Transglutaminases (TGase), synthesized as a zymogen (pro-TGase) in Streptomyces sp., are important enzymes in food industry. Due to the important applications of TGase in food industry, obtaining robust and food-safe TGase-producing strains has attracted much attention during the past decade. In this study, Streptomyces hygroscopicus pro-TGase was efficiently expressed and secreted by a food-grade host, Yarrowia lipolytica, without antibiotic markers. The pro-TGase gene was cloned into integrative vectors pINA1296 (monocopy) and pINA1297 (multicopy), and was used to transform the Y. lipolytica Po1g or Po1h strain, respectively. Expression was driven by a recombinant hp4d promoter and secretion obtained using a XPR2 pre-sequence as a signal peptide. The highest yield of extracellular pro-TGase produced by the recombinant Po1h strain corresponded to 5.3 U/mL of TGase, a level 8.8 fold higher than that obtained using the recombinant Po1g strain. Asparagines in two potential Asn-linked glycosylation sites (Asn160 and Asn355) from pro-TGase were mutated to glutamine individually or simultaneously, yielding the deglycosylated variants N160Q, N355Q, and N160Q/N355Q. The activities of N160Q, N355Q and N160Q/N355Q constructs were respectively 5.3 U/mL, 7.8 U/mL, and 3.0 U/mL, equivalent to 100 %, 147 %, and 57 % of that from wild-type pro-TGase. The TGase yield of N355Q variant was raised to 35.3 U/mL of by using a glycerol feeding strategy in a 3 L fermenter. The optimal pH and temperature of the activated pro-TGase, and of its deglycosylated variants, were in the range of 5.0-6.0 pH and 40-45 °C, respectively. The half-life of the recombinant wild-type pro-TGase at 37 °C reached 34.0 min, and those of the variants were from 24.2 min to 11.5 min. In contrast to the wild-type pro-TGase, all of the variants had decreased specific activities, and both the K m and k cat values of the variants decreased accordingly. This study constitutes the first report of the heterologous expression of a pro-TGase in Y. lipolytica, and provides new possibilities for the efficient production of TGases used in food processing.

Land-use and land-cover changes constitute pivotal components in global environmental change research. Through an examination of spatiotemporal variations in land cover, we can deepen our understanding of land-cover change dynamics, shape appropriate policy frameworks, and implement targeted environmental conservation strategies. The judicious management of land is a critical determinant in fostering the sustainable growth of urban economies and enhancing quality of life for residents. This study harnessed remote sensing data to analyze land-cover patterns in Tianjin over five distinct time points: 2000, 2005, 2010, 2015, and 2020. It focused on evaluating the evolving dynamics, transition velocities, and transformation processes across various land categories within the region. Utilizing dynamic analysis and a transition matrix, the study traced shifts among different land-use classes. The center-of-gravity migration model was employed to elucidate land-cover pattern evolution. This research also integrated pertinent land-cover statistics to offer a holistic perspective on Tianjin’s land-cover transformations. Employing the CA–Markov model, we projected the prospective spatial layout of land cover for the area. Our findings revealed the following. (1) From 2000 to 2020, Tianjin experienced a significant reduction in cropland, forest, grassland, and water areas, alongside a substantial increase in impervious. (2) The impervious surface’s center of gravity, initially in Beichen District, shifted 4.20 km northwestward at an average rate of 0.84 km per year. (3) Principal component analysis indicated that the growth in the output value of the secondary and forestry industries is a key driver in expanding Tianjin’s impervious-surface area. (4) Predictions for 2025 suggest an increase in Tianjin’s impervious-surface area to 4659.78 km², with a concurrent reduction in cropland to 5656.18 km². The insights gleaned from this study provide a solid theoretical foundation and empirical evidence, aiding in the formulation of informed land-use strategies, the preservation of urban land resources, and guiding principles for sustainable urban development.

Intensive mining over recent decades has caused severe ground subsidence in mining regions, threatening safety and long-term sustainability. High-precision, continuous monitoring and prediction of subsidence are therefore urgently needed. Traditional methods—terrestrial surveying and GPS—offer limited coverage, sparse measurement points, high costs, and poor scalability, making them unsuitable for large-scale, long-term surface deformation monitoring. InSAR is widely used for ground deformation monitoring due to its wide-area coverage, long-term sampling, high spatial resolution, and millimeter-scale precision. However, conventional InSAR often fails in vegetated areas and under steep deformation gradients—common in mining zones. To overcome these limitations, this study applied SBAS-InSAR, a method better suited for large-magnitude, continuous subsidence monitoring in mining areas. This study proposed an enhanced hierarchical spatiotemporal dependency graph neural network (HSDGNN) integrated with a Long Short-Term Memory (LSTM) module to improve temporal feature representation. Using this model, this study predicted surface subsidence at the Dexing Copper Mine under environmental drivers. Key findings are as follows: (1) Surface subsidence exhibited pronounced spatial heterogeneity and strong temporal nonlinearity; major subsidence zones were localized in open-pit excavation areas and waste rock dumps, with peak subsidence rates reaching −126.121 mm/yr. (2) Precipitation and soil moisture emerged as the dominant environmental controls on subsidence, displaying distinct seasonal modulation and quantifiable lagged responses—up to several months—relative to subsidence onset. (3) The HSDGNN model achieved high predictive accuracy for both Mine 1 and Mine 2, attaining R² values of up to 0.9950. This work establishes a robust, scalable, and operationally viable framework for high-precision subsidence monitoring and forecasting in geologically and anthropogenically complex mining environments.