Combined Effect of Storm Surge and Overland Flow on Flooding in a Coastal Urban Area

The coastal regions in the U.S. East Coast and the Gulf of Mexico are under the risk of storm surge and precipitation-driven flooding. The adverse impacts of climate change including sea level rise (SLR), potential increase in intensity and frequency of extreme storms, and increase in precipitation intensity increases the vulnerability of coastal communities to flooding. The common practice for flood hazard assessment in urban coastal areas can result in some errors as the effect of storm surge and overland flow are not considered simultaneously. In this study, we combine the results of two hydrodynamic models, one for overland flow and the other for storm surge inundation, to develop an improved approach for flood hazard assessment

Functionally distinct and selectively phosphorylated GPCR subpopulations co-exist in a single cell.

G protein-coupled receptors (GPCRs) transduce pleiotropic intracellular signals in a broad range of physiological responses and disease states. Activated GPCRs can undergo agonist-induced phosphorylation by G protein receptor kinases (GRKs) and second messenger-dependent protein kinases such as protein kinase A (PKA). Here, we characterize spatially segregated subpopulations of β2-adrenergic receptor (β2AR) undergoing selective phosphorylation by GRKs or PKA in a single cell. GRKs primarily label monomeric β2ARs that undergo endocytosis, whereas PKA modifies dimeric β2ARs that remain at the cell surface. In hippocampal neurons, PKA-phosphorylated β2ARs are enriched in dendrites, whereas GRK-phosphorylated β2ARs accumulate in soma, being excluded from dendrites in a neuron maturation-dependent manner. Moreover, we show that PKA-phosphorylated β2ARs are necessary to augment the activity of L-type calcium channel. Collectively, these findings provide evidence that functionally distinct subpopulations of this prototypical GPCR exist in a single cell

Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models

Machine unlearning aims to remove information derived from forgotten data while preserving that of the remaining dataset in a well-trained model. With the increasing emphasis on data privacy, several approaches to machine unlearning have emerged. However, these methods typically rely on complete supervision throughout the unlearning process. Unfortunately, obtaining such supervision, whether for the forgetting or remaining data, can be impractical due to the substantial cost associated with annotating real-world datasets. This challenge prompts us to propose a supervision-free unlearning approach that operates without the need for labels during the unlearning process. Specifically, we introduce a variational approach to approximate the distribution of representations for the remaining data. Leveraging this approximation, we adapt the original model to eliminate information from the forgotten data at the representation level. To further address the issue of lacking supervision information, which hinders alignment with ground truth, we introduce a contrastive loss to facilitate the matching of representations between the remaining data and those of the original model, thus preserving predictive performance. Experimental results across various unlearning tasks demonstrate the effectiveness of our proposed method, Label-Agnostic Forgetting (LAF) without using any labels, which achieves comparable performance to state-of-the-art methods that rely on full supervision information. Furthermore, our approach excels in semi-supervised scenarios, leveraging limited supervision information to outperform fully supervised baselines. This work not only showcases the viability of supervision-free unlearning in deep models but also opens up a new possibility for future research in unlearning at the representation level

An Amplification-free CRISPR-SERS Biosensor for Specific, Sensitive and Rapid Detection of \u3ci\u3eSalmonella\u3c/i\u3e Typhimurium in Poultry

CRISPR (Clustered regularly interspaced short palindromic repeats) has been a cutting-edge biotechnology in both gene editing and biosensing due to its desirable features, such as high base resolution and set-and-forget operation. However, as a nucleic acid detection method, an DNA/RNA amplification procedure is still inevitable in CRISPR biosensors. In this study, a highly-ordered nanopore array SERS substrate, coated with silver nanoparticles, was fabricated and utilized as a platform for the specific detection of Salmonella Typhimurium (S. Typhimurium) without the time-consuming DNA amplification step. The smart sensing strategy was established by introducing an ssDNA labeled with a Raman active molecule as a signal report probe to show the changes during target identification and detection. The CRISPR-SERS biosensor was developed by combining the ultra-sensitive sensing mechanism of CRISPR technology with the SERS analytical method, aiming at detecting the invasin A gene (invA) of S. Typhimurium. The sensing materials and operation conditions were optimized to maximize the output signal and minimize the cost. The results showed that the proposed biosensor could specifically detect S. Typhimurium with a limit of detection (LOD) of 110 CFU/mL in poultry samples within 2 h. This CRISPR-SERS sensing strategy could provide an alternative way for pathogen detection in food safety monitoring

Dynamic Modeling of Inland Flooding and Storm Surge on Coastal Cities Under Climate Change Scenarios: Transportation Infrastructure Impacts in Norfolk, Virginia USA as a Case Study

