Protein-Polyelectrolyte Complexes and Micellar Assemblies.

In this review, we highlight the recent progress in our understanding of the structure, properties and applications of protein-polyelectrolyte complexes in both bulk and micellar assemblies. Protein-polyelectrolyte complexes form the basis of the genetic code, enable facile protein purification, and have emerged as enterprising candidates for simulating protocellular environments and as efficient enzymatic bioreactors. Such complexes undergo self-assembly in bulk due to a combined influence of electrostatic interactions and entropy gains from counterion release. Diversifying the self-assembly by incorporation of block polyelectrolytes has further enabled fabrication of protein-polyelectrolyte complex micelles that are multifunctional carriers for therapeutic targeted delivery of proteins such as enzymes and antibodies. We discuss research efforts focused on the structure, properties and applications of protein-polyelectrolyte complexes in both bulk and micellar assemblies, along with the influences of amphoteric nature of proteins accompanying patchy distribution of charges leading to unique phenomena including multiple complexation windows and complexation on the wrong side of the isoelectric point

A high-throughput WSN for structural health monitoring

A challenge with existing WSNs used for structural health monitoring (SHM) is how to increase the data transmission rate (DTR) for large amounts of sampling data. To handle this issue, this paper proposes a new design method of a high-throughput WSN with multi-radio sink node (M-RSN) which can increase the data transfer ability of WSN. Additionally, a tight scheduled approach and multi-radio time synchronization method are designed for the stable implementation of the proposed network. A high data throughput of 1020 Kbps of the developed network has been proved. To evaluate the effectiveness and robustness of the proposed network designing method, experiments for aircraft composite wing boxes monitoring are carried out. The evaluation results have shown the advantages of the proposed methods

Multivariate Functional Time Series Forecasting: Application to Age-Specific Mortality Rates

This study considers the forecasting of mortality rates in multiple populations. We propose a model that combines mortality forecasting and functional data analysis (FDA). Under the FDA framework, the mortality curve of each year is assumed to be a smooth function of age. As with most of the functional time series forecasting models, we rely on functional principal component analysis (FPCA) for dimension reduction and further choose a vector error correction model (VECM) to jointly forecast mortality rates in multiple populations. This model incorporates the merits of existing models in that it excludes some of the inherent randomness with the nonparametric smoothing from FDA, and also utilizes the correlation structures between the populations with the use of VECM in mortality models. A nonparametric bootstrap method is also introduced to construct interval forecasts. The usefulness of this model is demonstrated through a series of simulation studies and applications to the age-and sex-specific mortality rates in Switzerland and the Czech Republic. The point forecast errors of several forecasting methods are compared and interval scores are used to evaluate and compare the interval forecasts. Our model provides improved forecast accuracy in most cases