幽门螺杆菌尿素酶B亚单位特异性抗体检测方法的建立与评价

目的建立并评价幽门螺杆菌(Hp)尿素酶B亚单位(UreB)特异性抗体的检测方法。方法应用纯化的重组HpUreB作为包被抗原,建立检测HpUreB特异性抗体的ELISA间接法。以Hp抗体Western blot检测作为“金标准”,评价所建立方法的真实性和可靠性。结果检测血清特异性IgG的灵敏度为100·0%,特异度为97·7%,阳性预测值为97·2%,阴性预测值为100·0%,约登指数为0·977,符合率为98·7%,试验的一致率为98·5%;检测血清中总的特异性Ig灵敏度为97·1%,特异度为95·3%,阳性预测值为94·4%,阴性预测值为97·6%,约登指数为0·925,符合率为96·2%,试验的一致率为97·8%;检测唾液中特异性sIgA的灵敏度为96·2%,特异度为94·5%,阳性预测值为89·3%,阴性预测值为98·1%,约登指数为0·907,符合率为95·1%,试验的一致率为97·5%;检测粪便标本中的特异性sIgA的灵敏度为92·0%,特异度为90·2%,阳性预测值为85·2%,阴性预测值为94·9%,约登指数为0·822,符合率为90·9%,试验的一致率为98·6%,CV值均小于15%。结论该检测方法真实性、可靠性、重复性良好,能满足临床标本检测的要求。     

An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink

The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.     

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.     

两种方法提取琉璃苣叶多糖的理化性质 及生理活性

本研究旨在制备两种琉璃苣叶水溶性多糖，进一步探究其结构特征，并评价其降血糖、抗癌和免疫活性。采用纤维素酶辅助提取（BLP-1）和微波辅助提取法（BLP-2）制备琉璃苣叶水溶性多糖，并比较了BLP-1和BLP-2的理化性质，通过分析α-葡萄糖苷酶和α-淀粉酶的抑制效果来评价BLP-1和BLP-2的降血糖活性，通过检测HepG2、MX-1和A549细胞的抑制率来评价抗癌活性，通过检测RAW246.7的免疫因子分泌来评价免疫活性。结果表明，BLP-1和BLP-2具有不同的多糖和蛋白质含量。BLP-1和BLP-2由阿拉伯糖（Ara）、半乳糖（Gal）、葡萄糖（Glc）、木糖（Xyl）组成，但摩尔比组成不同，其分子量分别为20.1和22.6KDa。热重分析（TGA）表明BLP-2比BLP-1具有更稳定的结构。这两种多糖具有抑制α-葡萄糖苷酶和α-淀粉酶活性、抑制HepG-2、A549和MX-1癌细胞增殖以及激活巨噬细胞RAW 264.7细胞分泌免疫细胞因子以介导细胞免疫反应的潜力。两种多糖可作为新功能食品和医药产品中的生物活性成分，为进一步研究两种多糖的构效关系提供了理论依据。     

被动桩中土拱形成机理的平面有限元分析

国内外对被动桩中土拱形成机理的研究比较少,尤其针对不同土性土体中这类问题的对比研究更是少见。借助平面有限元数值方法,对粘性土与无粘性土中被动桩间的土拱形成机理作了研究和对比分析,并讨论了土拱形成过程与土拱效应随土体参数如粘聚力、内摩擦角、弹性模量、剪胀角、泊松比、桩土接触特征的变化规律。研究表明:土性不同时,土拱形成过程中桩周土体的受力和破坏形式不同;剪胀角、泊松比、桩土接触特征对桩间土拱形成机理和效应影响明显。     

MORM: A Multi-objective Optimized Replication Management strategy for cloud storage cluster

