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Catalytic peroxymonosulfate (PMS) activation processes don’t solely rely on electron transfer from dominant metal centers due to the complicated composition and interface environment of catalysts. Herein the synthesis of a cobalt based metal-organic framework containing polyvanadate [V 4 O 12 ] 4− cluster, Co 2 (V 4 O 12 )(bpy) 2 (bpy = 4,4’-bipyridine), is presented. The catalyst demonstrates superior degradation activity toward various micropollutants, with higher highest occupied molecular orbital (HOMO), via nonradical attack. The X-ray absorption spectroscopy and density functional theory (DFT) calculations demonstrate that Co sites act as both PMS trapper and electron donor. In situ spectral characterizations and DFT calculations reveal that the terminal oxygen atoms in the [V 4 O 12 ] 4− electron sponge could interact with the terminal hydrogen atoms in PMS to form hydrogen bonds, promoting the generation of SO 5 * intermediate via both dynamic pull and direct electron transfer process. Further, Co 2 (V 4 O 12 )(bpy) 2 exhibits long-term water purification ability, up to 40 h, towards actual wastewater discharged from an ofloxacin production factory. This work not only presents an efficient catalyst with an electron sponge for water environmental remediation via nonradical pathway, but also provides fundamental insights into the Fenton-like reaction mechanism. Peroxymonosulfate (PMS) activation might not solely rely on electron transfer from dominant metal centers. Here, authors found that the formation of hydrogen bond between PMS and [V 4 O 12 ] 4− in Co 2 (V 4 O 12 )(bpy) 2 catalyst provided extra electron transfer channel for achieving efficient PMS activation.

Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent reinforcement learning (MARL) tasks, given the combinatorially increased interactions among agents and with the environment. However, in MARL, the paradigm of offline pre-training with online fine-tuning has not been studied, nor even datasets or benchmarks for offline MARL research are available. In this paper, we facilitate the research by providing large-scale datasets and using them to examine the usage of the decision transformer in the context of MARL. We investigate the generalization of MARL offline pre-training in the following three aspects: 1) between single agents and multiple agents, 2) from offline pretraining to online fine tuning, and 3) to that of multiple downstream tasks with few-shot and zero-shot capabilities. We start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment, and then propose the novel architecture of multi-agent decision transformer (MADT) for effective offline learning. MADT leverages the transformer’s modelling ability for sequence modelling and integrates it seamlessly with both offline and online MARL tasks. A significant benefit of MADT is that it learns generalizable policies that can transfer between different types of agents under different task scenarios. On the StarCraft II offline dataset, MADT outperforms the state-of-the-art offline reinforcement learning (RL) baselines, including BCQ and CQL. When applied to online tasks, the pre-trained MADT significantly improves sample efficiency and enjoys strong performance in both few-short and zero-shot cases. To the best of our knowledge, this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalizability enhancements for MARL.

Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model’s feature learning capability. The proposed method achieved remarkable accuracy rates of 93.22% and 95.20% on the UCI and Kaggle datasets, respectively, underscoring its exceptional performance even with small sample sizes. Ablation experiments further validated the efficacy of the data preprocessing and feature selection techniques employed. Notably, the EPSOSA algorithm surpassed classical optimization algorithms in terms of convergence speed, while also demonstrating improved sensitivity and specificity. This model holds significant potential for facilitating early identification of high-risk patients, which could ultimately save lives and optimize the utilization of medical resources. Despite implementation challenges, including technical integration and data standardization, the algorithm shows promise for use in emergency settings and community health services for regular cardiac risk monitoring.

This paper presents a study on the low-velocity impact response of lightweight steel foam concrete (LSFC) composite slabs. The LSFC composite slab consisted of a W-shaped steel plate, foam concrete and oriented strand board (OSB). Low-velocity impact tests on the LSFC composite slabs were conducted by employing an ultra-high heavy-duty drop hammer testing machine. The tests revealed the failure mode, impact force and displacement response of LSFC composite slabs. The effects of density and thickness of foam concrete and drop height on the peak impact force and energy absorption ratio were investigated. A finite element (FE) model was set up to predict the impact resistance of the LSFC composite slabs, and a good agreement between simulation and test results was achieved. In addition, an equivalent-single-degree-of-freedom (ESDOF) model was set up to predict the displacement response of the LSFC composite slabs under impact loading.

Ginseng has been regarded as a precious medicinal herb with miraculous effects in Eastern culture. The primary chemical constituents of ginseng are saponins, and the physiological activities of ginsenosides determine their edible and medicinal value. The aim of this study is to comprehensively and systematically investigate the kinetic processes of 20(S)-protopanaxadiol (PPD) in rats and dogs, in order to promote the rational combination of ginseng as a drug and dietary ingredient. PPD was administered, and drug concentration in different biological samples were detected by liquid chromatography tandem mass spectrometry (LC/MS/MS) and radioactive tracer methods. Pharmacokinetic parameters such as absorption, bioavailability, tissue distribution, plasma protein binding rate, excretion rate, and cumulative excretion were calculated, along with inference of major metabolites. This study systematically investigated the absorption, distribution, metabolism, excretion (ADME) of PPD in rats and dogs for the first time. The bioavailabilities of PPD were relatively low, with oral absorption nearly complete, and the majority underwent first-pass metabolism. PPD had a high plasma protein binding rate and was relatively evenly distributed in the body. Following oral administration, PPD underwent extensive metabolism, potentially involving one structural transformation and three hydroxylation reactions. The metabolites were primarily excreted through feces and urine, indicating the presence of enterohepatic circulation. The pharmacokinetic processes of PPD following intravenous administration aligned well with a three-compartment model. In contrast, after gastric administration, it fitted better with a two-compartment model, conforming to linear pharmacokinetics and proportional elimination. There were evident interspecies differences between rats and dogs regarding PPD, but individual variations of this drug were minimal within the same species. This study systematically studied the kinetic process of PPD in rats and also investigated the kinetic characteristics of PPD in dogs for the first time. These findings lay the foundation for further research on the dietary nutrition and pharmacological effects of PPD.

