A preparation method for observing intracellular structures by scanning electron microscopy

In order to observe intracellular structures by scanning electron microscopy, excess cytoplasmic matrix must be removed from the fractured surface of cells. Previously we reported an Osmium-DMSO-Osmium method devised for this purpose. This method is very effective in revealing intracellular structures, but requires osmium tetroxide for initial fixation with some consequent disadvantages. In the present study, a revised Osmium-DMSO-Osmium method is reported, in which an aldehyde mixture is used as the initial fixative instead of osmium tetroxide. As fixation is carried out by perfusion in this revised method, better preservation of fine structures is achieved than by the original method, especially in the central nervous tissue which tends to suffer from post-mortem degeneration. Moreover this method can be applied to cytochemical studies of intracellular structures with a scanning electron microscope (SEM). In this study, acid phosphatase of lysosomes is demonstrated in a coloured SEM micrograph.     

外碳源投量对亚硝酸型反硝化过程N2O释放影响

为实现N_2O的减量化控制或资源化利用,接种普通活性污泥,以乙酸钠和硝酸盐为基质,通过淘洗反硝化聚磷菌,在SBR中以厌氧/缺氧交替运行的方式启动內源反硝化,单周期内约50%的NO-3-N转化为NO-2-N,N_2O释放速率随着亚硝酸的积累逐渐增大,N_2O转化率(释放量占TN去除的比例)为2.04%.在此基础上,分别取缺氧末和厌氧末污泥,对比研究胞内聚合物PHB合成前后,外碳源投加量(碳氮比为0,0.75和2.50)对亚硝酸型反硝化过程N_2O释放特性的影响,结果表明,外碳源存在时,N_2O释放量随总碳源的增加呈略微减少的趋势,转化率在0.24%~1.61%;而当仅利用內源物质进行反硝化时,N_2O的转化率高达15.90%,单位SS最大释放速率达71.29μg/(min·g),释放量是其余条件下的14~26倍.表明单独利用PHB进行亚硝酸型反硝化会大幅增加N_2O的释放.     

基于二维复数离散小波包变换的桥面裂缝检测

为了解决传统桥梁维护检测依靠检测人员进行现场测量,造成桥面检测效率低下且耗费巨大的问题,提出了一种基于二维复数离散小波包变换的桥面裂缝检测方法.采用无人机对桥面图像进行采集,并通过二维复数离散小波包变换来提取桥面裂痕特征.根据频域的角度范围设计了一维非分离滤波器计算小波变换需要的方向分量,并将该分量应用到通过2D-CWPT所获得的小波系数中,从而提取任意方向上的图像特征.采用提出的桥面裂缝检测方法对桥面裂缝进行检测,实验提取到了桥面裂纹的多方特征,验证了本方案的有效性.     

CPL: Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network

Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework （CPL） that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. Tile CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.     

低品位氧化锌粉矿制粒及碱性浸出

小于2 mm的氧化锌矿(含锌5.17%)与5%的水泥混合,制粒并固化,所得颗粒直径5~8 mm。固化3天、10天、45天的颗粒用碱性溶液浸出,最大浸出率分别为92.2%、87.3%、72.9%。减少固化时间能够缩短反应时间、增加颗粒中锌的溶解以及减少浸出剂中初始锌浓度的影响。实验表明颗粒最少需固化3天。动力学研究表明浸出过程受浸出剂通过脉石层的扩散控制,表观速率常数分别为3.51×10-2d-1、8.09×10-3d-1和4.74×10-3d-1。     

锌渣烟化炉连续吹炼的试验研究

进行了烟化炉连续吹炼的工业试验研究,试验结果表明,该工艺具有流程短,降低能耗,改善作业环境,降低成本的优点。     

一种定量评估复杂网络节点重要度的算法

提出一种定量评估复杂网络节点重要度的算法,定义虚拟的理想“核心节点”,将灰色关联度作为测度,评价网络中每个节点和理想“核心节点”的关联度,关联度越高,节点在网络中的重要性越大。仿真结果表明,与同类算法相比,该算法运算速度更快,适用于评价大型复杂网络中节点的重要度。     

基于模块度函数的加权蛋白质复合物识别算法

针对加权模块度函数聚类算法在蛋白质相互作用网络中进行复合物识别的准确率不高、召回率较低以及时间性能不佳等问题进行了研究,提出一种基于模块度函数的加权蛋白质复合物识别算法IWPC-MF（Algorithm for Identifying Weighted Protein Complexes based on Modularity Function）。融合点聚集系数改进边聚集系数,将改进后的边点聚集系数与基因共表达的皮尔逊相关系数结合来构建加权蛋白质网络;基于节点权重选取种子节点,遍历种子的邻居节点,设计节点间的相似度度量和蛋白质附着度来获取初始聚类模块;设计基于紧密度的蛋白质复合物模块度函数来合并初始模块,并最终完成复合物的识别,克服传统的模块度函数无法识别出重叠和规模较小的复合物的缺陷。将IWPC-MF算法应用在DIP数据上进行复合物的识别,实验结果表明IWPC-MF算法的准确率和召回率较高,能够较准确地识别蛋白质复合物。     

Sugeno测度空间基于复样本的统计学习理论

引入复g_λ随机变量、准范数的定义,给出了复g_λ随机变量的期望和方差的概念及若干性质;证明了基于复g_λ随机变量的马尔可夫不等式、契比雪夫不等式和辛钦大数定律;提出了Sugeno测度空间中复经验风险泛函、复期望风险泛函以及复经验风险最小化原则严格一致性等定义;证明并构建了基于复g_λ随机样本的统计学习理论的关键定理和学习过程一致收敛速度的界,为系统建立基于复g_λ随机样本的统计学习理论奠定了理论基础。     

基于有向复杂网络模型的形状描述与识别

针对传统复杂网络方法对形状的非刚性变形较为敏感等问题,在形状内部距离的基础上利用有向复杂网络进行形状分析.首先提取形状边界点作为复杂网络的节点,以形状边界点之间的内部距离作为复杂网络中节点之间的边权值构建初始网络;然后对初始复杂网络进行k近邻演化,得到不同演化时刻的有向子网络;最后提取各有向子网络的特征来实现复杂网络的特征描述,进而实现形状的特征表示.实验结果表明,该方法对常见的形状变形具有更强的鲁棒性;与传统的无向网络模型相比,具有更高的检索和分类精度.