The reverse order law for the $W$-weighted Drazin inverse of multiple matrices product

Guorong Wang,Zhaoliang Xu 한국전산응용수학회 2006 Journal of applied mathematics & informatics Vol.21 No.1-2

Effects of homogenization on the molecular flexibility and emulsifying properties of soy protein isolate

Yeye Xu,Guorong Wang,Xibo Wang,Jie Yu,Jian Wang,Zeyu Zhang,Rui Li 한국식품과학회 2018 Food Science and Biotechnology Vol.27 No.5

The sensitivity of soy protein isolate (SPI) to trypsin was characterized by its flexibility. The effects of different homogenization conditions on soy protein isolate flexibility and emulsifying properties were investigated. Set the homogenization pressure was 120 MPa (megapascal) and the homogenous number of times is 0–4 times, the flexibility increases with the increase of the homogenization times (0–3 times), the change trend of flexibility is not obvious (3–4 times). When the homogenization times was 0–3 times, the emulsifying activity increases, and the emulsifying activity was the strongest at 3 times, after homogenization 3 times, the change trend of emulsifying activity is not obvious, the trend of emulsification stability and emulsification activity were similar. The surface hydrophobicity increases with the increase of homogenization times, while the turbidity decreases. The other structural indicators such as Ultraviolet scanning and endogenous tryptophan fluorescence spectroscopy suggest that the structure of SPI becomes more stretch as the flexibility increases.

Effects of ultrasonic treatment on the gel properties of microbial transglutaminase crosslinked soy, whey and soy–whey proteins

Qiang Cui,Xibo Wang,Guorong Wang,Rui Li,Xiaodan Wang,Shuang Chen,Jingnan Liu,Lianzhou Jiang 한국식품과학회 2019 Food Science and Biotechnology Vol.28 No.5

This paper studied the influences of diverse ultrasonic power treatments on the physico-chemical properties of soy–whey mixed protein induced by microbial transglutaminase. Two groups of 15% (m/v) of protein solution-sole protein (as control group) and mixed protein were prepared and processed under different ultrasonic powers for 30 min. After ultrasonic power treatments, gel properties were significantly increased: under 300 W, the gel hardness of mixed protein reached a maximum of 998.9 g, with its water binding capacity scoring a maximum of 87%. According to the analysis of fluorescence emission spectrum, the fluorescence intensity and maximum absorption peak had changed, for different ultrasonic power treatments had exposed more groups. The Fourier Transform Infrared Spectroscopy also suggested that ultrasonic power treatments could change the secondary structure of gel samples. The scanning electron microscope demonstrated that the network structure of mixed protein gel displayed more regular and uniform after ultrasonic treatments.

The influence factors on CeSn0.8W0.6Ox/TiO2 for catalytic removals of NO, CO and C3H8

Guorong Sui,Zhiwei Xue,Dan Zhou,Yan Wang,Yuesong Shen,Yuhao Zong,Youlin Liu,Tai Qiu,Shemin Zhu 한국공업화학회 2017 Journal of Industrial and Engineering Chemistry Vol.51 No.-

Series of CeSn0.8W0.6Ox/TiO2 catalysts were tested for selective catalytic reduction of NO and forsynergistic catalytic removals of CO and C3H8 from diesel engine exhaust. Results revealed that catalyst12%-CeSn0.8W0.6Ox/TiO2 calcined at 500 C exhibited the optimal catalytic performance for NH3-SCR ofNO. The catalyst obtained more than 90% NO conversion at a wide temperature range of 252–456 C. BothCO and C3H8 could be oxidized into CO2 by the optimized catalyst. Moreover, excellent redox property,rich surface acidity and big specific surface area were the promotional factors for good catalyticperformance in catalytic removals of NO, CO and C3H8.

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

Wu, Guorong,Kim, Minjeong,Wang, Qian,Munsell, Brent C.,Shen, Dinggang IEEE 2016 IEEE Transactions on Biomedical Engineering Vol.63 No.7

<P>Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.</P>

THE REPRESENTATION AND PERTURBATIONOF THE W-WEIGHTED DRAZIN INVERSE

ZHAOLIANG XU,GUORONG WANG 한국전산응용수학회 2007 Journal of applied mathematics & informatics Vol.23 No.1

Let A and E be m×n matrices and W an n×m matrix, and let Ad,W denote the W-weighted Drazin inverse of A . In this paper, a new representation of the W-weighted Drazin inverse of A is given. Some new properties for the W-weighted Drazin inverse Ad,W and Bd,W are investigated, where B = A+E. In addition, the Banach-type perturbation theorem for the W-weighted Drazin inverse of A and B are established, and the perturbation bounds for kBd,W k and kBd,W −Ad,W k/kAd,W k are also presented. When A and B are square matrices and W is identity matrix, some known results in the literature related to the Drazin inverse and the group inverse are directly reduced by the results in this paper as special cases.

