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Dysregulation of mitochondrial homeostasis is common to all types of cardiovascular diseases. SIRT3 regulates apoptosis and autophagy, material and energy metabolism, mitochondrial oxidative stress, inflammation, and fibrosis. As an important mediator and node in the network of mechanisms, SIRT3 is essential to many activities. This review explains how SIRT3 regulates mitochondrial homeostasis and the tricarboxylic acid cycle to treat common cardiovascular diseases. A novel description of the impact of lifestyle factors on SIRT3 expression from the angles of nutrition, exercise, and temperature is provided.

The practical application of solar energy harvesting urgently calls for the facile preparation of high‐performance ultrablack surfaces with broadband and omnidirectional light absorption performance; however, some roadblocks still need to meet these requirements simultaneously. Herein, cone‐array ultrablack (CAUB) surfaces are prepared by combining feasible template replication and blade coating techniques. The elaborate micro‐sized cone structure and high absorptive materials (polydimethylsiloxane@carbon black, PDMS@CB) offer the CAUB extremely high, broadband, and omnidirectional light absorption over the solar spectrum with an average absorbance of 99.35%. Under one sun irradiation, the CAUB can efficiently absorb solar energy, reaching a surface temperature as high as 75 °C. A solar‐thermal‐electric generator (STEG) made from the CAUB film demonstrates real‐time solar‐heat‐electric conversion, achieving a high output power density of 330 µW cm−2. The CAUB film possesses excellent water repellency, flexibility, chemical solvent resistance, and potential for large‐scale production, satisfying the application requirements of solar energy harvesting and utilization. [ABSTRACT FROM AUTHOR]

Climate change is causing an intensification of soil drying and rewetting events, altering microbial functioning and potentially destabilizing soil organic carbon. After rewetting, changes in microbial community carbon use efficiency (CUE), investment in life history strategies, and fungal to bacterial dominance co‐occur. Still, we have yet to generalize what drives these dynamic responses. Here, we collated 123 time series of microbial community growth (G, sum of fungal and bacterial growth, evaluated by leucine and acetate incorporation, respectively) and respiration (R) after rewetting and calculated CUE = G/(G + R). First, we characterized CUE recovery by two metrics: maximum CUE and time to maximum CUE. Second, we translated microbial growth and respiration data into microbial investments in life history strategies (high yield (Y), resource acquisition (A), and stress tolerance (S)). Third, we characterized the temporal change in fungal to bacterial dominance. Finally, the metrics describing the CUE recovery, investment in life history strategies, and fungal to bacterial dominance after rewetting were explained by environmental factors and microbial properties. CUE increased after rewetting as fungal dominance declined, but the maximum CUE was explained by the CUE under moist conditions, rather than specific environmental factors. In contrast, higher soil pH and carbon availability accelerated the decline of microbial investment in stress tolerance and fungal dominance. We conclude that microbial CUE recovery is mostly driven by the shifting microbial community composition and the metabolic capacity of the community, whereas changes in microbial investment in life history strategies and fungal versus bacterial dominance depend on soil pH and carbon availability. [ABSTRACT FROM AUTHOR]

Crack detection plays a crucial role in evaluating the safety and durability of civil infrastructure. However, detecting cracks of uneven intensity in complex backgrounds is challenging. To overcome this problem, we propose a dual decoder network (CSMT) based on a multi-branch aggregation Transformer, which uses residual atrous spatial pyramid pooling (RASPP) and Transformer dual decoding branches to extract local and global features of different structures. To enhance global feature extraction, we designed a multi-branch aggregation Transformer (MAT) that adaptively weights the features of two attention heads from spatial and channel dimensions to achieve intra block feature aggregation between dimensions. Meanwhile, to obtain multi-scale semantic information, we constructed a new decoding branch, RASPP, which embeds a squeeze-and-excitation (SE) module and residual structures into standard ASPP. Finally, we propose a feature adaptive fusion module (FAM) to enhance feature fusion between adjacent layers and codec layers. Many experiments on three benchmark datasets have shown that the proposed CSMT segmentation network provides excellent performance in a variety of complex scenarios. [ABSTRACT FROM AUTHOR]

