Prediction of Protein-Protein Interactions by a Novel Model Based on Domain Information

Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.     

Comparative Study of Emergency Logistics Path Planning

As natural disasters attract extensive attention worldwide,it is significant for governments and researchers to determine how to deliver emergency supplies after natural disasters.Considering the disaster victims' urgent needs for supplies after the disaster,based on the previous research,the path planning model from the shortest path and the optimal time was created in this paper.As in the actual road the outbreak of emergencies might cause some secondary disasters,including landslides triggered by heavy rain,paralysis of traffic together with a large crowd caused by earthquakes,the theory and method of the complex network to explore the topological structure characteristics of the whole network in the affected area were applied.Then,combining with the actual situation of the road network,the reliability analysis of the points,edges as well as topological structures were presented and a mathematical model based on reliability was proposed.Finally,two kinds of emergency logistics path planning models were compared and analyzed by some examples.The results showed that the relationship between time optimality and reliability was relevant.Emergency logistics path planning should not only consider the optimal transportation time of emergency logistics,but also pay attention to the reliability of transportation from a global perspective.     

哈尔滨市新型冠状病毒肺炎疫情分析与复工建议

【目的】通过计算分析哈尔滨市的新型冠状病毒肺炎疫情的相关数据,研究哈尔滨市的疫情发展趋势,为政府疫情防控及工厂复工提供重要参考。【方法】利用哈尔滨市卫生健康委员会发布的官方数据,计算出此次疫情确诊病例环比增长率,绘制变化曲线图分析疫情发展现状。根据政府防疫强度的不同分段分析疫情的发展状况,用MATLAB R2018b结合SIR传染病模型,预测疫情发展趋势。【结果】目前疫情形势已趋于平缓,疫情已在2月末出现拐点,但防疫工作仍然严峻,对于即将复工的工厂,仍需完善防范举措。【结论】基于哈尔滨市疫情已有稳定好转的倾向,当务之急,一方面是防止疫情二次爆发,另一方面则是在保证安全的前提下平稳有序复工复产,争取使误工造成的损失最小化。     

前置分频器编码的等效分类及方法的推广

前置分频器是采用吞脉冲技术的程序分频器的关键部件。设计出性能优越的前置分频器,首先要考虑选择优越的10/11电路的编码方式,10/11电路采用5/6同步电路加固定除2异步电路合成较好。为使前置分频器达到高速与具有自启动特性的目的,选择5/6同步电路的合适的编码是重要的一环。5/6电路的编码多种多样,但是有的尽管形式不一样,其逻     

聚乙二醇(PEG)诱导鸡红血细胞和小白鼠腹水型肝癌细胞融合技术的初步探讨

1.用 PEG6000的不同浓度(30%,40%,50%)处理鸡红血细胞及小白鼠腹水型肝癌(Hca)细胞融合的结果,以40%对这两种细胞较为适宜,细胞融合的均数为2.9%.2.悬浮细胞的细胞融合是发生在 PEG 处理的10分钟后.3.PEG 加入细胞中时以边加边摇动细胞试管为好.     

跳频序列的时频二维汉明相关理论界

在高速移动环境中,传输时延和频率偏移是影响跳频信号的2大因素.因此,研究在传输时延和频率偏移同时存在下的时频二维跳频序列具有重大实用价值.时频二维汉明相关函数满足的理论界是判定跳频序列性能优劣的重要准则.因此,对时频二维汉明相关函数进行了研究,并给出了关于时频二维汉明相关函数、序列个数、频隙个数、序列长度和频移范围的新理论界,证明了刘元慧的理论界以及Peng-Fan界为新理论界的特殊情况.     

Regional Pole Assignment Robustness for Linear VSC System Subject to Structured Perturbations

IntroductionInmeansann×nidentitymatrix.ρ(T)denotesspectralradiusofT∈Cn×n.|T|mmeanstotaketheabsolutevalueofeachelementofmatrixT.ThesymbolsRe(s)andarg(s)standforrealpartandangleofacomplexnumbers.Furthermore,T≥Prepresentstij≥pijforeachiandj.Considerthemultivariablelinearsystemx(t)=(A ΔA)x(t) Bu(t)(1)wherex(t)isn-dimensionalstatevector,u(t)ism-dimensionalvector,andAisann×ntime-invariantnominalmatrix.ΔAstandsforann×ntime-invariantperturbationmatrix.Theperturbationsofthevariouselemen…     

玉溪市城区大气VOCs及其它污染物质量浓度时空特征分析

2014年8月至2015年7月在云南典型高原城市玉溪市5个监测点利用苏玛罐采样法采集VOCs样品,基于GC-MS分析方法获取了VOCs总质量浓度,同时利用空气自动监测设备监测了5个点位的大气NO_2、CO、SO_2、O_3(最大8 h)、PM_(10)和PM_(2.5)质量浓度.基于这些监测数据分析了玉溪市城区各种大气污染物的质量浓度分布特征及可能的来源.结果表明:监测期间玉溪市城区环境空气中VOCs、SO_2、NO_2、O_3、PM_(10)和PM_(2.5)的平均质量浓度分别为(19.02±18.31)、(20.11±25.53)、(16.08±11.21)、(34.54±32.79)、(49.51±34.09)μg·m~(-3)和(28.67±20.85)μg·m~(-3),CO质量浓度为(1.35±0.82)mg·m~(-3).从季节分布来看,各种污染物变化规律并不一致.VOCs质量浓度呈现秋、春高,冬、夏低的特点,O_3质量浓度呈现夏季高的明显特征,SO_2质量浓度呈现冬、秋高,春、夏低的特征,NO_2、CO、PM_(10)和PM_(2.5)质量浓度则表现为冬、春高,夏、秋低的特点.从空间分布特征来看,玉溪市中心交通密集区监测点大气环境中VOCs、NO_2、O_3、PM_(2.5)和CO质量浓度较其他监测点浓度较高,反映机动车尾气的显著贡献;而工业区监测点大气环境中SO_2和PM_(10)质量浓度较高,符合工业燃煤排放烟气显著影响的基本特征.另通过相关性分析和气团轨迹研究表明除了本地人为活动影响外,区域传输源对玉溪城区大气污染有重要影响.