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Age-related osteoporosis is characterized by the deterioration in bone volume and strength, partly due to the dysfunction of bone marrow mesenchymal stromal/stem cells (MSCs) during aging. Alpha-ketoglutarate (αKG) is an essential intermediate in the tricarboxylic acid (TCA) cycle. Studies have revealed that αKG extends the lifespan of worms and maintains the pluripotency of embryonic stem cells (ESCs). Here, we show that the administration of αKG increases the bone mass of aged mice, attenuates age-related bone loss, and accelerates bone regeneration of aged rodents. αKG ameliorates the senescence-associated (SA) phenotypes of bone marrow MSCs derived from aged mice, as well as promoting their proliferation, colony formation, migration, and osteogenic potential. Mechanistically, αKG decreases the accumulations of H3K9me3 and H3K27me3, and subsequently upregulates BMP signaling and Nanog expression. Collectively, our findings illuminate the role of αKG in rejuvenating MSCs and ameliorating age-related osteoporosis, with a promising therapeutic potential in age-related diseases. α-ketoglutarate is an intermediate of the Krebs Cycle that was recently reported to extend lifespan in C.Elegans. Here, the authors show that administration of α-ketoglutarate to mice reduces age-related bone loss, by ameliorating senescence of bone-marrow derived mesenchymal stem cells.

The refractive index measurement of seawater has proven significance in oceanography, while an optical heterodyne interferometer is an important, highly accurate, tool used for seawater refractive index measurement. However, for practical seawater refractive index measurement, the refractive index of seawater needs to be monitored for long periods of time, and the influence of drift error on the measurement results for these cases cannot be ignored. This paper proposes a drift error compensation algorithm based on wavelet decomposition, which can adaptively separate the background from the signal, and then calculate the frequency difference to compensate for the drift error. It is suitable for unstable signals, especially signals with large differences between the beginning and the end, which is common in actual seawater refractive index monitoring. The authors identify that the primary cause of drift error is the frequency instability of the acousto-optic frequency shifter (AOFS), and the actual frequency difference was measured through experimentation. The frequency difference was around 0.1 Hz. Simulation experiments were designed to verify the effectiveness of the algorithm, and the standard deviation of the optical length of the results was on the scale of 10−8 m. Liquid refractive index measurement experiments were carried out in a laboratory, and the measurement error was reduced from 36.942% to 0.592% after algorithm processing. Field experiments were carried out regarding seawater refractive index monitoring, and the algorithm-processing results are able to match the motion of the target vehicle. The experimental data were processed with different algorithms, and, according to the comparison of the results, the proposed algorithm performs better than other existing drift error elimination algorithms.

Realizing real-time and rapid monitoring of crop growth is crucial for providing an objective basis for agricultural production. To enhance the accuracy and comprehensiveness of monitoring winter wheat growth, comprehensive growth indicators are constructed using measurements of above-ground biomass, leaf chlorophyll content and water content of winter wheat taken on the ground. This construction is achieved through the utilization of the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE) model. Additionally, a correlation analysis is performed with the selected vegetation indexes (VIs). Then, using unmanned aerial vehicle (UAV) multispectral orthophotos to construct VIs and extract texture features (TFs), the aim is to explore the potential of combining the two as input variables to improve the accuracy of estimating the comprehensive growth indicators of winter wheat. Finally, we develop comprehensive growth indicator inversion models based on four machine learning algorithms: random forest (RF); partial least squares (PLS); extreme learning machine (ELM); and particle swarm optimization extreme learning machine (PSO-ELM), and the optimal model is selected by comparing the accuracy evaluation indexes of the model. The results show that: (1) The correlation among the comprehensive growth indicators (CGIs) constructed by EWM (CGIewm) and FCE (CGIfce) and VIs are all improved to different degrees compared with the single indicators, among which the correlation between CGIfce and most of the VIs is larger. (2) The inclusion of TFs has a positive impact on the performance of the comprehensive growth indicator inversion model. Specifically, the inversion model based on ELM exhibits the most significant improvement in accuracy. The coefficient of determination (R2) values of ELM-CGIewm and ELM- CGIfce increased by 20.83% and 20.37%, respectively. (3) The CGIfce inversion model constructed by VIs and TFs as input variables and based on the ELM algorithm is the best inversion model (ELM-CGIfce), with R2 reaching 0.65. Particle swarm optimization (PSO) is used to optimize the ELM-CGIfce (PSO-ELM-CGIfce), and the precision is significantly improved compared with that before optimization, with R2 reaching 0.84. The results of the study can provide a favorable reference for regional winter wheat growth monitoring.

