Mapping the structure of perceptual and visual-motor abilities in healthy young adults.

The ability to quickly detect and respond to visual stimuli in the environment is critical to many human activities. While such perceptual and visual-motor skills are important in a myriad of contexts, considerable variability exists between individuals in these abilities. To better understand the sources of this variability, we assessed perceptual and visual-motor skills in a large sample of 230 healthy individuals via the Nike SPARQ Sensory Station, and compared variability in their behavioral performance to demographic, state, sleep and consumption characteristics. Dimension reduction and regression analyses indicated three underlying factors: Visual-Motor Control, Visual Sensitivity, and Eye Quickness, which accounted for roughly half of the overall population variance in performance on this battery. Inter-individual variability in Visual-Motor Control was correlated with gender and circadian patters such that performance on this factor was better for males and for those who had been awake for a longer period of time before assessment. The current findings indicate that abilities involving coordinated hand movements in response to stimuli are subject to greater individual variability, while visual sensitivity and occulomotor control are largely stable across individuals.

New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.

Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.

Hyperspectral Image Classification with Nonlinear Methods

This thesis introduces two related lines of study on classification of hyperspectral images with nonlinear methods. First, it describes a quantitative and systematic evaluation, by the author, of each major component in a pipeline for classifying hyperspectral images (HSI) developed earlier in a joint collaboration [23]. The pipeline, with novel use of nonlinear classification methods, has reached beyond the state of the art in classification accuracy on commonly used benchmarking HSI data [6], [13]. More importantly, it provides a clutter map, with respect to a predetermined set of classes, toward the real application situations where the image pixels not necessarily fall into a predetermined set of classes to be identified, detected or classified with.

The particular components evaluated are a) band selection with band-wise entropy spread, b) feature transformation with spatial filters and spectral expansion with derivatives c) graph spectral transformation via locally linear embedding for dimension reduction, and d) statistical ensemble for clutter detection. The quantitative evaluation of the pipeline verifies that these components are indispensable to high-accuracy classification.

Secondly, the work extends the HSI classification pipeline with a single HSI data cube to multiple HSI data cubes. Each cube, with feature variation, is to be classified of multiple classes. The main challenge is deriving the cube-wise classification from pixel-wise classification. The thesis presents the initial attempt to circumvent it, and discuss the potential for further improvement.

Reduction of quantity smoked predicts future cessation among older smokers.

AIM: To examine whether smokers who reduce their quantity of cigarettes smoked between two periods are more or less likely to quit subsequently. STUDY DESIGN: Data come from the Health and Retirement Study, a nationally representative survey of older Americans aged 51-61 in 1991 followed every 2 years from 1992 to 1998. The 2064 participants smoking at baseline and the first follow-up comprise the main sample. MEASUREMENTS: Smoking cessation by 1996 is examined as the primary outcome. A secondary outcome is relapse by 1998. Spontaneous changes in smoking quantity between the first two waves make up the key predictor variables. Control variables include gender, age, education, race, marital status, alcohol use, psychiatric problems, acute or chronic health problems and smoking quantity. FINDINGS: Large (over 50%) and even moderate (25-50%) reductions in quantity smoked between 1992 and 1994 predict prospectively increased likelihood of cessation in 1996 compared to no change in quantity (OR 2.96, P<0.001 and OR 1.61, P<0.01, respectively). Additionally, those who reduced and then quit were somewhat less likely to relapse by 1998 than those who did not reduce in the 2 years prior to quitting. CONCLUSIONS: Reducing successfully the quantity of cigarettes smoked appears to have a beneficial effect on future cessation likelihood, even after controlling for initial smoking level and other variables known to impact smoking cessation. These results indicate that the harm reduction strategy of reduced smoking warrants further study.

THE DEVELOPMENT AND FEASIBILITY OF A BRIEF RISK REDUCTION INTERVENTION FOR NEWLY HIV-DIAGNOSED MEN WHO HAVE SEX WITH MEN.

Men who have sex with men (MSM) represent more than half of all new HIV infections in the United States. Utilizing a collaborative, community based approach, a brief risk reduction intervention was developed and pilot tested among newly HIV-diagnosed MSM receiving HIV care in a primary care setting. Sixty-five men, within 3 months of diagnosis, were randomly assigned to the experimental condition or control condition and assessed at baseline, 3-month, and 6-month follow-up. Effect sizes were calculated to explore differences between conditions and over time. Results demonstrated the potential effectiveness of the intervention in reducing risk behavior, improving mental health, and increasing use of ancillary services. Process evaluation data demonstrated the acceptability of the intervention to patients, clinic staff, and administration. The results provide evidence that a brief intervention can be successfully integrated into HIV care services for newly diagnosed MSM and should be evaluated for efficacy.

Human dimensions of bycatch reduction technology: Current assumptions and directions for future research

Bycatch reduction technology (BRT) modifies fishing gear to increase selectivity and avoid capture of non-target species, or to facilitate their non-lethal release. As a solution to fisheries-related mortality of non-target species, BRT is an attractive option; effectively implemented, BRT presents a technical 'fix' that can reduce pressure for politically contentious and economically detrimental interventions, such as fisheries closures. While a number of factors might contribute to effective implementation, our review of BRT literature finds that research has focused on technical design and experimental performance of individual technologies. In contrast, and with a few notable exceptions, research on the human and institutional context of BRT, and more specifically on how fishers respond to BRT, is limited. This is not to say that fisher attitudes are ignored or overlooked, but that incentives for fisher uptake of BRT are usually assumed rather than assessed or demonstrated. Three assumptions about fisher incentives dominate: (1) economic incentives will generate acceptance of BRT; (2) enforcement will generate compliance with BRT; and (3) 'participation' by fishers will increase acceptance and compliance, and overall support for BRT. In this paper, we explore evidence for and against these assumptions and situate our analysis in the wider social science literature on fisheries. Our goal is to highlight the need and suggest focal areas for further research. © Inter-Research 2008.

