Catalyst-free decarboxylative cross-coupling of -hydroxyphthalimide esters with -butyl 2-(trifluoromethyl)acrylate and its application.

Here, we demonstrate a practical method toward the facile synthesis of CF-containing amino acids through visible light promoted decarboxylative cross-coupling of a redox-active ester with -butyl 2-(trifluoromethyl)acrylate. The reaction was driven by the photochemical activity of electron donor-acceptor (EDA) complexes that were formed by the non-covalent interaction between a Hantzsch ester and a redox-active ester. The advantages of this protocol are its synthetic simplicity, rich functional group tolerance, and a cost-effective reaction system.

Segmentation and visualization of left atrium through a unified deep learning framework.

Purpose: Left atrium segmentation and visualization serve as a fundamental and crucial role in clinical analysis and understanding of atrial fibrillation. However, most of the existing methods are directly transmitting information, which may cause redundant information to be passed to affect segmentation performance. Moreover, they did not further consider atrial visualization after segmentation, which leads to a lack of understanding of the essential atrial anatomy.

Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End Encoder-Decoder Network.

Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex structure of CBV image makes fully automatic segmentation as a well-known challenge. In this paper, we propose an improved end-to-end encoder-decoder network for CBV segmentation from the pixel level view (Cardiac-DeepIED).

Direct Segmentation-Based Full Quantification for Left Ventricle via Deep Multi-Task Regression Learning Network.

Quantitative analysis of the heart is extremely necessary and significant for detecting and diagnosing heart disease, yet there are still some challenges. In this study, we propose a new end-to-end segmentation-based deep multi-task regression learning model (Indices-JSQ) to make a holonomic quantitative analysis of the left ventricle (LV), which contains a segmentation network (Img2Contour) and multi-task regression network (Contour2Indices). First, Img2Contour, which contains a deep convolutional encoder-decoder module, is designed to obtain the LV contour.