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Elucidating the anti-obesity phytochemicals in Chenpi and their molecular mechanisms

Food science and Human Wellness 2025年第4期14卷 1224-1238页

作者： Jinhai Luo Weiqi Yan Zhi Chen Baojun Xu Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science Department of Life SciencesBNU-HKBU United International CollegeZhuhai 519087China Centre for Cancer and Infl ammation Research School of Chinese MedicineHong Kong Baptist UniversityHong Kong 999077China Faculty of Biochemistry and Molecular Medicine University of OuluOulu 90220Finland

Obesity has become a significant global public health *** studies have found that the Chenpi has the anti-obesity ***,the anti-obesity phytochemicals and their mechanisms are still *** study investigated the anti-obesity phytochemicals and molecular mechanisms involved in treating obesity by Chenpi through network pharmacology and molecular docking.A total of 17 bioactive phytochemicals from Chenpi and its 475 related anti-obesity targets have been *** KEGG pathway analysis showed that the PI3K/Akt signaling pathway,MAPK signaling pathway,AMPK signaling pathway,and nuclear factor kappa B signaling pathway are the main signaling pathways involved in the anti-obesity effect of *** to molecular docking analysis,the phytochemicals of Chenpi can bind to central anti-obesity *** on the ADMET analysis and network pharmacology results,tangeretin exhibited the lowest predicted toxicity and potential for anti-obesity *** the in vitro lipid accumulation model,tangeretin effectively suppressed the free fatty acid-induced lipid in Hep G2 cells by upregulating the PI3K/Akt/GSK3βsignaling pathway based on the result of q-PCR and Western *** outcomes of this research give insights for future research on the anti-obesity phytochemicals and molecular mechanisms derived from Chenpi,also providing the theoretical basis for developing anti-obesity functional foods based on Chenpi.

关键词： Chenpi Phytochemicals Obesity Network pharmacology Molecular mechanism Signaling pathway

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An Enhanced Multi-Scale Collaborative Learning Network for Medical Image Segmentation in Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Yang, Aimin Li, Jiahao Bai, Yunjie Li, Jie Yang, Xingwang North China University of Science and Technology College of Science Hebei Tangshan063210 China North China University of Science and Technology Hebei Key Laboratory of Data Science and Application Hebei Tangshan063210 China North China University of Science and Technology Hebei Engineering Research Center of Iron Ore Optimization and Iron Pre-Process Intelligence Tangshan063210 China

Multi-organ segmentation in the abdomen is a key area in medical image segmentation and is essential for accurate diagnosis and treatment of diseases. The diversity of abdominal organs, differences in size, and ambiguous boundaries pose significant challenges to segmentation tasks, making it difficult for existing models to meet clinical demands in complex scenarios. To address this, this paper proposes a Multi-Scale Collaborative Learning Network (MSCL-Net) in conjunction with a medical Internet of Things (IoMT) platform, aiming to enhance the accuracy of abdominal organ segmentation. The introduction of IoMT technology dramatically expands the channels for obtaining medical image data. By collecting massive amounts of data in real-time from various scenarios, such as outpatient clinics and hospitals, doctors can quickly use the segmentation model to obtain preliminary segmentation results of CT images, which specialists can rapidly correct. This workflow improves clinical work efficiency and provides a large volume of highquality data for training the model, further enhancing its accuracy. Regarding model design, the Feature Space Module is used for global feature extraction, the Feature Extraction Module captures local semantic information at different scales, and the Feature Aggregation Module performs deep integration of local and global information. A considerable number of experimental results based on the Synapse dataset demonstrate that MSCL-Net outperforms existing advanced algorithms and achieves 81.71% in the Dice Similarity Coefficient (DSC) and 17.81 in the Hausdorff Distance at 95% (HD95) metrics, demonstrating its outstanding ability in medical image segmentation. © 2014 IEEE.

关键词： Diagnosis

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A model-free feature extraction procedure for interval-valued time series prediction

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Soft Computing 2025年第6期29卷 2727-2741页

作者： Tian, Wan Qin, Zhongfeng Hu, Tao School of Economics and Management Beihang University Beijing100191 China Beijing100191 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education Shanghai China School of Mathematical Sciences Capital Normal University Beijing100048 China

