Adaptive response and tolerance to weak acids in Saccharomyces cerevisiae boulardii: a metabolomics approach

Weak acids inhibit the growth of probiotics, such as Saccharomyces boulardii. We explored the tolerance of S. boulardii to different weak acids. S. boulardii had better fermentation ability under lactic acid conditions compared with acetic and butyric acid conditions; however, the budding of S. boulardii was significantly stronger than that of Saccharomyces cerevisiae under acetic acid conditions. Although the surface structure of S. boulardii was destroyed, it produced more daughter cells. S. boulardii metabolites were also significantly different from S. cerevisiae under acidic stress. The growth of S. boulardii under weak acid conditions differed significantly from that of S. cerevisiae. S. boulardii-mediated fingerprints under weak acid conditions were identified as latent biomarkers, related to fructose and mannose metabolism, tricarboxylic acid cycle, and the glycolysis pathway. Identified biomarkers will aid in the genetic engineering of S. boulardii and other Saccharomyces strains for improved acid resistance and biomass yield.     

弱相互作用调控表面活性剂自组装(Ⅱ)——表面活性剂的结构与设计

介绍了表面活性剂的结构、性质以及分类,综述了阴离子表面活性剂、阳离子表面活性剂、两性离子表面活性剂、非离子表面活性剂的分子间弱相互作用。另外,简述了低聚型表面活性剂和其他新型表面活性剂的结构及应用。     

火灾条件下拱顶油罐弱连接结构的失效分析

拱顶油罐受外部火焰烘烤发生失效爆炸，易造成严重人员伤亡和巨大经济损失。为对火灾条件下拱顶油罐的失效爆炸进行预警，通过高温材料力学性能实验平台对拱顶油罐弱连接结构进行破坏测试，得出不同火灾温度条件下弱连接结构失效的拉断力和应力值。实验结果表明，当温度高于300℃时，拉断力和失效应力值变化显著，其中450℃为变化峰值温度。在此基础上，构建了拱顶油罐失效压力计算模型，推导出了不同容积拱顶油罐在不同火灾温度下的失效压力。所得结果可作为拱顶油罐失效的判定依据，为油罐的爆炸预警提供数据支撑。     

改进ILoG算子的故障检测方法

针对强背景噪声干扰下轮对轴承故障特征微弱、难以准确检测的问题,提出了一种自适应改进高斯拉普拉斯(improved Laplacian of Gaussian,简称ILoG)算子的微弱故障检测方法。ILoG算子滤波器具有优良的信号突变特征检测能力,将其用于轮对轴承故障信号的冲击特征检测,同时利用水循环算法(water cycle algorithm,简称WCA)的寻优特性,并行搜寻筛选最佳的ILoG算子影响参数,通过对参数优化后ILoG算子滤波后信号做进一步包络解调分析,提取出轮对轴承微弱的故障特征信息。对实际轮对轴承外圈和内圈故障信号分析的结果表明,该方法可以有效检测出轴承微弱故障特征频率,故障检测效果优于小波阈值和多尺度形态学差值滤波方法。     

基于弱变异准则的测试用例约简方法

针对为数较多的测试用例增加了回归测试成本的问题，提出一种基于弱变异准则的测试用例约简方法。首先，基于弱变异准则获得测试用例和变异分支关系矩阵；然后，重复约简4种无效测试需求和子集测试用例；最后，结合人工鱼群算法选择当前最优测试用例，并且交替执行简化和测试用例选择操作直至覆盖所有测试需求。该方法针对6个经典程序与贪心算法和HGS算法相比，基于弱变异准则并且不改变或稍微改变变异评分的条件下，约简率分别提高了73.4%和8.2%，且耗时分别降低了25.3%和56.1%。实验结果表明，所提方法在回归测试中可有效约简测试用例，降低测试代价。     

面部运动单元检测研究综述

面部运动单元检测旨在让计算机从给定的人脸图像或视频中自动检测需要关注的运动单元目标。经过二十多年的研究，尤其是近年来越来越多的面部运动单元数据库的建立和深度学习的兴起，面部运动单元检测技术发展迅速。首先，阐述了面部运动单元的基本概念，介绍了已有的常用面部运动单元检测数据库，概括了包括预处理、特征提取、分类器学习等步骤在内的传统检测方法；然后针对区域学习、面部运动单元关联学习、弱监督学习等几个关键研究方向进行了系统性的回顾梳理与分析；最后讨论了目前面部运动单元检测研究存在的不足以及未来潜在的发展方向。     

Weak Delocalization in Graphene on a Ferromagnetic Insulating Film

Graphene has been predicted to develop a magnetic moment by proximity effect when placed on a ferromagnetic film, a promise that could open exciting possibilities in the fields of spintronics and magnetic data recording. In this work, the interplay between the magnetoresistance of graphene and the magnetization of an underlying ferromagnetic insulating film is studied in detail. A clear correlation between both magnitudes is observed but through a careful modeling of the magnetization and the weak localization measurements, that such correspondence can be explained by the effects of the magnetic stray fields arising from the ferromagnetic insulator is found. The results emphasize the complexity arising at the interface between magnetic and 2D materials.     

低压力下环保型络合剂和氧化剂对铝合金化学机械抛光的影响

在低平坦化压力下用壳寡糖(COS)环保型络合剂及H₂O₂氧化剂化学机械抛光铝合金，用原子力显微镜观测抛光后的表面质量，并用X射线光电子能谱仪分析其表面的钝化膜元素，用纳米压痕仪分析钝化膜的力学性能，研究COS及H₂O₂对铝合金CMP的作用机理。结果表明:H₂O₂质量分数为2%时，材料去除率随COS含量的增加而增大，当COS质量分数为0.32%时，材料的去除速率达861 nm/min，表面粗糙度最低为2.50 nm；COS质量分数为0.50%时，材料去除率随H₂O₂含量的增加先增大后减小，当H₂O₂质量分数为1.2%时，材料的抛光去除速率达840 nm/min，同时其表面粗糙度为3.52 nm。加入COS络合剂会在铝合金表面形成主要成分为Al-COS、Al₂O₃和Al(OH)₃的弱钝化膜。      

TSL:predicting popularity of Facebook content based on tie strength

The rapid development of online social networks leads to an explosion of information,however,there are great differences in the popularity of different messages,and accurate prediction is always a great difficulty is the current study.Popularity prediction of online content aims to predict the popularity in the future based on its early diffusion status.Existing models for popularity prediction were mostly based on discovering network features or fitting the equation into a varying time function that the accuracy of current popularity prediction model was not high enough.Therefore,with the help of the weak ties theory in sociology,the concept of tie strength was introduced and a multilinear regression equation was constructed combined with the early popularity.A TSL model to predict the popularity of Facebook’s well-known pages was proposed.The main contribution of this article was to solve the problem and few or no work based on sociology.A high linear correlation between the proportion of faithful fans was existed in Facebook homepage with frequent shares in the early and the future popularity.Compared with other baseline models,an experimental study of Facebook (including 1.54 million shares) illustrates the effectiveness of the proposed TSL model,and the performance is better than the existing similar methods.