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https://www.selleckchem.com/products/SB-525334.html Univariate analysis was used to compare the general condition, cognitive function, and each cognitive domain between the two groups, analyzing the relation between high-frequency hearing loss and cognitive function. Result We found that age, years of education, pure tone average (PTA), occupation, living condition, history of otologic diseases, years of self-reported hearing loss, and hypertension history were related to cognitive function. Furthermore, age, education experience, duration of self-reported hearing loss, and hypertension were independent factors (p 0.05); in contrast, the speech and abstract ability were significantly decreased in cases with high-frequency hearing loss (p less then 0.05). Conclusion The increase of PTA among the elderly may affect the overall cognition by reducing attention and orientation. High-frequency hearing loss alone can affect the language and abstract ability to a certain extent, which is worthy of more attention.Finding the common principal component (CPC) for ultra-high dimensional data is a multivariate technique used to discover the latent structure of covariance matrices of shared variables measured in two or more k conditions. Common eigenvectors are assumed for the covariance matrix of all conditions, only the eigenvalues being specific to each condition. Stepwise CPC computes a limited number of these CPCs, as the name indicates, sequentially and is, therefore, less time-consuming. This method becomes unfeasible when the number of variables p is ultra-high since storing k covariance matrices requires O(k p 2) memory. Many dimensionality reduction algorithms have been improved to avoid explicit covariance calculation and storage (covariance-free). Here we propose a covariance-free stepwise CPC, which only requires O(k n) memory, where n is the total number of examples. Thus for n less then less then p, the new algorithm shows apparent advantages. It computes component

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