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COVID-19 has had a worldwide impact. The consensus is that the sudden pause of global production and the shrinking international trade will contract the global economy. This study explores the shor...

Angiotensin converting enzyme 2 (ACE2) is a key receptor present on cell surfaces that directly interacts with the viral spike (S) protein of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). It is proposed that inhibiting this interaction can be promising in treating COVID-19. Here, the presence of ACE2 in extracellular vesicles (EVs) is reported and the EV-ACE2 levels are determined by protein palmitoylation. The Cys141 and Cys498 residues on ACE2 are S-palmitoylated by zinc finger DHHC-Type Palmitoyltransferase 3 (ZDHHC3) and de-palmitoylated by acyl protein thioesterase 1 (LYPLA1), which is critical for the membrane-targeting of ACE2 and their EV secretion. Importantly, by fusing the S-palmitoylation-dependent plasma membrane (PM) targeting sequence with ACE2, EVs enriched with ACE2 on their surface (referred to as PM-ACE2-EVs) are engineered. It is shown that PM-ACE2-EVs can bind to the SARS-CoV-2 S-RBD with high affinity and block its interaction with cell surface ACE2 in vitro. PM-ACE2-EVs show neutralization potency against pseudotyped and authentic SARS-CoV-2 in human ACE2 (hACE2) transgenic mice, efficiently block viral load of authentic SARS-CoV-2, and thus protect host against SARS-CoV-2-induced lung inflammation. The study provides an efficient engineering protocol for constructing a promising, novel biomaterial for application in prophylactic and therapeutic treatments against COVID-19.

As an important scan plane, four chamber view is routinely performed in both second trimester perinatal screening and fetal echocardiographic examinations. The biometrics in this plane including cardio-thoracic ratio (CTR) and cardiac axis are usually measured by sonographers for diagnosing congenital heart disease. However, due to the commonly existing artifacts like acoustic shadowing, the traditional manual measurements not only suffer from the low efficiency, but also with the inconsistent results depending on the operators' skills. In this paper, we present an anchor-free ellipse detection network, namely EllipseNet, which detects the cardiac and thoracic regions in ellipse and automatically calculates the CTR and cardiac axis for fetal cardiac biometrics in 4-chamber view. In particular, we formulate the network that detects the center of each object as points and regresses the ellipses' parameters simultaneously. We define an intersection-over-union loss to further regulate the regression procedure. We evaluate EllipseNet on clinical echocardiogram dataset with more than 2000 subjects. Experimental results show that the proposed framework outperforms several state-of-the-art methods. Source code will be available at https://git.openi.org.cn/capepoint/EllipseNet ., Comment: MICCAI 2021

Aimed to realize reliable but time efficient data transmission in vulnerable space environment, an optimized sliding window transmission method based on BATS code (Batched Sparse Code) is proposed. In the scheme the unequal sliding step length of the window are used. While the small degree Batches are used to encode the data in the nonoverlapping part of the window, the big degree Batches are used to encode the data in whole sliding window, and the probability distribution of the Batch degree is optimized through a heuristic algorithm. The results of the simulation experiment show that compared with the traditional successive sliding window, the transmission efficiency of our method is significantly improved, while the reliability is maintained at a similar level as the traditional method, hence the trade-off between the transmission reliability and time efficiency is improved.