Demand for high-protein beverages is rising, yet the poor thermal stability of soy protein isolate (SPI) limits its application because processing induces aggregation and gelation. Here, a low-concentration pre-modification strategy combining preheating and laccase (LAC) was developed to improve SPI thermal stability. Preheating partially unfolded SPI and exposed buried aromatic residues, enhancing LAC-catalyzed oxidation and radical generation. This promoted intermolecular covalent crosslinking and produced more stable protein particles. LAC improved SPI thermal stability in a level- and time-dependent manner, as shown by weaker gelation and better flowability after reheating. Particle-size analysis showed that LAC promoted aggregation before reheating and that the resulting aggregates remained relatively stable afterward. Structural analyses further indicated that LAC-induced stabilization involved conformational rearrangement and strengthened non-covalent interactions. Overall, combining preheating with LAC provides an effective strategy for improving SPI thermal stability in high-protein systems.
Knowledge graph (KG)-based recommendation has emerged as an effective solution to data sparsity by incorporating structured semantic information into user and item representations. However, existing KG-based recommendation approaches still face two core challenges: (i) over-reliance on explicit relations while insufficiently capturing latent connections between items, and (ii) feature degradation during high-order propagation, which leads to the dilution of informative signals in learned representations. To this end, we propose PLRA-KG, a pattern-derived latent relation augmentation framework for KG-based recommendation. PLRA-KG uncovers implicit item associations by generating syn-relations and syn-entities from global user preference patterns and relational structures. Specifically, PLRA-KG first performs relation-aware clustering via self-training to group items with strong association patterns. It then introduces a discernment-aware relation-pair selection mechanism to identify relation combinations that significantly influence user decisions. Based on the selected relation-pairs, a pattern-derived syn-relation generation strategy is designed to construct latent relations by jointly modeling global user preferences and relational value interactions. These generated syn-relations and syn-entities are subsequently integrated into the original KG, resulting in an augmented graph that better captures hidden semantic connectivity. Finally, PLRA-KG is optimized in a unified framework that jointly considers recommendation learning, KG embedding, and clustering objectives, enabling seamless integration with various KG-based recommendation models. Extensive experiments on multiple benchmark datasets demonstrate that PLRA-KG consistently improves both recommendation accuracy and diversity, achieving average improvements of approximately 6.64% in Recall, 7.83% in AD, 5.56% in Coverage, and 6.20% reduction in ARP. The source code is accessible at https://github.com/ZZP-RS/PLRA-KG.
Catamaran salvage ships are perfect for marine rescue, wreck salvage, ocean cleanup, and other tasks. The salvage ships have to arrive quickly at the salvage site, thus catamarans with good hydrodynamic performance are best. This paper proposes a parametric automatic optimization design method to obtain a new type of catamaran salvage ship with good resistance and to forecast its seakeeping performance during salvage operations. In this paper, the underwater hull of the catamaran salvage ship is constructed using full parameterization, and its design parameters are analyzed for sensitivity. The SOBOL algorithm is used for spatial sampling, and the optimization variable is the parameter with the largest correlation to the total resistance value for multiple conditions in the light-load and full-load conditions. Finally, the Tsearch algorithm is used to build a set of parametric catamaran salvage ship automatic calculation frameworks based on numerical simulation. According to the sensitivity analysis and automatic optimization results, the Catamaran salvage ship with good resistance performance is obtained, and the seakeeping performance of the ship under different salvage angles is forecasted. This article provides reference and practical guidance for the design optimization method of Catamaran salvage ships and its seakeeping performance analysis for salvage in waves.
Listeria monocytogenes is a common foodborne pathogen that poses a serious health risk. Faecalibacterium prausnitzii is one of the major members of the gut microbiota and is considered essential for maintaining gut health. The aim of this study was to investigate the effect of live F. prausnitzii (FP), pasteurized F. prausnitzii (pFP) and its cell-free supernatant (CFS) on the susceptibility of mice to L. monocytogenes infection and to explore the underlying mechanisms. Safety evaluation results indicated that FP, pFP, and CFS showed no toxic effects in healthy mice. In L. monocytogenes-infected mice, these treatments significantly decreased bacterial counts in various organs and feces, and alleviated inflammation. Furthermore, the interventions significantly upregulated short-chain fatty acid (SCFA) levels, particularly acetate and propionate, and modulated the gut microbiota composition after infection. In addition, RT-qPCR and western blot results showed that they reduced inflammatory factors and intestinal damage, which is associated with downregulation of the TLR4/NF-κB pathway. Taken together, these results suggest that FP, pFP and CFS attenuate L. monocytogenes infection in mice and could be potentially developed as an alternate strategy for prevention or mitigation of L. monocytogenes. FP, pFP and CFS reduced L. monocytogenes infection in mice FP, pFP and CFS enhanced intestinal epithelial barrier FP, pFP and CFS modulated the composition of the gut microbiota FP, pFP and CFS attenuated inflammation by TLR4/NF-κB pathway
To enhance the mechanical performance of the hydrogel after swelling, polyacrylic acid (PAA)/polyethylene glycol (PEG) dual-network hydrogels modified by nano-silica (SiO2) were prepared via free radical polymerisation. PAA molecular chains were subjected to chemical crosslinking via a crosslinking agent in conjunction with SiO2–KH570 to form a chemically crosslinked network. PEG chains subsequently interpenetrated this network, generating a dual-network hydrogel structure. Hydrogel preparation parameters were optimised by varying the neutralisation degree and PEG molecular weight, followed by systematic evaluation of its structure and properties. The results demonstrated that the neutralization degree primarily influences the mechanical properties by regulating the balance between electrostatic repulsion and hydrogen bond dissipation. The molecular weight of PEG determines the rigidity and flexibility of the hydrogel cross-linking network. Furthermore, the compressive performance of the hydrogel was enhanced by the introduction of nano-SiO2. Addition of 0.5
