Explore the vast range of titles available.

Sclerostin has traditionally been thought of as a local inhibitor of bone acquisition that antagonizes the profound osteoanabolic capacity of activated Wnt/β-catenin signaling, but serum sclerostin levels in humans exhibit a correlation with impairments in several metabolic parameters. These data, together with the increased production of sclerostin in mouse models of type 2 diabetes, suggest an endocrine function. To determine whether sclerostin contributes to the coordination of whole-body metabolism, we examined body composition, glucose homeostasis, and fatty acid metabolism in Sost−/− mice as well as mice that overproduce sclerostin as a result of adeno-associated virus expression from the liver. Here, we show that in addition to dramatic increases in bone volume, Sost−/− mice exhibit a reduction in adipose tissue accumulation in association with increased insulin sensitivity. Sclerostin overproduction results in the opposite metabolic phenotype due to adipocyte hypertrophy. Additionally, Sost−/− mice and those administered a sclerostin-neutralizing antibody are resistant to obesogenic diet-induced disturbances in metabolism. This effect appears to be the result of sclerostin’s effects on Wnt signaling and metabolism in white adipose tissue. Since adipocytes do not produce sclerostin, these findings suggest an unexplored endocrine function for sclerostin that facilitates communication between the skeleton and adipose tissue.

Purpose As the largest source country for international students, Chinese students often encounter challenges related to mental health and study abroad satisfaction during the process of cross-cultural adaptation. While physical activity is generally considered related to mental health, its potential mechanisms and contextual moderating factors have not been systematically studied. This study aims to explore the relationship between physical activity, mental health, and study abroad satisfaction, and to examine the moderating role of digital social media use. Methods A cross-sectional survey was conducted among 589 Chinese international students from nine countries. Ten validated scales were used to assess physical activity level, psychological wellbeing, study abroad satisfaction, and related psychosocial mechanisms. Structural equation modeling and the PROCESS macro were employed to test chain mediation and moderation effects. Results There is a significant positive association between physical activity and mental health. The study identified two chain mediation relationships: (1) social interaction competence → emotion regulation ability → positive coping strategies; (2) body image satisfaction → cultural adaptation level → social support level. In addition, a significant relationship exists between mental health and study abroad satisfaction. Moderation analysis indicates that digital social media use is related to the positive association between physical activity and mental health. Conclusions Physical activity is positively associated with the mental health of Chinese international students and is related to higher study abroad satisfaction through dual psychosocial pathways. The use of digital social media appears to play a positive moderating role in this process, suggesting that cross-cultural interventions may achieve better outcomes by combining physical activity with appropriate digital support strategies.

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS.

Structural integrity of seawater pipelines in nuclear power plants is a very important issue. In accordance with the operating technical guidelines, the human operators directly enter the pipe and inspect it at every maintenance test. However, in this regard, safety issues such as narrow space and harmful gas are emerging every year. In response to these needs, a quadruped robot that can inspect underground pipes and assist workers has been developed. The robot has an articulated robotic arm that can receive an impact sound of hammering a pipe wall to test pipe integrity. The state of the pipe was examined using a Convolutional Neural Network algorithm. On the other hand, moving in a plumbing environment requires stable walking ability. To determine the gait sequence, a hierarchical gait controller is proposed. The hybrid controller, which consists of joint impedance and torque control, calculated from Model Predictive Control, can switch the gait modes comparing the reference and the current foot contact condition at each control cycle.

Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.

Significance Interleukin-1 family members are highly inflammatory but IL-37 member broadly suppresses inflammation and specific immunity. Initially, the mechanism of this suppression was shown to be via translocation to the nucleus following cleavage of the precursor by intracellular caspase-1. We now show that recombinant forms of IL-37 limit inflammation by extracellular binding to surface receptors but require the IL-1 family decoy receptor IL-1R8. Unexpectedly, picomolar concentrations of the IL-37 precursor optimally suppress IL-1β, IL-6, and TNFα production from human blood M1 macrophages, suggesting a unique function for a coreceptor function of IL-1R8. Assessment of IL-37 as well as IL-1R8 levels may provide previously unidentified insights into how the host limits inflammation. Similar to IL-1α and IL-33, IL-1 family member IL-37b translocates to the nucleus and is associated with suppression of innate and adaptive immunity. Here we demonstrate an extracellular function of the IL-37 precursor and a processed form. Recombinant IL-37 precursor reduced LPS-induced IL-6 by 50% ( P < 0.001) in highly inflammatory human blood-derived M1 differentiated macrophages derived from selective subjects but not M2 macrophages. In contrast, a neutralizing monoclonal anti–IL-37 increased LPS-induced IL-6, TNFα and IL-1β ( P < 0.01). The suppression by IL-37 was consistently observed at low picomolar but not nanomolar concentrations. Whereas LPS induced a 12-fold increase in TNFα mRNA, IL-37 pretreatment decreased the expression to only 3-fold over background ( P < 0.01). Mechanistically, LPS-induced p38 and pERK were reduced by IL-37. Recombinant IL-37 bound to the immobilized ligand binding α-chain of the IL-18 receptor as well as to the decoy receptor IL-1R8. In M1 macrophages, LPS increased the surface expression of IL-1R8. Compared with human blood monocytes, resting M1 cells express more surface IL-1R8 as well as total IL-1R8; there was a 16-fold increase in IL-1R8 mRNA levels when pretreated with IL-37. IL-37 reduced LPS-induced TNFα and IL-6 by 50–55% in mouse bone marrow-derived dendritic cells, but not in dendritic cells derived from IL-1R8–deficient mice. In mice subjected to systemic LPS-induced inflammation, pretreatment with IL-37 reduced circulating and organ cytokine levels. Thus, in addition to a nuclear function, IL-37 acts as an extracellular cytokine by binding to the IL-18 receptor but using the IL-1R8 for its anti-inflammatory properties.

