Search Results (98)

This study aimed to investigate the efficacy of a composite coating composed of 150 mg/L ε-Poly-L-lysine (ε-PL) and 5 g/L chitosan (CTS) in extending the shelf life and maintaining the postharvest quality of fresh Tremella fuciformis. Freshly harvested T. fuciformis were treated by surface spraying, with distilled water serving as the control. The effects of the coating on storage quality, physicochemical properties, reactive oxygen species (ROS) metabolism, and membrane lipid metabolism were evaluated during storage at (25 ± 1) °C. The results showed that the ε-PL/CTS composite coating significantly retarded quality deterioration, as evidenced by reduced weight loss, maintained whiteness and color, and higher retention of soluble sugars, soluble solids, and soluble proteins. The coating also effectively limited water migration and loss. Mechanistically, the coated T. fuciformis exhibited enhanced antioxidant capacity, characterized by increased superoxide anion (O₂⁻) resistance capacity, higher activities of antioxidant enzymes (SOD, CAT, APX), and elevated levels of non-enzymatic antioxidants (AsA, GSH). This led to a significant reduction in malondialdehyde (MDA) accumulation, alongside improved DPPH radical scavenging activity and reducing power. Furthermore, the ε-PL/CTS coating preserved cell membrane integrity by inhibiting the activities of lipid-degrading enzymes (lipase, LOX, PLD), maintaining higher levels of key phospholipids (phosphatidylinositol and phosphatidylcholine), delaying phosphatidic acid accumulation, and consequently reducing cell membrane permeability. In conclusion, the ε-PL/CTS composite coating effectively extends the shelf life and maintains the quality of postharvest T. fuciformis by modulating ROS metabolism and preserving membrane lipid homeostasis. This study provides a theoretical basis and a practical approach for the quality control of fresh T. fuciformis. Full article

Developing stretchable strain sensors that combine both high sensitivity and a wide linear range is a critical requirement for health electronics, yet it remains challenging to meet the practical demands of daily health monitoring. This study proposes a novel heterogeneous surface strategy by in situ silver deposition on modified PDMS followed by MXene spray coating, constructing a multilevel microcrack strain sensor (MAP) using silver nanoparticles and MXene. This innovative multilevel heterogeneous microcrack structure forms a dual conductive network, which demonstrates excellent detection performance within GF_max = 487.3 and response time ≈65 ms across various deformation variables. And the seamless integration of the sensor arrays was designed and employed for the detection of human activities without sacrificing biocompatibility and comfort. Furthermore, by adopting advanced deep learning technology, these sensor arrays could identify different joint movements with an accuracy of up to 95%. These results provide a promising example for designing high-performance stretchable strain sensors and intelligent recognition systems. Full article

Diverging from conventional dephosphorization approaches, this study employs a novel pre-reduction and smelting separation (PR-SS) to efficiently co-recover iron and phosphorus from high-phosphorus oolitic iron ore, directly yielding Fe–P alloy, and the Fe–P alloy shows potential as feedstock for high-phosphorus weathering steel or wear-resistant cast iron, indicating promising application prospects. Using oolitic magnetite concentrate (52.06% Fe, 0.37% P) as feedstock, optimized conditions including pre-reduction at 1050 °C for 2 h with C/Fe mass ratio of 2, followed by smelting separation at 1550 °C for 20 min with 5% coke, produced a metallic phase containing 99.24% Fe and 0.73% P. Iron and phosphorus recoveries reached 99.73% and 99.15%, respectively. EPMA microanalysis confirmed spatial correlation between iron and phosphorus in the metallic phase, with undetectable phosphorus signals in vitreous slag. This evidence suggests preferential phosphorus enrichment through interfacial mass transfer along the pathway of the slag phase to the metal interface and finally the iron matrix, forming homogeneous Fe–P solid solutions. The phosphorus migration mechanism involves sequential stages: apatite lattice decomposition liberates reactive P₂O₅ under SiO₂/Al₂O₃ influence; slag–iron interfacial co-reduction generates Fe₃P intermediates; Fe₃P incorporation into the iron matrix establishes stable solid solutions. Full article

