level MVC, the crosstalk from irrelevant muscle is minimized through reducing the electrode size. Finally, a prosthesis is successfully demonstrated to precisely grasp a needle based on a 9 mm2 Alg-PAAm compliant electrode. The strategy to design such compliant electrodes provides the potential for improving the quality of dynamically weak sEMG signals to precisely control prosthesis in performing purposefully dexterous activity.Accepted versio