Role of arginase 2 in systemic metabolic activity and adipose tissue fatty acid metabolism in diet-induced obese mice

Visceral adipose tissue (VAT) inflammation and metabolic dysregulation are key components of obesity-induced metabolic disease. Upregulated arginase, a ureahydrolase enzyme with two isoforms (A1-cytosolic and A2-mitochondrial), is implicated in pathologies associated with obesity and diabetes. This study examined A2 involvement in obesity-associated metabolic and vascular disorders. WT and globally deleted A2(−/−) or A1(+/−) mice were fed either a high fat/high sucrose (HFHS) diet or normal diet (ND) for 16 weeks. Increases in body and VAT weight of HFHS-fed WT mice were abrogated in A2−/−, but not A1+/−, mice. Additionally, A2−/− HFHS-fed mice exhibited higher energy expenditure, lower blood glucose, and insulin levels compared to WT HFHS mice. VAT and adipocytes from WT HFHS fed mice showed greater A2 expression and adipocyte size and reduced expression of PGC-1α, PPAR-γ, and adiponectin. A2 deletion blunted these effects, increased levels of active AMPK-α, and upregulated genes involved in fatty acid metabolism. A2 deletion prevented HFHS-induced VAT collagen deposition and inflammation, which are involved in adipocyte metabolic dysfunction. Endothelium-dependent vasorelaxation, impaired by HFHS diet, was significantly preserved in A2−/− mice, but more prominently maintained in A1+/− mice. In summary, A2 is critically involved in HFHS-induced VAT inflammation and metabolic dysfunction

Segmented Learning for Class-of-Service Network Traffic Classification

Class-of-service (CoS) network traffic classification (NTC) classifies a group of similar traffic applications. The CoS classification is advantageous in resource scheduling for Internet service providers and avoids the necessity of remodelling. Our goal is to find a robust, lightweight, and fast-converging CoS classifier that uses fewer data in modelling and does not require specialized tools in feature extraction. The commonality of statistical features among the network flow segments motivates us to propose novel segmented learning that includes essential vector representation and a simple-segment method of classification. We represent the segmented traffic in the vector form using the EVR. Then, the segmented traffic is modelled for classification using random forest. Our solution's success relies on finding the optimal segment size and a minimum number of segments required in modelling. The solution is validated on multiple datasets for various CoS services, including virtual reality (VR). Significant findings of the research work are i) Synchronous services that require acknowledgment and request to continue communication are classified with 99% accuracy, ii) Initial 1,000 packets in any session are good enough to model a CoS traffic for promising results, and we therefore can quickly deploy a CoS classifier, and iii) Test results remain consistent even when trained on one dataset and tested on a different dataset. In summary, our solution is the first to propose segmentation learning NTC that uses fewer features to classify most CoS traffic with an accuracy of 99%. The implementation of our solution is available on GitHub.Comment: The paper is accepted to be appeared in IEEE GLOBECOM 202

Self-Deployment of Non-stationary Wireless Systems by Knowledge Management with Artificial Intelligence

status: Published onlin

Self-optimization of Wireless Systems with Knowledge Management: An Artificial Intelligence Approach

status: Published onlin

Simulation and retrofitting of mass exchange networks in fertilizer plants

Abstract This paper presents a simulation technique for optimizing a hydrogen integration network. By applying this technique, the minimum fresh hydrogen consumption can be determined. Quantitative relationship between sources and sinks streams were studied to get the flow rates of coupled source and sink, hydrogen consumption and hydrogen concentration in each stream. The introduced technique was applied on twelve sources and twelve sinks with any purity of hydrogen concentration. The hydrogen integration network was designed through two steps, the first step considers applying the data given in the LINGO program, while the second step considers using the LINGO results in the introduced excel program to obtain the retrofitted hydrogen integration network. The proposed technique was applied on several case studies to achieve the minimum consumption of fresh hydrogen for the obtained hydrogen integrated networks. The introduced model for simulation and retrofitting of mass exchange networks is easy to understand and the results showed that this model is more efficient for fertilizer, petrochemical and refinery plants