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Osteoarthritis (OA) is a prevalent progressive disorder. Moxibustion has found widespread use in clinical practice for OA, while its underlying mechanism remains elusive. To investigate whether moxibustion can ameliorate OA by influencing the metabolic processes in OA and to elucidate the specific metabolic mechanisms involved. C57BL/6J WT mice were randomly assigned to one of three groups: the SHAM group, the ACLT group, and the ACLT+M group. In the ACLT+M group, mice underwent moxibustion treatment at acupoints Shenshu (BL23) and Zusanli (ST36) for a continuous period of 28 days, with each session lasting 20 min. We conducted a comprehensive analysis to assess the impact of moxibustion on OA, focusing on pathological changes, intestinal flora composition, and serum metabolites. Moxibustion treatment effectively mitigated OA-related pathological changes. Specifically, moxibustion treatment resulted in the amelioration of articular cartilage damage, synovial inflammation, subchondral bone sclerosis when compared to the ACLT group. Moreover, 16S rDNA sequencing analysis revealed that moxibustion treatment positively influenced the composition of the flora, making it more similar to that of the SHAM group. Notably, moxibustion treatment led to a reduction in the abundance of Ruminococcus and Proteobacteria in the intestine. In addition, non-targeted metabolomics analysis identified 254 significantly different metabolites between the groups. Based on KEGG pathway analysis and the observed impact of moxibustion on OA-related inflammation, moxibustion therapy is closely associated with the cAMP-related signaling pathway. Moxibustion can relieve OA by regulating intestinal flora and via impacting cAMP-related signaling pathway. [Display omitted] • Moxibustion mitigates cartilage destruction, synovial inflammation, and subchondral bone sclerosis. • Metabolomics analysis were used to investigate the effective mechanisms of Moxibustion in treating OA. • Moxibustion promote cartilage protection via impacting cAMP-related signaling pathway. • Our results provide a theoretical basis for OA treatment through the application of Moxibustion. [ABSTRACT FROM AUTHOR]

Biomass-based energy systems are gaining popularity as a clean and renewable source of energy, and have the potential to be a major contributor to the transition towards a sustainable energy future. In this regard, a novel multi-generation energy system is developed based on the biomass combustion. Wood chip and rice straw biomass is combusted with air agent and triggers an integrated system comprises a gas turbine cycle, a proton exchange membrane electrolyzer, a supercritical carbon dioxide Brayton cycle, and a humidification-dehumidification desalination system. The performances of wood chip and rice straw in triggering the system are compared from environmental and energy indicators viewpoints. The system fueled by wood chip emits lower carbon dioxide emission compared to the system fueled by rice straw (8.294 g/kWh compared to 10.41 g/kWh). However, the rice straw-fueled system results in higher efficiency than the wood chip-fueled system (69.9% compared to 69.1%). The system fueled by wood chip produces 2.68 kg/h of hydrogen while this value if 1.96 kg/h for the system fueled by rice straw. The research validated the possibility of utilizing energy systems that incorporate wood chip and rice straw biomass across various generations and showed the superiority of the wood chip in most cases. [ABSTRACT FROM AUTHOR]

Low-Intensity Pulsed Ultrasound (LIPUS) holds therapeutic potential in promoting skeletal muscle regeneration, a biological process mediated by satellite cells and myoblasts. Despite their central roles in regeneration, the detailed mechanistic of LIPUS influence on satellite cells and myoblasts are not fully underexplored. In the current investigation, we administrated LIPUS treatment to injured skeletal muscles and C2C12 myoblasts over five consecutive days. Muscle samples were collected on days 6 and 30 post-injury for an in-depth histological and molecular assessment, both in vivo and in vitro with immunofluorescence analysis. During the acute injury phase, LIPUS treatment significantly augmented the satellite cell population, concurrently enhancing the number and size of newly formed myofibers whilst reducing fibrosis levels. At 30 days post-injury, the LIPUS-treated group demonstrated a more robust satellite cell pool and a higher myofiber count, suggesting that early LIPUS intervention facilitates satellite cell proliferation and differentiation, thereby promoting long-term recovery. Additionally, LIPUS markedly accelerated C2C12 myoblast differentiation, with observed increases in AMPK phosphorylation in myoblasts, leading to elevated expression of Glut4 and PGC-1α, and subsequent glucose uptake and mitochondrial biogenesis. These findings imply that LIPUS-induced modulation of myoblasts may culminate in enhanced cellular energy availability, laying a theoretical groundwork for employing LIPUS in ameliorating skeletal muscle regeneration post-injury. Utilizing the cardiotoxin (CTX) muscle injury model, we investigated the influence of LIPUS on satellite cell homeostasis and skeletal muscle regeneration. Our findings indicate that LIPUS promotes satellite cell proliferation and differentiation, thereby facilitating skeletal muscle repair. Additionally, in vitro investigations lend credence to the hypothesis that the regulatory effect of LIPUS on satellite cells may be attributed to its capability to enhance cellular energy metabolism. • LIPUS promotes skeletal muscle regeneration by enhancing the activity of satellite cells and myoblasts. • Early LIPUS intervention can promotes long-term recovery by facilitating satellite cell proliferation and differentiation. • LIPUS treatment markedly accelerates C2C12 myoblast differentiation by enhancing cellular energy availability. • The study lay a theoretical foundation forthe clinical application of LIPUS in improving skeletal muscle recovery. [ABSTRACT FROM AUTHOR]

