Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation.

Background: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence after ablation. This study aims to propose a novel algorithm based on Transformer using surface electrocardiogram (ECG) signals and clinical features can predict AF recurrence.

Methods: Between October 2018 to December 2021, patients who underwent index radiofrequency ablation for AF with at least one standard 10-second surface ECG during sinus rhythm were enrolled.

A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

Objective: This study aims to develop an artificial intelligence-based method to screen patients with left ventricular ejection fraction (LVEF) of 50% or lesser using electrocardiogram (ECG) data alone.

Methods: Convolutional neural network (CNN) is a class of deep neural networks, which has been widely used in medical image recognition. We collected standard 12-lead ECG and transthoracic echocardiogram (TTE) data including the LVEF value.

AChE inhibitory alkaloids from Coptis chinensis.

Coptis chinensis is a widely used traditional Chinese herbal medicine. In this work, 6 new alkaloids (coptisine A-F, 1-6) and 26 known alkaloids (7-32) were isolated from the chloroform extract of the rhizomes of C. chinensis.

Utilization of the saccharification residue of rice straw in the preparation of biochar is a novel strategy for reducing CO emissions.

Once rice straw has been bioconverted into biofuels, it is difficult to further biodegrade or decompose the saccharification residue (mainly lignin). Taking into account the pyrolysis characteristics of lignin, in this study the saccharification residue was used as a raw material for the preparation of biochar (biochar-SR), a potential soil amendment. Biochar was prepared directly from rice straw (biochar-O) with a yield of 32.

Design and composition of synthetic fungal-bacterial microbial consortia that improve lignocellulolytic enzyme activity.

Microbial interactions are important for metabolism as they can improve or reduce metabolic efficiency. To improve lignocellulolytic enzyme activity, a series of synergistic microbial consortia of increasing diversity and complexity were devised using fungal strains, including Trichoderma reesei, Penicillium decumbens, Aspergillus tubingensis, and Aspergillus niger. However, when a screened microbial community with cellulolytic capacity was added to the consortia to increase the number of strains, it engendered more microbial interactions with the above strains and universally improved the β-glucosidase activity of the consortia.

New triterpene saponins from the roots of Glycyrrhiza yunnanensis and their rapid screening by LC/MS/MS.

The roots of Glycyrrhiza yunnanensis Cheng f. et L. K.

Distortion of terahertz signals due to imperfect synchronization with chirped probe pulses.

Terahertz (THz) signals measured by means of the spectral-encoding technique with different temporal discrepancies between probe pulses and THz signals are investigated. It is found that imperfect synchronization between the chirped probe and THz pulses induce a distortion and this distortion affects significantly the retrieved THz spectrum if the temporal discrepancy is large. The distortion becomes more prominent if the probe pulse length is less than the optimal chirped probe pulse duration.