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Purification and Characterization of Novel Manganese Peroxidase from Rhizoctonia sp. SYBC-M3
Yujie Cai,Huiguang Wu,Xiangru Liao,Yanrui Ding,Jun Sun,Dabing Zhang 한국생물공학회 2010 Biotechnology and Bioprocess Engineering Vol.15 No.6
A novel manganese peroxidase of Rhizoctonia sp. SYBC-M3 (R-MnP) was purified by (NH4)2SO4fractionation, DEAE-cellulose-32 column chromatography,and Sephadex G100 column chromatography. The molecular mass of R-MnP was determined to be approximately 40.4 kDa by SDS-PAGE. The optimum temperature and pH for R-MnP were 55°C and 4.5, respectively. R-MnP was highly stability when the temperature was below 50°C. R-MnP could retain about 60% of its activity when the pH was between 4 and 6.5. However, R-MnP activity was inhibited by Fe3+, Cu2+, and Co3+ as well as increased by Zn2+ and Ca2+. R-MnP demonstrated oxidation of DMP,ABTS, veratryl alcohol, and guaiacol. The Km values of RMnP for H2O2 and Mn2+ were 25.3 and 53.9 μmol/L,respectively.
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Automatic brain labeling via multi-atlas guided fully convolutional networks
Fang, Longwei,Zhang, Lichi,Nie, Dong,Cao, Xiaohuan,Rekik, Islem,Lee, Seong-Whan,He, Huiguang,Shen, Dinggang Elsevier 2019 Medical image analysis Vol.51 No.-
<P><B>Abstract</B></P> <P>Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atlases to the target image, and then propagate the labels from the labeled atlases to the unlabeled target image. However, the registration step involves non-rigid alignment, which is often time-consuming and might lack high accuracy. Alternatively, patch-based methods have shown promise in relaxing the demand for accurate registration, but they often require the use of hand-crafted features. Recently, deep learning techniques have demonstrated their effectiveness in image labeling, by automatically learning comprehensive appearance features from training images. In this paper, we propose a <I>multi-atlas guided fully convolutional network (MA-FCN)</I> for automatic image labeling, which aims at further improving the labeling performance with the aid of prior knowledge from the training atlases. Specifically, we train our MA-FCN model in a patch-based manner, where the input data consists of <I>not only</I> a training image patch <I>but also</I> a set of its neighboring (i.e., most similar) affine-aligned atlas patches. The guidance information from neighboring atlas patches can help boost the discriminative ability of the learned FCN. Experimental results on different datasets demonstrate the effectiveness of our proposed method, by significantly outperforming the conventional FCN and several state-of-the-art MR brain labeling methods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Additional atlas intensity and corresponding label information are used to help the MA-FCN model to better label the target ROIs. </LI> <LI> The additional anatomical structural information is revised in the unique pathway in the MA-FCN model. </LI> <LI> The fusion of atlas information is in a hierarchical way. </LI> <LI> The proposed method does not need a non-rigid registration step for aligning atlases to the target image, which is efficient for brain labeling. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
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