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Objective: Our previous studies substantiated that the biological activity of Schisandra chinensis lignans during the treatment of Alzheimer’s disease (AD) was mediated by neurotransmitter levels, and 15 of its active components were identified. However, the pharmacokinetic and pharmacodynamic relationship of Schisandra chinensis lignans has been less studied. The objective of this study was to investigate the relationship between the pharmacokinetics and pharmacodynamics of Schisandra chinensis lignans in the treatment of AD, and to establish a pharmacokinetic-pharmacodynamic (PK-PD) model. Methods and Results: Herein, we established a microdialysis-ultra performance liquid chromatography-triple quadruple mass spectrometry (MD-LC-TQ-MS) technique that could simultaneously and continuously collect and quantitatively analyze the active compounds and neurotransmitters related to the therapeutic effects of Schisandra chinensis in awake AD rats. Eight lignans were detected in the hippocampus, and a PK-PD model was established. The fitted curves highlighted a temporal lag between the maximum drug concentration and the peak drug effect. Following treatment, the levels of four neurotransmitters tended to converge with those observed in the sham operation group. Conclusion: By establishing a comprehensive concentration-time-effect relationship for Schisandra chinensis lignans in AD treatment, our study provides novel insights into the in vivo effects of these lignans in AD rats.

Globular clusters harbor numerous millisecond pulsars, but long-period pulsars ( P ≳ 100 ms) are rarely found. In this study, we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), aiming to detect the existence of long-period pulsars. We estimated the impact of the median filtering algorithm in eliminating red noise on the minimum detectable flux density ( S min ) of pulsars. Subsequently, we successfully discovered two isolated long-period pulsars in M15 with periods approximately equal to 1.928451 and 3.960716 s, respectively. On the P − P ˙ diagram, both pulsars are positioned below the spin-up line, suggesting a possible history of partial recycling in X-ray binary systems disrupted by dynamical encounters later on. According to timing results, these two pulsars exhibit remarkably strong magnetic fields. If the magnetic fields were weakened during the accretion process, then a short duration of accretion might explain the strong magnetic fields of these pulsars.

As intelligent wearable devices, they will inevitably be subjected to various damages and disturbances from the external environment during daily use. Therefore, it is urgent to develop safeguarding materials with multiple protective properties. Herein, this work developed a flexible and breathable three-dimensional (3D) porous shear stiffening elastomer (SSE)/MXene (M-SSE) foam with impact/electromagnetic interference (EMI)/bacteria multiple protection performance for intelligent wearable devices. The continuous conductive MXene network in the 3D SSE porous structure made M-SSE foam exhibit excellent electromagnetic interference shielding property with a high shielding effectiveness of 34 dB. Attributed to the shear stiffening effect of porous SSE matrix, M-SSE foam possessed unique anti-impact and protection properties. The energy dissipation rate reached up to more than 85%, illustrating M-SSE foam could effectively attenuate the external impact force and absorb the impact energy. Inherited from the excellent photothermal performance of MXene, M-SSE foam achieved a considerable saturated temperature of 98 °C under 0.57 W/cm 2 laser power. Therefore, M-SSE foam showed extraordinary antimicrobial property for Staphylococcus aureus according to the principle of photothermal sterilization. Finally, for the development of intelligent wearable devices, conductive M-SSE foam could be used as an intelligent sensor to monitor various human movements owing to the highly sensitive property. This work greatly expanded the application prospect of multifunctional protective materials in various complex environments and promoted the development of multifunctional smart wearable devices in protection field.

