Physiological and pharmacological implications of AT1 versus AT2 receptors

Physiological and pharmacological implications of AT1versus AT2receptors. Angiotensin II (Ang II) has diverse physiological actions that lead, for instance, to increases in extracellular volume and peripheral vascular resistance and blood pressure, and it has also been implicated in the regulation of cell growth and differentiation. Molecular cloning and pharmacological studies have defined two major classes of Ang II receptors, designated AT1 and AT2. Most effects of Ang II are mediated by AT1 receptors. Much less is known about the physiological role of AT2 receptors. Recent evidence suggests involvement of AT2 receptors in development, cell differentiation, apoptosis, and regeneration in various tissues. AT1 and AT2 receptors have been shown to exert counteracting effects on cellular growth and differentiation, vascular tone, and the release of arginine vasopressin. In each condition, the AT2 receptor appears to down-modulate actions mediated by the AT1 receptor, resulting in decreased cellular proliferation, decreased levels of serum arginine vasopressin levels, or decreased vasoconstrictor responses. In addition, in neuronal cell lines, the AT2 receptor exerts antiproliferative actions and promotes neurite outgrowth, an effect accompanied by significant changes in the expression pattern of growth/differentiation-related genes

AB2CD: AI for Building Climate Damage Classification and Detection

We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards, utilizing remote sensing data. The xBD dataset, comprising diverse disaster events from across the globe, serves as the primary focus, facilitating the evaluation of deep learning models. We tackle the challenges of generalization to novel disasters and regions while accounting for the influence of low-quality and noisy labels inherent in natural hazard data. Furthermore, our investigation quantitatively establishes that the minimum satellite imagery resolution essential for effective building damage detection is 3 meters and below 1 meter for classification using symmetric and asymmetric resolution perturbation analyses. To achieve robust and accurate evaluations of building damage detection and classification, we evaluated different deep learning models with residual, squeeze and excitation, and dual path network backbones, as well as ensemble techniques. Overall, the U-Net Siamese network ensemble with F-1 score of 0.812 performed the best against the xView2 challenge benchmark. Additionally, we evaluate a Universal model trained on all hazards against a flood expert model and investigate generalization gaps across events, and out of distribution from field data in the Ahr Valley. Our research findings showcase the potential and limitations of advanced AI solutions in enhancing the impact assessment of climate change-induced extreme weather events, such as floods and hurricanes. These insights have implications for disaster impact assessment in the face of escalating climate challenges.Comment: 9 pages, 4 figure

Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation

Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natural hazards is an emerging priority for the support of natural disaster relief, risk management, and informing governmental policy decisions. Recent methods to achieve near real-time mapping increasingly leverage deep learning (DL). However, DL-based approaches are designed for one specific task in a single geographic region based on specific frequency bands of satellite data. Therefore, DL models used to map specific natural hazards struggle with their generalization to other types of natural hazards in unseen regions. In this work, we propose a methodology to significantly improve the generalizability of DL natural hazards mappers based on pre-training on a suitable pre-task. Without access to any data from the target domain, we demonstrate this improved generalizability across four U-Net architectures for the segmentation of unseen natural hazards. Importantly, our method is invariant to geographic differences and differences in the type of frequency bands of satellite data. By leveraging characteristics of unlabeled images from the target domain that are publicly available, our approach is able to further improve the generalization behavior without fine-tuning. Thereby, our approach supports the development of foundation models for earth monitoring with the objective of directly segmenting unseen natural hazards across novel geographic regions given different sources of satellite imagery.Comment: Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023