RESUMO
Herein, a study on a new lower critical solution temperature (LCST) polymer in an organic solvent by an electrochemical technique has been reported. The phase-transition behavior of poly(arylene ether sulfone) (PAES) was examined on 1,2-dimethoxyethane (DME). At a temperature above the LCST point, polymer molecules aggregated to create polymer droplets. These droplets subsequently collided with an ultramicroelectrode (UME), resulting in a new form of staircase current decrease. The experimental collision frequency and collision signal were analyzed in relation to the concentration of the polymer. In addition, the degree of polymer aggregation associated with temperature change was also observed.
RESUMO
The key component currently missing for the next generation of transparent and flexible displays is a high-performance polymer material that is flexible, while showing optical and thermal properties of glass. It must be transparent to visible light and show a low coefficient of thermal expansion (CTE). While specialty plastics such as aromatic polyimides are promising, reducing their CTE and improving transparency simultaneously proved challenging, with increasing coloration the main problem to be resolved. We report a new poly(amide-imide) material that is flexible and displays glass-like behavior with a CTE value of 4 parts per million/°C. This novel polymer was successfully used as a substrate to fabricate transparent and flexible indium-gallium-zinc oxide thin-film transistors.
RESUMO
Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author's webpage.