Thermoelectric fabrics can generate energy for wearable devices by utilizing the temperature difference between the human body and the environment. However, the performance of non-scarce materials is currently insufficient for large-scale applications. In this study, a high-conductivity MXene (Ti 3 C 2 T x ) was combined with cotton fabric via a dip-coating process, followed by in-situ polymerization to coat the surface with polypyrrole (PPy) and CuI nanocrystals. The introduction of MXene enhanced the fabric’s conductivity, while the electrostatic interaction and π-π conjugation between PPy and MXene modified the MXene layer defects and filled the gaps between layers, increasing the number of charge conduction paths and thus improving conductivity. The deposition of CuI nanocrystals further boosted the Seebeck coefficient. The resulting Cotton/MXene/PPy/CuI composite thermoelectric fabric achieved a conductivity of 12.6 S cm −1 , a Seebeck coefficient of 49.2 μV K −1 , and a power factor of 3050 nW m −1  K −2 , while also exhibiting excellent flexibility and stability. A thermoelectric generator (f-TEG) with 22 pairs of TE fabrics generated 44 mV at a ΔT of 30 K, which was boosted to 3.67 V, sufficient to power small electronic devices. This study provides new insights into energy supply solutions for portable thermoelectric generators and wearable devices.

Machine unlearning, which selectively removes harmful knowledge from a pre-trained model without retraining from scratch, is crucial for addressing privacy, regulatory compliance, and ethical concerns in Large Language Models (LLMs). However, existing unlearning methods often struggle to thoroughly remove harmful knowledge, leaving residual harmful knowledge that can be easily recovered. To address these limitations, we propose Knowledge Density-Guided Unlearning via Blocks Reinsertion (KUnBR), a novel approach that first identifies layers with rich harmful knowledge and then thoroughly eliminates the harmful knowledge via re-insertion strategy. Our method introduces knowledge density estimation to quantify and locate layers containing the most harmful knowledge, enabling precise unlearning. Additionally, we design a layer re-insertion strategy that extracts and re-inserts harmful knowledge-rich layers into the original LLM, bypassing gradient obstruction caused by cover layers and ensuring effective gradient propagation during unlearning. Extensive experiments conducted on several unlearning and general capability benchmarks demonstrate that KUnBR achieves state-of-the-art forgetting performance while maintaining model utility.

Fast radio bursts (FRBs) remain one of the most puzzling astrophysical phenomena. While most FRBs are detected only once or sporadically, we present the identification of FRB 20190520B as the first persistently active source over a continuous span of ~ four years. This rare long-term activity enabled a detailed investigation of its dispersion measure (DM) evolution. We also report that FRB 20190520B exhibits a substantial decrease in DM at a global rate of minus 12.4 plus or minus 0.3 pc cm^-3 yr^-1, exceeding previous FRB DM variation measurements by a factor of three and surpassing those observed in pulsars by orders of magnitude. The magnitude and consistency of the DM evolution, along with a high host DM contribution, strongly indicate that the source resides in a dense, expanding ionized medium, likely a young supernova remnant (SNR).

Generative recommendation has emerged as a transformative paradigm for capturing the dynamic evolution of user intents in sequential recommendation. While flow-based methods improve the efficiency of diffusion models, they remain hindered by the ``Noise-to-Data'' paradigm, which introduces two critical inefficiencies: prior mismatch, where generation starts from uninformative noise, forcing a lengthy recovery trajectory; and linear redundancy, where iterative solvers waste computation on modeling deterministic preference transitions. To address these limitations, we propose a Flow-based Average Velocity Establishment (Fave) framework for one-step generation recommendation that learns a direct trajectory from an informative prior to the target distribution. Fave is structured via a progressive two-stage training strategy. In Stage 1, we establish a stable preference space through dual-end semantic alignment, applying constraints at both the source (user history) and target (next item) to prevent representation collapse. In Stage 2, we directly resolve the efficiency bottlenecks by introducing a semantic anchor prior, which initializes the flow with a masked embedding from the user's interaction history, providing an informative starting point. Then we learn a global average velocity, consolidating the multi-step trajectory into a single displacement vector, and enforce trajectory straightness via a JVP-based consistency constraint to ensure one-step generation. Extensive experiments on three benchmarks demonstrate that Fave not only achieves state-of-the-art recommendation performance but also delivers an order-of-magnitude improvement in inference efficiency, making it practical for latency-sensitive scenarios.

