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1
Effect of drying temperature on the sugars, organic acids, limonoids, phenolics, and antioxidant capacities of lemon slices

Shenghua Ding,Rongrong Wang,Jing Zhang,Gaoyang Li,Juhua Zhang,Shiyi Ou,Yang Shan 한국식품과학회 2017 Food Science and Biotechnology Vol.26 No.6
- 복사/대출신청
Changes in contents of sugars, organic acids, limonoids, phenolics contents, and antioxidant capacities of lemon slices dried at different temperatures were evaluated. Air drying (AD) promoted losses of sugars, citric acid, ascorbic acid, extractable phenolics (EPs), and nonextractable phenolics (NEPs), while it introduced an increase in limonoids. Phenolics of lemon were mainly presented in their extractable form. Hesperidin and eriocitrin were the main EPs; protocatechuic acid and poncirin were the predominant NEPs. The decrease in extractable phenolic acid, EP, and NEP content in lemon is lower at low drying temperatures, while the increase in non-extractable phenolic acid content is higher at high drying temperatures. The antioxidant capacity of EP was higher than that of NEP. Phenolics contributed to antioxidant capacities of lemon slices, and flavonoids were the main contributors among phenolics. Considering limonoids contents and the high levels of EP, NEP, and antioxidant capacities, AD at 60 C could be an appreciate treatment for dehydrating lemon slices.
2
Estimating functional brain networks by incorporating a modularity prior

Qiao, Lishan,Zhang, Han,Kim, Minjeong,Teng, Shenghua,Zhang, Limei,Shen, Dinggang ACADEMIC PRESS 2016 NEUROIMAGE Vol.141 No.-
- 원문보기
Abstract Functional brain network analysis has become one principled way of revealing informative organization architectures in healthy brains, and providing sensitive biomarkers for diagnosis of neurological disorders. Prior to any post hoc analysis, however, a natural issue is how to construct “ideal” brain networks given, for example, a set of functional magnetic resonance imaging (fMRI) time series associated with different brain regions. Although many methods have been developed, it is currently still an open field to estimate biologically meaningful and statistically robust brain networks due to our limited understanding of the human brain as well as complex noises in the observed data. Motivated by the fact that the brain is organized with modular structures, in this paper, we propose a novel functional brain network modeling scheme by encoding a modularity prior under a matrix-regularized network learning framework, and further formulate it as a sparse low-rank graph learning problem, which can be solved by an efficient optimization algorithm. Then, we apply the learned brain networks to identify patients with mild cognitive impairment (MCI) from normal controls. We achieved 89.01% classification accuracy even with a simple feature selection and classification pipeline, which significantly outperforms the conventional brain network construction methods. Moreover, we further explore brain network features that contributed to MCI identification, and discovered potential biomarkers for personalized diagnosis.
3
Profiling Total Viable Bacteria in a Hemodialysis Water Treatment System

( Lihua Chen ),( Xuan Zhu ),( Menglu Zhang ),( Yuxin Wang ),( Tianyu Lv ),( Shenghua Zhang ),( Xin Yu ) 한국미생물 · 생명공학회 2017 Journal of microbiology and biotechnology Vol.27 No.5
- 원문보기 3
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Culture-dependent methods, such as heterotrophic plate counting (HPC), are usually applied to evaluate the bacteriological quality of hemodialysis water. However, these methods cannot detect the uncultured or viable but non-culturable (VBNC) bacteria, both of which may be quantitatively predominant throughout the hemodialysis water treatment system. Therefore, propidium monoazide (PMA)-qPCR associated with HPC was used together to profile the distribution of the total viable bacteria in such a system. Moreover, high-throughput sequencing of 16S rRNA gene amplicons was utilized to analyze the microbial community structure and diversity. The HPC results indicated that the total bacterial counts conformed to the standards, yet the bacteria amounts were abruptly enhanced after carbon filter treatment. Nevertheless, the bacterial counts detected by PMA-qPCR, with the highest levels of 2.14 × 107 copies/100 ml in softener water, were much higher than the corresponding HPC results, which demonstrated the occurrence of numerous uncultured or VBNC bacteria among the entire system before reverse osmosis (RO). In addition, the microbial community structure was very different and the diversity was enhanced after the carbon filter. Although the diversity was minimized after RO treatment, pathogens such as Escherichia could still be detected in the RO effluent. In general, both the amounts of bacteria and the complexity of microbial community in the hemodialysis water treatment system revealed by molecular approaches were much higher than by traditional method. These results suggested the higher health risk potential for hemodialysis patients from the up-to-standard water. The treatment process could also be optimized, based on the results of this study.