Observed differences between paddy and upland croplands in soil organic carbon (SOC) and soil inorganic carbon (SIC) may reflect environmental and data-provenance imbalances rather than land-use effects. We reconstructed independent SOC and SIC profile datasets across China, harmonized depths, and separated full-sample comparisons from a prespecified comparable subset. Among all topsoil SOC profiles retained after applying the inclusion criteria (334 paddy, 347 upland), the median Upland-minus-Paddy contrast was −2.12 g kg−1; within the shared subset (97/95), it attenuated to −0.35 g kg−1 (95% interval, −1.74 to 1.55). For 97 paddy soils, changing only the land-use indicator to upland while holding observed soil and environment states fixed yielded a mean SOC contrast of −0.58 g kg−1 (joint 95% interval, −1.02 to −0.15), with 78.4% negative. A layer-resolved Extra Trees S-learner supported this scenario with out-of-fold R2 = 0.72 and mean R2 = 0.72 across ten spatial partitions. The shared topsoil SIC subset retained only 9 paddy and 7 upland profiles, so adjusted SIC could not be estimated. Aridity index (AI) stratification showed the strongest negative SOC contrast in more humid high-AI croplands (−1.08 g kg−1; −1.62 to −0.56), whereas low- and middle-AI strata crossed zero; the high-minus-middle difference was −1.29 g kg−1 (−2.09 to −0.60). Among comparable croplands, observed topsoil SOC differed little between paddy and upland fields, but model predictions indicated lower SOC under upland land use, particularly in humid regions. These results suggest a modest SOC advantage of paddy croplands, while SIC differences remain unresolved.
Pig respiratory diseases present a major health challenge to the global pig farming industry, and early identification of pig cough is crucial for disease prevention, control, and minimizing economic losses. However, existing deep learning methods based on a single audio modality exhibited limited generalization ability in acoustically complex farming environments. Moreover, conventional static multimodal audio fusion methods failed to fully exploit complementary cross-modal information and lacked adaptability to dynamic pig production conditions. To tackle the above challenges, a novel multimodal audio fusion (MAF) attention mechanism was proposed, enabling dynamic fusion of multimodal audio by adaptively leveraging complementary information between audio modalities. Based on the MAF, a pig cough recognition model tailored for practical farming environments was developed. The results demonstrated that the proposed MAF-based dynamic fusion significantly improved the pig cough recognition compared to static fusion methods. The best performance was achieved by dynamically fusing the logarithmic Mel (log-mel) and Constant-Q Transform (CQT) spectrograms using the MAF (Mel + CQT_MAF), reaching an accuracy of 96.55% and consistently outperforming existing state-of-the-art pig cough recognition methods. Both the position and number of the MAF blocks significantly affected model performance. Optimal results were obtained with MAF-based classifier-wise fusion, which improved recognition accuracy by approximately 2.5% compared to early-stage fusion approaches. Increasing the number of MAF blocks did not necessarily lead to improved performance. Similarly, incorporating more audio modalities did not inherently improve recognition accuracy. Instead, the use of high-quality and complementary audio modalities proved critical for effective multimodal audio fusion. This study indicates that the proposed method is a powerful tool for pig cough recognition in farming environments.
Starch retrogradation, driven by reassociation and recrystallization of gelatinized amylose and amylopectin chains, induces undesirable firming, syneresis, and sensory deterioration, ultimately shortening the shelf-life of starch-based foods. Enzymatic modification has emerged as a promising route to retard retrogradation, but its efficacy critically depends on the structural features targeted by the enzyme, the susceptibility of the starch substrate, and the accessibility of catalytic sites. This review systematically interprets the structure-dependent mechanisms underlying enzymatic retrogradation regulation, covering the differential regulatory patterns of various enzyme systems. This review further clarifies the essential distinction between substrate structural susceptibility and enzyme catalytic accessibility, and summarizes advanced modification strategies based on porous starch construction and controlled starch swelling to optimize enzyme-substrate interfacial contact efficiency. In addition, mainstream detection techniques are compared based on the different levels of the studied starch structure. Finally, the practical applicability of enzymatic anti-retrogradation technologies in diverse staple and processed starch-based food systems are also summarized. We propose that the key design principle is controlled structural remodeling enabled by enzyme action within a defined processing window, rather than indiscriminate starch degradation. This work provides systematic theoretical insights and practical guidance for the quality stabilization and shelf-life extension of starch-based food products.
High-moisture extrusion is a critical technology for producing fibrous plant-based meat analogues, yet the molecular mechanisms governing protein conformational transitions across different extruder zones remain incompletely understood. This study employed dead-stop sampling with molecular dynamics (MD) simulations to investigate the structural evolution of chickpea protein isolate (CPI) during extrusion at 65% moisture content. In the mixing and melting zones (Z1–Z3), samples S1–S3 underwent extensive unfolding, evidenced by reduced β-sheet content (39.28%) and a surge in random coil content (29.16%). This transition was accompanied by a dramatic decrease in the storage modulus (G''<100 Pa), indicating formation of a highly disordered molten protein phase. Following cooling and shaping (Z4–Z6), rapid protein reorganization occurred in samples S4–S6. This process yielded a product (S6) with significantly increased β-sheet content (45.91%), disulfide bonds (1.77 μmol/g), and surface hydrophobicity (1863.20 μmol/L), which ultimately established stable fibrous structures. MD simulations demonstrated a temperature-dependent unfolding–refolding mechanism. Fluctuations in root-mean-square deviation (RMSD), secondary structure assignment (SSAA) and radius of gyration (RG) reflected global structural expansion and the exposure of hydrophobic clusters during heating, while changes in root-mean-square fluctuation (RMSF) and the number of hydrogen bonds (NH-bond) indicated local residue flexibility and internal network reconstruction during the heating and cooling cycles. Collectively, CPI fiber formation originates from a temperature-programmed transition in which thermal unfolding generates a viscoelastic protein melt, while cooling-induced entropy-driven reassembly into β-sheet-rich, anisotropic networks acts as the molecular switch for texturization. The findings reported here underpin the rational design and precise modulation of textural attributes and fibrillar morphology, facilitating the advancement of high-quality chickpea-based meat alternatives.
The growing demand for plant-based food systems has led to increased interest in legume proteins as natural emulsifiers. However, their poor solubility, slow interfacial adsorption, and limited emulsion stability restrict broader applications. Therefore, in this study, the effects of protein-glutaminase (PG)-induced deamidation on the physicochemical and interfacial properties of common vetch protein isolate (CVPI) and pea protein isolate (PPI) were systematically investigated to enhance their emulsifying performance. Enzymatically modified samples (ECVPI and EPPI) were prepared and compared with untreated samples. Deamidation markedly improved protein dispersibility and solubility, reflected by smaller particle sizes, increased surface hydrophobicity, and more negative ζ-potentials. Interfacial tensiometry and dilatational rheology demonstrated that ECVPI and EPPI rapidly adsorbed at the oil-water interface, forming dense and viscoelastic interfacial films. Deamidation (ECVPI & EPPI) generated an interfacial network with enhanced stiffness and resistance to deformation, indicating that deamidation enhanced intermolecular interactions. At the emulsion level, PG treatment significantly enhanced the interfacial activity of deamidated proteins, leading to decreased droplet sizes and improved stabilization of droplets upon storage. ECVPI-stabilized emulsions exhibited the smallest and most homogeneous droplets with strong electrostatic stabilization and minimal flocculation. These findings demonstrate that PG-induced deamidation effectively alters the interfacial behavior of legume proteins, providing a controlled design of stable plant-based emulsions.
