Anhui Normal University

About: Anhui Normal University is a education organization based out in Wuhu, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 7955 authors who have published 7309 publications receiving 117443 citations.

Contrasting patterns in elevational diversity between microorganisms and macroorganisms

Abstract: Aim Data and analyses of elevational gradients in diversity have been central to the development and evaluation of a range of general theories of biodiversity. Elevational diversity patterns have, however, been severely understudied for microbes, which often represent decomposer subsystems. Consequently, generalities in the patterns of elevational diversity across different trophic levels remain poorly understood. Our aim was to examine elevational gradients in the diversity of macroinvertebrates, diatoms and bacteria along a stony stream that covered a large elevational gradient.

M2-factor of coherent and partially coherent dark hollow beams propagating in turbulent atmosphere.

Abstract: Analytical formula is derived for the M2-factor of coherent and partially coherent dark hollow beams (DHB) in turbulent atmosphere based on the extended Huygens-Fresnel integral and the second-order moments of the Wigner distribution function. Our numerical results show that the M2- factor of a DHB in turbulent atmosphere increases on propagation, which is much different from its invariant properties in free-space, and is mainly determined by the parameters of the beam and the atmosphere. The relative M2-factor of a DHB increases slower than that of Gaussian and flat-topped beams on propagation, which means a DHB is less affected by the atmospheric turbulence than Gaussian and flat-topped beams. Furthermore, the relative M2-factor of a DHB with lower coherence, longer wavelength and larger dark size is less affected by the atmospheric turbulence. Our results will be useful in long-distance free-space optical communications.

Manifold regularized multitask feature learning for multimodality disease classification

Abstract: Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold-based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease-related brain regions useful for disease diagnosis.