Adhesion between Schwann cells (SCs, a type of glial cell in the peripheral nervous system) and their underlying substrates is a fundamental process that holds critical importance for the proper functioning of the peripheral nervous system. Conducting further in-depth research into the adhesion mechanisms of nerve cells is of paramount significance, as it can pave the way for the development of highly effective biomaterials and facilitate the repair of nerve injuries. Thyroid Receptor Interaction Protein 6 (TRIP6), a member of the ZYXIN family of LIM domain-containing proteins, serves as a key component of focal adhesions. It plays a pivotal role in regulating a diverse array of cellular responses, including the reorganization of the actin cytoskeleton and cell adhesion. Accumulated data indicate that RSC96 cells (rat Schwann cells), which are rat Schwann cells, exhibit integrin-based mechanosensitivity during the initial phase of adhesion, specifically within the first 24 h. This enables the cells to sense and respond to alterations in matrix stiffness. The results of immunofluorescence staining experiments revealed intriguing findings. An increase in matrix stiffness not only led to significant changes in the morphological parameters of RSC96 ells, such as circularity, aspect ratio, and cell spreading area, but also enhanced the expression levels of TRIP6, focal adhesion kinase (FAK), and vinculin within these cells. These changes collectively promoted the adhesion of RSC96 cells to the matrix. Furthermore, when TRIP6 expression was silenced in RSC96 cells cultured on hydrogels, a notable decrease in the expression of both FAK and vinculin was observed. This, in turn, had a detrimental impact on cell adhesion. In summary, the present study strongly suggests that TRIP6 may play a crucial role in promoting the adhesion of RSC96 cells to polyacrylamide hydrogels with varying stiffness. This research not only offers a fresh perspective on the study of the integrin-mediated force regulation of cell adhesion but also lays a solid foundation for potential applications in tissue engineering, regenerative medicine, and other related fields. Full article

This study introduces a novel framework for the inverse design of sustainable food packaging materials using generative adversarial networks (GANs) and the recently released OMat24 dataset containing 110 million DFT-calculated inorganic material structures. Our approach transforms traditional material discovery paradigms by enabling end-to-end design from desired performance metrics to material composition. We developed a GAN-driven inverse design architecture specifically optimized for food packaging applications, integrating sensor-derived data on critical constraints such as biodegradability and barrier properties directly into the generative process. This integration occurs at three levels: (1) sensor-measured properties define conditioning targets for the GAN, (2) sensor data train the property prediction network, and (3) sensor-based characterization validates generated materials. An enhanced EquiformerV2 graph neural network was employed to accurately predict the formation energy, stability, and sensor-measurable properties of candidate materials. The model achieved a mean absolute error of 12 meV/atom for formation energy on the OMat24 test set (25% improvement over baseline models), while predictions of sensor-measured functional properties reached $R^2$ values of 0.84–0.89 through the integration of experimental measurements and physics-based proxy models. The framework successfully generated over 100 theoretically viable candidate materials, with 20% exhibiting superior barrier properties and controlled degradation characteristics. Our computational approach demonstrated a 20–100× acceleration in screening efficiency compared to traditional DFT calculations while maintaining high accuracy. This work presents a significant advancement in computational materials discovery for sustainable packaging applications, offering a promising pathway to address the urgent global challenges of food waste and plastic pollution. Full article

Tumor-associated macrophages (TAMs) are pivotal for tumor development and progression. Reprogramming the M2-like pro-tumoral behavior of TAMs towards the M1-like anti-tumor phenotype to unleash their potential against tumors has become one of the most promising anti-tumor immunotherapy strategies. In this work, the natural product pseudolaric acid B (PAB, 1) was found to markedly decrease ARG1 mRNA expression and significantly increase NOS2 expression in the IL-4/IL-13-pre-stimulated RAW 264.7 cells through cellular phenotype screening of a series of pseudolaric acid-related natural products, suggesting its potential to reprogram the pro-tumoral TAMs towards the M1-like phenotype against tumors. Further chemical modification of the carboxylic acid moiety of 1 led to a series of amide or pyranoside derivatives with ARG1- and NOS2-modulating activity. Among them, hydrazineyl amide 12 stands out as the most potent, without significant diminution in cell viability. It inhibited the M2-like polarized tumor-promoting phenotype of macrophages, as evidenced by a decrease in CD206 expression and an increase in CD86 expression in flow cytometry, as well as a decrease in ARG1 protein level in Western blot assays. In addition, 12 could reverse the suppression of Ki67+, IFN γ+, and granzyme B⁺ CD8⁺ T cell proliferation and activation induced by pro-tumoral macrophages. More importantly, it could reshape the tumor immune microenvironment and inhibit tumor growth in immunocompetent murine tumor models. Hsp90 was predicted to be a potential target of 12 by a target fishing software, which was further demonstrated by molecular docking. Collectively, the amide derivative 12 of PAB demonstrated promising anti-tumor TAM-reprogramming activity, which is worthy of further investigation. Full article

