INFLUENCE OF NETWORK TOPOLOGY AND DATA COLLECTION ON NETWORK INFERENCE

This paper is based on wireless sensor network topology for sports training human data collection, and the collected data are studied and analyzed in depth. According to the application requirements of sports training, a sports training system consisting of an embedded data collection terminal, and a database server is designed using wireless sensor network technology. The hardware is designed with sensor nodes and base stations to collect athletes’ motion parameters in real time. The software is designed with node and base station control software and sports database management system to realize the receiving, storing, and analyzing of sports parameters. And the system experiments were conducted, and the experimental results show that this system meets the application requirements of sports training and provides an effective tool for scientific training decision research. The needs of designing sports training systems under wireless sensor networks are analyzed, and the system is designed and implemented. Our results confirm that the use of wireless sensor network technology in the design of the sports training system improves the system application performance by 16%. And the interactivity of the sports training system in practice has increased by 8%. All of these show that the design of the sports training system under the wireless sensor network meets the actual system application requirements and has a positive impact. The design of base station control, node control, and sports database software is implemented in the software system, which can effectively realize the collection, storage, and analysis of sports parameters. Finally, the designed wireless sensor network-based sports training system is tested, and the test results indicate that the system designed in this paper can meet the needs of sports training use.

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How to make methodological decisions when inferring social networks

10.1101/739789 ◽

2019 ◽

Author(s):

André C. Ferreira ◽

Rita Covas ◽

Liliana R. Silva ◽

Sandra C. Esteves ◽

Inês F. Duarte ◽

...

Keyword(s):

Social Networks ◽

Social Network ◽

Data Collection ◽

Network Inference ◽

A Priori ◽

Pilot Studies ◽

Multiple Hypotheses Testing ◽

Sociable Weavers ◽

Breeding Groups

ABSTRACTConstructing and analysing social networks data can be challenging. When designing new studies, researchers are confronted with having to make decisions about how data are collected and networks are constructed, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods, and risk generating false results arising from multiple hypotheses testing. We suggest an approach for making decisions when developing a network without jeopardising the validity of future hypothesis tests. We argue that choosing the best edge definition for a network can be made using a priori knowledge of the species, and testing hypotheses that are known and independent from those that the network will ultimately be used to evaluate. We illustrate this approach by conducting a pilot study with the aim of identifying how to construct a social network for colonies of cooperatively breeding sociable weavers. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then identified which combination of data collection and association definition maximised (i) the assortment of individuals into ‘breeding groups’ (birds that contribute towards the same nest and maintain cohesion when foraging), and (ii) socially differentiated relationships (more strong and weak relationships than expected by chance). Our approach highlights how existing knowledge about a system can be used to help navigate the myriad of methodological decisions about data collection and network inference.SIGNIFICANCE STATEMENTGeneral guidance on how to analyse social networks has been provided in recent papers. However less attention has been given to system-specific methodological decisions when designing new studies, specifically on how data are collected, and how edge weights are defined from the collected data. This lack of guidance can lead researchers into being less critical about their study design and making arbitrary decisions or trying several different methods driven by a given preferred hypothesis of interest without realising the consequences of such approaches. Here we show that pilot studies combined with a priori knowledge of the study species’ social behaviour can greatly facilitate making methodological decisions. Furthermore, we empirically show that different decisions, even if data are collected under the same context (e.g. foraging), can affect the quality of a network.

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Network Inference from Gene Expression Data with Distance Correlation and Network Topology Centrality

Algorithms ◽

10.3390/a14020061 ◽

2021 ◽

Vol 14 (2) ◽

pp. 61

Author(s):

Kuan Liu ◽

Haiyuan Liu ◽

Dongyan Sun ◽

Lei Zhang

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Network Topology ◽

Regulatory Networks ◽

Network Inference ◽

Expression Data ◽

Distance Correlation ◽

Non Linear ◽

Gene Regulatory

The reconstruction of gene regulatory networks based on gene expression data can effectively uncover regulatory relationships between genes and provide a deeper understanding of biological control processes. Non-linear dependence is a common problem in the regulatory mechanisms of gene regulatory networks. Various methods based on information theory have been developed to infer networks. However, the methods have introduced many redundant regulatory relationships in the network inference process. A recent measurement method called distance correlation has, in many cases, shown strong and computationally efficient non-linear correlations. In this paper, we propose a novel regulatory network inference method called the distance-correlation and network topology centrality network (DCNTC) method. The method is based on and extends the Local Density Measurement of Network Node Centrality (LDCNET) algorithm, which has the same choice of network centrality ranking as the LDCNET algorithm, but uses a simpler and more efficient distance correlation measure of association between genes. In this work, we integrate distance correlation and network topological centrality into the reasoning about the structure of gene regulatory networks. We will select optimal thresholds based on the characteristics of the distribution of each gene pair in relation to distance correlation. Experiments were carried out on four network datasets and their performance was compared.

