Autophagy is a complex process of autodigestion in conditions of cellular stress, and it might play an important role in the pathophysiology during carcinogenesis. We hypothesize that genetic variants of the autophagy pathway may influence clinical outcomes in prostate cancer patients. We genotyped 40 tagging single-nucleotide polymorphisms (SNPs) from 7 core autophagy pathway genes in 458 localized prostate cancer patients. Multivariate Cox regression was performed to evaluate the independent association of each SNP with disease progression. Positive findings were then replicated in an independent cohort of 504 advanced prostate cancer patients. After adjusting for known clinicopathologic factors, the association between ATG16L1 rs78835907 and recurrence in localized disease [hazard ratio (HR) 0.70, 95% confidence interval (CI) 0.54–0.90, P = 0.006] was replicated in more advanced disease (HR 0.78, 95% CI 0.64–0.95, P = 0.014). Additional integrated in silico analysis suggests that rs78835907 tends to affect ATG16L1 expression, which in turn is correlated with tumor aggressiveness and patient prognosis. In conclusion, genetic variants of the autophagy pathway contribute to the variable outcomes in prostate cancer, and discovery of these novel biomarkers might help stratify patients according to their risk of disease progression.

Abstract-A Bayesian approach is presented for model-based classification of images with application to synthetic-aperture radar. Posterior probabilities are computed for candidate hypotheses using physical features estimated from sensor data along with features predicted from these hypotheses. The likelihood scoring allows propagation of uncertainty arising in both the sensor data and object models. The Bayesian classification, including the determination of a correspondence between unordered random features, is shown to be tractable, yielding a classification algorithm, a method for estimating error rates, and a tool for evaluating performance sensitivity. The radar image features used for classification are point locations with an associated vector of physical attributes; the attributed features are adopted from a parametric model of high-frequency radar scattering. With the emergence of wideband sensor technology, these physical features expand interpretation of radar imagery to access the frequencyand aspect-dependent scattering information carried in the image phase.Index Terms-Model-based classification, parametric modeling, point correspondence, radar image analysis.

A compact and position-addressable blue ray scanning microscope (BSM) based on a commercially available Blu-ray disk pickup head (PUH) is developed for cell imaging with high resolution and low cost. The BSM comprises two objective lenses with numerical apertures (NAs) of 0.85 and 0.6 for focusing blue and red laser beams, respectively, on the sample slide. The blue and red laser beams are co-located adjacent to each other and move synchronously. A specially designed sample slide is used with a sample area and an address-patterned area for sample holding and address recognition, respectively. The blue laser beam is focused on the sample area and is used for fluorescent excitation and image capturing, whereas the red laser beam is focused on the address-patterned area and is used for address recognition and dynamic focusing. The address-patterned area is divided into 310 sectors. The cell image of each sector of the sampling area has a corresponding address pattern. Fluorescence images of monkey-derived kidney epithelial cells and fibroblast cells in which the F-actin is stained with fluorophore phalloidin CF 405 are measured by the BSM, with results comparable to those measured by a Leica TCS CP2 confocal microscope. The cell image of an area of interest can be easily tracked based on the coded address, and a large-area sample image can be accurately reconstructed from the sector images.

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We present a method for estimating classi cation performance of a model-based synthetic aperture radar SAR automatic target recognition ATR system. Target classi cation is performed by comparing a feature vector extracted from a measured SAR image chip with a feature vector predicted from a hypothesized target class and pose. The feature vectors are matched using a Bayes likelihood metric that incorporates uncertainty in both the predicted and extracted feature vectors. We adopt an attributed scattering center model for the SAR features. The scattering attributes characterize frequency and angle dependence of each scattering center in correspondence the geometry of its physical scattering mechanism. We develop two Bayes matchers that incorporate two di erent solutions to the problem of correspondence between predicted and extracted scattering centers. We quantify classi cation performance with respect to the number of scattering center features. We also present classi cation results when the matchers assume incorrect feature uncertainty statistics.