The Qinling giant panda has a high susceptibility to skin damage, which affects its survival. Although their healing efficacy in panda injuries remains unexplored, extracellular vesicles from adipose-derived mesenchymal stem cells (ADMSC-EVs) have shown promise in regenerative medicine. In this study, ADMSC-EVs were successfully obtained from Qinling giant pandas using ultracentrifugation, and proteomic techniques were used to analyze their composition and function. Primary skin fibroblasts from Qinling giant pandas were isolated and cultured to explore the effects of ADMSC-EVs on cell proliferation and migration. Additionally, a mouse model of skin injury was used to assess their wound healing effects. The ADMSC-EVs contained various substances, particularly proteins, with fifty unique proteins involved in transport, catabolism, and signal transduction identified. The application of ADMSC-EVs in a mouse model accelerated wound healing and promoted the regeneration of the epidermal and dermal layers. It facilitated the repair of skin appendages, including hair follicles and sebaceous glands. Additionally, ADMSC-EVs enhanced collagen deposition, stimulated angiogenesis, and reduced inflammation. Our findings confirm that ADMSC-EVs significantly improve skin healing, thus supporting the theoretical framework for the clinical use of giant panda extracellular vesicles and underscoring their potential for preserving the genetic resources of the Qinling giant panda. Full article

The digital economy has become an important engine for global economic development by promoting optimal resource allocation and advancing industrial restructuring. Based on the panel data from 279 prefecture-level cities in China from 2012 to 2021, this paper constructs the spatial association networks of urban digital economy using a modified gravity model and analyzes the complex network characteristics and driving factors of urban digital economy growth by the social network analysis methods and the Quadratic Assignment Procedure (QAP). This study finds that (1) the level of urban digital economy in China shows a rising trend year by year and displays an uneven spatial distribution. (2) Spatial association networks of urban digital economy are relatively well-connected, with increasing density and stability of spatial associations, yet some hierarchical structure remains, and overall connectivity still needs to be improved. (3) Most cities in the east region occupy the core positions within the complex network, significantly influencing the overall complex network through a “siphon effect”, while cities in the central region play more of a “bridge” role in the spatial association network. In contrast, cities in the northwest, northeast, and southwest regions are situated on the periphery of this spatial association network. (4) The economic development level, informatization level, technological innovation, urbanization level, industrial structure, and human capital contribute to the formation of the spatial association network of the digital economy. Based on these conclusions, specific policy implications for the future development of the spatial association network of the urban digital economy are proposed. Full article

The retail industry faces increasing challenges in matching supply with demand due to evolving consumer behaviors, market volatility, and supply chain disruptions. While existing approaches employ statistical and machine learning methods for demand forecasting, they often fail to capture complex temporal dependencies and lack the ability to simultaneously optimize inventory decisions. This paper proposes a novel multi-agent deep reinforcement learning framework that jointly optimizes demand forecasting and inventory management in retail supply chains, leveraging data from IoT sensors, RFID tracking systems, and smart shelf monitoring devices. Our approach combines transformer-based sequence modeling for demand patterns with hierarchical reinforcement learning agents that coordinate inventory decisions across distribution networks. The framework integrates both historical sales data and real-time sensor measurements, employing attention mechanisms to capture seasonal patterns, promotional effects, and environmental conditions detected through temperature and humidity sensors. Through extensive experiments on large-scale retail datasets incorporating sensor network data, we demonstrate that our method achieves 18.2% lower forecast error and 23.5% reduced stockout rates compared with state-of-the-art baselines. The results show particular improvements in handling promotional events and seasonal transitions, where traditional methods often struggle. Our work provides new insights into leveraging deep reinforcement learning for integrated retail operations optimization and offers a scalable solution for modern sensor-enabled supply chain challenges. Full article