We present a novel feature extraction procedure to predict interval-valued time series by combing transfer learning and imaging approaches. Initially, we represent interval-valued time series as a bivariate point-valued time series, and then transform each time series into images by employing imaging approaches such as recurrence plot, gramian angular summation/difference field, and Markov transition field, thereby obtaining an image dataset containing four classes. Based on this dataset, we train multiple candidate feature extraction networks, specifically ResNet with varying layers. Then we choose the penultimate layer of the feature extraction networks to extract the most relevant features from the images. To formulate prediction, we integrate the extracted features into a traditional prediction model. The proposed method are evaluated on three data-generating processes as well as the S&P 500 index, and the experimental results demonstrate a notable improvement in prediction performance compared to existing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Feature Selection

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Interactions between nanoplastics and Tetrahymena thermophila: Low toxicity vs. potential biodegradation

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Chemosphere 2025年 373卷 144166页

作者： Li, Aiyun Yuan, Fengyu Li, Lianshan Gu, Jihai Zhang, Yuming Li, Fengchao Tang, Ting Liu, Fengsong The Key Laboratory of Zoological Systematics and Application College of Life Sciences Hebei University Baoding071002 China Hebei Basic Science Center for Biotic Interaction Hebei University Baoding071002 China

Nanoplastics (NPs) are prevalent throughout the environment and have raised growing environmental concerns. Although numerous studies have examined the toxicological aspects of NPs, few have investigated their environmental fate and behavior when affected by organisms other than bacteria or fungi. Planktonic ciliates are essential components of aquatic ecosystems and play important roles in decomposing organic matter and transferring energy from the microbial food web to higher trophic levels. To investigate the interplay between NPs and the ciliate Tetrahymena thermophila, we executed a sequence of feeding experiments utilizing 50 nm polystyrene nanoplastics (PS-NPs). In the presence of sufficient nutrition, exposure to PS-NPs (even at concentrations up to 500 mg/L) did not significantly inhibit growth in Tetrahymena thermophila, indicating only a mild toxic effect of PS-NPs. When ingested by T. thermophila, the PS-NPs are repackaged into aggregates with lysosomal components in the food vacuole and finally expelled as compacted "fecal pellets". This process modifies the physical attributes of PS-NPs, including their hydrophilicity, aggregability, and buoyancy, influencing their transportation, retention, deposition dynamics, and ultimately their bioavailability within the environment. A total of 73 proteins were identified from the fecal pellets, containing various hydrolases. Gel permeation chromatography (GPC), Fourier transform infrared (FTIR), and thermogravimetric analysis (TGA) were used to identify changes in molecular weights, functional groups, and thermal stabilities of PS-NP residues in fecal pellets. The results verified the degradation of PS-NPs during the passage through the T. thermophila cell. © 2025 Elsevier Ltd

关键词： Gel permeation chromatography

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Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology

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IEEE Transactions on Information Theory 2025年第3期71卷 2292-2314页

作者： Cui, Manjun Zhang, Zhichao Han, Jie Chen, Yunjie Cao, Chunzheng Nanjing University of Information Science and Technology School of Mathematics and Statistics Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data Analysis Nanjing210044 China Nanjing University of Information Science and Technology School of Mathematics and Statistics Nanjing210044 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Hainan Normal University Key Laboratory of Computational Science and Application of Hainan Province Haikou571158 China Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China

The conventional Cohen's distribution can't meet the requirement of additive noises jamming signals high-performance denoising under the condition of low signal-to-noise ratio, it is necessary to integrate the metaplectic transform for non-stationary signal fractional domain time-frequency analysis. In this paper, we embark on blending time-frequency operators and coordinate operator fractionizations to formulate the definition of the joint fractionizations metaplectic Wigner distribution (JFMWD), based on which we integrate the generalized metaplectic convolution to address the unified representation issue of the convolution type of metaplectic Cohen's distribution (CMCD), whose special cases and essential properties are also derived. We blend Wiener filter principle and fractional domain filter mechanism of the metaplectic transform to design the least-squares adaptive filter method in the JFMWD domain, giving birth to the least-squares adaptive filter-based CMCD whose kernel function can be adjusted with the input signal automatically to achieve the minimum mean-square error (MSE) denoising in Wigner distribution domain. We discuss the optimal symplectic matrices selection strategy of the proposed adaptive CMCD through the minimum MSE minimization modeling and solving. Some examples are also carried out to demonstrate that the proposed filtering method outperforms some state-of-the-arts including the classical Gaussian filter, the Cohen's distribution filtering methods with fixed kernel functions, the classical Wiener filter, the adaptive generalized linear canonical convolution-based filtering method, the adaptive Cohen's distribution filtering method, the adaptive JFMWD-based Cohen's distribution filtering method, and the adaptive generalized metaplectic convolution-based Cohen's distribution filtering method in noise suppression. © 1963-2012 IEEE.