A series of novel non-covalent peptidomimetic proteasome inhibitors possessing bulky group at the C-terminus and N -alkylation at the N -terminus were designed with the aim to increase metabolic stabilities in vivo. All the target compounds were screened for their inhibitory activities against human 20S proteasome, and most analogs exhibited notable potency compared with the positive control bortezomib with IC 50 values lower than 10 nM, which also displayed potent cytotoxic activities against multiple myeloma (MM) cell lines and human acute myeloid leukemia (AML) cells. Furthermore, whole blood stability and in vivo proteasome inhibitory activity experiments of selected compounds were conducted for further evaluation, and the representative compound 43 (IC 50 = 8.39 ± 2.32 nM, RPMI-8226: IC 50 = 15.290 ± 2.281 nM, MM-1S: IC 50 = 9.067 ± 3.103 nM, MV-4-11: IC 50 = 2.464 ± 0.713 nM) revealed a half-life extension of greater than 9-fold (329.21 min VS 36.79 min) and potent proteasome inhibitory activity in vivo. The positive results confirmed the reliability of the metabolism guided optimization strategy, and the analogs discovered are potential leads for exploring new anti -MM drugs. [Display omitted] • A series of non-covalent proteasome inhibitors with bulky group and N -alkylation were synthesized and evaluated. • Compounds 43 and 53 exhibited excellent proteasome inhibitory activities. • Compounds 43 and 53 displayed potent antiproliferative activities against RPMI-8226, MM-1S and MV-4-11 cell lines. • Compound 43 showed high metabolic stability in vitro. [ABSTRACT FROM AUTHOR]

• A cleaner vanadium extraction process with ultrasonic-H 2 O 2 synergistic acid leaching was developed. • Under the new process, vanadium recovery and H 2 O 2 decomposition rate increased by 63.30% and 106.38%. • The ultrasonic process requires only 50% of the energy consumption of conventional conditions. Residual vanadium accounts for about 1% of calcification roasted-acid leaching tailings after industrial vanadium extraction. Vanadium pollution is primarily induced by the stacking of tailings. In this study, the conventional leaching process based on sulfuric acid is compared with the ultrasound-H 2 O 2 synergistic enhanced process in the extraction of residual vanadium from calcification roasted-acid leaching tailings. Besides, the experimental process parameters are also optimized. Under the optimal conditions, the vanadium leaching rate increases by 63.64%, and the energy consumption decreases by 50%. The experimental enhancement mechanism is clarified by exploring the decomposition patterns of H 2 O 2 under ultrasound in combination with the characterization results based on electron paramagnetic resonance (EPR). Under the action of ultrasound, H 2 O 2 is effectively decomposed into •OH radicals to provide higher oxidation potential, which accelerates the reaction process. Through the characterization and analysis of the samples before and after the experiment, it is found that H 2 O 2 enhances the cavitation effect of ultrasound. This effectively destroys the composite oxide structure on the surface of tailing particles, thus increasing the reaction area. In conclusion, the ultrasound-H 2 O 2 synergistic vanadium leaching process exhibits broad application prospects in the management and recovery of industrial tailings. [ABSTRACT FROM AUTHOR]

Hypervirulent carbapenem-resistant Klebsiella pneumoniae (hv-CRKP) has been widely reported and poses a global threat. However, the comprehensive genetic structure of ST11-KL64 hv-CRKP and the possible evolutionary mechanisms from a genetic structure perspective of this high-risk clone remain unclear. Here, a bla KPC-2 - bla NDM-1 -positive ST11-KL64 hv-CRKP isolate was obtained from a human bloodstream infection (BSI). Whole-genome sequencing and bioinformatics analyses revealed that it contained a fusion plasmid, pKPTCM2–1. pKPTCM2–1 is a conjugative plasmid composed of an oriT -positive pLVPK-like virulence plasmid and a type IV secretion system-produced bla NDM-1 -bearing IncX3 plasmid mediated by IS 26 -based co-integration. This progress generated 8-bp target site duplications (TGAAAACC) on both sides. The fusion plasmid possessed self-transferability and could be transferred to bla KPC-2 -harboring ST11-KL64 CRKP to form the ST11-KL64 hv-CRKP clone. The pLVPK-like-positive ST11-KL64 strain exhibited virulence levels similar to those of the typical hypervirulent K. pneumoniae NTUH-2044. The mutation, Tet(A) (A276S), which was believed to lead to tigecycline resistance was observed. Overall, this high-risk clone has emerged as a tremendous threat in fatal BSIs and thus, targeted surveillance is an urgent need to contain the hv-CRKP clones. • One bla KPC-2 - bla NDM-1 -positive ST11-KL64 hv-CRKP in human bloodstream infection. • oriT -positive pLVPK-like virulence plasmid and bla NDM-1 -bearing IncX3 plasmid fusion. • One mutation of Tet(A) (A276S) maybe lead to tigecycline resistance. [ABSTRACT FROM AUTHOR]