Reduction of malaria prevalence by indoor residual spraying: a meta-regression analysis.

Indoor residual spraying (IRS) has become an increasingly popular method of insecticide use for malaria control, and many recent studies have reported on its effectiveness in reducing malaria burden in a single community or region. There is a need for systematic review and integration of the published literature on IRS and the contextual determining factors of its success in controlling malaria. This study reports the findings of a meta-regression analysis based on 13 published studies, which were chosen from more than 400 articles through a systematic search and selection process. The summary relative risk for reducing malaria prevalence was 0.38 (95% confidence interval = 0.31-0.46), which indicated a risk reduction of 62%. However, an excessive degree of heterogeneity was found between the studies. The meta-regression analysis indicates that IRS is more effective with high initial prevalence, multiple rounds of spraying, use of DDT, and in regions with a combination of Plasmodium falciparum and P. vivax malaria.

G protein signaling and vein graft intimal hyperplasia: reduction of intimal hyperplasia in vein grafts by a Gbetagamma inhibitor suggests a major role of G protein signaling in lesion development.

Vein grafting results in the development of intimal hyperplasia with accompanying changes in guanine nucleotide-binding (G) protein expression and function. Several serum mitogens that act through G protein-coupled receptors, such as lysophosphatidic acid, stimulate proliferative pathways that are dependent on the G protein betagamma subunit (Gbetagamma)-mediated activation of p21ras. This study examines the role of Gbetagamma signaling in intimal hyperplasia by targeting a gene encoding a specific Gbetagamma inhibitor in an experimental rabbit vein graft model. This inhibitor, the carboxyl terminus of the beta-adrenergic receptor kinase (betaARK(CT)), contains a Gbetagamma-binding domain. Vein graft intimal hyperplasia was significantly reduced by 37% (P<0.01), and physiological studies demonstrated that the normal alterations in G protein coupling phenotypically seen in this model were blocked by betaARK(CT) treatment. Thus, it appears that Gbetagamma-mediated pathways play a major role in intimal hyperplasia and that targeting inhibitors of Gbetagamma signaling offers novel intraoperative therapeutic modalities to inhibit the development of vein graft intimal hyperplasia and subsequent vein graft failure.

Spectrotemporal CT data acquisition and reconstruction at low dose.

PURPOSE: X-ray computed tomography (CT) is widely used, both clinically and preclinically, for fast, high-resolution anatomic imaging; however, compelling opportunities exist to expand its use in functional imaging applications. For instance, spectral information combined with nanoparticle contrast agents enables quantification of tissue perfusion levels, while temporal information details cardiac and respiratory dynamics. The authors propose and demonstrate a projection acquisition and reconstruction strategy for 5D CT (3D+dual energy+time) which recovers spectral and temporal information without substantially increasing radiation dose or sampling time relative to anatomic imaging protocols. METHODS: The authors approach the 5D reconstruction problem within the framework of low-rank and sparse matrix decomposition. Unlike previous work on rank-sparsity constrained CT reconstruction, the authors establish an explicit rank-sparse signal model to describe the spectral and temporal dimensions. The spectral dimension is represented as a well-sampled time and energy averaged image plus regularly undersampled principal components describing the spectral contrast. The temporal dimension is represented as the same time and energy averaged reconstruction plus contiguous, spatially sparse, and irregularly sampled temporal contrast images. Using a nonlinear, image domain filtration approach, the authors refer to as rank-sparse kernel regression, the authors transfer image structure from the well-sampled time and energy averaged reconstruction to the spectral and temporal contrast images. This regularization strategy strictly constrains the reconstruction problem while approximately separating the temporal and spectral dimensions. Separability results in a highly compressed representation for the 5D data in which projections are shared between the temporal and spectral reconstruction subproblems, enabling substantial undersampling. The authors solved the 5D reconstruction problem using the split Bregman method and GPU-based implementations of backprojection, reprojection, and kernel regression. Using a preclinical mouse model, the authors apply the proposed algorithm to study myocardial injury following radiation treatment of breast cancer. RESULTS: Quantitative 5D simulations are performed using the MOBY mouse phantom. Twenty data sets (ten cardiac phases, two energies) are reconstructed with 88 μm, isotropic voxels from 450 total projections acquired over a single 360° rotation. In vivo 5D myocardial injury data sets acquired in two mice injected with gold and iodine nanoparticles are also reconstructed with 20 data sets per mouse using the same acquisition parameters (dose: ∼60 mGy). For both the simulations and the in vivo data, the reconstruction quality is sufficient to perform material decomposition into gold and iodine maps to localize the extent of myocardial injury (gold accumulation) and to measure cardiac functional metrics (vascular iodine). Their 5D CT imaging protocol represents a 95% reduction in radiation dose per cardiac phase and energy and a 40-fold decrease in projection sampling time relative to their standard imaging protocol. CONCLUSIONS: Their 5D CT data acquisition and reconstruction protocol efficiently exploits the rank-sparse nature of spectral and temporal CT data to provide high-fidelity reconstruction results without increased radiation dose or sampling time.