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Yue-Ying Yang,1,2,* Jing Liu,2,* Yi-Tong Liu,3,4 Hsiao-Hui Ong,2 Qian-Min Chen,2,4 Ce-Belle Chen,5 Mark Thong,6 Xinni Xu,6 Sui-Zi Zhou,3,4 Qian-Hui Qiu,3,4 De-Yun Wang2 1Department of Otolaryngology-Head and Neck Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2Department of Otolaryngology, Infectious Diseases Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 3Department of Otolaryngology-Head and Neck Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, People’s Republic of China; 4The Second School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China; 5Centre for Ion Beam Applications, Department of Physics, National University of Singapore, Singapore; 6Department of Otolaryngology-Head and Neck Surgery, National University Hospital, National University Health System, Singapore*These authors contributed equally to this workCorrespondence: Qian-Hui Qiu, Department of Otolaryngology-Head and Neck Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, No. 106 Zhongshan Road II, Guangzhou, 510080, People’s Republic of China, Tel +86 20 83827812, Email qiuqianhui@hotmail.com De-Yun Wang, Department of Otolaryngology, Infectious Diseases Translational Research Programme, Yong Loo Lin School of Medicine, NUHS Tower Block, 1E Kent Ridge Road, 119228, Singapore, Tel + 65 6772 5373/5370/5371, Fax +65 6775 3820, Email entwdy@nus.edu.sgPurpose: Radiotherapy (RT) is the mainstay treatment for head and neck cancers. However, chronic and recurrent upper respiratory tract infections and inflammation have been commonly reported in patients post-RT. The underlying mechanisms remain poorly understood.Method and Materials: We used a well-established model of human nasal epithelial cells (hNECs) that forms a pseudostratified layer in the air-liquid interface (ALI) and exposed it to single or repeated moderate dose γ-irradiation (1Gy). We assessed the DNA damage and evaluated the biological properties of hNECs at different time points post-RT. Further, we explored the host immunity alterations in irradiated hNECs with polyinosinic-polycytidylic acid sodium salt (poly [I:C]) and lipopolysaccharides (LPS).Results: IR induced DNA double strand breaks (DSBs) and triggered DNA damage response in hNECs. Repeated IR significantly reduced basal cell proliferation with low expression of p63/KRT5 and Ki67, induced cilia loss and inhibited mucus secretion. In addition, IR decreased ZO-1 expression and caused a significant decline in the transepithelial electrical resistance (TEER). Moreover, hyperreactive response against pathogen invasion and disrupted epithelial host defense can be observed in hNECs exposed to repeated IR.Conclusion: Our study suggests that IR induced prolonged structural and functional impairments of hNECs may contribute to patients post-RT with increased risk of developing chronic and recurrent upper respiratory tract infection and inflammation.Keywords: human nasal epithelial cells, irradiation, DNA double strand breaks, epithelial barrier, host defense

In the article, we prove that the double inequalities $$\begin{array}{*{20}{c}} {{G^p}\left[ {{\lambda _1}a + \left( {1 - {\lambda _1}} \right)b,{\lambda _1}b + \left( {1 - {\lambda _1}} \right)a} \right]{A^{1 - p}}\left( {a,b} \right) < T\left[ {A\left( {a,b} \right),G\left( {a,b} \right)} \right]} \\ { < {G^p}\left[ {\mu _1^{}a + \left( {1 - {\mu _1}} \right)a} \right]{A^{1 - p}}\left( {a,b} \right),\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;} \\ {{C^s}\left[ {{\lambda _2}a + \left( {1 - {\lambda _2}} \right)b,{\lambda _2}b + \left( {1 - {\lambda _2}} \right)a} \right]{A^{1 - s}}\left( {a,b} \right) < T\left[ {A\left( {a,b} \right),Q\left( {a,b} \right)} \right]} \\ { < {C^s}\left[ {{\mu _2}a + \left( {1 - {\mu _2}} \right)b,{\mu _2}b + {{\left( {1 - {\mu _2}} \right)}_a}} \right]{A^{1 - p}}\left( {a,b} \right)\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;} \end{array}$$ hold for all a, b > 0 with a ≠ b if and only if $${\lambda _1} \le 1/2 - \sqrt {1 - {{\left({2/\pi} \right)}^{2/p}}} /2$$ , $${\mu _1} \ge 1/2 - \sqrt {2p} /\left({4p} \right),{\lambda _2} \le 1/2 + \sqrt {{2^{3/\left({2s} \right)}}{{\left({\varepsilon \left({\sqrt 2 /2} \right)/\pi} \right)}^{1/s}} - 1} /2$$ and $${\mu _2} \ge 1/2 + \sqrt s /\left({4s} \right)$$ if λ1, μ1 ∈ (0, 1/2), λ2, μ2 ∈ (1/2, 1), p ≥ 1 and s ≥ 1/2, where $$G\left({a,b} \right) = \sqrt {ab} $$ , A(a, b) = (a + b)/2, $$T\left({a,b} \right) = 2\int_0^{\pi /2} {\sqrt {{a^2}{{\cos}^2}t + {b^2}{{\sin}^2}t} dt/\pi} $$ , $$Q\left({a,b} \right) = \sqrt {\left({{a^2} + {b^2}} \right)/2} $$ , C(a, b) = (a2 + b2)/(a + b) and $$\varepsilon (r) = \int_0^{\pi /2} {\sqrt {1 - {r^2}{{\sin}^2}t}} {\rm{d}}t$$ .

