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Multi-omics elucidation of key odor-active compounds and their association with lipid-derived flavor precursors in textured peanut protein-based fermented sausage analogs

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LWT 2025年 225卷

作者： Li, Ying Blank, Imre Liu, Xin Feng, Xiaoxiao Fan, Yuxia Yu, Yanyang Cui, Zhiyong Zhu, Yiwen Chen, Hongqiang Xu, Xinglian Tang, Changbo Zhou, Guanghong Liu, Yuan Key Laboratory of Meat Processing and Quality Control Ministry of Education Key Laboratory of Meat Processing Ministry of Agriculture and Rural Affairs Jiangsu Synergetic Innovation Center of Meat Processing and Quality Control College of Food Science and Technology Nanjing Agricultural University Nanjing210095 China School of Food Science and Engineering Ningxia University Yinchuan750021 China North Information Control Research Academy Group Co. Ltd. Norinco Group Nanjing211153 China Department of Food Science & Technology School of Agriculture & Biology Shanghai Jiao Tong University Shanghai200240 China

Flavor plays a pivotal role in shaping consumer preferences for meat alternatives, yet the interaction between lipid composition and aroma formation in sustainable peanut protein-based fermented sausage analogues remains unexplored. This study investigated how textured peanut proteins (TNPs) influence lipid variation, aroma development and sensory perception in fermented sausage analogues. By employing multi-omics techniques (volatilomics and lipidomics) combined with sensory evaluation, we revealed that integrating TNPs at varying proportions (25 %, 50 %, 75 %, and 100 %) resulted in significant changes in the volatiles associated with meat-like aromas (nitrogen oxides and sulfur-containing compounds) and taste attributes (umami, richness, bitterness, and astringency), along with the identification of key volatiles and 44 differentially expressed lipids. 3-sec-Butyl-2-methoxypyrazine, 2,3-butanedione, and 1-octen-3-ol were primary contributors to unique flavors, with sensory panelists significantly preferring the 25 % TNPs analogues (P © 2025 The Authors

关键词： Nitrogen oxides

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Kernel Bayesian tensor ring decomposition for multiway data recovery

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Neural Networks 2025年 189卷 107500页

作者： Huang, Zhenhao Zhou, Guoxu Qiu, Yuning Chen, Xinqi Zhao, Qibin School of Automation Guangdong University of Technology Guangzhou510006 China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Tensor ring (TR) decomposition has emerged as the prevailing method for tensor completion. Earlier approaches have situated TR decomposition within a probabilistic framework, yielding satisfactory outcomes. However, these methods ignore side information or are inherently incapable of leveraging it. In response to this challenge, we propose a variational inference-based kernel Bayesian TR (VKBTR) method that integrates side information, low-rankness, and sparse learning. By incorporating kernel matrices into the TR factors, we can effectively leverage the intrinsic properties of the data (e.g., the smoothness in images and videos) to improve performance across different tasks. Additionally, by introducing a sparsity-inducing hierarchical prior on the latent factors, the proposed method enables automatic selection of the TR rank. Leveraging the variational inference algorithm enables us to achieve the update of posterior parameters effectively. Extensive experiments conducted on synthetic data, color images, face images, and color video data have shown that, with the assistance of side information, VKBTR significantly improves performance in completion tasks compared to other state-of-the-art methods. © 2025 Elsevier Ltd

关键词： Variational techniques

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Base flow optimization and sensitivity analysis of magnetohydrodynamic micropolar planar Poiseuille flow with micro-magnetic rotation and thermo-magnetic effects

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Physics of Fluids 2025年第5期37卷

作者： Tian, Xiyan Chen, Neng Na, Xin Wen, Jianxin Hu, Jiayu Liu, Xiaoming Key Laboratory of National Education Ministry for Electromagnetic Processing of Materials Northeastern University POB 314 Shenyang110819 China School of Metallurgy Northeastern University POB 314 Shenyang110819 China Department of Information and Control Engineering Shenyang Urban Construction University Shenyang110167 China

