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Why Do Weak-Binding M–N–C Single-Atom Catalysts Possess Anomalously High Oxygen Reduction Activity?
Mastitis-related Staphylococcus aureus-derived extracellular vesicles induce a pro-inflammatory response in bovine monocyte-derived macrophages
Abstract Staphylococcus aureus (S. aureus) is one of the most common causative agents of mammary gland infection and mastitis, but the specific role of S. aureus-derived extracellular vesicles (SaEVs) in mastitis has been poorly studied to date. Here, we aimed to investigate the response of bovine monocyte-derived macrophages (boMdM) to SaEVs of the genotype B (GTB) mastitis-related strain M5512B. Specifically, we evaluated the effects on the actin cytoskeleton, gene expression, and the SaEV proteomic cargo. Furthermore, we assessed to what extent the cellular and molecular response of boMdM to SaEVs differed from peripheral mononuclear blood cells (PBMCs) used for in vitro derivation of the former. We observed that SaEVs induced morphological changes in boMdM, leading to a pro-inflammatory and pyroptosis-related increased gene expression. Additionally, our study revealed that boMdM and PBMCs exhibited stimulus-specific differing responses. The proteomic analysis of SaEVs identified clusters of proteins related to virulence and antibiotic resistance, supporting the theory that S. aureus might use EVs to evade host defences and colonize the mammary gland. Our results bring new insights into how SaEVs might impact the host during an S. aureus infection, which can be useful for future S. aureus vaccine development.
In-Situ Thermometry Reveals Fragmentation Behavior Based on Local Temperature in α-Olefin Polymerization Catalysts
Titania nanotubes with aminated reduced graphene oxide as efficient photocatalysts for antibacterial application under visible light
Low-Temperature Exsolution of Rh from Mixed ZnFeRh Oxides toward Stable and Selective Catalysts in Liquid-Phase Hydroformylation
Numerical and machine learning based evaluation of ethylene glycol based hybrid nano-structured (TiO2-SWCNTs) fluid flow
Beyond the Vibrational Stark Effect: Unraveling the Large Redshifts of Alkyne C–H Bond in Solvation Environments
Author Correction: Optimization of process parameters of catalytic pyrolysis using natural zeolite and synthetic zeolites on yield of plastic oil through response surface methodology
Bio-orthogonal Labeling of Chitin in Native Pathogenic Candida Species via the Chitin Scavenge Pathway
Publisher Correction: Repurposing raltegravir for reducing inflammation and treating cancer: a bioinformatics analysis
Three-Dimensional Covalent Organic Frameworks with lil Topology
Information seeking and the expected utility of information about COVID-19 can be associated with uncertainty and related attitudes
Self-Assembled Monolayers of Triazolylidenes on Gold and Mixed Gold/Dielectric Substrates
Development of a new hybrid model to enhance streamflow estimation using artificial neural network and reptile search algorithm
Abstract A new metaheuristic optimizer combined with artificial neural networks is proposed for streamflow prediction. Hence, the study aimed to forecast monthly streamflow of the main rivers in Urmia, Iran, by considering data shortage and using artificial neural network (ANN) models. By combining three variables: temperature, precipitation, and streamflow, we formulated five patterns, where 70% of the data were used for model training, and 30% for model testing. To improve the performance of ANN, we evaluated a new optimization algorithm, reptile search algorithm (RSA), and compared the results with combinations of ANN, particle swarm optimization algorithm (PSO), and whale optimization algorithm (WOA) models. The results of the ANN + RSA were promising at most stations and patterns. At Band station streamflow simulation testing gave RMSE, MAE, and NSE of 1.65, 1.21 MCM/month, and 0.80, respectively. At Babaroud station they were 4.01, 3.0 MCM/month and 0.68, respectively, at Nazlo station 5.62, 3.79 MCM/month, and 0.69, respectively, and at Tapik station 5.69, 3.82 MCM/month, and 0.59, respectively. However, the results of the ANN + PSO hybrid model were better than ANN + RSA. The impact of different parameters on the accuracy of streamflow prediction varied depending on model and streamflow station, indicating that the models do not perform consistently across different locations, times, and conditions. The inclusion of lagged monthly streamflow in the model was an influential input parameter. The results demonstrated that the new algorithm consistently improved predictions, enhancing the performance of traditional algorithms. The findings of this study highlight advantage of the ANN + RSA hybrid model for specific areas, suggesting its potential application in other similar hydrological problems for further validation.