SIRT2 as a Potential Biomarker in Lung Adenocarcinoma: Implications for Immune Infiltration.

SIRT2 play important roles in cell cycle and cellular metabolism in the development of non-small cell lung cancer (NSCLC), and SIRT2 exhibits its therapeutic effect on NSCLC tumors with high expression of SIRT2. Nevertheless, the clinical relevance of SIRT2 in lung adenocarcinoma (LUAD), particularly its impact on tumor growth and prognostic implications, remains obscure. This investigation entailed a comprehensive analysis of SIRT2 mRNA and protein expression levels in diverse tumor and corresponding healthy tissues, utilizing databases such as TIMER 2.0, UALCAN, and HPA. Prognostic correlations of SIRT2 expression in LUAD patients, stratified by distinct clinicopathological characteristics, were evaluated using the KM Plotter database. Additionally, the TCGA and TIMER 2.0 databases were employed to assess the relationship between SIRT2 and immune infiltration, as well as to calculate immunity, stromal, and estimation scores, thus elucidating the role of SIRT2 in modulating tumor immunotherapy responses. Furthermore, Gene Set Enrichment Analysis (GSEA) was utilized to elucidate SIRT2's biological functions in pan-cancer cells. Our findings revealed a marked reduction in both mRNA and protein levels of SIRT2 in LUAD tumors relative to healthy tissue. Survival analysis indicated that diminished SIRT2 expression correlates with adverse prognostic outcomes in LUAD. Furthermore, SIRT2 expression demonstrated a significant association with various clinicopathologic attributes of LUAD patients, influencing survival outcomes across different clinicopathologic states. Functional enrichment analyses highlighted SIRT2's involvement in cell cycle regulation and immune response. Notably, SIRT2 exhibited a positive correlation with immune cell infiltration, including natural killer (NK) cells, macrophages, and dendritic cells (DCs). In summary, SIRT2 was a potential prognostic biomarker for LUAD and and a new immunotherapy target.

Managing energy-water-carbon-food nexus for cleaner agricultural greenhouse production: A control system approach.

Poverty, food insecurity and climate change are global issues facing humanity, threatening social, economic and environmental sustainability. Greenhouse cultivation provides a potential solution to these challenges. However, some greenhouses operate inefficiently and need to be optimized for more economical and cleaner crop production. In this paper, an economic model predictive control (EMPC) method for a greenhouse is proposed. The goal is to manage the energy-water­carbon-food nexus for cleaner production and sustainable development. First, an optimization model that minimizes the greenhouse's operating costs, including costs associated with greenhouse heating/cooling, ventilation, irrigation, carbon dioxide (CO2) supply and carbon emissions taking into account both the CO2 equivalent (CO2-eq) emissions caused by electrical energy consumption and the negative emissions caused by crop photosynthesis, is developed and solved. Then, a sensitivity analysis is carried out to study the impact of electricity price, supplied CO2 price and social cost of carbon (SCC) on the optimization results. Finally, a model predictive control (MPC) controller is designed to track the optimal temperature, relative humidity, CO2 concentration and incoming radiation power in presence of system disturbances. Simulation results show that the proposed approach increases the operating costs by R186 (R denotes the South African currency, Rand) but reduces the total cost by R827 and the carbon emissions by 1.16 tons when compared with a baseline method that minimizes operating costs only. The total cost is more sensitive to changes in SCC than that in electricity price and supplied CO2 price. The MPC controller has good tracking performance under different levels of system disturbances. Greenhouse environmental factors are kept within specified ranges suitable for crop growth, which increases crop yields. This study can provide effective guidance for growers' decision-making to achieve sustainable development goals.

Active nonlinear partial-state feedback control of contacting force for a pantograph-catenary system.

In this paper, a nonlinear partial-state feedback control is designed for a 3-DOF pantograph-catenary system by using backstepping approach, such that the contacting force of the closed-loop system is capable of tracking its reference profile. In the control design, the pantograph-catenary model is transformed into a triangular form, facilitating the utilization of backstepping. Derivatives of virtual controls in backstepping are calculated explicitly. A high-order differentiator is designed to estimate the unknown time derivatives of elasticity coefficient; and an observer is proposed to reconstruct the unmeasurable states. It can be proved theoretically that, with the proposed nonlinear partial-state feedback control, the tracking error of the contacting force is ultimately bounded with tunable ultimate bounds. Theoretical results are demonstrated by numerical simulations.