关键词： Adaptive filtering

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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Relativistic effects in the strong and electromagnetic decays of the meson

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The European Physical Journal C 2025年第3期85卷 1-7页

作者： Jia, Man Li, Wei Pei, Su-Yan Du, Xin-Yao Ning, Guo-Zhu Wang, Guo-Li Department of Physics Hebei University Baoding China Hebei Key Laboratory of High-precision Computation and Application of Quantum Field Theory Hebei University Baoding China Hebei Research Center of the Basic Discipline for Computational Physics Hebei University Baoding China College of Science Hebei Agriculture University Baoding China

In this paper, we solve the complete Salpeter equation and use the obtained relativistic wave function to calculate the strong and radiative electromagnetic decays of the $${D^*}$$ meson. We obtain the results $$\Gamma (D^{*}(2007)^{0}\rightarrow D^{0}\pi ^{0})=34.6~\textrm{keV}$$ and $$\Gamma (D^{*}(2007)^{0}\rightarrow D^{0}\gamma )=19.4~\textrm{keV}$$ , and the estimated full width is $$\Gamma (D^{*}(2007)^{0})=54.0~\textrm{keV}$$ . The focus of this study is on the relativistic corrections. In our method, the wave function of the D meson is not a pure S-wave, but includes both a non-relativistic S-wave and a relativistic P-wave, while the wave function of the $$D^*$$ meson includes a non-relativistic S-wave as well as both relativistic P-wave and D-wave. Therefore, in this case, the decay $${D^{*}\rightarrow {D}\gamma }$$ is not a non-relativistic M1 transition, but rather an $$M1+E2+M3+E4$$ decay. We find that in a strong decay $$D^{*}\rightarrow {D}{\pi }$$ , the non-relativistic contribution is dominant, while in an electromagnetic decay $${D^{*}\rightarrow {D}\gamma }$$ , the relativistic correction is dominant.

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Customized priors for enhanced multispectral and hyperspectral image sharpening

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Information Fusion 2025年 122卷

作者： Zhang, Junkang Zhang, Haoyang Wang, Tingting Fang, Faming Zhang, Guixu Xu, Ming School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Shanghai China

In recent years, deep unfolding (DU) models have gained significant traction in multi-source image fusion applications, including remote sensing pansharpening and the integration of multispectral and hyperspectral images, owing to their enhanced interpretability. However, despite embedding physical degradation models, these DU models still rely on implicit learning for their priors, which can impede interpretability. In this paper, we present a novel image fusion model based on maximum a posterior (MAP) framework. This model enhances interpretability and fusion performance by incorporating two innovative prior modules. The first module employs codebook to capture the spectral traits of the target image, subsequently integrating these insights within our DU framework. The second module specializes in uncovering the intrinsic correlations between multi-source images, ensuring that the fusion process effectively leverages the rich, diverse information encoded within each modality. Specifically, we have effectively combined the self-similarity and cross-modal similarity paradigms into a unified cross image prior module. To counteract the redundancy present in images, we have integrated an agent mechanism that significantly simplifies the computational load of our prior module. Quantitative and qualitative experiments on multiple benchmark datasets demonstrate that the proposed method achieves more robustness and high-quality results than other state-of-the-art sharpening methods. © 2025 Elsevier B.V.

关键词： Miocene

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LLM-MalDetect: A Large Language Model-Based Method for Android Malware Detection

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IEEE Access 2025年 13卷 81347-81364页

作者： Feng, Ruirui Chen, Hui Wang, Shuo Monjurul Karim, Md Jiang, Qingshan Hebei University College of Mathematics and Information Science Baoding071002 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Key Laboratory for High Performance Data Mining Shenzhen518055 China Shenzhen Polytechnic University School of Artificial Intelligence Shenzhen518055 China

Android malware poses a significant cybersecurity threat, enabling unauthorized data access, financial fraud, and device compromise. Although deep learning methods are widely used for malware detection, they often struggle with stability and adaptability in the face of evolving threats. While large language models (LLMs) have shown promise in this area, their application to Android malware detection remains underexplored, particularly with regard to optimizing the semantic relationships within Android application packages (APKs). To address this gap, we introduce LLM-MalDetect, a novel framework that improves LLM-based APK analysis by explicitly modeling semantic dependencies and leveraging structured prompt engineering for optimized detection. Our approach formalizes LLM adaptation through a robust string-based feature extraction method and a tailored fine-tuning strategy to enhance precision. Evaluations on benchmark datasets demonstrate that LLM-MalDetect achieves up to 98.97% accuracy, outperforms existing methods in terms of robustness, and enables real-time analysis. © 2013 IEEE.

关键词： Android malware

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