The Madden–Julian Oscillation (MJO) is a crucial predictability source on a subseasonal-to-seasonal (S2S) timescale. Therefore, the models participating in the World Weather Research Programme and the World Climate Research Programme (WWRP/WCRP) S2S prediction project focus on accurately predicting and analyzing the MJO. This study provides a detailed description of the configuration within the Institute of Atmospheric Physics at the Chinese Academy of Sciences (IAP-CAS) S2S forecast system. We assess the accuracy of the IAP-CAS model's MJO forecast using traditional Real-time Multivariate MJO (RMM) analysis and cluster analysis. Then, we explain the reasons behind any bias observed in the MJO forecast. Comparing the 20-year hindcast with observations, we found that the IAP-CAS ensemble mean has a skill of 24 d. However, the ensemble spread still has potential for improvement. To examine the MJO structure in detail, we use cluster analysis to classify the MJO events during boreal winter into four types: fast-propagating, slow-propagating, standing, and jumping patterns of MJO. The model exhibits biases of overestimated amplitude and faster propagation speed in the propagating MJO events. Upon further analysis, it was found that the model forecasted a wetter background state. This leads to stronger forecasted convection and coupled waves, especially in the fast MJO events. The overestimation of the strength and length of MJO-coupled waves results in a faster MJO mode and quicker dissipation in the IAP-CAS model. These findings show that the IAP-CAS skillfully forecasts signals of MJO and its propagation, and they also provide valuable guidance for improving the current MJO forecast by developing the ensemble system and moisture forecast. [ABSTRACT FROM AUTHOR]

Extended reality (XR) is an immersive technology widely applied in various fields. Due to the real-time interaction required between users and virtual environments, XR applications are highly sensitive to latency. Furthermore, handling computationally intensive tasks on wireless XR devices leads to energy consumption, which is a critical performance constraint for XR applications. It has been noted that the XR task can be decoupled to several subtasks with mixed serial–parallel relationships. Furthermore, the evaluation of XR application performance involves both subjective assessments from users and objective evaluations, such as of energy consumption. Therefore, in edge computing environments, ways to integrate task offloading for XR subtasks to meet users' demands for XR applications is a complex and challenging issue. To address this issue, this paper constructs a wireless XR system based on mobile edge computing (MEC) and conducts research on the joint optimization of multi-user communication channel access and task offloading. Specifically, we consider the migration of partitioned XR tasks to MEC servers and formulate a joint optimization problem for communication channel access and task offloading. The objective is to maximize the ratio of quality of experience (QoE) to energy consumption while meeting the user QoE requirements. Subsequently, we introduce a deep reinforcement learning-based algorithm to address this optimization problem. The simulation results demonstrate the effectiveness of this algorithm in meeting user QoE demands and improving energy conversion efficiency, regardless of the XR task partitioning strategies employed. [ABSTRACT FROM AUTHOR]

Process monitoring and fault diagnosis systems are essential for ensuring the quality and safety of industrial processes. Real industrial process data always has dynamic features, autocorrelation features, and non‐Gaussian distributions, making many multivariate statistical process monitoring (MSPM) methods, such as principal component analysis (PCA), not work well. To solve the problem, a new process monitoring method (named DPCA‐ICA‐moving window Kolmogorov–Smirnov test [MWKS]) is proposed in the paper, which is a combination of DPCA and independent component analysis (ICA) with adaptive weight parameters and introduces a moving window technique and two‐sample K‐S test to obtain the K‐S distance of the commonly used Hotelling's T2 and square predictive error as new monitoring parameters. Experimental results on the Tennessee Eastman chemical process demonstrate the effectiveness and accuracy of the proposed method, which shows it has better performance than existing commonly used methods. The average fault detection rate (FDR) is 93.131%, while the average false alarm rate (FAR) of the proposed method is only 0.1%. [ABSTRACT FROM AUTHOR]

Farm leftovers, particularly crop residues, are a key source of renewable energy in Canada. The nation's robust agricultural industry provides ample biomass, derived from forestry and agriculture resources, for energy generation. Crop residues, such as straws and husks, play a crucial role in this biomass reservoir, contributing to biofuel production and greenhouse gas mitigation efforts. Focusing on supply chains, waste management, and emission reduction, this study evaluates the sustainability of wheat straw, an agricultural biomass by-product. The environmental issues of various approaches to managing agricultural biomass were explored. Following an evaluation of biomass features, conversion methods, and economic and environmental advantages, the results show anaerobic digestion to be the most sustainable approach. Four metrics were examined in relation to social elements, and numerous aspects were considered as inputs in the evaluation of transportation costs. The use of electric trucks versus fuel-based trucks resulted in an 18% reduction in total operating costs and a 58% reduction in consumption costs. This study examined CO2 emissions over four different transportation distances. The data indicate that a significant reduction of 36% in kg CO2 equivalent emissions occurred when the distance was lowered from 100 km to 25 km. These findings offer insights for creating practical plans that should increase the sustainability of agricultural biomass leftovers. [ABSTRACT FROM AUTHOR]

