Arene Trifunctionalization with Highly Fused Ring Systems through a Domino Aryne Nucleophilic and Diels-Alder Cascade.

A convenient and efficient domino aryne process was developed under transition-metal-free conditions to generate a range of tetra- and pentacyclic ring systems. This transformation was realized via a 1,2-benzdiyne through a nucleophilic and Diels-Alder reaction cascade using styrene as the diene moiety. Three new chemical bonds, namely one C-N and two C-C bonds, and two benzofused rings could be constructed concomitantly, which was made possible by distinct chemoselective control at both the 1,2-aryne and 2,3-aryne stages. Moreover, in-depth studies were carried out on the domino aryne precursors and controlling the diastereoselectivity.

Switchable chemoselective aryne reactions between nucleophiles and pericyclic reaction partners using either 3-methoxybenzyne or 3-silylbenzyne.

Arynes are known to serve as highly reactive benzene-based synthons, which have gained numerous successes in preparing functionalized arenes. Due to the superb electrophilic nature of these fleeting species, however, it is challenging to modulate the designated aryne transformation chemoselectively, when substrates possess multiple competing reaction sites. Here, we showcase our effort to manipulate chemoselective control between two major types of aryne transformations using either 3-methoxybenzyne or 3-silylbenzyne, where nucleophilic addition-triggered reactions and non-polar pericyclic reactions could be differentiated. This orthogonal chemoselective protocol is found to be applicable between various nucleophiles, i.e., imidazole, N-tosylated/N-alkyl aniline, phenol, and alcohol, and an array of pericyclic reaction partners, i.e., furan, cyclopentadiene, pyrrole, cycloheptatrienone, and cyclohexene. Beyond arylation reactions, C-N bond insertion, Truce-Smiles rearrangement, and nucleophilic annulation are appropriate reaction modes as well. Moreover, this chemoselective protocol can find potential synthetic application.

Automatic 12-Leading Electrocardiogram Classification Network with Deformable Convolution.

Electrocardiogram (ECG) is an electrical signal that helps monitor the physiology of the heart. A complete ECG record includes 12 leads, each reflecting features from a different angle of the heart. In recent years, various deep learning algorithms, especially convolutional neural networks (CNN), have been applied to detect ECG features. However, the conventional CNN can only extract the local features and cannot extract the data correlation across the leads of ECG. Based on deformable convolution networks (DCN), this article proposes a new neural network structure (DCNet) to detect ECG features. The network architecture consists of four DCN blocks and a classification layer. For the ECG classification task, in a DCN block, the combination of normal convolution and deformable convolution with better effect was testified by the experiments. Based on the feature learning capability of DCN, the architecture can better extract the characteristics between leads. Using the public 12-leading ECG data in CPSC-2018, the diagnostic accuracy of this architecture is the highest, reaching 86.3%, which is superior to other common network architectures with good results in ECG signal classification.Clinical relevance-In this paper, we proposed an effective automatic ECG classification model that can reduce medical staff workload.