a-mouse-56th-nephrectomy-model-that-induces-experimental-uremic

A Mouse 5/6<sup>th</sup> Nephrectomy Model That Induces Experimental Uremic Cardiomyopathy

Chronic kidney disease (CKD) is a great risk factor for cardiovascular disease events and mortality, and progressively develops to the clinical phenotype ...

enhanced-visual-slam-path-planning-for-autonomous-navigation-wheeled

Enhanced Visual SLAM and Path Planning for Autonomous Navigation of Wheeled Mobile Robots

This research focuses on important technologies used in wheeled mobile robot autonomous navigation, such as path planning optimization, system ...

Marshall University Joan C. Edwards School of Medicine

Universiti Sains

Other than genetic regulation of salt sensitivity of blood pressure, many factors have been shown to regulate renal sodium handling which contributes to long-term blood pressure regulation and have been extensively reviewed. Here we present our progress on the Na/K-ATPase signaling mediated sodium reabsorption in renal proximal tubules, from cardiotonic steroids-mediated to reactive oxygen species (ROS)-mediated Na/K-ATPase signaling that contributes to experimental salt sensitivity.

Na/K-ATPase Signaling and Salt Sensitivity: The Role of Oxidative Stress.

Water turbidity is an important indicator for evaluating water clarity and plays an important role in environmental protection and ecological balance. Due to the subtle changes in water turbidity images, the differences captured are often too subtle to be classified. Convolutional neural networks (CNN) are widely used in image classification and perform well in feature extraction and classification. This study explored the application of convolutional neural networks in water turbidity classification. The innovation lies in applying CNN to water turbidity images, focusing on optimizing the CNN model to improve prediction accuracy and efficiency. The study proposed four CNN models for water turbidity classification based on artificial intelligence, and adjusted the number of model layers to improve prediction accuracy. Experiments were conducted on noise-free and noisy datasets to evaluate the accuracy and running time of the models. The results show that the CNN-10 model with a dropout layer has a classification accuracy of 96.5% under noisy conditions. This study has opened up new applications of CNN in fine-grained image classification, and further demonstrated the effectiveness of convolutional neural networks in water turbidity image classification through experiments.

ArticleTotal : 2	Year
Publication title Cited by 40	2017
Publication title Cited by 1	2025

Article	Year
Na/K-ATPase Signaling and Salt Sensitivity: The Role of Oxidative Stress. Antioxidants (Basel, Switzerland)\| PubMed ID: 28257114	2017
An improved CNN model in image classification application on water turbidity. Scientific reports\| PubMed ID: 40175397	2025

Ying Nie

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