Genomic mining for complex disease traits with “random chemistry”

Our rapidly growing knowledge regarding genetic variation in the human genome offers great potential for understanding the genetic etiology of disease. This, in turn, could revolutionize detection, treatment, and in some cases prevention of disease. While genes for most of the rare monogenic diseases have already been discovered, most common diseases are complex traits, resulting from multiple gene–gene and gene-environment interactions. Detecting epistatic genetic interactions that predispose for disease is an important, but computationally daunting, task currently facing bioinformaticists. Here, we propose a new evolutionary approach that attempts to hill-climb from large sets of candidate epistatic genetic features to smaller sets, inspired by Kauffman’s “random chemistry” approach to detecting small auto-catalytic sets of molecules from within large sets. Although the algorithm is conceptually straightforward, its success hinges upon the creation of a fitness function able to discriminate large sets that contain subsets of interacting genetic features from those that don’t. Here, we employ an approximate and noisy fitness function based on the ReliefF data mining algorithm. We establish proof-of-concept using synthetic data sets, where individual features have no marginal effects. We show that the resulting algorithm can successfully detect epistatic pairs from up to 1,000 candidate single nucleotide polymorphisms in time that is linear in the size of the initial set, although success rate degrades as heritability declines. Research continues into seeking a more accurate fitness approximator for large sets and other algorithmic improvements that will enable us to extend the approach to larger data sets and to lower heritabilities.     

Whatever happened to "What might have been"? Regrets, happiness, and maturity.

Although lost opportunities and mistaken expectations are unpleasant to think and talk about, these experiences may have a role to play in personality development. Drawing on research using narratives of lost possible selves, the authors review the relations of regrettable experiences to 2 important and independent aspects of maturity, happiness and complexity. Thinking about a lost possible self is related to concurrent regrets, distress, and lowered well-being; however, elaborating on a lost possible self is related, concurrently, to complexity and predicts complexity, prospectively, over time. In this article, the authors describe the role that regrettable experiences have in promoting both happiness and complexity. Finally, expanding on previous work, the authors examine potential affordances of happy maturity and suggest psychological capacities that may promote happy maturity. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Thermal Annealing and Propagation of Shockley Stacking Faults in 4H-SiC PiN Diodes

Stacking faults within 4H-SiC PiN diodes are known to be detrimental to device operation. Here, we present electroluminescence (EL) images of 4H-SiC PiN diodes providing evidence that electrically and optically stimulated Shockley stacking fault (SSF) propagation is a reversible process at temperatures as low as 210°C. Optical beam induced current (OBIC) images taken following complete optical stressing of a PiN diode and that lead to a small number of completely propagated SSFs provide evidence that such defects propagate across the n–/p+ interface and continue to grow throughout the p+ layer. These observations bring about questions regarding the validity of the currently accepted driving force mechanism for SSF propagation.     

An improved topographic mapping technique from airborne lidar: application in a forested hillside

We developed a robust method to reconstruct a digital terrain model (DTM) by classifying raw light detection and ranging (lidar) points into ground and non-ground points with the help of the Progressive Terrain Fragmentation (PTF) method. PTF applies iterative steps for searching terrain points by approximating terrain surfaces using the triangulated irregular network (TIN) model constructed from ground return points. Instead of using absolute slope or offset distance, PTF uses orthogonal distance and relative angle between a triangular plane and a node. Due to this characteristic, PTF was able to classify raw lidar points into ground and non-ground points on a heterogeneous steep forested area with a small number of parameters. We tested this approach by using a lidar data set covering a part of the Angelo Coast Range Reserve on the South Fork of the Eel River in Mendocino County, California, USA. We used systematically positioned 16 reference plots to determine the optimal parameter that can be used to separate ground and non-ground points from raw lidar point clouds. We tested at different admissible hillslope angles (15° to 20°), and the minimum total error (1.6%) was acquired at the angle value of 18°. Because classifying raw lidar points into ground and non-ground points is the basis for other types of analyses, we expect that our study will provide more accurate terrain approximation and contribute to improving the extraction of other forest biophysical parameters.     

An interleaved sampling scheme for the characterization of single qubit dynamics

In this paper, we demonstrate that interleaved sampling techniques can be used to characterize the Hamiltonian of a qubit and its environmental decoherence rate. The technique offers a significant advantage in terms of the number of measurements that are required to characterize a qubit. When compared to the standard Nyquist?CShannon sampling rate, the saving in the total measurement time for the interleaved method is approximately proportional to the ratio of the sample rates.     

A Bayesian nonparametric approach to modeling motion patterns

The most difficult??and often most essential??aspect of many interception and tracking tasks is constructing motion models of the targets. Experts rarely can provide complete information about a target??s expected motion pattern, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, Bayesian nonparametric models of target motion are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides an adaptive representation for each individual motion pattern, while the DP prior allows us to represent an unknown number of motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area.     

Enhanced ionic conductivity and phase meta-stability of nano-sized thin film yttria-doped zirconia (YDZ)

Yttria-doped zirconia (YDZ) thin films with nanometric sized grains were prepared by reactive RF sputtering and their oxygen ion conductivities were systematically measured as a function of yttria doping with levels in the range 0.5–9.1 mol.% Y₂O₃. Enhanced oxygen ion conductivities, as derived from impedance spectra, were observed when compared with values reported for bulk YSZ. Furthermore, the peak conductivity for the YDZ films was observed to occur at considerably reduced yttria levels, i.e., at 6.5 mol.% Y₂O₃ (for T > ～400 °C) and at 3.2 mol.% Y₂O₃ (for T < ～300 °C) vs. 9 mol.% Y₂O₃ in bulk YSZ. Based on an analysis of the Raman spectra, these results are believed to result from the extended meta-stability of the cubic phase to reduced yttria levels at nanometric grain sizes.     

Timing HIV infection with a simple and accurate population viral dynamics model

Clinical trials for HIV prevention can require knowledge of infection times to subsequently determine protective drug levels. Yet, infection timing is difficult when study visits are sparse. Using population nonlinear mixed-effects (pNLME) statistical inference and viral loads from 46 RV217 study participants, we developed a relatively simple HIV primary infection model that achieved an excellent fit to all data. We also discovered that Aptima assay values from the study strongly correlated with viral loads, enabling imputation of very early viral loads for 28/46 participants. Estimated times between infecting exposures and first positives were generally longer than prior estimates (average of two weeks) and were robust to missing viral upslope data. On simulated data, we found that tighter sampling before diagnosis improved estimation more than tighter sampling after diagnosis. Sampling weekly before and monthly after diagnosis was a pragmatic design for good timing accuracy. Our pNLME timing approach is widely applicable to other infections with existing mathematical models. The present model could be used to simulate future HIV trials and may help estimate protective thresholds from the recently completed antibody-mediated prevention trials.     

审慎校正功率因子

通用电源设计（即那些适应低于100V至高于240V交流输入的设计）的一个期望结果是，功率管理电路与电源无关，且设计者只需考虑那些与预期负载有关的问题。这种功率管理方法通过减少为全球客户群体服务所需的电源种类而能产生相当大的规模经济。类似的优势还能扩展到供应链、制造业与库存管理运作。