• An integrated framework is proposed to predict lane-changing risk. • Driving intentions are recognized using LSTM neural network. • LGBM algorithm achieves a higher lane-changing risk prediction accuracy. • Feature importance analysis is conducted using LGBM classifier. Proactive lane-changing (LC) risk prediction can assist driver's LC decision-making to ensure driving safety. However, most previous studies on LC risk prediction did not consider the driver's intention recognition, which made it difficult to guarantee the timeliness and practicability of LC risk prediction. Moreover, the difference in driving risks and its influencing factors between LC to left lane (LCL) and LC to right lane (LCR) have rarely been investigated. To bridge the above research gaps, this study proposes a proactive LC risk prediction framework which integrates the LC intention recognition module and LC risk prediction module. The Long Short-term Memory (LSTM) neural network with time-series input was employed to recognize the driver's LC intention. The Light Gradient Boosting Machine (LGBM) algorithm was then applied to predict the LC risk. Feature importance analysis was lastly conducted to obtain the key features that affect the LC risk. The highD trajectory dataset was used for framework validation. Results show that the recognition accuracy of the driver's LCL, LCR and lane-keeping (LK) intentions based on the proposed LSTM model are 97%, 96% and 97%, respectively. Meanwhile, the LGBM algorithm outperforms other machine learning algorithms in LC risk prediction. The results from feature importance analysis show that the interaction characteristics of the LC vehicle and its preceding vehicle in the current lane have the greatest impact on the LC risk. The proposed framework could potentially be implemented in advanced driver-assistance system (ADAS) or autonomous driving system for improved driving safety. [ABSTRACT FROM AUTHOR]

• A hybrid deep learning model was developed for delay propagation patterns. • Train timetables were modeled as images. • A cost-sensitive technique was used to address the data imbalance challenge. • The proposed model shows satisfactory performance on different situations. As a vital component of train operational control, train delay propagation pattern discovery is critically important for both railway controllers and passengers. In this study, we present a carefully designed deep learning model, called FCF-Net, that comprises fully connected neural networks (FCNN) and convolutional neural networks (CNN) for train delay propagation pattern recognition in railway systems. FCF-Net first uses a CNN component that handles train timetables as images to capture interactions of train events and an FCNN component to capture the influence of non-operational features separately; then it uses another FCNN component to combinedly learn the dependencies between operational and non-operational features. In addition, considering the imbalance of train delay data, a cost-sensitive technique that assigns different misclassification costs for different class was used to better deal with the imbalanced data. The main goal of the FCF-Net is to realize efficient and accurate train delay propagation pattern recognition by mining potential knowledge from train operation data. The predictive and computational performance of the model was tested and evaluated on data from two high-speed railway lines with different operational features in China. The results show that FCF-Net, once trained with sufficient data, outperforms conventional deep learning with common loss and other state-of-the-art deep learning models for train delay propagation pattern recognition, indicating its capability in knowledge discovery from train operation data. In addition, the computational results show that FCF-Net exhibits more efficient training process than existing state-of-the-art deep learning models. [ABSTRACT FROM AUTHOR]

Predicting the population density of key areas of the city is crucial. It helps reduce the spread risk of Covid-19 and predict individuals' travel needs. Although current researches focus on using the method of clustering to predict the population density, there is almost no discussion about using spatial-temporal models to predict the population density of key areas in a city without using actual regional images. We abstract 997 key areas and their regional connections into a graph structure and propose a model called Word Embedded Spatial-temporal Graph Convolutional Network (WE-STGCN). WE-STGCN is mainly composed of the Spatial Convolution Layer, the Temporal Convolution Layer, and the Feature Component. Based on the data set provided by the DataFountain platform, we evaluate the model and compare it with some typical models. Experimental results show that WE-STGCN has 53.97% improved to baselines on average and can commendably predicting the population density of key areas. [ABSTRACT FROM AUTHOR]