Detecting students’ classroom behaviors from instructional videos is important for instructional assessment, analyzing students’ learning status, and improving teaching quality. To achieve effective detection of student classroom behavior based on videos, this paper proposes a classroom behavior detection model based on the improved SlowFast. First, a Multi-scale Spatial-Temporal Attention (MSTA) module is added to SlowFast to improve the ability of the model to extract multi-scale spatial and temporal information in the feature maps. Second, Efficient Temporal Attention (ETA) is introduced to make the model more focused on the salient features of the behavior in the temporal domain. Finally, a spatio-temporal-oriented student classroom behavior dataset is constructed. The experimental results show that, compared with SlowFast, our proposed MSTA-SlowFast has a better detection performance with mean average precision (mAP) improvement of 5.63% on the self-made classroom behavior detection dataset.

Background Low back pain (LBP) is very common in women of child bearing age (WCBA) and is a major burden on individuals and society, but studies about it have not been reported. Methods Trend analyses were based on data from the Global Burden of Disease, Injuries, and Risk Factors Study (GBD) 2021. We investigated global trends in the number of prevalence cases, prevalence and the years lived with disability (YLDs), and the YLDs rate for LBP in the WCBA. We used joint point regression analyses to report average annual changes and identify the most variable years in the global trend. Simultaneously, we further analyzed these trends by stratifying them by the region, nation, and age. Results Globally, the number of the prevalence and the YLDs for LBP increased substantially from 1990 to 2021(the prevalence: 39.65%) (the YLDs: 39.53%), whereas the prevalence and the YLDs rate of WCBA LBP decreased (the prevalence: AAPC: -0.1355 [95% CI -0.1419 to -0.1288) (the YLDs: AAPC: -0.1365 [95% CI -0.1427 to -0.1302]). Regionally, the prevalence and the YLDs rate of LBP among WCBA in the medium socio-demographic index region showed an increasing trend(the prevalence: AAPC: 0.1291 [95% CI: 0.118 to 0.1396]) (the YLDs: AAPC: 0.1371 [95% CI: 0.1251 to 0.1499]). Nationally, Thailand and Vietnam were the countries with the largest increases in YLD rates(Thailand: AAPC: 0.7742 [95% CI: 0.7589 to 0.7904]) (Vietnam: AAPC: 0.7364 [95% CI: 0.7263 to 0.7468]). In age patterns, the highest prevalence and YLDs rates of LBP were found among women in the 45–49 age group(the prevalence: 14,412.77 per 100,000 in 2021 [95% UI: 10,482.27 to 19,077.3]) (the YLDs: 1,639.56 per 100,000 population (95% UI: 1,035.24 to 2,494.9]). Conclusion Despite declines in the prevalence and YLDs of WCBA LBP, the burden remains high. Age- and region-specific prevention and healthcare strategies should be optimized to meet the needs of WCBA and reduce the burden of disease.

Objective To assess geographical disparities in hospice and palliative care (HPC) based on provider-reported practices across Chinese provinces and to identify associated socioeconomic and health system factors. Study design Descriptive cross-sectional study. Methods A large cross-sectional survey was conducted among 6,393 healthcare providers from 903 institutions across 87 pilot cities in 29 provinces using a multi-stage stratified sampling strategy. Provider practice levels were measured using a validated 14-item scale (Cronbach’s α = 0.98; score range 14–70), reflecting the frequency of essential HPC activities. Spatial patterns were assessed using Global and Local Moran’s I, and factors associated with practice levels were examined using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). Results The average practice level score was 53.35 ± 1.52. Significant spatial clustering of HPC practice levels was observed, with high-high clusters in Shandong and Yunnan and low-low clusters in Xinjiang. Higher practice levels were unexpectedly associated with lower GDP per capita (β = -0.07, 95%CI: -1.31 to -0.13) and fewer hospitals per 10,000 people (β = -0.67, 95%CI: -1.24 to -0.10). Conclusions Substantial geographic disparities in provider practice levels reveals inequities in HPC service delivery across China. These findings underscore the need for region-specific interventions, strengthened community-based services, and policy frameworks to improve equitable access to HPC nationwide.