Cold chain temperature management is crucial for preserving product quality and safety across various industries. While Computational Fluid Dynamics (CFD) provides detailed insights into thermal analysis and fluid dynamics, its computational intensity limits practical applications. This study presents a novel hybrid approach combining CFD and machine learning to enhance both computational efficiency and prediction accuracy in cold storage temperature management. A validated 3D CFD model was developed to analyze temperature distribution and airflow patterns in a refrigerated container with multiple storage boxes. Taking advantage of the cold room’s symmetrical design along its longitudinal axis significantly reduced computational requirements while maintaining model accuracy. Over 200 cases were simulated by varying key process parameters to generate training data for machine learning models. Random Forest (RF) and Neural Network (NN) models were developed and compared, with RF demonstrating consistently superior performance across all storage locations. Feature importance analysis revealed cold air temperature as the dominant control variable, while SHAP analysis identified optimal operational ranges for air velocity and heat transfer coefficients that balance product quality with energy efficiency. This research work also revealed distinct patterns in the influence of process parameters, with cold air and ambient temperatures showing hierarchical impacts on system performance. The hybrid methodology successfully addresses the computational limitations of traditional CFD approaches while maintaining high prediction accuracy, offering a practical solution for sustainable temperature management in cold storage applications. Finally, this research provides valuable insights for optimizing cold chain operations and demonstrates the potential of hybrid modeling approaches in thermal management systems. Full article

This study conducted a systematic comparison of four drying methods (vacuum freeze-drying, spray drying, spray freeze-drying, and hot air drying) on abalone bioactive peptides, investigating their effects on physicochemical properties and nutritional composition. Scanning electron microscopy revealed distinct morphological characteristics: hot-air-dried samples showed compact structures with large particles, and vacuum-freeze-dried samples exhibited flaky morphology, while spray-freeze-dried and spray-dried samples demonstrated advantageous smaller particle sizes. Spray freeze-drying achieved superior emulsification capacity and fat absorption, significantly higher than hot air drying. The enhanced performance was attributed to increased exposure of hydrophobic amino acid residues and improved surface activity. Regarding nutritional composition, vacuum freeze-drying demonstrated optimal protein and total amino acid preservation, while spray freeze-drying showed the highest retention of Ca and Fe. Interestingly, hot air drying exhibited superior vitamin A retention, attributed to its fat-soluble nature and stability below 100 °C. The particle size reduction in spray-freeze-dried samples enhanced solvent–solute contact area, contributing to improved solubility and consequently superior foaming properties. These findings provide valuable insights into the relationship between drying methods and product characteristics, offering guidance for optimizing processing conditions in marine protein production. Full article

Banana (Musa spp.) is susceptible to low-temperature stress and other environmental stresses, which can hinder the growth and development. Succinic semialdehyde dehydrogenase (SSADH) is critical for GABA biosynthesis and plays a crucial role in plants. However, the SSADH genes of bananas have not been studied. This study found 19 MaSSADHs, 18 MbSSADHs, and 18 MiSSADHs from the banana genome. According to the phylogenetic tree, these genes can be categorized into five branches. This study cloned the MaSSADH1-14 from banana. The subcellular localization assays of MaSSADH1-14 in tobacco leaves confirmed that the presence of SSADH was not only localized mitochondrion but also localized chloroplast. The cis-elements of the SSADH gene family are related to the potential regulation of the banana SSADH gene family; their involvement in diverse stress responses. Transcriptomic data was utilized to examine the effect of MaSSADH genes under cold stress in bananas. The results of RT-qPCR were consistent with transcriptome data. These results showed that most MaSSADHs are passively responsive to low-temperature treatment. In addition, transient overexpression of MaSSADH1-14 in Nicotiana benthamiana leaves resulted in the content of GABA increasing, indicating that MaSSADH1-14 may be involved in the accumulation of GABA of banana. Collectively, these results improve knowledge of the SSADH gene family in banana and establish a basis for comprehending its biological roles in response to low temperatures. Full article