Enabling large language models to scale and reliably use hundreds of tools is critical for real-world applications, yet challenging due to the inefficiency and error accumulation inherent in flat tool-calling architectures. To address this, we propose Hybrid Toolset Agentization & Adaptation (HTAA), a hierarchical framework for scalable tool-use planning. We propose a novel toolset agentization paradigm, which encapsulates frequently co-used tools into specialized agent tools, thereby reducing the planner's action space and mitigating redundancy. To ensure effective coordination, we design Asymmetric Planner Adaptation, a trajectory-based training paradigm that aligns the high-level planner with agent tools via backward reconstruction and forward refinement. To validate the performance of HTAA, we conduct experiments on a real-world internal dataset, InfoVerify, based on the POI validation workflow of China's largest online large-scale ride-hailing platform, featuring long-horizon executable tool trajectories. Experiments on InfoVerify and widely-used benchmarks show that HTAA consistently achieves higher task success rates, requires short tool calling trajectories, and significantly reduces context overhead compared to strong baselines. Furthermore, in a production deployment, HTAA substantially reduces manual validation effort and operational cost, demonstrating its practical efficacy.

In this study, we report multi-year polarization measurements of four repeating FRBs initially discovered by CHIME: FRBs~20190117A, 20190208A, 20190303A, and 20190417A. We observed the four repeating FRBs with FAST, detecting a total of 66 bursts. Two bursts from FRB~20190417A exhibit a circular polarization signal-to-noise ratio greater than 7, with the highest circular polarization fraction recorded at 35.7%. While the bursts from FRBs 20190208A and 20190303A are highly linearly polarized, those from FRBs~20190117A and 20190417A show depolarization due to multi-path propagation, with _RM = 2.78 0.05 rad m\\(^-2\\) and 5.19 0.09 rad m\\(^-2\\), respectively. The linear polarization distributions among five repeating FRB--FRBs~20190208A, 20190303A, 20201124A, 20220912A, and 20240114A--are nearly identical but show distinct differences from those of non-repeating FRBs. FRBs~20190117A, 20190303A, and 20190417A exhibit substantial rotation measure (RM) variations between bursts, joining other repeating FRBs in this behavior. Combining these findings with published results, 64% of repeating FRBs show RM variations greater than 50 rad m\\(^-2\\), and 21\\% exhibit RM reversals. A significant proportion of repeating FRBs reside in a dynamic magneto-ionic environment. The structure function of RM variations shows a power-law index of \\( (0-0.8)\\), corresponding to a shallow power spectrum \\( = -( + 2) -(2.0-2.8)\\) of turbulence, if the RM variations are attributed to turbulence. This suggests that the variations are dominated by small-scale RM density fluctuations. We perform K-S tests comparing the RMs of repeating and non-repeating FRBs, which reveal a marginal dichotomy in the distribution of their RMs.We caution that the observed dichotomy may be due to the small sample size and selection biases.

Globular clusters harbor numerous millisecond pulsars, but long-period pulsars (\\(P \\gtrsim 100\\) ms) are rarely found. In this study, we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundred-meter Aperture Spherical radio Telescope (FAST), aiming to detect the existence of long-period pulsars. We estimated the impact of the median filtering algorithm in eliminating red noise on the minimum detectable flux density (\\(S_{\\rm min}\\)) of pulsars. Subsequently, we successfully discovered two isolated long-period pulsars in M15 with periods approximately equal to 1.928451 seconds and 3.960716 seconds, respectively. On the \\(P-\\dot{P}\\) diagram, both pulsars are positioned below the spin-up line, suggesting a possible history of partial recycling in X-ray binary systems disrupted by dynamical encounters later on. According to timing results, these two pulsars exhibit remarkably strong magnetic fields. If the magnetic fields were weakened during the accretion process, then a short duration of accretion might explain the strong magnetic fields of these pulsars.