Behavior characteristics of micropolar magnetohydrodynamic fluid with the combination of macroscopic thermo-magnetic effects and micromagnetorotation (MMR) effect are studied in this research. The complex mechanisms of the base flow variation influence on planar Poiseuille flow are analyzed, and the transition process in this flow system is further investigated. The sensitivity function including the gradient information of base flow variations is derived via the adjoint variational method. Lagrange function is constructed and iteratively solved to determine the optimal base flow that maximizes flow instability for a specified base flow variation. With energy gradient theory, interaction mechanism between thermo-magnetic effect, MMR effect, and the instability caused by variations in base flow is analyzed. The results reveal that the most sensitive to variations in the base flow within the micropolar planar Poiseuille flow appears in the region nearest to the walls. This area also exhibits the largest energy gradient in the base flow velocity profile, where the base flow begins to distort. When the base flow variation is within 2%, the magnetic field mainly plays a suppressive role in the instability of the base flow variation, while the thermal field, micropolar effects, and side effects contribute to instability. Conversely, the magnetization effect partially suppresses flow instability. As the base flow variation continues to increase, the effects of the magnetic field and the microstructure parameters start to reverse in sequence. As the base flow variation reaches around 10%, the critical Reynolds number tends to stabilize. © 2025 Author(s).

关键词： Magnetic levitation

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Unsupervised Multivariate Time Series Anomaly Detection by Feature Decoupling in Federated Learning Scenarios

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Yifan Ding, Xi Tang, Yateng Guan, Jihong Zhou, Shuigeng Fudan University School of Computer Science Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tencent Weixin Group Guangzhou510308 China Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China

Anomalies are usually regarded as data errors or novel patterns previously unseen, which are quite different from most observed data. Accurate detection of anomalies is crucial in various application scenarios. This paper focuses on unsupervised anomaly detection from Multivariate Time Series (MTS), as real-world data collected from sources such as wearable devices, medical equipment and industrial machines typically manifest as MTS and are often unlabeled. Anomaly detection in MTS represents a data-driven challenge that traditionally requires substantial centralized data for training models. However, in practice, data are frequently distributed among multiple institutions, with privacy concerns restricting unrestricted access. To address these issues, we introduce Feature Decoupling Federated Learning (FDFL), an approach designed to collaboratively train a representation learning network over multiple clients for unsupervised anomaly detection in MTS. Unlike previous methods that simply integrate MTS anomaly detection algorithms with federated learning strategies, FDFL specifically addresses heterogeneity among clients by decoupling the representation network into shared and private branches through a contrastive learning mechanism. This method aggregates shared parameters during each federated round while maintaining client-specific private parameters locally. Additionally, we develop a self-attention block that integrates the representations derived from both shared and private parameters to reconstruct MTS and identify anomalies based on reconstruction errors. Extensive experiments conducted on three publicly available datasets demonstrate that FDFL outperforms existing algorithms in most cases, highlighting the effectiveness and superiority of our proposed method in MTS anomaly detection. © 2020 IEEE.

关键词： Federated learning

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Shape Embedding and Knowledge Mining Network for Generalized Few-Shot Remote Sensing Segmentation

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IEEE Geoscience and Remote Sensing Letters 2025年 22卷

作者： Qiu, Zifeng Liu, Hongyu Xiong, Hang Di, Chengliang Fang, Hao Cong, Runmin Beijing Jiaotong University Institute of Information Science Beijing100044 China The54th Research Institute of CETC Shijiazhuang050081 China China Communication System Co. Ltd. Hebei Branch Shijiazhuang050081 China Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China

