Flux balance models of metabolism generally utilize synthesis of biomass as the main determinant of intracellular fluxes. However, the biomass constraint alone is not sufficient to predict realistic fluxes in central heterotrophic metabolism of plant cells because of the major demand on the energy budget due to transport costs and cell maintenance. This major limitation can be addressed by incorporating transport steps into the metabolic model and by implementing a procedure that uses Pareto optimality analysis to explore the trade-off between ATP and NADPH production for maintenance. This leads to a method for predicting cell maintenance costs on the basis of the measured flux ratio between the oxidative steps of the oxidative pentose phosphate pathway and glycolysis. We show that accounting for transport and maintenance costs substantially improves the accuracy of fluxes predicted from a flux balance model of heterotrophic Arabidopsis cells in culture, irrespective of the objective function used in the analysis. Moreover, when the new method was applied to cells under control, elevated temperature and hyper-osmotic conditions, only elevated temperature led to a substantial increase in cell maintenance costs. It is concluded that the hyper-osmotic conditions tested did not impose a metabolic stress, in as much as the metabolic network is not forced to devote more resources to cell maintenance.
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Water Res X
May 2025
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.
Pumps in Water Distribution Networks (WDNs) adequately provide effective pressure where low elevation or high head losses are detected within the system. One of the most effective strategies to ensure economic sustainability is Pump Scheduling (PS), assuring the optimization of pump management and enabling significant energy cost saving. Meta-heuristic algorithms can be applied to Pump Scheduling, given their ability to provide reliable global solutions, further complemented by limited computational efforts.
View Article and Find Full Text PDFFront Plant Sci
January 2025
Department of Biology, University of Mississippi, University, MS, United States.
Temperature control is crucial for live cell imaging, particularly in studies involving plant responses to high ambient temperatures and thermal stress. This study presents the design, development, and testing of two cost-effective heating devices tailored for confocal microscopy applications: an aluminum heat plate and a wireless mini-heater. The aluminum heat plate, engineered to integrate seamlessly with the standard 160 mm × 110 mm microscope stage, supports temperatures up to 36°C, suitable for studies in the range of non-stressful warm temperatures (e.
View Article and Find Full Text PDFLett Appl Microbiol
January 2025
Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur 303002, India.
Azo dyes constitute 60-70% of commercially used dyes and are complex, carcinogenic, and mutagenic pollutants that negatively impact soil composition, water bodies, flora, and fauna. Conventional azo dye degradation techniques have drawbacks such as high production and maintenance costs, use of hazardous chemicals, membrane clogging, and sludge generation. Constructed Wetland-Microbial Fuel Cells (CW-MFCs) offer a promising sustainable approach for the bio-electrodegradation of azo dyes from textile wastewater.
View Article and Find Full Text PDFMed Sci Monit
January 2025
Department of Nephrology, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China.
BACKGROUND For patients with end-stage renal disease, arteriovenous fistulas (AVFs) are often used for hemodialysis, but stenosis can impair their function. Traditional inpatient procedures to address AVF stenosis are effective but resource-intensive, prompting the need for alternative approaches like day surgery to optimize care and reduce costs. This study evaluated the feasibility of a day surgery model for AVF stenosis treatment in maintenance hemodialysis (MHD) patients, aiming to develop a cost-effective and high-quality care model.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. In real production environments, the amount of time series data that can be collected during the manufacturing process is limited, which can lead to a decline in model performance.
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