Er3+-doped glass-polymer composite thin films fabricated using combinatorial pulsed laser deposition

Siloxane Polymer exhibits low loss in the 800-1500 nm range which varies between 0.01 and 0.66 dB cm1. It is for such low loss the material is one of the most promising candidates in the application of engineering passive and active optical devices [1, 2]. However, current polymer fabrication techniques do not provide a methodology which allows high structurally solubility of Er3+ ions in siloxane matrix. To address this problem, Yang et al.[3] demonstrated a channel waveguide amplifier with Nd 3+-complex doped polymer, whilst Wong and co-workers[4] employed Yb3+ and Er3+ co-doped polymer hosts for increasing the gain. In some recent research we demonstrated pulsed laser deposition of Er-doped tellurite glass thin films on siloxane polymer coated silica substrates[5]. Here an alternative methodology for multilayer polymer-glass composite thin films using Er3+ - Yb3+ co-doped phosphate modified tellurite (PT) glass and siloxane polymer is proposed by adopting combinatorial pulsed laser deposition (PLD). © 2011 IEEE.

Low-cost PCB-integrated polymer waveguide sensor for gas detection

An optical waveguide sensor formed directly on low-cost PCB substrates is presented for the first time. The device integrates polymer waveguides functionalized with chemical dyes, photonic and electronic components and allows multiple-gas detection. © 2011 OSA.

Gene function prediction from synthetic lethality networks via ranking on demand.

MOTIVATION: Synthetic lethal interactions represent pairs of genes whose individual mutations are not lethal, while the double mutation of both genes does incur lethality. Several studies have shown a correlation between functional similarity of genes and their distances in networks based on synthetic lethal interactions. However, there is a lack of algorithms for predicting gene function from synthetic lethality interaction networks. RESULTS: In this article, we present a novel technique called kernelROD for gene function prediction from synthetic lethal interaction networks based on kernel machines. We apply our novel algorithm to Gene Ontology functional annotation prediction in yeast. Our experiments show that our method leads to improved gene function prediction compared with state-of-the-art competitors and that combining genetic and congruence networks leads to a further improvement in prediction accuracy.