The vanadium redox flow battery is considered one of the most promising candidates for use in large-scale energy storage systems. However, its commercialization has been hindered due to the high manufacturing cost of the vanadium electrolyte, which is currently prepared using a costly electrolysis method with limited productivity. In this work, we present a simpler method for chemical production of impurity-free V 3.5+ electrolyte by utilizing formic acid as a reducing agent and Pt/C as a catalyst. With the catalytic reduction of V 4+ electrolyte, a high quality V 3.5+ electrolyte was successfully produced and excellent cell performance was achieved. Based on the result, a prototype catalytic reactor employing Pt/C-decorated carbon felt was designed, and high-speed, continuous production of V 3.5+ electrolyte in this manner was demonstrated with the reactor. This invention offers a simple but practical strategy to reduce the production cost of V 3.5+ electrolyte while retaining quality that is adequate for high-performance operations. The vanadium redox flow battery is promising for commercial applications, but is hampered by high-cost electrolytes that are typically prepared via electrolysis. Here the authors demonstrate cost-effective chemical production of a high-quality vanadium electrolyte using platinum nanoparticles as a catalyst.

Maintenance of bone mass is coordinated with adipose tissue function through the secretion of hormones and endocrine factors that act on the opposing tissue. Sclerostin, a small glycoprotein produced by osteocytes embedded within the bone matrix, potently suppresses bone formation by antagonizing Wnt/β-catenin signaling while stimulating adipose tissue accumulation via the same mechanism of action. Since sclerostin-deficient mice develop pockets of multilocular adipocytes in subcutaneous adipose, we investigate the influence of sclerostin on thermogenic and β3-adrenergic stimuli-induced white adipose tissue beiging. Here, we report that Sost gene expression in bone and serum sclerostin levels are induced by β3-adrenergic agonists via an adipose-to-bone relay. Gene knockout studies suggest sclerostin acts to inhibit adipose tissue beiging by modulating β-catenin, as male Sost -/- mice display a greater abundance of beige adipocytes after chronic treatment with CL316,243 or cold exposure. Likewise, housing at thermoneutrality is sufficient to eliminate the decrease in fat mass and increased insulin sensitivity evident in sclerostin mutants under standard conditions. We also demonstrate that co-administration of a β3-adrenergic agonist and a sclerostin neutralizing antibody synergistically influences metabolic parameters in a mouse obesity model. These data suggest utility in interrogating this interaction in the treatment of metabolic disorders. This manuscript from Choquette and colleagues finds that sclerostin gene expression is up-regulated in bone by β3-adrenergic signaling via an adipose to bone relay and acts as an endocrine factor to inhibit adipose tissue beiging in mice.

An integrated navigation system is a promising solution to improve positioning performance by complementing estimated positioning in each sensor, such as a global positioning system (GPS), an inertial measurement unit (IMU), and an odometer sensor. However, under GPS-disabled environments, such as urban canyons or tunnels where the GPS signals are difficult to receive, the positioning performance of the integrated navigation system decreases. Therefore, deep learning-based integrated navigation systems have been proposed to ensure seamless localization under various positioning conditions. Nevertheless, the conventional deep learning-based systems are applied with a lack of consideration of context features on surface condition, wheel slip, and movement pattern, which are factors causing positioning performance. In this paper, a context-aware integrated navigation system (CAINS) is proposed to ensure seamless localization, especially under GPS-disabled conditions. In the proposed CAINS, two deep learning layers are designed with context-aware and state estimation layers. The context-aware layer extracts vehicle context features from IMU data, while the state estimation layer predicts the GPS position increments by modeling the relationship between context features, velocity, attitude, and position increments. From simulation results, it is confirmed that the positioning accuracy can be significantly improved based on the proposed CAINS when compared with conventional navigation systems.

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized.