In face recognition, Principal Component Analysis (PCA) is often used to extract a low dimensional face representation based on the eigenvector of the face image autocorrelation matrix. Kernel Principal Component Analysis (Kernel PCA) has recently been proposed as a non-linear extension of PCA. While PCA is able to discover and represent linearly embedded manifolds, Kernel PCA can extract low d...

UNLABELLED Clinical significance of autoantibodies to parietal cells (PCA) in patients with autoimmune thyroid diseases (ATD) remains contradictory. AIM To characterize the frequency of PCA in patients with ATD; to clarify the role of gender and age in PCA positivity; to analyze the association of PCA with gastric or haematologic symptoms and with the levothyroxine dose required to achieve a ...

Prostate cancer (PCa) is the most common non-cutaneous malignancy in men and is responsible for the second most common age-adjusted death rate due to cancer among men in the US, after lung cancer [1]. The prevalence of PCa increases with age with the chance of detecting PCa in an 80 year-old male being as high as 60% [2]. While PCa is generally considered a disease of “old age” with most men dy...

AIMS To clarify the features of the background electroencephalographic (EEG) activities in clinically well preterm infants born at less than 27 weeks gestation and to outline their chronological changes with increasing postconceptional age (PCA). METHODS EEGs of clinically well premature infants born at less than 27 weeks gestation were recorded during the early postnatal period. The infants ...

Tumors from patients with high-grade aggressive prostate cancer (PCa) exhibit increased expression of monoamine oxidase A (MAOA), a mitochondrial enzyme that degrades monoamine neurotransmitters and dietary amines. Despite the association between MAOA and aggressive PCa, it is unclear how MAOA promotes PCa progression. Here, we found that MAOA functions to induce epithelial-to-mesenchymal trans...

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...