Cartilage defects are common joint disorders that, if left untreated, may progress to severe degenerative joint conditions. Inflammatory response plays a critical role in the pathogenesis of cartilage damage. Hydrogels incorporating diacerein, an anti‐inflammatory drug used in clinical settings, can mitigate inflammation that impairs cartilage repair. It is hypothesized that the direct injection of a hydrogel scaffold combining diacerein and polydopamine into cartilage defect sites can enhance localized treatment, reduce surgical risks, and expedite recovery. Therefore, in this study, a hydrogel infused with diacerein is developed to investigate its efficacy for cartilage restoration. By crosslinking poly(ethylene glycol) diacrylate, four‐arm polyethylene glycol‐functionalized diacerein, hyaluronic acid, and polydopamine, an injectable hydrogel with superior properties is achieved. In vitro evaluations confirm the mechanical strength and biocompatibility of the hydrogel, and in vivo studies demonstrate its effectiveness in cartilage repair and anti‐inflammatory activity in a rat model. These findings indicate that hydrogels are promising materials for addressing cartilage defects and advancing tissue engineering and biological implantation strategies. In this paper, an injectable hydrogel is prepared that can be utilized as a tissue engineering scaffold for cartilage repair. This scaffold is capable of promoting cartilage regeneration and reducing inflammation caused by cartilage defects, thus holding broad application prospects in the repair of cartilage defects.

Adaptive multi-agent cooperation with especially unseen partners is becoming more challenging in multi-agent reinforcement learning (MARL) research, whereby conventional deep-learning-based algorithms suffer from the poor new-player-generalization problem, possibly caused by not considering theory-of-mind theory (ToM). Inspired by the ToM personality in cognitive psychology, where a human can easily resolve this problem by predicting others' intuitive personality first before complex actions, we propose a biologically-plausible algorithm named the mixture of personality (MoP) improved spiking actor network (SAN). The MoP module contains a determinantal point process to simulate the formation and integration of different personality types, and the SAN module contains spiking neurons for efficient reinforcement learning. The experimental results on the benchmark cooperative overcooked task showed that the proposed MoP-SAN algorithm could achieve higher performance for the paradigms with (learning) and without (generalization) unseen partners. Furthermore, ablation experiments highlighted the contribution of MoP in SAN learning, and some visualization analysis explained why the proposed algorithm is superior to some counterpart deep actor networks.

Background Depression and obesity among adolescents have become major public health problems. They have common biological mechanisms and overlapping risk factors, such as lack of exercise, poor diet, and sleep disorders. Although there are corresponding interventions for these two diseases, there is still a lack of a comprehensive and systematic intervention approach to address their coexistence. The study will integrate existing intervention strategies and develop various intervention measures at individual, family, school, and medical institution levels. The aMOST and SCA framework will be employed to provide personalized and effective treatment for adolescents. Following its implementation, the comprehensive intervention strategy will undergo evaluation using the RE-AIM framework and will be promoted through online public accounts and academic conferences.  Methods and analysis This research on adolescent depression and obesity will comprise three. First, a comprehensive intervention strategy will be developed based on the aMOST model through screening, optimizing, and refining phases. Second, the intervention will be implemented using the SCA in three steps: cross-sectional study for screening, cluster randomized controlled trial for high-risk students, and clinical referral. Third, the strategy will be evaluated and promoted based on the RE-AIM framework. Quality control will be ensured in both development and implementation phases. Statistical analyses were performed using Excel2016, SPSS, and R for different phases of the study. Count data will be described with percentages and compared using χ 2 or Fisher’s exact probability test and measurement data was characterized by mean, median, etc., and compared using MMRM, t-tests, or Wilcoxon rank sum tests.  Discussion This study aims to evaluate the effectiveness of the intervention strategy and examine its implementation within families, schools, and medical institutions. Following this intervention research, the comprehensive intervention program targeting adolescent depression and obesity holds immense potential in China, as it can facilitate and support changes to healthy lifestyle behaviors, thereby reducing the risks of adolescent depression and obesity.  Trial registration NCT06489990.

Vegetable oil is a carbon-rich resource applied in liquid fermentation for compounds of interest. In this study, olive oil demonstrated the best effect on improving the liquid fermentation of a medicinal fungus Poria cocos (Schw.) Wolf compared to rapeseed, coix seed, palm, peanut, and soybean oils. When 2% (v/v) olive oil was initially added to the medium, biomass reached a maximum value of 11.7 g L−1, presenting a 3.1-fold enhancement compared to the blank control. Due to the stronger basal metabolism, the total triterpenoid yields also exhibited a significant improvement of ~3.4-fold, reaching 0.68 g L−1. Spectrophotometry, along with fluorescence and chemiluminescence probe assays, demonstrated that olive oil affected the fungus membrane fluidity and level of reactive oxygen species and nitrogen oxide in mycelium cells. Transcriptome analysis confirmed that olive oil was used as a carbon resource and elicitor that affected mycelia growth, which simultaneously produced some slight effects on metabolic processes, including fatty acid degradation, TCA cycle, and glycolysis/gluconeogenesis. Our study represents an attractive strategy for the industrial fermentation of filamentous fungi.