Philosophical Studies - It is widely accepted that supervenience is a minimal commitment of physicalism. In this article, however, I aim to argue that physicalism should be exempted from the... 相似文献
Introduction: Hypertension has shown to be an important risk factor for the decline in cognitive function. Aim of our study is to investigate the presence of cognitive impairment of the elders with hypertension and other confounding factors.
Methods: This study was conducted on 400 veterans who were matched one-to-one with the confounding factors for assessing the presence of mild cognitive impairment using both MMSE and Montreal Cognitive Assessment (MoCA). The 13 related factors of patient data were studied.
Results: The prevalence rate of cognitive impairment was 29.25%. Age (OR 2.679, 95%CI 1.663–6.875), sleep impairment (OR 1.117, 95%CI 1.754–7.422), uncontrolled hypertension (OR 1.522, 95%CI 1.968–4.454), type 2 diabetes (OR 2.464, 95%CI 1.232–4.931), and hyperlipidaemia (OR 1.411, 95%CI 1.221–8.988) are the risk factors for the cognitive deterioration, while the protective factors are high level of education (OR 0.032, 95%CI 0.007–0.149) and regular exercise (OR 0.307, 95%CI 0.115–0.818).
Discussion: Because some vascular disease risk factors, such as hypertension, can be treated effectively, cognitive decline related to these risk factors, and vascular disease per se, may be prevented or its course modified through more aggressive treatment and improved compliance. 相似文献
Brain activation detection is an important problem in fMRI data analysis. In this paper, we propose a data-driven activation detection method called neighborhood one-class SVM (NOC-SVM). Based on the probability distribution assumption of the one-class SVM algorithm and the neighborhood consistency hypothesis, NOC-SVM identifies a voxel as either an activated or non-activated voxel by a weighted distance between its near neighbors and a hyperplane in a high-dimensional kernel space. The proposed NOC-SVM are evaluated by using both synthetic and real datasets. On two synthetic datasets with different SNRs, NOC-SVM performs better than K-means and fuzzy K-means clustering and is comparable to POM. On a real fMRI dataset, NOC-SVM can discover activated regions similar to K-means and fuzzy K-means. These results show that the proposed algorithm is an effective activation detection method for fMRI data analysis. Furthermore, it is stabler than K-means and fuzzy K-means clustering. 相似文献