209 research outputs found

    Regularized Functional Regression Models with Applications to Brain Imaging.

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    Positron emission tomography (PET) is an imaging technique that provides useful information about brain metabolism to help clinicians in the early diagnosis of Alzheimer's disease (AD). In order to identify the brain areas that show significant signals, many statistical methods have been developed for the analysis of brain imaging data. However, most of them neglect accounting for spatial information in imaging data. One way to address this problem is to treat each image as a realization of a functional predictor. This dissertation includes three research projects concerning regularized functional regression models via Haar wavelets for the analysis of brain imaging data, particularly PET images. The first project develops a lasso penalized 3D functional linear regression model by viewing PET image as a 3D functional predictor and cognitive impairment as the response variable, aiming to identify the most predictive voxels with the underlying assumption that only a few brain areas are truly predictive. The PET images are obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The second project concerns a lasso penalized 3D functional logistic regression model for classification of PET images from ADNI database. ADNI participants were classified into three groups during their initial visits: AD, Mild Cognitive Impairment (MCI) and Normal Control (NC). The model is applied to all the pairwise classifications using baseline PET images. The third project develops a regularized 3D multiple functional logistic regression model that can account for the group structure among voxels. Cerebral cortex can be partitioned into multiple regions. Treating each region as a group, within-group and groupwise regularization is imposed into the estimation to identify the most predictive voxels. This model is applied to the prediction of MCI-to-AD conversion using ADNI MCI subjects’ baseline PET images. All proposed models are evaluated through extensive simulation studies which are based on simulated data and slices extracted from ADNI PET images. Comparisons with existing methods for the prediction performance are also conducted using ADNI data. The results suggest that the proposed models are able to not only identify the predictive voxels, but also achieve higher prediction accuracy than existing methods in general.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99975/1/xuejwang_1.pd

    GENHOP: An Image Generation Method Based on Successive Subspace Learning

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    Being different from deep-learning-based (DL-based) image generation methods, a new image generative model built upon successive subspace learning principle is proposed and named GenHop (an acronym of Generative PixelHop) in this work. GenHop consists of three modules: 1) high-to-low dimension reduction, 2) seed image generation, and 3) low-to-high dimension expansion. In the first module, it builds a sequence of high-to-low dimensional subspaces through a sequence of whitening processes, each of which contains samples of joint-spatial-spectral representation. In the second module, it generates samples in the lowest dimensional subspace. In the third module, it finds a proper high-dimensional sample for a seed image by adding details back via locally linear embedding (LLE) and a sequence of coloring processes. Experiments show that GenHop can generate visually pleasant images whose FID scores are comparable or even better than those of DL-based generative models for MNIST, Fashion-MNIST and CelebA datasets.Comment: 10 pages, 5 figures, accepted by ISCAS 202

    Protective effect of astragalus injection against myocardial injury in septic young rats via inhibition of JAK/STAT signal pathway and regulation of inflammation

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    Purpose: To investigate the protective effect of astragalus injection against myocardial injury in septic young rats, and the underlying mechanism of action. Methods: Seventy-two healthy Sprague Dawley (SD) rats were randomly selected and used to establish a young rat model of sepsis. The young rats were randomly divided into 3 groups: sham, model and astragalus injection groups. Each group had 24 young rats. Serum cardiac troponin I (cTnI), IL-10, IL-6, JAK2 and STAT3 were measured after op. Results: Compared with sham group, serum cTnI level in the model group was significantly higher, while serum cTnI level of the drug group was significantly lower than that of the model group (p < 0.05). Compared with model group, the level of IL-10 in the myocardial tissue of the drug group was significantly elevated, while IL-6 level was lower (p < 0.05). Relative to sham rats, myocardial JAK2 and STAT3 protein levels in model rats were high. However, myocardial JAK2 and STAT3 proteins in the drug-treated rats were significantly downregulated, relative to model rats (p < 0.05). Conclusion: Astragalus injection upregulates IL-10 and IL-6 in rats by inhibiting the activation of JAK/STAT signal pathway, and via maintenance of pro-inflammation/anti-inflammation balance. Thus, astragalus exerts protective effect against myocardial injury in sepsis, and can potentially be developed for use as such in clinical practice. Keywords: Astragalus injection, JAK/STAT signal pathway, Pro-inflammatory/anti-inflammatory imbalance, Sepsis, Myocardial injur

    An efficient decision support system for flood inundation management using intermittent remote-sensing data

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    Abstract: Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model’s performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions
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