Wenlu Zhang

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The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue(More)
Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. A challenge in using multimodality data is that the data are commonly incomplete; namely, some modality might be missing for some subjects. In this work, we proposed a deep learning based framework for estimating multi-modality imaging data. Our method takes(More)
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate(More)
The mammalian brain contains cells of a large variety of types. The phenotypic properties of cells of different types are largely the results of distinct gene expression patterns. Therefore, it is of critical importance to characterize the gene expression patterns in the mammalian brain. The Allen Developing Mouse Brain Atlas provides spatiotemporal in situ(More)
Differential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expression patterns can provide insights into the relationship between(More)
A central theme in learning from image data is to develop appropriate image representations for the specific task at hand. Traditional methods used handcrafted local features combined with high-level image representations to generate image-level representations. Thus, a practical challenge is to determine what features are appropriate for specific tasks.(More)
We consider the co-clustering of time-varying data using evolutionary co-clustering methods. Existing approaches are based on the spectral learning framework, thus lacking a probabilistic interpretation. We overcome this limitation by developing a probabilistic model in this paper. The proposed model assumes that the observed data are generated via a(More)
We consider the mining of hidden block structures from time-varying data using evolutionary co-clustering. Existing methods are based on the spectral learning framework, thus lacking a probabilistic interpretation. To overcome this limitation, we develop a probabilistic model for evolutionary co-clustering in this paper. The proposed model assumes that the(More)
In this work, we discuss the porting to the GPU platform of the latest production version of the Gyrokinetic Torodial Code (GTC), which is a petascale fusion simulation code using particle-in-cell method. New GPU parallel algorithms have been designed for the particle push and shift operations. The GPU version of the GTC code was bench-marked on up to 3072(More)