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D-Mannitol (hereinafter as mannitol) is a six-carbon sugar alcohol with diverse applications in food and pharmaceutical industries. To overcome the drawbacks of the chemical hydrogenation method commonly used for mannitol production at present, there is a need to search for novel prospective mannitol production strategies that are of high yield and low cost. In this study, we present a novel approach for the stoichiometric synthesis of mannitol via an in vitro synthetic enzymatic biosystem using the low-cost starch as substrate. By dividing the overall reaction pathway into three modules which could be executed sequentially in one pot, our design aimed at the stoichiometric conversion of starch-based materials into mannitol in an ATP-independent and cofactor-balanced manner. At optimized conditions, high product yields of around 95-98% were achieved using both 10 g/L and 50 g/L maltodextrin as substrate, indicating the potential of our designed system for industrial applications. This study not only provides a high-efficient strategy for the synthesis of mannitol but also expands the product scope of sugar alcohols by the in vitro synthetic enzymatic biosystems using low-cost starch-based materials as the input. Key points: • We described a design-build-test-learn pipeline to construct in vitro biosystems. • The designed system comprised six key enzymes and another three enzymes. • The system converted maltodextrin stoichiometrically to mannitol in one pot. [ABSTRACT FROM AUTHOR]

The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data. This study investigated the capability and strategy of GF-2 multispectral data for land use and land cover (LULC) classification in a region of the North China Plain. The pixel-based and object-based classifications using maximum likelihood (MLC) and support vector machine (SVM) classifiers were evaluated to determine the classification strategy that was suitable for GF-2 multispectral data. The validation results indicated that GF-2 multispectral data achieved satisfactory LULC classification performance, and object-based classification using the SVM classifier achieved the best classification accuracy with an overall classification accuracy of 94.33% and kappa coefficient of 0.911. Therefore, considering the LULC classification performance and data characteristics, GF-2 satellite data could serve as a valuable and reliable high-resolution data source for land surface monitoring. Future works should focus on improving LULC classification accuracy by exploring more classification features and exploring the potential applications of GF-2 data in related applications. [ABSTRACT FROM AUTHOR]

In the karst regions of southwest China, rocky desertification is one of the most serious problems in land degradation. The bedrock exposure rate is an important index to assess the degree of rocky desertification in karst regions. Because of the inherent merits of macro-scale, frequency, efficiency, and synthesis, remote sensing is a promising method to monitor and assess karst rocky desertification on a large scale. However, actual measurement of the bedrock exposure rate is difficult and existing remote-sensing methods cannot directly be exploited to extract the bedrock exposure rate owing to the high complexity and heterogeneity of karst environments. Therefore, using unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data for Xingren County, Guizhou Province, quantitative extraction of the bedrock exposure rate based on multi-scale remote-sensing data was developed. Firstly, we used an object-oriented method to carry out accurate classification of UAVimages. From the results of rock extraction, the bedrock exposure rate was calculated at the 30 m grid scale. Parts of the calculated samples were used as training data; other data were used for model validation. Secondly, in each grid the band reflectivity of Landsat-8 OLI data was extracted and a variety of rock and vegetation indexes (e.g., NDVI and SAVI) were calculated. Finally, a network model was established to extract the bedrock exposure rate. The correlation coefficient of the network model was 0.855, that of the validation model was 0.677 and the root mean square error of the validation model was 0.073. This method is valuable for wide-scale estimation of bedrock exposure rate in karst environments. Using the quantitative inversion model, a distribution map of the bedrock exposure rate in Xingren County was obtained. [ABSTRACT FROM AUTHOR]

Background: Haloacid dehalogenase (HAD)-like hydrolases represent the largest superfamily of phosphatases, which release inorganic phosphate from phosphate containing compounds, such as sugar phosphates. The HAD-like phosphatases with highly substrate specificity, which perform irreversible dephosphorylation, are always integrated into in vitro synthetic enzymatic biosystems as the last enzymatic step for the cost-efficient production of biochemicals. Therefore, identification and characterization of substrate specificity of HAD-like phosphatases are important for exploring their application. Results: In this study, a hyperthermophilic HAD-like phosphatase from Archaeoglobus fulgidus (AfPase) was cloned, expressed, and characterized. AfPase was identified as a type I Mg2+-dependent HAD-like phosphatase with high optimal temperature and thermostability. Among the tested phosphate containing compounds, AfPase exhibited the highest catalytic activity on p-nitrophenyl phosphate, followed by dihydroxyacetone phosphate (DHAP). On the basis of the high catalytic activity of AfPase to generate 1,3-dihydroxyacetone (DHA) from DHAP, an in vitro synthetic enzymatic biosystem containing this phosphatase and other five enzymes was constructed for the biosynthesis of DHA from inexpensive maltodextrin in one pot. About 14 mM (1.26 g/L) DHA was produced from 10 g/L maltodextrin. Conclusions: A hyperthermophilic HAD-like phosphatase from Archaeoglobus fulgidus was characterized carefully, and the success of an in vitro synthetic enzymatic biosystem containing this phosphatase provided a promising approach for DHA production from maltodextrin. [ABSTRACT FROM AUTHOR]

Over the past decades, croplands in China experienced rapid losses as a result of nationwide urbanization, industrialization, socioeconomic development, and implementation of ecological projects. These developments were driven by continuous economic reforms and environmental protection policies. Croplands are precious land resources influenced by overpopulation in China. Cropland losses threaten national food security. This study analyzed spatial patterns of cropland changes, driving forces, and its adaptation. Object-based approach was adopted to accurately measure cropland changes using remote sensing data. Conversion matrix and statistical regression model were used to explore impacts of socioeconomic factors on cropland changes. A case study on Beijing revealed that cropland in Beijing decreased by 47% in the past 35 years. Primary driving forces in cropland loss included urban expansion, implementation of ecological projects, orchard plantation, and water resource protection. Increased migration and economic development resulted in urban expansion. To promote sustainable development and to meet demands of urban and environmental protection, cropland adaptation was performed by increasing value-added commercial crop plantation, improving crop yield, and reducing cultivation intensity for effective water consumption. [ABSTRACT FROM AUTHOR]