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As a promising feedstock, alkali-extracted xylan from lignocellulosic biomass is desired for producing xylose, which can be used for renewable biofuels production. In this study, an efficient pathway has been established for low-cost and high-yield production of xylose by hydrolysis of alkali-extracted xylan from agricultural wastes using an endo-1,4-xylanase (XYLA) from Bacillus safensis TCCC 111022 and a β-xylosidase (XYLO) from B. pumilus TCCC 11573. The optimum activities of recombinant XYLA (rXYLA) and XYLO (rXYLO) were 60 ℃ and pH 8.0, and 30 ℃ and pH 7.0, respectively. They were stable over a broad pH range (pH 6.0-11.0 and 7.0-10.0). rXYLO showed a relatively high xylose tolerance up to 100 mM. Furthermore, the yield of xylose from wheat straw, rice straw, corn stover, corncob and sugarcane bagasse by rXYLA and rXYLO was 63.77%, 71.76%, 68.55%, 53.81%, and 58.58%, respectively. This study demonstrated a strategy to produce xylose from agricultural wastes by integrating alkali-extracted xylan and enzymatic hydrolysis.

Nowadays, alkali-tolerant β-xylosidases and their molecular mechanism of pH adaptability have been poorly studied. Here, a novel GH43 β-xylosidase (XYLO) was isolated from Bacillus clausii TCCC 11004, and the recombinant β-xylosidase (rXYLO) was most active at pH 8.0 and stable in a broad pH range (7.0-11.0), exhibiting superior alkali tolerance. Molecular dynamics simulation indicated that XYLO showed a notable overall structural stability and an enlargement of substrate binding pocket under alkaline condition, resulting in the formation of a new hydrogen bond between substrate and Arg286 of XYLO, and the tight binding played a key role in improving the XYLO activity with the increasing pH. Moreover, rXYLO with an endo-xylanase resulted in high xylose yields by hydrolyzing alkali-extracted xylan from agricultural wastes. This work would provide an alkali-tolerant β-xylosidase, enhance the understanding for the relationship of structure and activity adapted to the high-alkaline environment, and promote its application in xylose production.

This paper presents a new stereo based on effective cost aggregation. Firstly, a color-based image segmentation algorithm is used to segment the reference image into blocks with different colors, and they are taken as the matching window to search the similar region in the target image. Secondly, in order to eliminate the mismatching of brightness difference and noise, Census transform is conducted with both reference image and target image to obtain the bit strings. At the same time, for the sake of matching efficiency, it is of great necessary to rectify the dynamic disparity scopes of the pixel to be matched by the disparity of neighborhood pixels. At last, the bit strings can be used to execute the cost calculation by a new kind of cost aggregation function. By moving the window in the horizontal direction, we can figure out the percentage of the mismatch pixels in every segmented block respectively. The minimum value means which corresponding the horizontal shift is expressed as the best disparity of this segmented block. The experimental results demonstrate that our method can not only improve the accuracy of the depth discontinuities, but also has the high calculation efficiency. Besides, it has good robustness in different brightness environments.