In the article, we provide the sharp bounds for the Sándor–Yang mean in terms of certain families of the two-parameter geometric and arithmetic mean and the one-parameter geometric and harmonic means. As applications, we present new bounds for a certain Yang mean and the inverse tangent function.

The aroma components of Chinese sourdough steamed breads (CSSBs) play a vital role in CSSBs flavor. Fifteen aroma-active compounds of eight CSSBs fermented with sourdoughs from different regions were identified by gas chromatography-olfactometry and aroma extract dilution analysis. Odor activity values (OAVs) of (E,E)-2,4-decadienal, naphthalene, 1-pentanol, 1-heptanol, 2-pentylfuran, 1-octen-3-ol were high, and these compounds provided CSSBs with the green, floral, fruity, alcoholic, nutty, sweet, fatty and mushroom odor. The aroma properties of Nanyang and Weinan samples belonged to the same category, while Taian sample had unique odor. Moreover, Xi'an sample had a rich and distinct aroma, such as strong green, floral and fatty odor. In addition, the aroma profiles of CSSBs fermented by different types of sourdough (Lao-mian starter or Jiao-zi starter) were significantly different. The OAVs and concentrations of key aroma components in CSSBs from different origins were diverse, which made different CSSBs had their own unique aroma characteristics.

Effects of different process steps on volatiles and aroma compounds of “Jiaozi” steamed breads (JSBs) fermented by Jiaozi starter were investigated for finding the key process steps related to aroma formation. Thirty aroma-active compounds were identified using Gas chromatography-mass spectrometry (GC-MS) and GC-olfactometry, which provided green, fatty, mushroom, mossy, fruity, sweaty, floral odors to JSBs. GC-MS analysis showed that the concentration of volatiles in JSB dough increased gradually during first-mixing and primary fermentation; decreased after second-mixing, kneading, molding and secondary fermentation; and increased greatly after steaming. Cluster analysis indicated that the aroma profile of fresh cooked JSBs was different from that of JSB dough. Fermentation is an important stage of aroma formation of JSBs. Furthermore, steaming is also a key process step in the formation of JSBs aroma, which endows JSBs unique aroma characteristic that is different from those produced by fermentation.

In this paper, we prove that the double inequalities \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$\begin{aligned}& {\alpha }N_{QA}(a,b)+({1-\alpha })G(a,b)< TD \bigl[A(a,b),G(a,b) \bigr]< {\beta }N_{QA}(a,b)+({1-\beta })G(a,b), \\& {\lambda }N_{AQ}(a,b)+({1-\lambda })G(a,b)< TD \bigl[A(a,b),G(a,b) \bigr]< {\mu }N_{AQ}(a,b)+({1-\mu })G(a,b) \end{aligned}$$ \end{document}αNQA(a,b)+(1−α)G(a,b)0$\end{document}a,b>0 with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$a\neq b$\end{document}a≠b if and only if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\alpha \leq 3/8$\end{document}α≤3/8, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\beta \geq 4/ [\pi ( \log (1+\sqrt{2})+\sqrt{2}) ]=0.5546 \cdots $\end{document}β≥4/[π(log(1+2)+2)]=0.5546⋯ , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\lambda \leq 3/10$\end{document}λ≤3/10 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mu \geq 8/ [\pi (\pi +2) ]=0.4952 \cdots $\end{document}μ≥8/[π(π+2)]=0.4952⋯ , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$TD(a,b)$\end{document}TD(a,b), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$G(a,b)$\end{document}G(a,b), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$A(a,b)$\end{document}A(a,b) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$N_{QA}(a,b)$\end{document}NQA(a,b), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$N_{AQ}(a,b)$\end{document}NAQ(a,b) are the Toader, geometric, arithmetic and two Neuman means of a and b, respectively.

