Ex vivo 3D osteocyte network construction with primary murine bone cells

Osteocytes reside as three-dimensionally（3D） networked cells in the lacunocanalicular structure of bones and regulate bone and mineral homeostasis. Despite of their important regulatory roles, in vitro studies of osteocytes have been challenging because：（1） current cell lines do not sufficiently represent the phenotypic features of mature osteocytes and（2） primary cells rapidly differentiate to osteoblasts upon isolation. In this study, we used a 3D perfusion culture approach to：（1） construct the 3D cellular network of primary murine osteocytes by biomimetic assembly with microbeads and（2） reproduce ex vivo the phenotype of primary murine osteocytes, for the first time to our best knowledge. In order to enable 3D construction with a sufficient number of viable cells, we used a proliferated osteoblastic population of healthy cells outgrown from digested bone chips. The diameter of microbeads was controlled to：（1） distribute and entrap cells within the interstitial spaces between the microbeads and（2） maintain average cell-to-cell distance to be about 19 mm. The entrapped cells formed a 3D cellular network by extending and connecting their processes through openings between the microbeads. Also, with increasing culture time, the entrapped cells exhibited the characteristic gene expressions（SOST and FGF23） and nonproliferative behavior of mature osteocytes. In contrast, 2D-cultured cells continued their osteoblastic differentiation and proliferation. This 3D biomimetic approach is expected to provide a new means of：（1） studying flow-induced shear stress on the mechanotransduction function of primary osteocytes,（2） studying physiological functions of 3D-networked osteocytes with in vitro convenience,and（3） developing clinically relevant human bone disease models.

The Budget Constrained Multi-product Newsboy Problem with Reactive Production:A Problem from Entrepreneurial Network Construction

This paper develops an extended newsboy model and presents a formula- tion for this model. This new model has solved the budget contained multi-product newsboy problem with the reactive production. This model can be used to describe the status of entrepreneurial network construction. We use the Lagrange multiplier procedure to deal with our problem, but it is too complicated to get the exact solu-tion. So we introduce the homotopy method to deal with it. We give the flow chart to describe how to get the solution via the homotopy method. We also illustrate our model in both the classical procedure and the homotopy method. Comparing the two methods, we can see that the homotopy method is more exact and efficient.

Optimal blockchain network construction methodology based on analysis of configurable components for enhancing Hyperledger Fabric performance

Presently,blockchain technology has been widely applied in various application domains such as the Internet of Things(IoT),supply chain management,healthcare,etc.So far,there has been much confusion about whether blockchain performs with scale,and admittedly,a lack of information about best practices that can improve the performance and scale.This paper proposes a novel blockchain network construction methodology to improve the performance of Hyperledger Fabric.As a highly scalable permissioned blockchain platform,Hyperledger Fabric supports a wide range of enterprise use cases from finance to governance.A comprehensive evaluation is performed by observing various configurable network components that can affect the blockchain performance.To demonstrate the significance of the proposed methodology,we set up the experiment environment for the baseline and the test network using optimized parameters,respectively.The experimental results indicate that the test network's performance is enhanced effectively compared to the baseline in transaction throughput and transaction latency.

Improvement of tree-like network constructal method for heat conduction optimization

The analysis of the “tree-like network” construct method has been repeated. The high effective conduction channel distribution has been optimized again, without the premise that the new order assembly construct must be assembled by the optimized last order construct. It is proved that the “tree-like network” construct method is faultiness. A more optimal construct is obtained,; when the thermal conductivity; the proportion of the two heat conduction materials are constant, the limit of the minimum heat resistance is derived. All these conclusions can be used to guide the engineering application.

A DATA MINING METHOD BASED ON CONSTRUCTIVE NEURAL NETWORKS

In this letter,Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies,fuzzy theory is adopted to improve the covering algorithms. The threshold of covering algorithms is redefined. "Extended area" for test samples is built. The inference of the outlier is eliminated. Furthermore,"Sphere Neighborhood (SN)" are constructed. The membership functions of test samples are given and all of the test samples are determined accordingly. The method is used to mine large wireless monitor data (about 3×107 data points),and knowledge is found effectively.

Construction of NIM sub-network regulated by Liuwei Dihuang decoction and its characteristics

OBJECTIVE To construct the neuroendocrine immunomodulation(NIM) sub-network regulated by Liuwei Dihuang decoction(LW) and analyze its characteristics.METHODS We took the GSE57273 in GEO database and screened the differentially expressed genes(DEGs)(P<0.01) by the GEO2 R tool as gene expression signature of LW.The global PPI network was constructed in the context of whole PPI network through direct interaction algorithm and forest algorithm respectively.Then the enrichment and the topological characteristics of NIM signaling molecules were evaluated by permutation test.Finally,we abstracted the NIM sub-network by NIMNT,which combined the NIM molecular network and forest algorithm,and analyzed the topological characteristics of it by the Network Analyzer(release 2.7) plugin in Cytoscape v3.5.1.RESULTS We got 2468 DEGs in the gene expression signature of LW.After analyzing the global PPI network of LW got by two kinds of algorithms,we found that the NIM signaling molecules significantly enriched and located in important positions in the global PPI network.The NIM sub-network regulated by LW contained 1099 nodes and 1056 edges.We screened out 22 hub nodes(Degree>10).Among them,there were 19 NIM signaling molecules in which only ESR1 changed significantly and 3 non-DEGs(NFATC2,RARA,TP53).However,the down.stream of the hub nodes were significantly changes.CONCLUSION The results suggested that LW might mainly regulate the non-hub nodes to recovery of the network imbalance of the body in the state of disease.

