SFRP4 signalling of apoptosis and angiostasis uses nitric oxide-cGMP-permeability axis of endothelium

Nitric oxide (NO) plays a critical role in endothelial functions such as cellular migration, vascular permeability and angiogenesis. Angiogenesis, the formation of new blood vessels from "pre-existing" ones is a carefully regulated process and essential during reproduction, development and wound healing. Previously our lab group reported that Secreted Frizzled-Related Protein 4 (sFRP4) could inhibit angiogenesis in both in vitro and in vivo conditions. sFRP4 belongs to a family of secreted glycoproteins that function as antagonists of the canonical Wnt signalling pathway. Although the pro-apoptotic role of sFRP4 is well discussed in literature, little is known in regards to its anti-angiogenic property. The objective of this study was to elucidate sFRP4 implications in NO biology of the endothelium. Results demonstrate that sFRP4 causes endothelial dysfunction by suppressing NO-cGMP signaling and elevating corresponding ROS levels. The imbalance between NO and ROS levels results in apoptosis and subsequent leakiness of endothelium as confirmed in vivo (Texas red/Annxin - CAM assay) and in vitro (Monolayer permeability assay) conditions. Furthermore utilizing peptides synthesized from the CRD domain of sFRP4, our results showed that while these peptides were able to cause endothelial dysfunctions, they did not cause apoptosis of the endothelial cells. Thereby confirming that sFRP4 can mediate its anti-angiogenic effect independent of its pro-apoptotic property. In conclusion, the current study reports that sFRP4-mediated anti-angiogenesis occurs as a result of impaired NO-cGMP signaling which in turn allow for elevation of redox levels and promotion of apoptosis of endothelial cells

Ocean barrier layers’ effect on tropical cyclone intensification

Improving a tropical cyclone’s forecast and mitigating its destructive potential requires knowledge of various environmental factors that influence the cyclone’s path and intensity. Herein, using a combination of observations and model simulations, we systematically demonstrate that tropical cyclone intensification is significantly affected by salinity-induced barrier layers, which are “quasi-permanent” features in the upper tropical oceans. When tropical cyclones pass over regions with barrier layers, the increased stratification and stability within the layer reduce storm-induced vertical mixing and sea surface temperature cooling. This causes an increase in enthalpy flux from the ocean to the atmosphere and, consequently, an intensification of tropical cyclones. On average, the tropical cyclone intensification rate is nearly 50% higher over regions with barrier layers, compared to regions without. Our finding, which underscores the importance of observing not only the upper-ocean thermal structure but also the salinity structure in deep tropical barrier layer regions, may be a key to more skillful predictions of tropical cyclone intensities through improved ocean state estimates and simulations of barrier layer processes. As the hydrological cycle responds to global warming, any associated changes in the barrier layer distribution must be considered in projecting future tropical cyclone activity

Marine Dynamics and Productivity in the Bay of Bengal

The Bay of Bengal provides important ecosystem services to the Bangladesh delta. It is also subject to the consequences of climate change as monsoon atmospheric circulation and fresh water input from the major rivers are the dominating influences. Changes in marine circulation will affect patterns of biological production through alterations in the supply of nutrients to photosynthesising plankton. Productivity in the northern Bay will also be sensitive to changes in riverborne nutrients. In turn, these changes could influence potential fish catch. The Bay also affects the physical environment of Bangladesh: relative sea-level rise is expected to be in the range of 0.5–1.7 m by 2100, and changing climate could affect the development of tropical cyclones over the Bay

Coronary arterial fistulas

ABSTRACT: A coronary arterial fistula is a connection between one or more of the coronary arteries and a cardiac chamber or great vessel. This is a rare defect and usually occurs in isolation. Its exact incidence is unknown. The majority of these fistulas are congenital in origin although they may occasionally be detected after cardiac surgery. They do not usually cause symptoms or complications in the first two decades, especially when small. After this age, the frequency of both symptoms and complications increases. Complications include 'steal' from the adjacent myocardium, thrombosis and embolism, cardiac failure, atrial fibrillation, rupture, endocarditis/endarteritis and arrhythmias. Thrombosis within the fistula is rare but may cause acute myocardial infarction, paroxysmal atrial fibrillation and ventricular arrhythmias. Spontaneous rupture of the aneurysmal fistula causing haemopericardium has also been reported. The main differential diagnosis is patent arterial duct, although other congenital arteriovenous shunts need to be excluded. Whilst two-dimensional echocardiography helps to differentiate between the different shunts, coronary angiography is the main diagnostic tool for the delineation of the anatomy. Surgery was the traditional method of treatment but nowadays catheter closure is recommended using a variety of closure devices, such as coils, or other devices. With the catheter technique, the results are excellent with infrequent complications. DISEASE NAME AND SYNONYMS: Coronary arterial fistulas Coronary arterial fistulas or malformation

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Self-organizing maps of typhoon tracks allow for flood forecasts up to two days in advance

[[abstract]]Typhoons are among the greatest natural hazards along East Asian coasts. Typhoon-related precipitation can produce flooding that is often only predictable a few hours in advance. Here, we present a machine-learning method comparing projected typhoon tracks with past trajectories, then using the information to predict flood hydrographs for a watershed on Taiwan. The hydrographs provide early warning of possible flooding prior to typhoon landfall, and then real-time updates of expected flooding along the typhoon’s path. The method associates different types of typhoon tracks with landscape topography and runoff data to estimate the water inflow into a reservoir, allowing prediction of flood hydrographs up to two days in advance with continual updates. Modelling involves identifying typhoon track vectors, clustering vectors using a self-organizing map, extracting flow characteristic curves, and predicting flood hydrographs. This machine learning approach can significantly improve existing flood warning systems and provide early warnings to reservoir management.[[notice]]補正完