Low-lying coastal cities across the world are vulnerable to the combined impact of rainfall and storm tide. However, existing approaches lack the ability to model the combined effect of these flood mechanisms, especially under climate change and sea level rise (SLR). Thus, to increase flood resilience of coastal cities, modeling techniques to improve the understanding and prediction of the combined effect of these flood hazards are critical. To address this need, this study presents a modeling system for assessing the combined flood impact on coastal cities under selected future climate scenarios that leverages ocean modeling with land surface modeling capable of resolving urban drainage infrastructure within the city. The modeling approach is demonstrated in quantifying the impact of possible future climate scenarios on transportation infrastructure within Norfolk, Virginia, USA. A series of combined storm events are modeled for current (2020) and projected future (2070) climate scenarios. The results show that pluvial flooding causes a larger interruption to the transportation network compared to tidal flooding under current climate conditions. By 2070, however, tidal flooding will be the dominant flooding mechanism with even nuisance flooding expected to happen daily due to SLR. In 2070, nuisance flooding is expected to cause a 4.6% total link close time (TLC), which is more than two times that of a 50-year storm surge (1.8% TLC) in 2020. The coupled flood model was compared with a widely used but physically simplistic bathtub method to assess the difference resulting from the more complex modeling presented in this study. The results show that the bathtub method overestimated the flooded area near the shoreline by 9.5% and 3.1% for a 10-year storm surge event in 2020 and 2070, respectively, but underestimated the flooded area in the inland region by 9.0% and 4.0% for the same events. The findings demonstrate the benefit of sophisticated modeling methods compared to more simplistic bathtub approaches, in climate adaptive planning and policy in coastal communities

Anti-platelet aggregation activities of different grades of Angelica sinensis and their therapeutic mechanisms in rats with blood deficiency: insights from metabolomics and lipidomics analyses

In traditional Chinese medicine, the radix of Angelica sinensis (Oliv.) Diels (RAS) is mainly used to replenish and invigorate the blood circulation. This study investigated anti-platelet aggregation activities were used by New Zealand rabbits, and high-performance liquid chromatography data were obtained to determine the spectrum–effect relationship for different commercial grades of RAS. Plasma and urine metabolites were examined using ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry-based metabolomics to elucidate the mechanisms underlying the role of these metabolites in a rat model of blood deficiency (BD). Plasma and spleen metabolites were additionally examined using ultra-performance liquid chromatography plus Q-Exactive tandem mass spectrometry-based lipidomics to clarify the mechanisms of RAS in treating BD. The third grade of RAS exhibited the best activity in replenishing and invigorating blood in vitro and in vivo. Ferulic acid, ligustilide, senkyunolide I, uridine, and guanine are quality markers of anti-platelet aggregation activity. Based on the metabolomics results, 19 potential biomarkers were screened in plasma, and 12 potential metabolites were detected in urine. In lipidomics analyses, 73 potential biomarkers were screened in plasma, and 112 potential biomarkers were screened in the spleen. RAS may restore lipid metabolism by regulating disorders of glycerophospholipid and sphingolipid metabolism, the tricarboxylic acid cycle, amino acid metabolism (thereby improving energy metabolism), and arachidonic acid metabolism (thereby promoting blood circulation). These results provide a deeper understanding of the effects of different grades of RAS and a scientific reference for the establishment of grading standards and for the clinical use of RAS

Urban Flood Extent Segmentation and Evaluation from Real-World Surveillance Camera Images Using Deep Convolutional Neural Network

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. High-definition web cameras can be an alternative tool with the models trained on the data it collected. In conclusion, DCNN-based models can extract flood extent from camera images of urban flooding. The challenges with using these models on real-world data identified through this research present opportunities for future research

Family and job microsystems as mediators between social integration and depression among rural-to-urban migrant workers in China: does having sons make a difference?

BackgroundRural-to-urban migrant workers are a vulnerable group at risk of developing depression. Based on the social-ecological systems theory, this study investigates the impact of the lack of social integration on depression, considering the mediating roles of migrant workers’ microsystems (family happiness and job burnout). Additionally, the study explores whether having sons influences these associations.MethodsThe sample of 4,618 rural-to-urban migrant workers was obtained from the 2018 wave of the China Labor Force Dynamics Survey (CLDS). All the measures in the survey exhibited good reliability, including the Center for Epidemiological Research Depression Scale (CES-D), family happiness, job burnout, and social integration. The data were primarily analyzed using a structural equation model.ResultsSocial integration had a direct impact on depression among migrant workers. Additionally, it indirectly affected depression through the mediating roles of family happiness not job burnout. The moderating effect of having sons mainly occurred on the path from social integration to family happiness.LimitationsThe cross-sectional design impeded the ability to draw causal inferences.ConclusionThis finding highlights the potential benefits of social integration and family happiness in promoting early prevention of depression among migrant workers. It indicates that the inclination toward having sons among migrant workers continues to impact their mental health