In recent years, generalized few-shot segmentation (GFSS) has received widespread attention from scholars by virtue of its superiority in low-data regimes. Most of the existing research focuses on natural image processing, and few studies have been devoted to the practical but challenging topic of remote sensing image (RSI) understanding. In this paper, we propose a Shape Embedding and Knowledge Mining Network (SKNet) for generalized few-shot RSI segmentation. Specifically, the framework is divided into two key stages: (a) In the base class learning stage, shape representation embedding is introduced to enhance the network's ability to perceive remote sensing objects. Simultaneously, we introduce the self-reconstruction constraint to prevent new unseen classes from merging, thereby improving the representation uniqueness of these classes. (b) In the novel class learning stage, a base class knowledge mining mechanism is designed to update the prototypes of the novel class by using the prototype representation of the base class, so as to enhance the discrimination ability of the network. We validated our methods on the adapted version of OpenEarthMap and iSAID datasets. In comparison with existing GFSS methods, the proposed approach demonstrates an advancement. © 2004-2012 IEEE.

关键词： Semantic Segmentation

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3D bioprinting of a dermal scaffold for full-thickness skin tissue regeneration

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Bio-Design and Manufacturing 2025年第1期8卷 68-84,I0039-I0041页

作者： Lu Han Zixian Liu Meng Li Zhizhong Shen Jianming Wang Shengbo Sang Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Electronic Information and Optical EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China General Hospital of TISCO Taiyuan 030809China

Dermal substitutes have provided a template for the regeneration and reconstruction of the ***,the healed skin tissue often exhibits abnormal morphology and functionality,including scarring and *** this study,a composite bioink composed of methacrylated gelatin(GelMA)and chitosan oligosaccharide(COS)was proposed for printing a dermal scaffold using digital light processing(DLP)*** GelMA/COS bioink exhibited suitable porosity,swelling,degradation rate,and mechanical *** inclusion of COS demonstrated antibacterial effects against both Gram positive and Gram-negative bacteria,while simultaneously fostering the proliferation of human dermal fibroblasts(HDFs).Additionally,the application of COS could effectively reduce the expression levels of fibrosis-related genes,such as collagen I,collagen III,and fibronectin *** three-dimensionally printed cell-laden dermal scaffold exhibited excellent shape fidelity and high cellular viability,facilitating the extension of HDFs along the scaffold and the simultaneous secretion of extracellular matrix ***,the HDF-laden dermal scaffold transplanted into full-thickness skin defect sites in nude mice was shown to accelerate wound closure,reduce inflammation,and improve wound ***,the DLP-printed dermal scaffold provides an appealing approach for effectively treating full-thickness skin defects in clinical settings.

关键词： 3D printing Dermal scaffold Photo-cross-linking Skin tissue regeneration

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SN-LiDAR: Semantic Neural Fields for Novel Space-time View LiDAR Synthesis

arXiv

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arXiv 2025年

作者： Chen, Yi Deng, Tianchen Zhao, Wentao Wang, Xiaoning Xi, Wenqian Chen, Weidong Wang, Jingchuan Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200030 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200030 China Ruijin Hospital Shanghai Jiao Tong University School of Medicine China Renji hospital Shanghai Jiao Tong University School of Medicine China

Recent research has begun exploring novel view synthesis (NVS) for LiDAR point clouds, aiming to generate realistic LiDAR scans from unseen viewpoints. However, most existing approaches do not reconstruct semantic labels, which are crucial for many downstream applications such as autonomous driving and robotic perception. Unlike images, which benefit from powerful segmentation models, LiDAR point clouds lack such large-scale pre-trained models, making semantic annotation time-consuming and labor-intensive. To address this challenge, we propose SN-LiDAR, a method that jointly performs accurate semantic segmentation, high-quality geometric reconstruction, and realistic LiDAR synthesis. Specifically, we employ a coarse-to-fine planar-grid feature representation to extract global features from multi-frame point clouds and leverage a CNN-based encoder to extract local semantic features from the current frame point cloud. Extensive experiments on SemanticKITTI and KITTI-360 demonstrate the superiority of SN-LiDAR in both semantic and geometric reconstruction, effectively handling dynamic objects and large-scale scenes. Codes will be available on https://***/dtc111111/SN-Lidar. © 2025, CC BY-NC-SA.

