Energy Storage Performance of Polymer-Based Dielectric Composites with Two-Dimensional Fillers.

Dielectric capacitors have garnered significant attention in recent decades for their wide range of uses in contemporary electronic and electrical power systems. The integration of a high breakdown field polymer matrix with various types of fillers in dielectric polymer nanocomposites has attracted significant attention from both academic and commercial sectors. The energy storage performance is influenced by various essential factors, such as the choice of the polymer matrix, the filler type, the filler morphologies, the interfacial engineering, and the composite structure.

Recent Advances on Conducting Polymers Based Nanogenerators for Energy Harvesting.

With the rapid growth of numerous portable electronics, it is critical to develop high-performance, lightweight, and environmentally sustainable energy generation and power supply systems. The flexible nanogenerators, including piezoelectric nanogenerators (PENG) and triboelectric nanogenerators (TENG), are currently viable candidates for combination with personal devices and wireless sensors to achieve sustained energy for long-term working circumstances due to their great mechanical qualities, superior environmental adaptability, and outstanding energy-harvesting performance. Conductive materials for electrode as the critical component in nanogenerators, have been intensively investigated to optimize their performance and avoid high-cost and time-consuming manufacture processing.

Alzheimer's Disease Affects Severity of Asthma Through Methylation Control of Foxp3 Promoter.

Recent studies suggest that severity of asthma can be modulated by neuropsychiatric conditions, while the underlying mechanisms are not clear. Here, we used ovalbumin (OVA) to induce asthma in APP/PS1 mice, a mouse model of Alzheimer's disease (AD), or in their wildtype control C57BL/6J mice. We found that all hallmarks of asthma by OVA were significantly attenuated in APP/PS1 mice, compared to age- and gender-matched C57BL/6J mice.

State of the art prediction of HIV-1 protease cleavage sites.

Motivation: Understanding the substrate specificity of human immunodeficiency virus (HIV)-1 protease is important when designing effective HIV-1 protease inhibitors. Furthermore, characterizing and predicting the cleavage profile of HIV-1 protease is essential to generate and test hypotheses of how HIV-1 affects proteins of the human host. Currently available tools for predicting cleavage by HIV-1 protease can be improved.

Using Gaussian process with test rejection to detect T-cell epitopes in pathogen genomes.

A major challenge in the development of peptide-based vaccines is finding the right immunogenic element, with efficient and long-lasting immunization effects, from large potential targets encoded by pathogen genomes. Computer models are convenient tools for scanning pathogen genomes to preselect candidate immunogenic peptides for experimental validation. Current methods predict many false positives resulting from a low prevalence of true positives.

How to find simple and accurate rules for viral protease cleavage specificities.

Background: Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate.

Bioinformatic approaches for modeling the substrate specificity of HIV-1 protease: an overview.

HIV-1 protease has a broad and complex substrate specificity, which hitherto has escaped a simple comprehensive definition. This, and the relatively high mutation rate of the retroviral protease, makes it challenging to design effective protease inhibitors. Several attempts have been made during the last two decades to elucidate the enigmatic cleavage specificity of HIV-1 protease and to predict cleavage of novel substrates using bioinformatic analysis methods.

Detection of cleavage sites for HIV-1 protease in native proteins.

Predicting novel cleavage sites for HIV-1 protease in non-viral proteins is a difficult task because of the scarcity of previous cleavage data on proteins in a native state. We introduce a three-level hierarchical classifier which combines information from experimentally verified short oligopeptides, secondary structure and solvent accessibility information from prediction servers to predict potential cleavage sites in non-viral proteins. The best classifier using secondary structure information on the second level classification of the hierarchical classifier is the one using logistic regression.

Comprehensive bioinformatic analysis of the specificity of human immunodeficiency virus type 1 protease.

Rapidly developing viral resistance to licensed human immunodeficiency virus type 1 (HIV-1) protease inhibitors is an increasing problem in the treatment of HIV-infected individuals and AIDS patients. A rational design of more effective protease inhibitors and discovery of potential biological substrates for the HIV-1 protease require accurate models for protease cleavage specificity. In this study, several popular bioinformatic machine learning methods, including support vector machines and artificial neural networks, were used to analyze the specificity of the HIV-1 protease.

Why neural networks should not be used for HIV-1 protease cleavage site prediction.

Unlabelled: Several papers have been published where nonlinear machine learning algorithms, e.g. artificial neural networks, support vector machines and decision trees, have been used to model the specificity of the HIV-1 protease and extract specificity rules.