The Isorhamnetin-Containing Fraction of Philippine Honey Produced by the Stingless Bee Tetragonula biroi Is an Antibiotic against Multidrug-Resistant Staphylococcus aureus

Honey exhibits antibacterial and antioxidant activities that are ascribed to its diverse secondary metabolites. In the Philippines, the antibacterial and antioxidant activities, as well as the bioactive metabolite contents of the honey, have not been thoroughly described. In this report, we investigated the in vitro antibacterial and antioxidant activities of honey from Apis mellifera and Tetragonula biroi, identified the compound responsible for the antibacterial activity, and compared the observed bioactivities and metabolite profiles to that of Manuka honey, which is recognized for its antibacterial and antioxidant properties. The secondary metabolite contents of honey were extracted using a nonionic polymeric resin followed by antibacterial and antioxidant assays, and then spectroscopic analyses of the phenolic and flavonoid contents. Results showed that honey extracts produced by T. biroi exhibits antibiotic activity against Staphylococcal pathogens as well as high antioxidant activity, which are correlated to its high flavonoid and phenolic content as compared to honey produced by A. mellifera. The bioassay-guided fractionation paired with Liquid Chromatography Mass Spectrometry (LCMS) and tandem MS analyses found the presence of the flavonoid isorhamnetin (3-methylquercetin) in T. biroi honey extract, which was demonstrated as one of the compounds with inhibitory activity against multidrug-resistant Staphylococcus aureus ATCC BAA-44. Our findings suggest that Philippine honey produced by T. biroi is a potential nutraceutical that possesses antibiotic and antioxidant activities.     

Mass Spectrometry-Based Identification of Bioactive Bee Pollen Proteins: Evaluation of Allergy Risk after Bee Pollen Supplementation

Bee pollen, because of its high content of nutrients, is a very valuable medicinal and nutritional product. However, since its composition is not completely studied, the consumption of this product may cause adverse effects, including allergic reactions. Therefore, this study aimed to discover and characterize the bioactive proteins of bee pollen collected in Poland, focusing mainly on the allergens. For this purpose, the purified and concentrated pollen aqueous solutions were analyzed using the nanoLC-MALDI-TOF/TOF MS analytical platform. As a result of the experiments, 197 unique proteins derived from green plants (Viridiplantae) and 10 unique proteins derived from bees (Apis spp.) were identified. Among them, potential plant allergens were discovered. Moreover, proteins belonging to the group of hypothetical proteins, whose expression had not been confirmed experimentally before, were detected. Because of the content of bioactive compounds—both beneficial and harmful—there is a critical need to develop guidelines for standardizing bee pollen, especially intended for consumption or therapeutic purposes. This is of particular importance because awareness of the allergen content of bee pollen and other bee products can prevent health- or life-threatening incidents following the ingestion of these increasingly popular “superfoods”.     

一种基于人工蜂群算法的混沌信号盲分离方法

混沌信号所固有的非周期、宽带频谱和对初值极度敏感等特性使得对这类信号进行盲分离极为困难. 针对这一问题, 提出一种新的盲分离方法, 该方法通过相空间重构来构造代价函数, 将混沌信号的盲分离转化为一个无约束优化问题, 并利用人工蜂群算法进行求解. 不同于现有的独立成分分析方法仅使用混合信号的统计特性来解决分离问题, 该方法能充分利用混合信号内在的动态特性, 因而在处理混沌信号这种确定性信号时能获得更好的分离效果. 此外, 正交矩阵的参数化表示有效地降低了盲分离问题的复杂性, 使优化过程能快速收敛. 实验结果表明, 该方法具有较快的收敛速度和较高的数值精度, 在分离混沌信号时其整体性能优于现有的几种盲分离方法. 同时, 在分离混沌-高斯混合信号的实验中该方法也展现出优异良好的性能, 这表明该文的方法有应用潜力.     

