Back propagation artificial neural network for community Alzheimer's disease screening in China

AIzheimer＇s disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer＇s disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 （95% confidence interval： 0.868-0.968）, sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer＇s disease in patients selected from the community.

Finding Statistically Significant Communities in Networks with Weighted Label Propagation

Various networks exist in the world today including biological, social, information, and communication networks with the Internet as the largest network of all. One salient structural feature of these networks is the formation of groups or communities of vertices that tend to be more connected to each other within the same group than to those outside. Therefore, the detection of these communities is a topic of great interest and importance in many applications and different algorithms including label propagation have been developed for such purpose. Speaker-listener label propagation algorithm (SLPA) enjoys almost linear time complexity, so desirable in dealing with large networks. As an extension of SLPA, this study presented a novel weighted label propagation algorithm (WLPA), which was tested on four real world social networks with known community structures including the famous Zachary's karate club network. Wilcoxon tests on the communities found in the karate club network by WLPA demonstrated an improved statistical significance over SLPA. Withthehelp of Wilcoxon tests again, we were able to determine the best possible formation of two communities in this network relative to the ground truth partition, which could be used as a new benchmark for assessing community detection algorithms. Finally WLPA predicted better communities than SLPA in two of the three additional real social networks, when compared to the ground truth.

A weight's agglomerative method for detecting communities in weighted networks based on weight's similarity

This paper proposes the new definition of the community structure of the weighted networks that groups of nodes in which the edge＇s weights distribute uniformly but at random between them. It can describe the steady connections between nodes or some similarity between nodes＇ functions effectively. In order to detect the community structure efficiently, a threshold coefficient t~ to evaluate the equivalence of edges＇ weights and a new weighted modularity based on the weight＇s similarity are proposed. Then, constructing the weighted matrix and using the agglomerative mechanism, it presents a weight＇s agglomerative method based on optimizing the modularity to detect communities. For a network with n nodes, the algorithm can detect the community structure in time O（n2 log~）. Simulations on networks show that the algorithm has higher accuracy and precision than the existing techniques. Furthermore, with the change of t~ the algorithm discovers a special hierarchical organization which can describe the various steady connections between nodes in groups.

Bacterial community network complexity and role of stochasticity decrease during primary succession

In microbial ecology,there is limited understanding of the mechanisms governing patterns in community structure across space and time.Here,we studied the changes of bacterial co-occurrence network structure over four primary successional soils after glacier retreat,including a sand dune system and three glacier foreland series,varying in timescale from centuries to millennia.We found that in all series,network structure was most complex in the earliest stages of succession,and became simpler over time.Richness and abundance of keystone species and network stability also declined over time.It appears that with less productive,nutrient poor and physiologically extreme conditions of early succession,closer interactions among bacterial species are ecologically selected for.These may take the form of consortia(with positive interactions)or stronger niche exclusion(with negative interactions).Additionally,we quantified the relative roles of different structuring processes on bacterial community using a bin-based null model analysis(iCAMP).With each successional series,community composition was initially governed by stochasticity,but as succession proceeded there was a progressive increase in deterministic selection over time,correlated with decreasing pH.Overall,our results show a consistency among the four series in long-term processes of community succession,with more integrated networks and greater stochasticity in early stages.

WebGIS在管网管理中的应用探索

通过比对WebGIS的使用优势、性能特点和过往只使用C/S结构的管网管理系统的缺陷、不足,根据管网的日常管理维护中的要求以及管理系统所必备的共通功能,分析得出WebGIS在管网管理中的结构设计以及它的功能应用。

环状给水管网设计和调度的虚拟仪器

基于矩阵运算和迭代平差方法,在LabVIEW的编程平台上,开发环状给水管网水力计算的虚拟仪器。对管段流量基础解系赋值,代入节点连续方程求解管段流量。并由管段上的水头损失形成的闭合差获得环及管段的校正流量,经迭代逼近真值。闭合差可控制在给定精度。虚拟仪器的监控盘面和操作象是普通仪器。管长、管径和节点流量等设计参数能够成批传入,或在界面上写入。不需了解编程方法和数据库结构,既可开展设计,又能应用于生产现场的优化控制和调度,也是制定检修预案和进行管网技术改造的有力工具。

基于BIM的地下管网实时监测系统开发研究

通过使用BIM(Building Information Modeling)核心建模软件Revit完成对Y校园地下供水管网、供热管网模型绘制与精细化处理;分析了B/S架构(Browser/Server,浏览器/服务器模式,是WEB兴起后的一种网络结构模式)下的BIM技术运用的可行性,在此基础上运用HTML5(HTML5是构建Web内容的一种语言描述方式)、CSS(Cascading Style Sheets)与JavaScript(是一种具有函数优先的轻量级,解释型或即时编译型的编程语言)等技术,选取Forge作为主要平台,进行对地下管网BIM模型的轻量化处理,通过Web编译,最终实现BIM轻量化模型在Web端的显示;初步构建地下管网实时监测系统,阐述了地下管网实施监测系统的总体设计思路与开发流程。

Aggregate sizes regulate the microbial community patterns in sandy soil profile

Soil microorganisms play a key role in the function of soil ecosystem,yet our knowledge about how microbial communities respond to the typically sandy soil environmental properties along the soil profile is still insufficient.We investigated the soil microbial community patterns from top(0–20 cm)to clay-layer(>80 cm)of the typical sandy soils in three regions in China with different levels of precipitation,including Lishu County in Jilin Province(LS),Langfang City in Hebei Province(LF)and Zhengzhou City in Henan Province(ZZ).Our findings showed that small-size aggregates(<0.5 mm)rather than large ones(³0.5 mm)dominated the soil profile.The relative abundances of Actinobacteria,Crenarchaeota and Firmicutes were highly related to aggregate proportions of the deep clay-layer soil.The network analysis revealed the distinct community patterns among modules,evidencing niche differentiation along the soil profile.The keystone species OTU_11292 was observed having migrated clearly into the other module of the clay-layer soil.Different roles of the OTU_30(belonging to Gemmatimonadetes)in soil processes might partly explain the different microbial distribution between top-and clay-layer soils.These findings provided new insights into the candidate mechanisms of microbial diversity maintenance and community patterning of sandy soils,which were necessary for better understanding of ecological rules guiding long-term agricultural practice.