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.

High mobility N-channel and P-channel nanocrystalline silicon thin-film transistors

We report high hole and electron mobilities in nanocrystalline silicon (nc-Si:H) top-gate staggered thin-film transistors (TFTs) fabricated by direct plasma-enhanced chemical vapor deposition (PECVD) at 260°C. The n-channel nc-Si:H TFT with n+ nc-Si:H ohmic contacts shows a field-effect electron mobility (μnFE) of 130 cm2/Vs, which increases to 150 cm2/Vs with Cr-silicide contacts, along with a field-effect hole mobility (μhFE) of 25 cm2/Vs. To the best of our knowledge, the hole and electron mobilities reported here are the highest achieved to date using direct PECVD. © 2005 IEEE.

Electron spin resonance signature of the oxygen vacancy in HfO 2

The oxygen vacancy has been inferred to be the critical defect in HfO 2, responsible for charge trapping, gate threshold voltage instability, and Fermi level pinning for high work function gates, but it has never been conclusively identified. Here, the electron spin resonance g tensor parameters of the oxygen vacancy are calculated, using methods that do not over-estimate the delocalization of the defect wave function, to be g xx = 1.918, g yy = 1.926, g zz = 1.944, and are consistent with an observed spectrum. The defect undergoes a symmetry lowering polaron distortion to be localized mainly on a single adjacent Hf ion. © 2012 American Institute of Physics.

Comparison among MCNP-based depletion codes applied to burnup calculations of pebble-bed HTR lattices

The double-heterogeneity characterising pebble-bed high temperature reactors (HTRs) makes Monte Carlo based calculation tools the most suitable for detailed core analyses. These codes can be successfully used to predict the isotopic evolution during irradiation of the fuel of this kind of cores. At the moment, there are many computational systems based on MCNP that are available for performing depletion calculation. All these systems use MCNP to supply problem dependent fluxes and/or microscopic cross sections to the depletion module. This latter then calculates the isotopic evolution of the fuel resolving Bateman's equations. In this paper, a comparative analysis of three different MCNP-based depletion codes is performed: Montburns2.0, MCNPX2.6.0 and BGCore. Monteburns code can be considered as the reference code for HTR calculations, since it has been already verified during HTR-N and HTR-N1 EU project. All calculations have been performed on a reference model representing an infinite lattice of thorium-plutonium fuelled pebbles. The evolution of k-inf as a function of burnup has been compared, as well as the inventory of the important actinides. The k-inf comparison among the codes shows a good agreement during the entire burnup history with the maximum difference lower than 1%. The actinide inventory prediction agrees well. However significant discrepancy in Am and Cm concentrations calculated by MCNPX as compared to those of Monteburns and BGCore has been observed. This is mainly due to different Am-241 (n,γ) branching ratio utilized by the codes. The important advantage of BGCore is its significantly lower execution time required to perform considered depletion calculations. While providing reasonably accurate results BGCore runs depletion problem about two times faster than Monteburns and two to five times faster than MCNPX. © 2009 Elsevier B.V. All rights reserved.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.