Association between the microbiomes of tonsil and saliva samples isolated from pediatric patients subjected to tonsillectomy for the treatment of tonsillar hyperplasia

Oral microbes have the capacity to spread throughout the gastrointestinal system and are strongly associated with multiple diseases. Given that tonsils are located between the oral cavity and the laryngopharynx at the gateway of the alimentary and respiratory tracts, tonsillar tissue may also be affected by microbiota from both the oral cavity (saliva) and the alimentary tract. Here, we analyzed the distribution and association of the microbial communities in the saliva and tonsils of Korean children subjected to tonsillectomy because of tonsil hyperplasia (n = 29). The microbiome profiles of saliva and tonsils were established via 16S rRNA gene sequencing. Based on the alpha diversity indices, the microbial communities of the two groups showed high similarities. According to Spearman’s ranking correlation analysis, the distribution of Treponema, the causative bacterium of periodontitis, in saliva and tonsils was found to have a significant positive correlation. Two representative microbes, Prevotella in saliva and Alloprevotella in tonsils, were negatively correlated, while Treponema 2 showed a strong positive correlation between saliva and tonsils. Taken together, strong similarities in the microbial communities of the tonsils and saliva are evident in terms of diversity and composition. The saliva microbiome is expected to significantly affect the tonsil microbiome. Furthermore, we suggest that our study creates an opportunity for tonsillar microbiome research to facilitate the development of novel microbiome-based therapeutic strategies.Subject terms: Comparative genomics, Metagenomics     

Learning Multi Labels from Single Label——An Extreme Weak Label Learning Algorithm

This paper presents a novel algorithm for an extreme form of weak label learning, in which only one of all relevant labels is given for each training sample. Using genetic algorithm, all of the labels in the training set are optimally divided into several non-overlapping groups to maximize the label distinguishability in every group. Multiple classifiers are trained separately and ensembled for label predictions. Experimental results show significant improvement over previous weak label learning algorithms.     

Dynamic behavior and stability analysis of nonlinear modes in the fourth-order generalized Ginzburg–Landau model with near $$\mathcal{PT}$$ PT -symmetric potentials

Nonlinear Dynamics - Parametrically excited oscillators are used in several domains, in particular to improve the dynamical behaviour of systems like in the case of the parametric amplification or...     

基于模糊集相容性的模糊控制规则优化方法

在简要介绍模糊模型的完备性和相容性的基础上 ,根据模糊集贴近度 ,提出模糊集相容性的概念 ;通过对模糊控制规则表特征的分析 ,进一步完善规则相容性概念及其定量评价方法 ;最后给出模糊控制规则模型自寻优优化方法。仿真结果表明 ,该方法可以大大提高控制系统的控制性能。     

浅析基于电子商务网络安全PKI技术的角色访问控制模型

文本提出了一种区别于传统自主访问控制策略和强制访问控制策略的RBAC网络安全策略,并提出了RBAC的面向对象模型,实现用户身份认证与访问权限的逻辑分离,且易于控制,既能有效的改进系统安全性能,又极大地减少了权限管理的负担。     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.     

Synthesis and association of poly(N,N‐dimethylacrylamide) copolymers with perfluorocarbon pendent groups connected through polyethylene‐glycol tethers

The synthesis is reported of copolymers of N,N‐dimethylacrylamide (DMA) and methacrylates containing 2,2′‐dihydroperfluorodecanoyl (RF) groups separated from the methacrylate by long polyethylene glycol (PEG) tether groups (between 1000 and 14,000 Da). At concentrations of between 1 and 8 wt % the copolymers with macromonomer contents of 1 mol % or less give gels in organic solvents such as dioxane, THF, or methanol, as well as in water. Given the low molecular weights, this indicates very efficient association of very low numbers of RF groups. Association and gel formation is enormously enhanced in the presence of longer PEG tethers. This is consistent with smaller poly(N,N,‐dimethylacrylamide) (PDMA) intermolecular excluded volume effects that are mediated by the longer PEG tethers and possibly by the incompatibility of PEG and PDMA that may lead to the formation of PEG microdomains. This increases the local concentrations of the RF groups in the PEO domains that are not diluted by the PDMA chains, as would be the case in the absence of PEG tethers. © 2003 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 42: 360–373, 2004