Energy-water management system based on robust predictive control for open-field cultivation.

Food availability has been endangered by recent global events, where agriculture, the main food source for the global population, is expected to increase even more to fulfill the growing food demand. Along with food production, water and energy consumption are also increased, leading to over-extraction of groundwater and an excess emission of greenhouse gases due to fossil fuel consumption. In this context, a balance of these three resources is crucial; therefore, the water-energy-food nexus is considered to address the previous issues by designing an energy-water management system based on robust predictive control. This controller estimates the future worst-case scenario for multiple climatic conditions, such as solar radiation, ambient temperature, wind speed, precipitation, and groundwater recharge, to define an optimal irrigation volume, maximize crop growth, and minimize water consumption. At the same time, the controller schedules daily irrigation and groundwater extraction, considering energy availability from solar generation and storage, to fulfill the previously defined irrigation volume while minimizing operating costs. Climate prediction is done through fuzzy prediction intervals, whose lower or upper bound are used as worst-case to include climate uncertainty on the controller design. The energy-water management system is tested in different experiments, where results show that considering a robust approach ensures maximum crop development, avoids over-extraction of groundwater, and prioritizes renewable energy sources. This work proposes a robust energy-water management system designed to be sustainable. Considering the water-energy-food nexus, the system ensures food security and proper resource allocation, tackling global starvation, water availability, and energy access.

A new level of regulation in gluconeogenesis: metabolic state modulates the intracellular localization of aldolase B and its interaction with liver fructose-1,6-bisphosphatase.

Understanding how glucose metabolism is finely regulated at molecular and cellular levels in the liver is critical for knowing its relationship to related pathologies, such as diabetes. In order to gain insight into the regulation of glucose metabolism, we studied the liver-expressed isoforms aldolase B and fructose-1,6-bisphosphatase-1 (FBPase-1), key enzymes in gluconeogenesis, analysing their cellular localization in hepatocytes under different metabolic conditions and their protein-protein interaction in vitro and in vivo. We observed that glucose, insulin, glucagon and adrenaline differentially modulate the intracellular distribution of aldolase B and FBPase-1. Interestingly, the in vitro protein-protein interaction analysis between aldolase B and FBPase-1 showed a specific and regulable interaction between them, whereas aldolase A (muscle isozyme) and FBPase-1 showed no interaction. The affinity of the aldolase B and FBPase-1 complex was modulated by intermediate metabolites, but only in the presence of K(+). We observed a decreased association constant in the presence of adenosine monophosphate, fructose-2,6-bisphosphate, fructose-6-phosphate and inhibitory concentrations of fructose-1,6-bisphosphate. Conversely, the association constant of the complex increased in the presence of dihydroxyacetone phosphate (DHAP) and non-inhibitory concentrations of fructose-1,6-bisphosphate. Notably, in vivo FRET studies confirmed the interaction between aldolase B and FBPase-1. Also, the co-expression of aldolase B and FBPase-1 in cultured cells suggested that FBPase-1 guides the cellular localization of aldolase B. Our results provide further evidence that metabolic conditions modulate aldolase B and FBPase-1 activity at the cellular level through the regulation of their interaction, suggesting that their association confers a catalytic advantage for both enzymes.

Fuzzy-model-based hybrid predictive control.

In this paper we present a method of hybrid predictive control (HPC) based on a fuzzy model. The identification methodology for a nonlinear system with discrete state-space variables based on combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for HPC design, where the optimization problem is solved by the use of genetic algorithms (GAs). An illustrative experiment on a hybrid tank system is conducted to demonstrate the benefits of the proposed approach.

Lack of iNKT cells in patients with combined immune deficiency due to hypomorphic RAG mutations.

Hypomorphic mutations of the RAG genes in humans are associated with a spectrum of clinical and immunologic presentations that range from T(-) B(-) severe combined immune deficiency (SCID) to Omenn syndrome. In most cases, residual V(D)J recombination activity allows for development of few T-cell clones, which expand in the periphery and infiltrate target organs, resulting in tissue damage. Invariant natural killer T (iNKT) cells play an important immunoregulatory role and have been associated with protection against autoimmunity. We now report on 5 unrelated cases of combined immune deficiency due to hypomorphic RAG mutations, and demonstrate the absence of iNKT cells in all 5 patients. These findings suggest that lack of this important immunoregulatory cell population may contribute to the pathophysiology of Omenn syndrome.