Potential for an underwater glider component as part of the Global Ocean Observing System

The contributions of autonomous underwater gliders as an observing platform in the in-situ global ocean observing system (GOOS) are investigated. The assessment is done in two ways: First, the existing in-situ observing platforms contributing to GOOS (floats, surface drifters, moorings, research/commercial ships) are characterized in terms of their current capabilities in sampling key physical and bio-geochemical oceanic processes. Next the gliders’ capabilities are evaluated in the context of key applications. This includes an evaluation of 140 references presented in the peer-reviewed literature. It is found that GOOS has adequate coverage of sampling in the open ocean for several physical processes. There is a lack of data in the present GOOS in the transition regions between the open ocean and shelf seas. However, most of the documented scientific glider applications operate in this region, suggesting that a sustained glider component in the GOOS could fill that gap. Glider data are included for routine product generation (e.g. alerts, maps). Other noteworthy process-oriented applications where gliders are important survey tools include local sampling of the (sub)mesoscale, sampling in shallow coastal areas, measurements in hazardous environments, and operational monitoring. In most cases, the glider studies address investigations and monitoring of processes across multiple disciplines, making use of the ease to implement a wide range of sensors to gliders. The maturity of glider operations, the wide range of applications that map onto growing GOOS regional needs, and the maturity of glider data flow all justify the formal implementation of gliders into the GOOS. Remaining challenges include the execution of coordinated multinational missions in a sustained mode as well as considering capacity-building aspects in glider operations as well as glider data use

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate