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Background and Aims: Endoscopic ultrasonography (EUS) is useful for the diagnosis of gastrointestinal stromal tumors (GISTs), but is limited by subjective interpretation. Studies on artificial intelligence (AI)-assisted diagnosis are under development. Here, we used a meta-analysis to evaluate the diagnostic performance of AI in the diagnosis of GISTs using EUS images. Methods: PubMed, Ovid Medline, Embase, Web of science, and the Cochrane Library databases were searched for studies based on the EUS using AI for the diagnosis of GISTs, and a meta-analysis was performed to examine the accuracy. Results: Overall, 7 studies were included in our meta-analysis. A total of 2431 patients containing more than 36,186 images were used as the overall dataset, of which 480 patients were used for the final testing. The pooled sensitivity, specificity, positive, and negative likelihood ratio (LR) of AI-assisted EUS for differentiating GISTs from other submucosal tumors (SMTs) were 0.92 (95% confidence interval [CI] 0.89–0.95), 0.82 (95% CI 0.75–0.87), 4.55 (95% CI 2.64–7.84), and 0.12 (95% CI 0.07–0.20), respectively. The summary diagnostic odds ratio (DOR) and the area under the curve were 64.70 (95% CI 23.83–175.69) and 0.950 (Q* = 0.891). Conclusions: AI-assisted EUS showed high accuracy for the automatic endoscopic diagnosis of GISTs, which could be used as a valuable complementary method for the differentiation of SMTs in the future. [ABSTRACT FROM AUTHOR]

In this paper, a graph regularized nonlinear ridge regression (RR) model is proposed for remote sensing data analysis, including hyper-spectral image classification and atmospheric aerosol retrieval. The RR is an efficient linear regression method, especially in handling cases with a small number of training samples or with correlated features. However, large amounts of unlabeled samples exist in remote sensing data analysis. To sufficiently explore the information in unlabeled samples, we propose a graph regularized RR (GRR) method, where the vertices denote labeled or unlabeled samples and the edges represent the similarities among different samples. A natural assumption is that the predict values of neighboring samples are close to each other. To further address the nonlinearly separable problem in remote sensing data caused by the complex acquisition process as well as the impacts of atmospheric and geometric distortions, we extend the proposed GRR into a kernelized nonlinear regression method, namely KGRR. To evaluate the proposed method, we apply it to both classification and regression tasks and compare with representative methods. The experimental results show that KGRR can achieve favorable performance in terms of predictability and computation time. [ABSTRACT FROM PUBLISHER]

In this article, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval. First, we adopt a low-rank representation (LRR) model to learn a powerful representation of the spectral response. Then, a graph regularization is incorporated into the LRR model to capture the local structure information and the non-linear property of the remote-sensing data. Since it is easy to acquire the rich results retrieved by satellite, we use them as a baseline to construct the graph. Finally, the learned feature representation is fed into Support Vector Machines (SVMs) to retrieve AOD. Experiments are conducted on two widely used data sets acquired by different sensors, and the experimental results show that the proposed method can achieve superior performance compared to the physical models and other state-of-the-art empirical models. [ABSTRACT FROM AUTHOR]

Purpose: The use of contrast-enhanced sonography (CEUS) has yielded promising results in the differentiation of thyroid nodules. We conducted this meta-analysis to assess its performance in identifying and distinguishing between benign and malignant thyroid nodules.Methods: PubMed, Medline, Embase, and the Cochrane Library were searched for studies published through the end of December 2013. Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve were calculated.Results: A total of 13 studies were included in this meta-analysis. For the diagnosis of malignant thyroid nodules worldwide, the overall mean rates of sensitivity and specificity of CEUS were 90% (95% confidence interval [CI], 88-93%) and 86% (95% CI, 83-89%), respectively. The summary diagnostic odds ratio was 52.83 (95% CI, 21.71-128.55), and the area under the curve for the summary receiver operating characteristic curve was 0.94 (95% CI, 0.90-0.98).Conclusions: This meta-analysis indicates that CEUS may be a valuable supplemental method, with high rates of sensitivity and specificity, to use for identifying and distinguishing between benign and malignant thyroid nodules. [ABSTRACT FROM AUTHOR]

Novel low-molecular-weight organic gelators (LMOGs) with propeller-shaped triphenylamine as a 'core' and tiny methyl acrylate groups as 'tails,' are developed, which would not be thought to be a candidate for gel formation due to the absence of planar rigid aromatic building blocks and long-chain alkyls. The sensitive gel-sol transformation can be easily achieved upon heating or cooling accompanied by molecular aggregation or disaggregation. H-H 2D NOESY is employed to prove that the π-π stacking between the interlaced tiny 'tails' and the interaction between the superimposed phenyl and carbonyl groups of adjacent mole­cules are the dominant interaction forces. What is more, this soft interaction model can be regarded as a powerful tool in the design and construction of supramolecular structures in the future. At present, tris(4-methyl cinnamate)amine synthesized by a single step, but with a gel-formation ability and an aggrega­tion-induced fluorescence emission property, has great potential application as functional soft matter in amorphous materials, photoelectric materials, and so on. [ABSTRACT FROM AUTHOR]