Inhibitors of the renin–angiotensin system: The potential role in the pathogenesis of COVID-19

Coronavirus disease 2019 (COVID-19), which initially began in China, has spread to other countries of Asia, Europe, America, Africa and Oceania, with the number of confirmed cases and suspected cases increasing each day. According to recently published research, it was found that the majority of the severe cases were elderly, and many of them had at least one chronic disease, especially cardiovascular diseases. Angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEIs/ARBs) are the most widely used drugs for cardiovascular diseases. The clinical effect of ACEIs/ARBs on patients with COVID-19 is still uncertain. This paper describes their potential role in the pathogenesis of COVID-19, which may provide useful in the advice of cardiologists and physicians

Multidimensional sound propagation in 3D high-order topological sonic insulator

High-order topological insulators (TIs) develop the conventional bulk-boundary correspondence theory and rise the interest in searching innovative topological materials. To realize a high-order TI with a wide passband of 1D and 2D transportation modes, we design non-trivial and trivial 3D sonic crystals whose combination mimics the Su-Schrieffer-Heeger model. The high-order topological boundary states can be found at the interfaces, including 0D corner state, 1D hinge state, and 2D surface state. The fabricated sample with the bent two-dimensional and one-dimensional acoustic channels exhibits the multidimensional sound propagation in space, and also verifies the transition between the complete band gap, hinge states, and surface states within the bulk band gap. Among them, the bandwidth of the single-mode hinge state achieves a large relative bandwidth 9.1%, in which sound transports one-dimensionally without significant leak into the surfaces or the bulk. The high-order topological states in the study pave the way for multidimensional sound manipulation in space.Comment: 21 pages, 7 figure

APICom: Automatic API Completion via Prompt Learning and Adversarial Training-based Data Augmentation

Based on developer needs and usage scenarios, API (Application Programming Interface) recommendation is the process of assisting developers in finding the required API among numerous candidate APIs. Previous studies mainly modeled API recommendation as the recommendation task, which can recommend multiple candidate APIs for the given query, and developers may not yet be able to find what they need. Motivated by the neural machine translation research domain, we can model this problem as the generation task, which aims to directly generate the required API for the developer query. After our preliminary investigation, we find the performance of this intuitive approach is not promising. The reason is that there exists an error when generating the prefixes of the API. However, developers may know certain API prefix information during actual development in most cases. Therefore, we model this problem as the automatic completion task and propose a novel approach APICom based on prompt learning, which can generate API related to the query according to the prompts (i.e., API prefix information). Moreover, the effectiveness of APICom highly depends on the quality of the training dataset. In this study, we further design a novel gradient-based adversarial training method {\atpart} for data augmentation, which can improve the normalized stability when generating adversarial examples. To evaluate the effectiveness of APICom, we consider a corpus of 33k developer queries and corresponding APIs. Compared with the state-of-the-art baselines, our experimental results show that APICom can outperform all baselines by at least 40.02\%, 13.20\%, and 16.31\% in terms of the performance measures EM@1, MRR, and MAP. Finally, our ablation studies confirm the effectiveness of our component setting (such as our designed adversarial training method, our used pre-trained model, and prompt learning) in APICom.Comment: accepted in Internetware 202

PigGIS: Pig Genomic Informatics System

Pig Genomic Information System (PigGIS) is a web-based depository of pig (Sus scrofa) genomic learning mainly engineered for biomedical research to locate pig genes from their human homologs and position single nucleotide polymorphisms (SNPs) in different pig populations. It utilizes a variety of sequence data, including whole genome shotgun (WGS) reads and expressed sequence tags (ESTs), and achieves a successful mapping solution to the low-coverage genome problem. With the data presently available, we have identified a total of 15 700 pig consensus sequences covering 18.5 Mb of the homologous human exons. We have also recovered 18 700 SNPs and 20 800 unique 60mer oligonucleotide probes for future pig genome analyses. PigGIS can be freely accessed via the web at and

Temporal and Quantitative Analysis of Atherosclerotic Lesions in Diet-Induced Hypercholesterolemic Rabbits

The diet-induced atherosclerotic rabbit is an ideal model for atherosclerosis study, but temporal changes in atherosclerotic development in hypercholesterolemic rabbits are poorly understood. Japanese white rabbits were fed a high-cholesterol diet to induce sustained hypercholesterolemia, and each group of 10–12 animals was then sacrificed at 6, 12, 16, or 28 weeks. The rabbit aortas were harvested, and the sizes of the gross and intima atherosclerotic lesions were quantified. The cellular component of macrophages (Mφs) and smooth muscle cells (SMCs) in aortic intimal lesions was also quantified by immunohistochemical staining, and the correlation between plasma cholesterol levels and the progress of atherosclerotic lesions was studied. The ultrastructure of the atherosclerotic lesions was observed by transmission electron microscopy (TEM). Widely variable atherosclerotic plaques were found from 6 weeks to 28 weeks, and the lesional progress was closely correlated with cholesterol exposure. Interestingly, a relatively reduced accumulation of Mφ, an increased numbers of SMCs, and a damaged endothelial layer were presented in advanced lesions. Moreover, SMCs were closely correlated with cholesterol exposure and lesional progress for the whole period. Cholesterol exposure directly determines atherosclerotic progress in a rabbit model, and the changes in the cellular component of advanced lesions may affect plaque stability in an atherosclerotic rabbit model

Prediction of Tropical Cyclones’ Characteristic Factors on Hainan Island Using Data Mining Technology

A new methodology combining data mining technology with statistical methods is proposed for the prediction of tropical cyclones’ characteristic factors which contain latitude, longitude, the lowest center pressure, and wind speed. In the proposed method, the best track datasets in the years 1949~2012 are used for prediction. Using the method, effective criterions are formed to judge whether tropical cyclones land on Hainan Island or not. The highest probability of accurate judgment can reach above 79%. With regard to TCs which are judged to land on Hainan Island, related prediction equations are established to effectively predict their characteristic factors. Results show that the average distance error is improved compared with the National Meteorological Centre of China