Prostate cancer (PCa) is the most common type of cancer among men over 60 years old. The aggressiveness and mortality of PCa can be correlated with obesity. Adipose tissue-derived cytokines such as adiponectin may explain the correlation between PCa and obesity. Since the correlation between adiponectin and aggressive PCa is still not fully evaluated, we aimed to investigate the probable role o...

OBJECTIVES To review the historical origins and current evidence for the belief that testosterone (T) causes prostate cancer (pCA) growth. METHODS Review of the historical literature regarding T administration and pCA, as well as more recent studies investigating the relationship of T and pCA. RESULTS In 1941 Huggins and Hodges reported that marked reductions in T by castration or estrogen ...

Prostate cancer (PCa) is a common malignant tumor and the second leading cause of morbidity and mortality in men worldwide. Considering the prevalence and effects of PCa in males, an understanding of the molecular mechanisms underlying PCa tumorigenesis are essential and may provide novel therapeutic strategies for treating PCa. Bloom syndrome protein (BLM) is a member of the RecQ helicase fami...

BACKGROUND Hip fractures are associated with increased mortality and are a known adverse effect of androgen deprivation therapy (ADT) for prostate cancer (PCa). It was our aim to evaluate how mortality after hip fracture is modified by PCa and ADT. METHODS PCa dataBase Sweden (PCBaSe 2.0) is based on the National PCa Register and also contains age and county-matched PCa-free men. We selected ...

Since it is not possible to compute E-scores for 10-mers from PBM data, we used 10-mer pseudo-intensities to perform an analysis similar to the one described in the main text for 8-mer E-scores. We estimated the 10-mers intensities from the 7-mer and 8-mer median intensities. Let X = X1X2 . . . Xk be a DNA site of length k, with Ik(X) it’s median intensity, and Ek(X) it’s energy. Then the inten...

1 Intro The theme of these two lectures is that for L2 methods we need not work in infinite dimensional spaces. In particular, we can unadaptively find and work in a low dimensional space and achieve about as good results. These results question the need for explicitly working in infinite (or high) dimensional spaces for L2 methods. In contrast, for sparsity based methods (including L1 regulari...

In this project we investigate two machine learning methods, one supervised and one unsupervised, that will allow the information content of Yelp data to be efficiently conveyed to the users. The first is matrix completion via the novel ”max-norm” constraint which out results show to be more powerful than the traditional nuclear norm minimization. The second is text summary via sparse PCA which...

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utili...

Principal Component Analysis (PCA) is one of the feature extraction techniques, commonly used in human facial recognition systems. PCA yields high accuracy rates when requiring lower dimensional vectors; however, the computation during covariance matrix and Eigenvalue Decomposition (EVD) stages leads to a high degree of complexity that corresponds to the increase of datasets. Thus, this researc...