There are several cutting edge applications needing PCA methods for data on tori and we propose a novel torus-PCA method with important properties that can be generally applied. There are two existing general methods: tangent space PCA and geodesic PCA. However, unlike tangent space PCA, our torus-PCA honors the cyclic topology of the data space whereas, unlike geodesic PCA, our torus-PCA produ...

Principal component analysis (PCA) is an extensively used dimensionality reduction technique, with important applications in many fields such as pattern recognition, computer vision and statistics. It employs the eigenvectors of the covariance matrix of the data to project it on a lower dimensional subspace. Kernel PCA, a generalized version of PCA, performs PCA implicitly in a nonlinearly tran...

an ideal fusion method preserves the spectral information in fused image without spatial distortion. the pca is believed to be a well-known pan-sharpening approach and being widely used for its efficiency and high spatial resolution. however, it can distort the spectral characteristics of multispectral images. the current paper tries to present a new fusion method based on the same concept. in ...

Normal human monocytes and macrophages. as well as leukemic promyelocytes. generate potent procoagulant activity (PCA) resembling thromboplastin. In the present study only mild PCA was detected in a leukemic promyelocytic cell line (HL-60) and in promyelocytic cells induced to differentiate into neutrophils by dimethylsulfoxide (DMSO). After exposure to 1 2-O-tetradecanoylphorbol1 3-acetate (TP...

PLZF is a transcription repressor, which plays a critical role in development, spermatogenesis and oncogenesis. Down-regulation of PLZF has been found in various tumor cell lines. There has been virtually no tissue study on the expression of PLZF in prostate cancer (PCa). PCa is a heterogeneous disease, most of which are indolent and non-lethal. Currently there are no biomarkers that distinguis...

The first advance in the history of studies on prostate cancer (PCa) and androgens was the development of treatment with castration and administration of estrogen by Charles B. Huggins, who won the Nobel Prize in Physiology and Medicine. Since then, and for 70 years, androgen deprivation therapy has been the standard therapy for advanced PCa and the center of studies on PCa. However, recent adv...

The dysregulation of miR‑126 has been reported to correlate with the progression of several cancer types. The present study demonstrated that miR‑126 was significantly downregulated in prostate cancer (PCa) tissues compared with normal prostate tissues. In vitro and in vivo studies indicated that forced overexpression of miR‑126 significantly suppressed the proliferation of PCa cell lines. Addi...

Robust PCA methods are typically based on batch optimization and have to load all the samples into memory during optimization. This prevents them from efficiently processing big data. In this paper, we develop an Online Robust PCA (OR-PCA) that processes one sample per time instance and hence its memory cost is independent of the number of samples, significantly enhancing the computation and st...

BACKGROUND Prostate cancer (PCa) is the most commonly diagnosed cancer and kills about 28,000 American men annually. Although progress has been made in understanding the molecular features of different forms of the disease, PCa is considered incurable when it becomes resistant to standard therapies. Prostate specific antigen (PSA) test has been a gold standard of diagnosis for PCa, however, it ...

The method of sparse principal component analysis (S-PCA) proposed by Zou, Hastie, and Tibshirani (2006) is an attractive approach to obtain sparse loadings in principal component analysis (PCA). S-PCA was motivated by reformulating PCA as a least-squares problem so that a lasso penalty on the loading coefficients can be applied. In this article, we propose new estimates to improve S-PCA in the...