Research and Design of Network Management Construction Based on Mobile AGENT

The technical characters of mobile agent （MA） originated in the distributional artificial intelligence domain is introduced. A network management construction based on agent （NMCA） is then proposed. The NMCA structure features are elaborated in detail. A prototype design of NMCA is given by using the jKQML programming. The establishment of NMCA platform will be helpful to reduce the correspondence load of network management and improves the efficiency and the expansion ability of network management systems.

Self-Constructing Neural Network Modeling and Control of an AGV

Tracking precision of pre-planned trajectories is essential for an auto-guided vehicle (AGV). The purpose of this paper is to design a self-constructing wavelet neural network (SCWNN) method for dynamical modeling and control of a 2-DOF AGV. In control systems of AGVs, kinematical models have been preferred in recent research documents. However, in this paper, to enhance the trajectory tracking performance through including the AGV’s inertial effects in the control system, a learned dynamical model is replaced to the kinematical kind. As the base of a control system, the mathematical models are not preferred due to modeling uncertainties and exogenous inputs. Therefore, adaptive dynamic and control models of AGV are proposed using a four-layer SCWNN system comprising of the input, wavelet, product, and output layers. By use of the SCWNN, a robust controller against uncertainties is developed, which yields the perfect convergence of AGV to reference trajectories. Owing to the adaptive structure, the number of nodes in the layers is adjusted in online and thus the computational burden of the neural network methods is decreased. Using software simulations, the tracking performance of the proposed control system is assessed.

Discussion on Construction of CDMA2000 1xEV-D0 Network

The research advances on 3G CDMA standards are outlined and the network architecture and networking characteristics of CD- MA2000 1x EV-DO and CDMA2000 1x EV-DV are described.Em- phasis is put on the key technical issues that may occur when China Unicom upgrades CDMA2000 1x to CDMA2000 1x EV-DO.

新型电力系统主动构网机理与技术路径

随着新一代“双高”电力系统的发展,新能源并网变换系统逐渐由随网型向构网型演变,当新能源机组集群运行时,并网情况将更加复杂。变换器的构网能力需亟待研究,同时也是电力能源转型需承担的重要使命。该文立足并网系统内外形态的全局性和传统并网变换系统性能分析的局限性,对并网变换器的主动构网支撑机理进行再揭示。首先,对虚拟同步技术与构网机理进行简要评述;其次,就集群并网系统的建模问题、多机集群并网运行时的耦合交互问题、支撑构网能力的储能配置和运行边界等3个关键技术问题,进行深入研究;最后,针对上述3个关键技术问题,进行技术路径探索和研判。该文的思路为形成适用于未来新能源集群并网运行的系统化理论和实用化方法奠定一定基础。

Improving the Input of Classified Neural Networks Through Feature Construction

A general classification algorithm of neural networks is unable to obtain satisfied results because of the uncertain problems existing among the features in moot classification programs, such as interaction. With new features constructed by optimizing decision trees of examples, the input of neural networks is improved and an optimized classification algorithm based on neural networks is presented. A concept of dispersion of a classification program is also introduced too in this paper. At the end of the paper, an analysis is made with an example.