Background: LACS (long-chain acyl-CoA synthetase) genes are widespread in organisms and have multiple functions in plants, especially in lipid metabolism. However, the origin and evolutionary dynamics of the LACS gene family remain largely unknown. Results: Here, we identified 1785 LACS genes in the genomes of 166 diverse plant species and identified the clades (I, II, III, IV, V, VI) of six clades for the LACS gene family of green plants through phylogenetic analysis. Based on the evolutionary history of plant lineages, we found differences in the origins of different clades, with Clade IV originating from chlorophytes and representing the origin of LACS genes in green plants. The structural characteristics of different clades indicate that clade IV is relatively independent, while the relationships between clades (I, II, III) and clades (V, VI) are closer. Dispersed duplication (DSD) and transposed duplication (TRD) are the main forces driving the evolution of plant LACS genes. Network clustering analysis further grouped all LACS genes into six main clusters, with genes within each cluster showing significant co-linearity. Ka/Ks results suggest that LACS family genes underwent purifying selection during evolution. We analyzed the phylogenetic relationships and characteristics of six clades of the LACS gene family to explain the origin, evolutionary history, and phylogenetic relationships of different clades and proposed a hypothetical evolutionary model for the LACS family of genes in plants. Conclusions: Our research provides genome-wide insights into the evolutionary history of the LACS gene family in green plants. These insights lay an important foundation for comprehensive functional characterization in future research. [ABSTRACT FROM AUTHOR]

The photonic crystal waveguide (PCW) possesses remarkable capabilities in manipulating light beams and light–matter interactions within the subwavelength range. This property renders it a highly promising structure for the miniaturization of optical devices. We delve into the mode characteristics in two-dimensional PCWs on thin film lithium niobate, establish the correlation between the single-mode region in the PCW and its photonic crystal duty cycle, and observe mode hybridization in the waveguide. A lithium niobate PCW with sidewall angles can realize single-mode transmission or mode conversion by adjusting the width of its defective waveguides, and it is theoretically and experimentally verified that a change in the width of the waveguide shifts the operating wavelength. The results of the mode analysis are useful in the design of waveguide structures for photonic crystal-based electro-optical modulators and optical sensors. [ABSTRACT FROM AUTHOR]

The Madden-Julian Oscillation (MJO) is a crucial predictability source on a sub-seasonal to seasonal (S2S) timescale. Therefore, the models participating in the WWRP/WCRP S2S prediction project focus on accurately predicting and analyzing the MJO. This study provided a detailed description of the configuration within the IAP-CAS S2S forecast system. We assessed the accuracy of the IAP-CAS model's MJO forecast using traditional RMM analysis and cluster analysis. Then, we explained the reasons behind any bias observed in the MJO forecast. Comparing the 20-year hindcast with observations, we found that the IAP-CAS ensemble mean has a skill of 24 days. However, there is still room for improvement in the ensemble spread. To examine the MJO structure in detail, we used cluster analysis to classify the MJO events during boreal winter into four types: fast-propagating, slow-propagating, standing, and jumping patterns of MJO. The model exhibits biases of overestimated amplitude and faster propagation speed in the propagating MJO events. Upon further analysis, it was found that the model forecasted a wetter background state. This leads to more intense forecasted convection and stronger coupled winds, especially in the fast MJO events. However, the horizontal moisture advection effect for eastward propagation is overestimated in IAP-CAS due to the wetter state and more substantial MJO circulations, which results in a faster MJO mode. These findings show that the IAP-CAS skilfully forecasts signals of MJO and its propagation, and they also provide valuable guidance for improving the current MJO forecast by developing the ensemble system and moisture forecast. [ABSTRACT FROM AUTHOR]

The ever-increasing development of the information society brings convenience to human beings but also poses serious electromagnetic pollution problems. 3D-printed electromagnetic interference (EMI) shielding is regarded as a prospective solution as incident electromagnetic waves can be effectively reflected/scattered and lost in 3D-printed porous structures. Especially, the diversity of materials and structural design of 3D printing enable 3D-printed electromagnetic interference shields display extraordinary EMI shielding performance and considerable development potential in multi-scenario applications. This paper provides a comprehensive review of recent advances in the application of 3D printing technologies in EMI shielding. This review focuses on the mechanism of electromagnetic shielding, the electromagnetic shielding features of 3D-printed structures and the corresponding fabrication methods, as well as simulation/test techniques. Finally, the recent development of 3D-printed transparent electromagnetic shields and thermal management electromagnetic shields are also introduced, and future research and development directions for next-generation EMI shielding are envisioned. [ABSTRACT FROM AUTHOR]

