Activation of aldehyde dehydrogenase-2 improves ischemic random skin flap survival in rats

ObjectiveRandom skin flaps have many applications in plastic and reconstructive surgeries. However, distal flap necrosis restricts wider clinical utility. Mitophagy, a vital form of autophagy for damaged mitochondria, is excessively activated in flap ischemia/reperfusion (I/R) injury, thus inducing cell death. Aldehyde dehydrogenase-2 (ALDH2), an allosteric tetrameric enzyme, plays an important role in regulating mitophagy. We explored whether ALDH2 activated by N-(1,3-benzodioxol-5-ylmethyl)-2,6-dichlorobenzamide (Alda-1) could reduce the risk of ischemic random skin flap necrosis, and the possible mechanism of action.MethodsModified McFarlane flap models were established in 36 male Sprague-Dawley rats assigned randomly to three groups: a low-dose Alda-1 group (10 mg/kg/day), a high-dose Alda-1 group (20 mg/kg/day) and a control group. The percentage surviving skin flap area, neutrophil density and microvessel density (MVD) were evaluated on day 7. Oxidative stress was quantitated by measuring the superoxide dismutase (SOD) and malondialdehyde (MDA) levels. Blood perfusion and skin flap angiogenesis were assessed via laser Doppler flow imaging and lead oxide-gelatin angiography, respectively. The expression levels of inflammatory cytokines (IL-1β, IL-6, and TNF-α), vascular endothelial growth factor (VEGF), ALDH2, PTEN-induced kinase 1 (PINK1), and E3 ubiquitin ligase (Parkin) were immunohistochemically detected. Indicators of mitophagy such as Beclin-1, p62, and microtubule-associated protein light chain 3 (LC3) were evaluated by immunofluorescence.ResultsAlda-1 significantly enhanced the survival area of random skin flaps. The SOD activity increased and the MDA level decreased, suggesting that Alda-1 reduced oxidative stress. ALDH2 was upregulated, and mitophagy-related proteins (PINK1, Parkin, Beclin-1, p62, and LC3) were downregulated, indicating that ALDH2 inhibited mitophagy through the PINK1/Parkin signaling pathway. Treatment with Alda-1 reduced neutrophil infiltration and expressions of inflammatory cytokines. Alda-1 significantly upregulated VEGF expression, increased the MVD, promoted angiogenesis, and enhanced blood perfusion.ConclusionALDH2 activation can effectively enhance random skin flap viability via inhibiting PINK1/Parkin-dependent mitophagy. Moreover, enhancement of ALDH2 activity also exerts anti-inflammatory and angiogenic properties

Multi-objective planning of multi-type distributed generation considering timing characteristics and environmental benefits

This paper presents a novel approach to multi-type distributed generation (DG) planning based on the analysis of investment and income brought by grid-connected DG. Firstly, the timing characteristics of loads and DG outputs, as well as the environmental benefits of DG are analyzed. Then, on the basis of the classification of daily load sequences, the typical daily load sequence and the typical daily output sequence of DG per unit capacity can be computed. The proposed planning model takes the location, capacity and types of DG into account as optimization variables. An improved adaptive genetic algorithm is proposed to solve the model. Case studies have been carried out on the IEEE 14-node distribution system to verify the feasibility and effectiveness of the proposed method and model

Interplay of socio-economic and environmental factors in shaping urban plant biodiversity: a comprehensive analysis

Urban environments are dynamic landscapes shaped by a multitude of factors, including environmental conditions and socio-economic influences. This study systematically investigates how various factors shape urban plant diversity in Haikou City, Hainan Province, China, focusing on 30 key drivers including socio-economic aspects, biophysical conditions, landscape elements, and management practices. Our research methodology involved a comprehensive analysis of these factors’ impact on six types of urban plant species: spontaneous, native spontaneous, exotic spontaneous, cultivated, native cultivated, and exotic cultivated. Conducted in urban areas with varying population densities and landscape features, our sampling approach aimed to understand the species’ distribution patterns. We discovered significant correlations between plant species diversity and specific environmental and socio-economic variables. Our results indicate that spontaneous species are prevalent in densely populated areas with strong social ties, whereas areas rich in tree and shrub cover see fewer such species. Native cultivated species favor more serene, less urbanized landscapes, while exotic cultivated species are predominantly found in economically affluent areas with diverse vegetation. These findings offer valuable insights for urban planning and biodiversity conservation, emphasizing the need for customized greening strategies that align with local environmental and social contexts. By adopting such tailored approaches, urban planners can more effectively manage landscapes, enrich green spaces, and foster biodiverse, sustainable ecosystems. This research not only enhances our understanding of urban plant biodiversity but also lays the groundwork for future studies and policy-making, promoting harmonious integration of diverse plant life within urban settings

A multi-agent reinforcement learning-based method for server energy efficiency optimization combining DVFS and dynamic fan control

With the rapid development of the digital economy and intelligent industry, the energy consumption of data centers (DCs) has increased significantly. Various optimization methods are proposed to improve the energy efficiency of servers in DCs. However, existing solutions usually adopt model-based heuristics and best practices to select operations, which are not universally applicable. Moreover, existing works primarily focus on the optimization methods for individual components, with a lack of work on the joint optimization of multiple components. Therefore, we propose a multi-agent reinforcement learning-based method, named MRDF, combining DVFS and dynamic fan control to achieve a trade-off between power consumption and performance while satisfying thermal constraints. MRDF is model-free and learns by continuously interacting with the real server without prior knowledge. To enhance the stability of MRDF in dynamic environments, we design a data-driven baseline comparison method to evaluate the actual contribution of a single agent to the global reward. In addition, an improved Q-learning is proposed to deal with the large state and action space of the multi-core server. We implement MRDF on a Huawei Taishan 200 server and verify the effectiveness by running benchmarks. Experimental results show that the proposed method improves energy efficiency by an average of 3.9% compared to the best baseline solution, while flexibly adapting to different thermal constraints