类风湿关节炎铁死亡特征基因CeRNA网络构建及免疫表现

背景:研究发现铁死亡相关基因在类风湿关节炎的发病机制中占据重要地位,但目前尚缺乏关于类风湿关节炎铁死亡特征基因的免疫表现及CeRNA互作网络的构建,而机器学习作为生物信息学中强大的特征基因选择算法能更精确地筛选出在类风湿关节炎发病机制中占主导地位的铁死亡特征基因。目的:利用生物信息学与机器学习方法筛选类风湿关节炎铁死亡特征基因,并分析铁死亡特征基因与免疫浸润的相关性及铁死亡特征基因CeRNA的网络构建。方法:从GEO数据库获取与类风湿关节炎相关的芯片,利用R语言提取铁死亡相关基因及其差异基因表达;使用机器学习方法对差异基因进行筛选,即运用LASSO回归与SVM-RFE方法进行特征基因筛选,对两者过滤后的基因进行再次交集,最终得到类风湿关节炎的特征基因,运用ROC曲线评估筛选后的疾病特征基因诊断疾病的准确性;利用CIBERSORT算法分析类风湿关节炎与正常滑膜组织的免疫浸润情况,并分析铁死亡特征基因与免疫细胞的相关性,最后构建类风湿关节炎铁死亡疾病特征基因的CeRNA网络并对疾病特征基因进行验证。结果与结论:①得到与类风湿关节炎相关铁死亡基因150个,其中55个上调基因,95个下调基因;②GO与KEGG富集分析分别得到18个GO显著相关条目与30个KEGG条目,主要涉及金属离子稳态、有铁离子稳态与氧化应激反应等;③机器学习分析最终获得疾病特征基因GABARAPL1、SAT1;④GSEA分析发现脂肪细胞因子信号通路、药物代谢细胞色素P450、脂肪酸代谢、PPAR信号通路、酪氨酸代谢主要集中在GABARAPL1高表达时,趋化因子信号通路、肠道免疫网络对IGA产生的影响主要集中在SAT1高表达时;⑤免疫浸润分析发现类风湿关节炎与9种免疫细胞与正常组织存在明显差异,其中浆细胞、T细胞CD8、T细胞滤泡辅助器在疾病组中为高表达状态,其余为低表达。单基因与免疫细胞相关性分析发现GABARAPL1在树突状静息细胞、激活的NK细胞、巨噬细胞M1等为正相关,其中与树突状静息细胞的相关性最为显著,SAT1与T细胞CD4与γδT细胞为正相关,与NK静息细胞为负相关;⑥GSVA分析发现抗坏血酸和醛酸代谢在SAT1表现为上调的基因高表达水平时候为上调,而B细胞受体信号通路、TOLL样受体信号通路、T细胞受体信号通路、自然杀伤细胞介导的细胞毒性等表现为下调,PPAR信号通路、烟酸盐和烟酰胺的代谢、色氨酸代谢、脂肪酸代谢、类固醇生物合成等在GABARAPL1表现为下调趋势;⑦60种长链非编码RNA可能在导致类风湿关节炎发展过程中发挥关键作用。提示:类风湿关节炎的发生与类风湿关节炎铁死亡疾病特征基因的异常表达显著相关,特征基因通过影响相关信号通路诱导疾病的发生发展,并通过对类风湿关节炎相关长链非编码RNA介导的ceRNA网络进行分析,识别出潜在的治疗靶点及信号通路,为进一步阐明其发病机制,并为后续的实验研究提供参考依据。

数字化环境监测与企业绿色创新——基于环境监测网络建设的准自然实验

数字化环境监测将数字技术应用于环境监测领域,旨在提升政府环境治理决策的科学性,而实现海量监测数据互联互通的关键是构建生态环境监测网络。本文基于2013—2020年沪深A股上市公司的绿色专利数据,利用各省份陆续出台生态环境监测网络建设方案事件构建强度双重差分模型,检验数字化环境监测的微观创新效应及其机制。研究发现,环境监测网络建设能够促进企业绿色创新,且政府环境执法和环保补贴的效率优化是实现环境监测效应的有效路径。进一步检验发现,对于政府数字化水平较高、联网监控的重点排污单位占比较高的省份、环境规制强度较高的地市以及研发投入较高的企业,环境监测效应更明显。此外,环境监测网络建设对企业绿色创新的数量和质量均有显著的提升作用。本文从微观层面提供数字化环境监测促进企业绿色创新的积极证据,拓展了环境监测效应及企业绿色创新影响因素相关文献,对新时期推动数字政府建设和实现经济绿色发展具有一定启示。

Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine

Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.

陆域大地电磁基础网规划与展望

阵列式大地电磁测深是区域地球物理调查的重点发展方向之一,对标国外先进水平和国家社会的基础性需求,中国阵列式大地电磁测深工作程度依然较低,缺乏整体规划,开展全国性观测计划亟需提上日程。系统总结了当前区域地球物理学的发展趋势及国内外大地电磁网建设现状,提出了合作共建陆域多层级大地电磁基础网和大数据中心的顶层设计和规划布局,并对中国大地电磁基础网未来的重点发展方向进行了展望和思考。相关工作的顺利开展将为中国的区域地球物理调查拓展一项新属性参数,推动构建物性地球、结构地球和地球物理场四维模型,进而变革区域地球物理研究范式和工作模式,支撑中国深地探测走向世界前列。

Predicting Compressive Strength of Recycled Concrete for Construction 3D Printing Based on Statistical Analysis of Various Neural Networks

Construction 3D printing is changing construction industry, but for its immaturity, there are still many problems to be solved. One of the major problems is to study materials for construction 3D printing. Because printed buildings are very different from traditional buildings, there are special requirements for printing materials. Based on environmental and cost considerations, the recycled concrete as printing material is a perfect choice. In order to study and develop the construction 3D printing materials, it is necessary to predict the properties of them. As one of the most effective artificial intelligence algorithms, artificial neural network can deal with multi-parameter and nonlinear problems, and it can provide useful reference to predict the performance of recycled concrete for 3D printing. However, since there are many types and parameters for neural network, it is difficult to select the optimal neural network with excellent prediction performance. In this paper, by comparing different types of neural networks and statistically analyzing the distribution of the root-mean-square error (RMSE) and the coefficient of determination (R2) of these neural networks, we can determine the best performance among four neural networks and finally select the suitable one to predict the performance of 3D printing concrete.