Background/Aims: In the present study, we examined whether DNA methylation of the brain-derived neurotrophic factor (BDNF) promoter is associated with the manifestation and clinical presentation of Alzheimer's disease (AD). Methods: Of 20 patients with AD and 20 age-matched normal controls (NCs), the DNA methylation of the BDNF promoter (measured using peripheral blood samples) was completely analyzed in 12 patients with AD and 6 NCs. The resulting methylation levels were compared statistically. Next, we investigated the correlation between the DNA methylation levels and the clinical presentation of AD. Results: The total methylation ratio (in %) of the 20 CpG sites was significantly higher in the AD patients (5.08 ± 5.52%) than in the NCs (2.09 ± 0.81%; p < 0.05). Of the 20 CpG sites, the methylation level at the CpG4 site was significantly higher in the AD subjects than in the NCs (p < 0.05). Moreover, the methylation level was significantly and negatively correlated with some neuropsychological test subscores (registration, recall, and prehension behavior scores; p < 0.05). Conclusion: These results suggest that the DNA methylation of the BDNF promoter may significantly influence the manifestation of AD and might be associated with its neurocognitive presentation.
This paper proposes an accurate, dense and robust wide baseline stereo correspondence matching method combining ASIFT (Affine-SIFT) and POC (Phase-Only Correlation).ASIFT-based matching is robust against perspective defor mation of the stereo images, while the corresponding points are sparse. POC-based matching can find dense correspon dence, while the corresponding points are not reliable in the case of the wide-baseline stereo. The complementary use of ASIFT and POC makes it possible to find accurate and dense stereo correspondence regardless of the length of camera baseline. Through a set of experiments, we demonstrate that the proposed method exhibits efficient performance compared with the conventional methods. We also apply the proposed method to 3D reconstruction from multi-view images.
A method is presented for measuring accurate and dense 3D shapes of an object from two views captured with a moving consumer digital camera. Since existing 3D measurement systems require special and expensive measurement equipment such as laser scanners or technical knowledge such as camera calibration, it is difficult for ordinary consumers to use existing systems practically in their daily life. An easy-to-use 3D measurement system was achieved by using a Structure from Motion algorithm based on feature matching to estimate camera parameters and area-based matching to determine an accurate and dense correspondence. Experiments demonstrated that the proposed method makes it possible to measure a 3D shape with accuracy comparable to that of a laser scanner.
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