Effective data management is an important issue for a large-scale distributed environment such as data cloud. This can be achieved by using file replication, which efficiently reduces file service time and access latency, increases file availability and improves system load balancing. However, replication entails various costs such as storage and energy consumption for holding replicas. This article proposes a multi-objective offline optimization approach for replica management, in which we view the various factors influencing replication decisions such as mean file unavailability, mean service time, load variance, energy consumption and mean access latency as five objectives. It makes decisions of replication factor and replication layout with an improved artificial immune algorithm that evolves a set of solution candidates through clone, mutation and selection processes. The proposed algorithm named Multi-objective Optimized Replication Management (MORM) seeks the near optimal solutions by balancing the trade-offs among the five optimization objectives. The article reports a series of experiments that show the effectiveness of the MORM. Experimental results conclusively demonstrate that our MORM is energy effective and outperforms default replication management of HDFS (Hadoop Distributed File System) and MOE (Multi-objective Evolutionary) algorithm in terms of performance and load balancing for large-scale cloud storage cluster.     

一种瓦斯涌出量系统预测辨识模型的研究

针对煤与瓦斯突出灾害中瓦斯涌出量的辨识预测问题,结合采煤工作面瓦斯涌出量系统的现场实际特点,提出了混沌免疫遗传优化算法(CIGOA)与Elman神经网络相结合的耦合算法(CIGOA-ENN)。利用GIGOA的全局寻优能力替代梯度下降法,以克服Elman神经网络固有的缺陷。并根据输入的数据,构造基于CIGOA和ENN耦合算法的瓦斯涌出量系统辨识预测模型。利用矿区采集的现场监测数据进行仿真预测,实验表明该预测模型与BPNN,GA-ENN等神经网络预测模型相比,其收敛速度更快、收敛精度更高、鲁棒性更强,为解决煤矿瓦斯涌出量的预测问题提供了一个行之有效的方法。     

布病胶体金免疫层析检测方法的建立

目的建立诊断布病的胶体金免疫层析方法。方法以B.abortus 544A全菌免疫BALB/c小鼠,以杂交瘤细胞技术制备单抗,纯化后以胶体金标记,选择最佳反应模式组装试纸条,并对其进行鉴定。结果已筛选4B8、1G9、1C9和1E24株稳定分泌单抗的细胞株,以其单抗互相配对组合的试纸条均能检出B.abortus 544A菌。其中以1G9为包被抗体、4B8为金标抗体的试纸检测效果最佳,与以B.abortus 544A菌多抗为包被抗体、4B8单抗为金标抗体的试纸对比,检测结果一致,且不与其他菌发生交叉反应。对PCR检测为阳性的70份牛血清样品,两种试纸条检测结果的符合率均为100%。结论所建立的诊断布病的胶体金免疫层析法快速、简便、准确,有望用于布病的诊断。     

（急）PDMS/ZrO2/SiO2超疏水辐射制冷薄膜的制备及性能

被动日间辐射制冷（PDRC）技术由于不需要外部能源、绿色清洁无污染而受到广泛关注。该文通过将聚二甲基硅氧烷（PDMS）和二氧化锆（ZrO2）颗粒进行复合形成分散液，将其浇铸成膜后得到PDMS/ZrO2辐射制冷薄膜材料，然后通过喷涂PDMS/SiO2分散液对其进行疏水化处理制备了一种超疏水辐射制冷薄膜材料。通过优化ZrO2粒径和喷涂液中SiO2用量，薄膜表面接触角可达156.6 ° ± 2 °，滚动角为0.3 ° ± 0.1 °，呈现优异的自清洁性能。其太阳光反射率高达95.3%，红外发射率大于90%。在太阳光直射下，薄膜可实现平均9.99 ℃的降温效果。薄膜的自清洁性能使其表面不受泥土污染从而具有稳定持久的辐射降温功能。除此之外，薄膜具有优异的机械性能、耐摩擦性能以及耐酸/碱溶液和紫外光照稳定性。     

基于模糊免疫PID的左心室辅助装置控制系统仿真

为了提高左心室辅助装置运行的稳定性,研究了模糊免疫PID控制在左心室辅助装置控制系统中的应用.首先,分析了辅助装置的工作原理及其系统建模;其次,对模糊免疫PID控制算法进行分析;最后,进行了基于模糊免疫PID左心室辅助装置控制的仿真.仿真结果表明,模糊免疫PID控制技术比常规PID控制具有更好的控制稳定性.