Automatically detecting neighbourhood constraint interactions using Comet

Local Search has been shown to be capable of producing high quality solutions in a variety of hard constraint and optimisation problems. Typically implementing a Local Search algorithm is done in a problem specic manner. In the last few years a variety of approaches have emerged focussed on easing the implementation and creating a clean separation between the algorithm and problem. We present a system which can deduce information about the interactions between problem constraints and the search neighbourhoods whilst maintaining a loose coupling between these components. We apply this technique to the International Timetabling Competition instances and show an implementation expressed in Comet

Hormone Replacement Therapy and Risk for Neurodegenerative Diseases

Over the past two decades, there has been a significant amount of research investigating the risks and benefits of hormone replacement therapy (HRT) with regards to neurodegenerative disease. Here, we review basic science studies, randomized clinical trials, and epidemiological studies, and discuss the putative neuroprotective effects of HRT in the context of Alzheimer's disease, Parkinson's disease, frontotemporal dementia, and HIV-associated neurocognitive disorder. Findings to date suggest a reduced risk of Alzheimer's disease and improved cognitive functioning of postmenopausal women who use 17β-estradiol. With regards to Parkinson's disease, there is consistent evidence from basic science studies for a neuroprotective effect of 17β-estradiol; however, results of clinical and epidemiological studies are inconclusive at this time, and there is a paucity of research examining the association between HRT and Parkinson's-related neurocognitive impairment. Even less understood are the effects of HRT on risk for frontotemporal dementia and HIV-associated neurocognitive disorder. Limits to the existing research are discussed, along with proposed future directions for the investigation of HRT and neurodegenerative diseases

Reduced dimensionality spin-orbit dynamics of CH3 + HCl reversible arrow CH4 Cl on ab initio surfaces

A reduced dimensionality quantum scattering method is extended to the study of spin-orbit nonadiabatic transitions in the CH3 + HCl reversible arrow CH4 + Cl(P-2(J)) reaction. Three two-dimensional potential energy surfaces are developed by fitting a 29 parameter double-Morse function to CCSD(T)/IB//MP2/cc-pV(T+d)Z-dk ab initio data; interaction between surfaces is described by geometry-dependent spin-orbit coupling functions fit to MCSCF/cc-pV(T+d)Z-dk ab initio data. Spectator modes are treated adiabatically via inclusion of curvilinear projected frequencies. The total scattering wave function is expanded in a vibronic basis set and close-coupled equations are solved via R-matrix propagation. Ground state thermal rate constants for forward and reverse reactions agree well with experiment. Multi-surface reaction probabilities, integral cross sections, and initial-state selected branching ratios all highlight the importance of vibrational energy in mediating nonadiabatic transition. Electronically excited state dynamics are seen to play a small but significant role as consistent with experimental conclusions. (C) 2011 American Institute of Physics. [doi:10.1063/1.3592732

Risky Decision Making Assessed With the Gambling Task in Adults with HIV

Decision making was assessed using a laboratory gambling task in 67 adults with the Human Immunodeficiency Virus (HIV+) and in 19 HIV-seronegative (HIV−) control participants. Neurocognitive test performance across several domains was also analyzed to examine potential cognitive mechanisms of gambling task performance. As predicted, the HIV+ group performed worse on the gambling task, indicating greater risky decision making. Specifically, the HIV+ group selected more cards from the “risky” or disadvantageous deck that included relatively large payoffs but infrequent large penalties. The control group also selected such risky cards but quickly learned to avoid them. Exploratory analyses also indicated that in the HIV+ group, but not in the control group, gambling task performance was correlated with Stroop Interference performance and long delay free recall on the California Verbal Learning Test, suggesting the role of inhibitory processes and verbal memory in the poorer gambling task performance in HIV. These findings indicate the usefulness of the gambling task as a laboratory tool to examine risky decision making and cognition in the HIV population

Sensation Seeking and Visual Selective Attention in Adults with HIV/AIDS

The association between sensation seeking and visual selective attention was examined in 31 adults with the Human Immunodeficiency Virus (HIV). Sensation seeking was measured with Zuckerman’s Sensation Seeking Scale Form V (SSS-V). Selective attention was assessed with a perceptual span task, where a target letter-character must be identified in a quickly presented array of nontarget letter-characters. As predicted, sensation seeking was strongly associated (R2 = .229) with perceptual span performance in the array size 12 condition, where selective attention demands were greatest, but not in the easier conditions. The Disinhibition, Boredom Susceptibility, and Experience Seeking subscales of the SSS-V were associated with span performance. It is argued that personality factors such as sensation seeking may play a significant role in selective attention and related cognitive abilities in HIV positive adults. Furthermore, sensation seeking differences might explain certain inconsistencies in the HIV neuropsychology literature

inSPOT: The First Online STD Partner Notification System Using Electronic Postcards

Deb Levine and colleagues describe an innovative online e-card service for partner notification, initial evaluation results, and future research needs

Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype

We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes phenotypes. At the heart of data assimilation is the mechanistic model, here an endocrine model. Such models can vary in complexity, contain testable hypotheses about important mechanics that govern the system (eg, nutrition’s effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data

Ensemble Kalman Methods With Constraints

Ensemble Kalman methods constitute an increasingly important tool in both state and parameter estimation problems. Their popularity stems from the derivative-free nature of the methodology which may be readily applied when computer code is available for the underlying state-space dynamics (for state estimation) or for the parameter-to-observable map (for parameter estimation). There are many applications in which it is desirable to enforce prior information in the form of equality or inequality constraints on the state or parameter. This paper establishes a general framework for doing so, describing a widely applicable methodology, a theory which justifies the methodology, and a set of numerical experiments exemplifying it