Rhamsan gum is a type of water-soluble exopolysaccharide produced by species of Sphingomonas bacteria. The optimal fermentation medium for rhamsan gum production by Sphingomonas sp. CGMCC 6833 was explored definition. Single-factor experiments indicate that glucose, soybean meal, K(2)HPO(4) and MnSO(4) compose the optimal medium along with and initial pH 7.5. To discover ideal cultural conditions for rhamsan gum production in a shake flask culture, response surface methodology was employed, from which the following optimal ratio was derived: 5.38 g/L soybean meal, 5.71 g/L K(2)HPO(4) and 0.32 g/L MnSO(4). Under ideal fermentation rhamsan gum yield reached 19.58 g/L ± 1.23 g/L, 42.09% higher than that of the initial medium (13.78 g/L ± 1.38 g/L). Optimizing the fermentation medium results in enhanced rhamsan gum production.
The optimal temperature for the microbial polysaccharide fermentation is no higher than 30 °C, which is economically undesirable due to additional cooling cost. To solve this problem in the case of welan gum production, we obtained the high-temperature-tolerant-producing strain, Sphingomonas sp. HT-1 by atmospheric and room-temperature plasma-induced mutation. Using HT-1, we obtained a concentration and 1 % aqueous viscosity of 26.8 ± 0.34 g/L and 3.50 ± 0.05 Pa s at a comparatively higher optimal temperature (37 °C). HT-1 was further characterized to understand the mechanism by which these properties are improved. Results indicated that high yield could be attributed to the following: (1) enhanced intracellular synthesis, demonstrated by an increase in the activities of key enzymes, and (2) accelerated cross-membrane substrate uptake and product secretion, indicated by improved membrane fluidity and permeability. Temperature tolerance could be attributed to the overexpression of the investigated heat shock proteins and oxidative stress proteins.
Breast tumor segmentation is useful to diagnose breast cancer. However, challenges, such as intensity inhomogeneity and shadowing artifacts arise in this task. To address these two issues, this paper proposes a robust ultrasound image segmentation method based on correction learning. At first, a novel idea of correction learning is introduced. In contrast to traditional methods that develop the complex models to obtain accurate segmentation results, correction learning aims to detect the erroneous segmentation in advance and correct this automatically by only using simple method. The proposed correction learning method mainly involves two steps: coarse segmentation and correction learning. First, an active contour model is firstly constructed for coarse segmentation by introducing assumption of bias field and local intensity clustering property. Then, correction learning is developed to address the erroneous segmentation and to improve the segmentation performance. In this paper, correction learning mainly contains Internal Tumor Block Correction (ITBC) and Boundary Block Correction(BBC). In order to correct the erroneous segmentation caused by intensity inhomogeneity, the internal tumor blocks detection model based on Simple Linear Iterative Clustering(SLIC) and Support Vector Machine (SVM) is learned to detect these segmented blocks. Based on the detection result, the incorrect segmented blocks can be corrected. In addition, BBC is proposed to correct the erroneous segmentation of boundary blocks which are caused by shadowing artifacts. Our experiment results on the constructed database demonstrate the effectiveness and robustness of the proposed method.
Porous materials are promising media for designing medical instruments, drug carriers, and bioimplants because of their excellent biocompatibility, ease of design, and large variation of elastic moduli. In this study, a computational strategy using the finite element method is developed to model the porous microstructures and to predict the relevant elastic moduli considering the actual characteristics of the micropores and their distributions. First, an element-based approach is presented to generate pores of different shapes and sizes according to the experimental observations. Then, a computational scheme to evaluate the effective moduli of macroscopically isotropic porous materials based on their micro-mechanics is introduced. Next, the accuracy of our approach is verified with the analytical solutions of the extreme bounds of the elastic isotropic moduli of a simplified model and with the experimental data available in the literature. Finally, the influence of the shape of pores and their distribution modes are assessed.
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