Peripheral nerve injury is a common complication in trauma, and regeneration and function recovery are clinical challenges. It is indispensable to find a suitable material to promote peripheral nerve regeneration due to the limited capacity of peripheral nerve regeneration, which is not an easy task to design a material with good biocompatibility, appropriate degradability. Magnesium has captured increasing attention during the past years as suitable materials. However, there are little types of research on magnesium promoting peripheral nerve regeneration. In this review, we conclude the possible mechanism of magnesium ion promoting peripheral nerve regeneration and the properties and application of different kinds of magnesium-based biomaterials, such as magnesium filaments, magnesium alloys, and others, in which we found some shortcomings and challenges. So, magnesium can promote peripheral nerve regeneration with both challenge and potential.

Reinforcement learning has potential in the area of intelligent transportation due to its generality and real-time feature. The Q-learning algorithm, which is an early proposed algorithm, has its own merits to solve the train timetable rescheduling (TTR) problem. However, it has shortage in two aspects: Dimensional limits of action and a slow convergence rate. In this paper, a deep deterministic policy gradient (DDPG) algorithm is applied to solve the energy-aimed train timetable rescheduling (ETTR) problem. This algorithm belongs to reinforcement learning, which fulfills real-time requirements of the ETTR problem, and has adaptability on random disturbances. Superior to the Q-learning, DDPG has a continuous state space and action space. After enough training, the learning agent based on DDPG takes proper action by adjusting the cruising speed and the dwelling time continuously for each train in a metro network when random disturbances happen. Although training needs an iteration for thousands of episodes, the policy decision during each testing episode takes a very short time. Models for the metro network, based on a real case of the Shanghai Metro Line 1, are established as a training and testing environment. To validate the energy-saving effect and the real-time feature of the proposed algorithm, four experiments are designed and conducted. Compared with the no action strategy, results show that the proposed algorithm has real-time performance, and saves a significant percentage of energy under random disturbances.

Background Enhancing public health emergency (PHE) management capacities has become a critical challenge in global public health governance. During the Coronavirus Disease 2019 (COVID-19) pandemic, Shanghai and Shenzhen implemented region-specific measures tailored to local conditions, reflecting China’s overarching control strategy. Systematically analysis of prevention and control policies is essential for optimizing PHE responses. While existing research has primarily focused on policy-outcome relationships through case studies or quantitative models, the application of policy tools across different pandemic stages remains underexplored. Methods To analyze the distribution and evolution of policy tools across pandemic stages, this study integrated the policy tool perspective with the “4Rs” crisis management theory to construct a two-dimensional analytical framework. Quantitative text analysis was employed within this framework to code and quantify pandemic prevention and control policies issued between January 2020 and December 2022. Policy texts were collected from the official websites of local governments and the Peking University Law website. Results From the perspective of policy tools, both Shanghai and Shenzhen predominantly relied on authority tools, followed by incentive tools, with system-changing tools being the least utilized. From the crisis management dimension, the frequency of policy tool usage peaked during the crisis outbreak period, dropped significantly during the crisis receding period, and slightly rebounded during the crisis recovery period. The two-dimensional analysis revealed that, apart from Shanghai’s emphasis on incentive tools during the crisis receding period, authority tools dominated across all crisis management stages in both cities. Additionally, as the pandemic progressed, the use of capacity-building tools and incentive tools increased significantly. Conclusions Chinese local governments primarily applied authority tools to drive institutional improvements, complemented by incentive tools and capacity-building tools to enhance policy effectiveness and public engagement. Optimizing PHE management requires dynamic adjustments to policy tools based on crisis stage characteristics, balancing rigidity with flexibility and immediate responses with long-term system development. The findings may provide valuable references for governments worldwide in formulating follow-up PHE policies and offer a replicable framework for future analyses in this field.

Diabetes has long been considered a risk factor in implant therapy and impaired wound healing in soft and hard oral tissues. Magnesium has been proved to promote bone healing under normal conditions. Here, we elucidate the mechanism by which Mg2+ promotes angiogenesis and osseointegration in diabetic status. We generated a diabetic mice model and demonstrated the alveolar bone healing was compromised, with significantly decreased angiogenesis. We then developed Mg-coating implants with hydrothermal synthesis. These implants successfully improved the vascularization and osseointegration in diabetic status. Mechanically, Mg2+ promoted the degradation of Kelch-like ECH-associated protein 1 (Keap1) and the nucleation of nuclear factor erythroid 2-related factor 2 (Nrf2) by up-regulating the expression of sestrin 2 (SESN2) in endothelial cells, thus reducing the elevated levels of oxidative stress in mitochondria and relieving endothelial cell dysfunction under hyperglycemia. Altogether, our data suggested that Mg2+ promoted angiogenesis and osseointegration in diabetic mice by regulating endothelial mitochondrial metabolism.