Land use change driven by vegetation succession significantly enhances soil carbon storage, yet the microbial mechanisms underlying this process remain poorly understood. This study aims to elucidate the mechanistic linkages between bacterial community dynamics and organic matter carbon stabilization across four vegetation succession stages on the Loess Plateau: abandoned farmland (AF), grassland stage (GS), shrub-land stage (SS), and forest stage (FS). We analyzed soil organic matter carbon (SOM_C) fractions, physicochemical properties, and bacterial communities (16S rRNA sequencing), employing structural equation modeling to quantify causal pathways. The results showed that the content of soil total organic matter carbon (TOM_C), labile organic matter carbon (LOM_C), dissolved organic matter carbon (DOM_C), and microbial biomass carbon (MBC) increased progressively with succession, peaking in the FS, with 23.87 g/kg, 4.13 g/kg, 0.33 mg/kg, and 0.14 mg/kg, respectively. Furthermore, vegetation succession also led to heterogeneity in the bacterial community structure. The number of soil bacterial operational taxonomic units (OTUs) for the four succession stages was 9966, 13,463, 14,122, and 10,413, with the shrub-land stage showcasing the highest OTUs. Nine bacterial taxa were strongly correlated with SOM_C stabilization. Affected by soil bacteria, soil physicochemical properties and litter biomass directly influence SOM_C, with the physicochemical pathway (path coefficient: 0.792, p < 0.001) having a greater impact on organic matter carbon than the litter pathway (path coefficient: 0.221, p < 0.001). This study establishes that vegetation succession enhances SOM_C content not only through increased litter inputs but also by reshaping bacterial communities toward taxa that stabilize carbon via physicochemical interactions. Full article

Flexible grippers based on magnetically sensitive rubber have garnered significant research attention due to their high gripping adaptability and ease of control. However, current research designs often separate the excitation device from the flexible finger, which can lead to potential interference or damage to other electronic components in the working environment and an inability to simultaneously ensure safety and gripping performance. In this paper, we propose an integrated magnetically controlled bionic flexible gripper that combines the excitation device and the flexible finger. We derive a formula for calculating the magnetic field generated by the excitation device, model and simulate the device, and find that the optimal magnetic field effect is achieved when the core-to-coil size ratio is 1:5. Additionally, we fabricated flexible fingers with different NdFeB volume ratios and experimentally determined that a volume ratio of 20% yields relatively better bending performance. The integrated magnetically controlled bionic flexible gripper described in this paper can adaptively grasp items such as rubber, column foam, and electrical tape, achieving maximum grasping energy efficiency of 0.524 g per millitesla (g/mT). These results highlight its potential advantages in applications such as robotic end-effectors and industrial automatic sorting. Full article

With the proliferation of mobile devices and payment systems in modern financial services, there is an increasing need to process and analyze continuous streams of transaction data for credit risk assessment. Leveraging the inherent symmetries in financial markets and data structures, this paper introduces DeepCreditRisk, a symmetry-aware deep learning framework that addresses key challenges while maintaining critical invariance properties in financial data representation. The framework incorporates three main components: an adaptive temporal fusion mechanism, a heterogeneous graph neural network, and an attention-based interpretable output layer. The temporal fusion mechanism effectively models both short-term fluctuations and long-term trends in financial time series, while the heterogeneous graph neural network captures intricate relationships within the financial ecosystem. The framework maintains important symmetrical properties in both temporal and structural representations, ensuring balanced feature learning and invariant risk assessment. The attention-based output layer preserves representation symmetry while enhancing model interpretability. Extensive experiments on a large-scale credit risk dataset demonstrate DeepCreditRisk’s superior performance, achieving a 7.2% improvement in the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and an 18.6% improvement in the Kolmogorov–Smirnov (KS) statistic over state-of-the-art baseline models. The framework maintains high predictive power across various time horizons and provides interpretable insights into feature importance. DeepCreditRisk represents a significant advancement in applying deep learning to credit risk assessment, offering financial institutions a more accurate, robust, and transparent approach for evaluating creditworthiness and managing risk. Full article