关键词： Semantic Segmentation

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A Parallel Read-Write Circuit With Fast Amplitude-Adaptive Matching Scheme to Memristor Crossbar Array

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IEEE Transactions on Circuits and systems I: Regular Papers 2025年

作者： Jiang, Ningye Jiang, Mingxuan Huang, Haoen Yang, Le Huang, Yi Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430074 China Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan430205 China University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States

Memristor crossbar array (MCA) is a computing-in-memory (CIM) module for computational acceleration. However, conventional read-write (R-W) circuits for MCA rely heavily on external components and have shortcoming in long writing times. To address these issues, we propose a parallel R-W circuit with a fast amplitude-adaptive matching (AAM) scheme. The fast AAM scheme is designed to accelerate time of writing memristors in MCA by adaptively adjusting the writing voltage to match an optimal amplitude. The architecture of parallel R-W circuit are outlined with the implementation of R-W processes. The design principles of parallel R-W unit, fast AAM unit, and associated control logic are described for analyzing their functions within the circuit. By integrating the parallel R-W circuit with MCA, a computing block is leveraged in a neural network for classification tasks. The experimental results demonstrate that the neural network with multiple computing blocks maintains high accuracy across various datasets (over 80%). Furthermore, the proposed AAM scheme achieves an average 54% reduction in writing time compared to that one without AAM scheme. Compared to the pulse-width scheme, the parallel R-W circuit exhibits an average 3.05× speedup in adjusting a 5×5 MCA, and the speedup efficiency is higher as the increases of MCA size. © 2025 IEEE.

关键词： Memristors

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Pathological axonal enlargement in connection with amyloidosis,lysosome destabilization,and bleeding is a major defect in Alzheimer’s disease

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Neural Regeneration Research 2026年第2期21卷 790-799页

作者： Hualin Fu Jilong Li Chunlei Zhang Guo Gao Qiqi Ge Xinping Guan Daxiang Cui Institute of Nano Biomedicine and Engineering School of Sensing Science and EngineeringSchool of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghaiChina National Center for Translational Medicine Shanghai Jiao Tong UniversityShanghaiChina Department of Automation Shanghai Jiao Tong UniversityShanghaiChina The Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina

Alzheimer’s disease is a multi-amyloidosis disease characterized by amyloid-βdeposits in brain blood vessels,microaneurysms,and senile *** amyloid-βdeposition affects axon pathology has not been examined *** used immunohistochemistry and immunofluorescence staining to analyze the forebrain tissue slices of Alzheimer’s disease *** axonal amyloidosis with distinctive axonal enlargement was observed in patients with Alzheimer’s *** average,amyloid-β-positive axon diameters in Alzheimer’s disease brains were 1.72 times those of control brain ***,axonal amyloidosis was associated with microtubule-associated protein 2 reduction,tau phosphorylation,lysosome destabilization,and several blood-related markers,such as apolipoprotein E,alpha-hemoglobin,glycosylated hemoglobin type A1C,and *** destabilization in Alzheimer’s disease was also clearly identified in the neuronal soma,where it was associated with the co-expression of amyloid-β,Cathepsin D,alpha-hemoglobin,actin alpha 2,and collagen type *** suggests that exogenous hemorrhagic protein intake influences neural lysosome ***,the data showed that amyloid-β-containing lysosomes were 2.23 times larger than control ***,under rare conditions,axonal breakages were observed,which likely resulted in Wallerian *** summary,axonal enlargement associated with amyloidosis,micro-bleeding,and lysosome destabilization is a major defect in patients with Alzheimer’s *** finding suggests that,in addition to the well-documented neural soma and synaptic damage,axonal damage is a key component of neuronal defects in Alzheimer’s disease.

关键词： Alzheimer’s disease amyloid-β amyloidosis axonal enlargement hemoglobin hemorrhage lysosome destabilization neuropil thread tau Wallerian degeneration

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