In the paper, we find the greatest values α1, α2, α3, α4 and the least values β1, β2, β3, β4 such that the double inequalities α1A(a, b) + (1− α1)H(a, b) 0 with a 6= b. Here H(a, b), G(a, b), A(a, b), Q(a, b), C(a, b)and N(a, b) denote the classical harmonic, geometric, arithmetic, quadratic, contraharmonic and Neumant means of a and b, respectively. Mathematics Subject Classification: 26E6

Association between ion channel functional subtype and its genes expression is important for exploring function of ion channel, annotating function of an unknown subtype and probing into molecular mechanism of ion channel diseases. In this study, we began with noise reduction by standardizing original micro-array data, which consisted of human and mouse gene expression profiles, and then we employed principle component analysis (PCA) together with fuzzy C-mean clustering algorithm to analyze the pre-processed gene expression profiles. PCA is applied to rebuild the feature space of human gene in 21 dimensions as well as the feature space of mouse gene in 26 dimensions. Using this method we largely reduced computational complexity without losing much information involved in the original data. Subsequently, fuzzy C-mean clustering was used to classify the ion channel genes of human and mouse in their reduced feature space. In the end, four ion channel functional subtypes, such as potassium ion channels, calcium ion channel, chloride ion channel, and receptor-mediated ion channel were clustered in both human and mouse gene feature space. We applied two statistic ways to conduct significance test of the findings. In one way, we randomly sampled the data for each functional subtype of the ion channel genes and recorded the true positive rate. As a result, in both human and mouse gene feature spaces, genes that belong to one functional subtype were more likely to be clustered together than expected by chance. In the other way, we performed Kappa test and used the functional subtypes as gold standard. The result showed that consistency between the ion channel gene clusters and the ion channel gene subtypes was significantly high for both human and mouse. These results indicate that ion channel genes within the same functional subtype tend to be co-expressed at least at the mRNA-level.

With the rapid development of the electricity, the advantage of using the electric vehicles has become more attractive than before. Firstly, to prove the emission of carbon dioxide and the consumption of fossil fuels have been reduced, we establish a time series prediction model. The result indicates that the spread of electric vehicles not only does good to the environment but also makes good impact on the economy. Secondly, in order to provide a model of the amount and type of electricity generation, by comparative analysis, we searched out the perfect interval of various kinds of energy sources, adequately satisfy the demands of the government and all social circles. To insure safe, efficient and effective transportation, the introduction of widespread use of electric vehicles becomes a necessary trend. Hence, the development of electric vehicles must go through a zigzag road and be full of challenges.

Association between ion channel functional subtype and its genes expression is important for exploring function of ion channel, annotating function of an unknown subtype and probing into molecular mechanism of ion channel diseases. In this study, we began with noise reduction by standardizing original micro-array data, which consisted of human and mouse gene expression profiles, and then we employed principle component analysis (PCA) together with fuzzy C-mean clustering algorithm to analyze the pre-processed gene expression profiles. PCA is applied to rebuild the feature space of human gene in 21 dimensions as well as the feature space of mouse gene in 26 dimensions. Using this method we largely reduced computational complexity without losing much information involved in the original data. Subsequently, fuzzy C-mean clustering was used to classify the ion channel genes of human and mouse in their reduced feature space. In the end, four ion channel functional subtypes, such as potassium ion channels, calcium ion channel, chloride ion channel, and receptor-mediated ion channel were clustered in both human and mouse gene feature space. We applied two statistic ways to conduct significance test of the findings. In one way, we randomly sampled the data for each functional subtype of the ion channel genes and recorded the true positive rate. As a result, in both human and mouse gene feature spaces, genes that belong to one functional subtype were more likely to be clustered together than expected by chance. In the other way, we performed Kappa test and used the functional subtypes as gold standard. The result showed that consistency between the ion channel gene clusters and the ion channel gene subtypes was significantly high for both human and mouse. These results indicate that ion channel genes within the same functional subtype tend to be co-expressed at least at the mRNA-level.