Characterization of a GH8 β-1,4-Glucanase from Bacillus subtilis B111 and Its Saccharification Potential for Agricultural Straws

( Zhen Huang ),( Guorong Ni ),( Xiaoyan Zhao ),( Fei Wang ),( Mingren Qu ) 한국미생물 · 생명공학회 2021 Journal of microbiology and biotechnology Vol.31 No.10

Herein, we cloned and expressed an endo-β-1,4-glucanase gene (celA1805) from Bacillus subtilis B111 in Escherichia coli. The recombinant celA1805 contains a glycosyl hydrolase (GH) family 8 domain and shared 76.8% identity with endo-1,4-β-glucanase from Bacillus sp. KSM-330. Results showed that the optimal pH and temperature of celA1805 were 6.0 and 50°C, respectively, and it was stable at pH 3-9 and temperature ≤50°C. Metal ions slightly affected enzyme activity, but chemical agents generally inhibited enzyme activity. Moreover, celA1805 showed a wide substrate specificity to CMC, barley β-glucan, lichenin, chitosan, PASC and avicel. The K_m and V_max values of celA1805 were 1.78 mg/ml and 50.09 μmol/min/mg. When incubated with cellooligosaccharides ranging from cellotriose to cellopentose, celA1805 mainly hydrolyzed cellotetrose (G4) and cellopentose (G5) to cellose (G2) and cellotriose (G3), but hardly hydrolyzed cellotriose. The concentrations of reducing sugars saccharified by celA1805 from wheat straw, rape straw, rice straw, peanut straw, and corn straw were increased by 0.21, 0.51, 0.26, 0.36, and 0.66 mg/ml, respectively. The results obtained in this study suggest potential applications of celA1805 in biomass saccharification.

THE REPRESENTATION AND PERTURBATION OF THE W-WEIGHTED DRAZIN INVERSE

Xu, Zhaoliang,Wang, Guorong 한국전산응용수학회 2007 Journal of applied mathematics & informatics Vol.23 No.1

Let A and E be $m{\times}n$ matrices and W an $n{\times}m$ matrix, and let $A_{d,w}$ denote the W-weighted Drazin inverse of A. In this paper, a new representation of the W-weighted Drazin inverse of A is given. Some new properties for the W-weighted Drazin inverse $A_{d,w}\;and\;B_{d,w}$ are investigated, where B=A+E. In addition, the Banach-type perturbation theorem for the W-weighted Drazin inverse of A and B are established, and the perturbation bounds for ${\parallel}B_{d,w}{\parallel}\;and\;{\parallel}B_{d,w}-A_{d,w}{\parallel}/{\parallel}A_{d,w}{\parallel}$ are also presented. When A and B are square matrices and W is identity matrix, some known results in the literature related to the Drazin inverse and the group inverse are directly reduced by the results in this paper as special cases.

Automatic labeling of MR brain images by hierarchical learning of atlas forests : Automatic labeling of MR brain images

Zhang, Lichi,Wang, Qian,Gao, Yaozong,Wu, Guorong,Shen, Dinggang Wiley (John WileySons) 2016 Medical physics Vol.43 No.3

<P>Purpose: Automatic brain image labeling is highly demanded in the field of medical image analysis. Multiatlas-based approaches are widely used due to their simplicity and robustness in applications. Also, random forest technique is recognized as an efficient method for labeling, although there are several existing limitations. In this paper, the authors intend to address those limitations by proposing a novel framework based on the hierarchical learning of atlas forests. Methods: Their proposed framework aims to train a hierarchy of forests to better correlate voxels in the MR images with their corresponding labels. There are two specific novel strategies for improving brain image labeling. First, different from the conventional ways of using a single level of random forests for brain labeling, the authors design a hierarchical structure to incorporate multiple levels of forests. In particular, each atlas forest in the bottom level is trained in accordance with each individual atlas, and then the bottom-level forests are clustered based on their capabilities in labeling. For each clustered group, the authors retrain a new representative forest in the higher level by using all atlases associated with the lower-level atlas forests in the current group, as well as the tentative label maps yielded from the lower level. This clustering and retraining procedure is conducted iteratively to yield a hierarchical structure of forests. Second, in the testing stage, the authors also present a novel atlas forest selection method to determine an optimal set of atlas forests from the constructed hierarchical structure (by disabling those nonoptimal forests) for accurately labeling the test image. Results: For validating their proposed framework, the authors evaluate it on the public datasets, including Alzheimer's disease neuroimaging initiative, Internet brain segmentation repository, and LONI LPBA40. The authors compare the results with the conventional approaches. The experiments show that the use of the two novel strategies can significantly improve the labeling performance. Note that when more levels are constructed in the hierarchy, the labeling performance can be further improved, but more computational time will be also required. Conclusions: The authors have proposed a novel multiatlas-based framework for automatic and accurate labeling of brain anatomies, which can achieve accurate labeling results for MR brain images. (C) 2016 American Association of Physicists in Medicine.</P>

Scalable joint segmentation and registration framework for infant brain images

Dong, Pei,Wang, Li,Lin, Weili,Shen, Dinggang,Wu, Guorong Elsevier 2017 Neurocomputing Vol.229 No.-

<P><B>Abstract</B></P> <P>The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We developed an efficient approach to deal with the tissue segmentation and registration for the infant brain MR images. </LI> <LI> Our proposed framework is scalable to various registration tasks in early brain development studies. </LI> <LI> Promising results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old. </LI> <LI> The proposed technique can be very useful for many ongoing early brain development studies. </LI> </UL> </P>