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5,731 results on '"Qin, Jing"'

495. Two-stage Geometric Information Guided Image Reconstruction

Author

Qin, Jing and Guo, Weihong

Subjects
Mathematics - Optimization and Control, Computer Science - Computer Vision and Pattern Recognition
Abstract

In compressive sensing, it is challenging to reconstruct image of high quality from very few noisy linear projections. Existing methods mostly work well on piecewise constant images but not so well on piecewise smooth images such as natural images, medical images that contain a lot of details. We propose a two-stage method called GeoCS to recover images with rich geometric information from very limited amount of noisy measurements. The method adopts the shearlet transform that is mathematically proven to be optimal in sparsely representing images containing anisotropic features such as edges, corners, spikes etc. It also uses the weighted total variation (TV) sparsity with spatially variant weights to preserve sharp edges but to reduce the staircase effects of TV. Geometric information extracted from the results of stage I serves as an initial prior for stage II which alternates image reconstruction and geometric information update in a mutually beneficial way. GeoCS has been tested on incomplete spectral Fourier samples. It is applicable to other types of measurements as well. Experimental results on various complicated images show that GeoCS is efficient and generates high-quality images.

Published
2014

496. Combining isotonic regression and EM algorithm to predict genetic risk under monotonicity constraint

Author

Qin, Jing, Garcia, Tanya P., Ma, Yanyuan, Tang, Ming-Xin, Marder, Karen, and Wang, Yuanjia

Subjects
Statistics - Applications
Abstract

In certain genetic studies, clinicians and genetic counselors are interested in estimating the cumulative risk of a disease for individuals with and without a rare deleterious mutation. Estimating the cumulative risk is difficult, however, when the estimates are based on family history data. Often, the genetic mutation status in many family members is unknown; instead, only estimated probabilities of a patient having a certain mutation status are available. Also, ages of disease-onset are subject to right censoring. Existing methods to estimate the cumulative risk using such family-based data only provide estimation at individual time points, and are not guaranteed to be monotonic or nonnegative. In this paper, we develop a novel method that combines Expectation-Maximization and isotonic regression to estimate the cumulative risk across the entire support. Our estimator is monotonic, satisfies self-consistent estimating equations and has high power in detecting differences between the cumulative risks of different populations. Application of our estimator to a Parkinson's disease (PD) study provides the age-at-onset distribution of PD in PARK2 mutation carriers and noncarriers, and reveals a significant difference between the distribution in compound heterozygous carriers compared to noncarriers, but not between heterozygous carriers and noncarriers., Comment: Published in at http://dx.doi.org/10.1214/14-AOAS730 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2014
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497. Maximum Smoothed Likelihood Component Density Estimation in Mixture Models with Known Mixing Proportions

Author

Yu, Tao, Li, Pengfei, and Qin, Jing

Subjects
Statistics - Methodology, Mathematics - Statistics Theory
Abstract

In this paper, we propose a maximum smoothed likelihood method to estimate the component density functions of mixture models, in which the mixing proportions are known and may differ among observations. The proposed estimates maximize a smoothed log likelihood function and inherit all the important properties of probability density functions. A majorization-minimization algorithm is suggested to compute the proposed estimates numerically. In theory, we show that starting from any initial value, this algorithm increases the smoothed likelihood function and further leads to estimates that maximize the smoothed likelihood function. This indicates the convergence of the algorithm. Furthermore, we theoretically establish the asymptotic convergence rate of our proposed estimators. An adaptive procedure is suggested to choose the bandwidths in our estimation procedure. Simulation studies show that the proposed method is more efficient than the existing method in terms of integrated squared errors. A real data example is further analyzed.

Published
2014

498. Electroacupuncture Promotes Neural Proliferation in Hippocampus of Perimenopausal Depression Rats via Wnt/β-Catenin Signaling Pathway

Author

Qin Jing, Lu Ren, Xue Deng, Nan Zhang, Martin Fu, Ge Wang, Xi-Rong Jiang, Shu-Ru Lin, and Cai-Rong Ming

Subjects
Electroacupuncture, Neural Proliferation, Perimenopausal Depression, Wnt/β-catenin Signaling Pathway, Other systems of medicine, RZ201-999
Abstract

Background and Objective: Perimenopausal depression is caused by the impaired function of the ovarium before menopause and with a series of symptoms. Electroacupuncture (EA) therapy has been demonstrated to improve clinically depression. However, the mechanism underlying its therapeutic activity remains unknown. This study aimed to investigat the effects of EA treatment on the hippocampal neural proliferation through Wnt signaling pathway. Methods: Chronic unpredictable mild stress (CUMS) combined with bilateral ovariectomy (OVX) were used to establish a rat model of perimenopausal depression. The open field test (OFT) and sucrose preference test (SPT) were used to assess depression-like behaviors in rats. ELISAs were used to measure estrogen (E2), luteinizing hormone (LH) and gonadotropin-releasing hormone (GnRH) levels in the serum. RT-PCR and Western blot assay were utilized for measuring the mRNA expressions and protein expressions of GSK-3β/β-catenin. Results: Four-week EA treatment at three points including “Shenshu” (BL23), “Baihui” (GV20) and “Sanyinjiao” (SP6) simultaneously ameliorated depression-like behaviors in rats with CUMS and OVX, whereas rescued the decreased serum level of E2 and prevented the increased serum levels of GnRH and LH. EA treatment ameliorated CUMS and OVX-induced alterations of glycogen synthase kinase-3β (GSK-3β) and β-catenin mRNA levels, β-catenin and phosphorylated β-catenin (p-β-catenin) protein levels. Conclusions: The results showed that EA treatment promoted hippocampal neural proliferation in perimenopausal depression rats via activating the Wnt/β-catenin signaling pathway, indicating that EA may represent an efficacious therapy for perimenopausal depression.

Published
2020
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