基于改进蜂群算法的果酒总黄酮软测量模型

针对RBF神经网络和支持向量机对果酒总黄酮软测量过程中,存在速度慢和精度低的缺点,提出了基于改进人工蜂群算法的果酒总黄酮软测量模型。该模型利用混沌变量的遍历性和随机性特点,对标准人工蜂群算法进行改进,在种群初始化阶段引入混沌机制,确保个体分布的均匀性,并用信息素和灵敏度模型代替轮盘赌选择策略,使模型避免了过早收敛和提前停滞。仿真实验结果表明,该模型提高了果酒总黄酮软测量的精度,具有收敛速度快,抗噪性较强的特点,便于实现果酒总黄酮的在线测量。     

Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning

The discrimination and classification of allergy-relevant pollen was studied for the first time by mid-infrared Fourier transform infrared (FT-IR) microspectroscopy together with unsupervised and supervised multivariate statistical methods. Pollen samples of 11 different taxa were collected, whose outdoor air concentration during the flowering time is typically measured by aerobiological monitoring networks. Unsupervised hierarchical cluster analysis provided valuable information about the reproducibility of FT-IR spectra of the same taxon acquired either from one pollen grain in a 25 × 25 μm² area or from a group of grains inside a 100 × 100 μm² area. As regards the supervised learning method, best results were achieved using a K nearest neighbors classifier and the leave-one-out cross-validation procedure on the dataset composed of single pollen grain spectra (overall accuracy 84%). FT-IR microspectroscopy is therefore a reliable method for discrimination and classification of allergenic pollen. The limits of its practical application to the monitoring performed in the aerobiological stations were also discussed. Figure Traditional and innovative methods for the identification of airborne pollen grains     

A multilevel image thresholding using the honey bee mating optimization

Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the maximum entropy thresholding (MET) has been widely applied. In this paper, a new multilevel MET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. This proposed method is called the maximum entropy based honey bee mating optimization thresholding (MEHBMOT) method. Three different methods such as the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO) and the Fast Otsu’s method are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed MEHBMOT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other three thresholding methods, the segmentation results using the MEHBMOT algorithm is the best and its computation time is relatively low. Furthermore, the convergence of the MEHBMOT algorithm can rapidly achieve and the results validate that the proposed MEHBMOT algorithm is efficient.     

基于频率和模态保证准则的结构内部多缺陷反演

静响应(位移、应变等)在实际问题的反演分析中很难由安装在结构上的一组传感器记录得到,而结构的动力特性(频率、振型)和 动力响应(加速度、速度、动位移)在实际问题中较易通过传感器采集得到. 文中基于频率残差和模态保证准则构建了反演分析模型的目标函数,并结合频域内动力扩展有限元法和人工蜂群智能优化算法 的优点,扩展有限元法通过引入非连续位移模式在不重新划分网格的情况下通过改变水平集函数反映缺陷的数量、位置及大小, 避免了反演分析每次迭代过程中的网格重剖分,人工蜂群智能优化算法在每次迭代中都采用全局和局部搜索,找到最优解的概 率大幅增大并可很好地避免局部最优,同时,通过引入拓扑变量,将缺陷的数量纳入到反演分析过程中,迭代过程中可智能反演出缺陷的数目,建立了结构内部多缺陷(孔洞、裂纹)的反演分析模型. 通过若干算例的分析表明:建立的反演分析模型能够较为准确地探测出结构内部圆形、椭圆形以及裂纹状缺陷的数量、位置及大小,且算法具有较好的鲁棒性.      

光敏感核不育水稻W6184s花药和花粉发育的细胞形态学观察

光敏感核不育水稻W6184s细胞形态学观察结果;花粉败育主要发生在小孢子发育后期,具大液泡的小孢子不能进入配子体发育阶段;小孢子发育的早期,少数小孢子母细胞就表现异常而导致花粉败育。在小孢子发育的同时,绒毡层出现各种异常现象。     

罂粟花粉十七肽类似物和片段的合成及其抗肿瘤活性研究

以从罂粟花粉中分离得到的具有促免疫和抗肿瘤活性的十七肽为先导化合物,设计了4个类似物和3个片段,并用固相法合成,通过CD谱测定了它们在溶液中的初级二级结构,同时测定了它们对人胃癌和人膀胱癌肿瘤细胞的抑制活性,初步讨论了构效关系.