This paper contributes to the existing knowledge on the correlation between green innovation and financial performance among small and medium‐sized enterprises (SMEs). It examines the effects of two types of green innovation, specifically green product innovation and green process innovation, on the financial performance of SMEs. Additionally, it investigates the moderating role of economic support and predetermined targets, which are essential components of government subsidy programs, in relation to the aforementioned associations. Based on the panel dataset collected from Chinese manufacturing and energy industries covering the period of 2011–2021, the results show that (1) green product innovation may not positively impact the financial performance of SMEs; (2) green process innovation could enhance the financial performance of SMEs; (3) economic support may positively moderate the relationship between green process innovation and the financial performance of SMEs, while predetermined targets may negatively influence this relationship. These findings have considerable implications for practitioners, policymakers, and researchers in the industrial sector. [ABSTRACT FROM AUTHOR]

Objective To explore the roles of iron overload in pro - atherogenic activation of foam cells. Methods RAW264.7 and MOVAS cells were stimulated by oxLDL, ferrimine citrate and deferoxamine respectively. Prussian Blue and Oil Red O staining were used to detect iron deposition and foam cell. CCK-8 test, DHE probe, ELISA, RT-qPCR were performed to detect the cell death rate, reactive oxygen species (ROS) generation, lipid peroxidation molecules [glutathione peroxidase (GSH), glutathione peroxidase 4 (GPX4), malondialdehyde (MDA) content] and the mRNA level of ATP binding cassette transporter A1 (ABCA1), ATP binding cassette transporter G1 (ABCG1), inductible nitris oxide synthase (iNOS), arginase-1 (Arg-1), a-smooth muscle actin (a-SMA), smooth muscle 22 alpha (SM22a), osteopontin (OPN), Interleukin-ip (IL-ip), tumor necrosis factor-a (TNF-a). Results Iron overload could reduced reverse cholesterol transporters (ABCA1 and ABCG1), promote foam cells generation, increased cell death rate, induced the expression of lipid peroxidation molecules (GSH, GPX4, MDA), and promoted pro-inflammatory M1 marker of macrophage and synthetic marker expression of vascular smooth muscle cell (VSMC) and inflammatory cytokines (IL-ip, TNF-a). Conclusion Iron overload promotes the generation of foam cells derived from macrophages and smooth muscle cells and transform them into pro - atherosclerotic phenotype, aggravates cell lipid peroxidation and inflammatory reaction, which contributes to the progress of atherosclerosis. [ABSTRACT FROM AUTHOR]

The structural phase evolution and superconductivity of perovskite RbPbI3 under pressure have been investigated by employing first-principles calculations. The findings suggest that RbPbI3 undergoes three structural phase transitions from ppPv-Pnma to ppPv-Pnma(I) phases at 4.6 GPa, from ppPv-Pnma(I) to ppPv-Pnma(V) phases at 19 GPa, and from ppPv-Pnma(V) to Pm phases at 69.2 GPa, respectively. Two novel phases ppPv-Pnma(V) and Pm are predicted, differing from previously reported results. Meanwhile, an obvious piezochromic effect appears in the ppPv-Pnma(I) phase due to the change of the band gap under pressure tuning. Quite surprisingly, the electron–phonon coupling calculations reveal that the ppPv-Pnma(V) phase may be a potential low-pressure superconductor with a Tc of 8 K (7.3 K) at 20 GPa, which is slightly higher than those of other similar perovskites or alloys (∼3 K). Our studies further provide theoretical support for exploring high-Tc low-pressure superconductivity and piezochromic effects in halide perovskites. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results Conclusion Aging skin, exacerbated by external factors like UV radiation and pollutants, is a major cosmetic concern. Taurine, renowned for its antioxidant and anti‐inflammatory properties, may combat skin aging. We performed mendelian randomization (MR) analysis to investigate the causal link between taurine and immune cells linked to skin aging.To investigate the association between taurine and immune cells using mendelian randomization, to thereby explore the mechanism through which taurine exerts anti‐aging effects on the skin via immune modulation.A MR approach was employed using taurine‐level data from the Ieu Open GWAS Project and immunocyte traits from a large European cohort. MR‐Egger regression, weighted median estimation, and inverse variance weighting all provided statistical insights into causality. Sensitivity analyses assessed the heterogeneity and pleiotropy among the genetic instruments used.MR analysis identified a causal relationship between taurine levels and 10 immunocyte phenotypes, with taurine found to be negatively and positively associated with three and seven phenotypes, respectively. Sensitivity analysis revealed no significant heterogeneity or pleiotropy, suggesting reliable MR findings.This study provides insights into the immunological pathways by which taurine contributes to skin anti‐aging effects, suggesting that increasing taurine levels could offer a novel strategy for anti‐aging skincare. [ABSTRACT FROM AUTHOR]