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Novel Network Topology and Transporting Protocol for Data Collection from Large Scale Smart Electricity Meters

Proceedings of the 2015 International Forum on Energy, Environment Science and Materials ◽

10.2991/ifeesm-15.2015.5 ◽

2015 ◽

Author(s):

Feng Zhou ◽

Yingying Cheng ◽

Ji Xiao ◽

Jie Du

Keyword(s):

Data Collection ◽

Network Topology ◽

Large Scale

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Graph combinatorics based group-level network inference

10.1101/758490 ◽

2019 ◽

Author(s):

Shuo Chen ◽

Qiong Wu ◽

L. Elliot Hong

Keyword(s):

Statistical Inference ◽

Network Topology ◽

Network Inference ◽

Brain Regions ◽

Superior Performance ◽

Omics Data ◽

Group Level ◽

Data Set ◽

Joint Inference ◽

Brain Connectome

AbstractWe consider group-level statistical inference for networks, where outcomes are multivariate edge variables constrained in an adjacency matrix. The graph notation is used to represent a network, where nodes are identical biological units (e.g. brain regions) shared across subjects and edge-variables indicate the strengths of interactive relationships between nodes. Edge-variables vary across subjects and may be associated with covariates of interest. The statistical inference for multivariate edge-variables is challenging because both localized inference on individual edges and the joint inference of a combinatorial of edges (network-level) are desired. Different from conventional multivariate variables (e.g. omics data), the inference of a combinatorial of edges is closely linked with network topology and graph combinatorics. We propose a novel objective function with 𝓁0 norm regularization to robustly capture subgraphs/subnetworks from the whole brain connectome and thus reveal the latent network topology of phenotype-related edges. Our statistical inferential procedure and theories are constructed based on graph combinatorics. We apply the proposed approach to a brain connectome study to identify latent brain functional subnetworks that are associated with schizophrenia and verify the findings using an independent replicate data set. The results demonstrate that the proposed method achieves superior performance with remarkably increased replicability.

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BrainNET: Inference of brain network topology using Machine Learning

10.1101/776641 ◽

2019 ◽

Author(s):

Gowtham Krishnan Murugesan ◽

Chandan Ganesh ◽

Sahil Nalawade ◽

Elizabeth M Davenport ◽

Ben Wagner ◽

...

Keyword(s):

Machine Learning ◽

Network Topology ◽

Network Inference ◽

Learning Algorithm ◽

Ground Truth ◽

Brain Network ◽

Fmri Data ◽

Method Performance ◽

Graph Metrics ◽

Network Topologies

AbstractObjectiveTo develop a new fMRI network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI.MethodsBrainNET is based on Extremely Randomized Trees (ERT) to estimate network topology from fMRI data and modified to generate an adjacency matrix representing brain network topology, without reliance on arbitrary thresholds. Open source simulated fMRI data of fifty subjects in twenty-eight different simulations under various confounding conditions with known ground truth was used to validate the method. Performance was compared with correlation and partial correlation (PC). The real-world performance was then evaluated in a publicly available Attention-deficit/hyperactivity disorder (ADHD) dataset including 134 Typically Developing Children (mean age: 12.03, males: 83), 75 ADHD Inattentive (mean age: 11.46, males: 56) and 93 ADHD Combined (mean age: 11.86, males: 77) subjects. Network topologies in ADHD were inferred using BrainNET, correlation, and PC. Graph metrics were extracted to determine differences between the ADHD groups.ResultsBrainNET demonstrated excellent performance across all simulations and varying confounders in identifying true presence of connections. In the ADHD dataset, BrainNET was able to identify significant changes (p< 0.05) in graph metrics between groups. No significant changes in graph metrics between ADHD groups was identified using correlation and PC.

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Blockchain-Based Automated System for Identification and Storage of Networks

Security and Communication Networks ◽

10.1155/2021/6694281 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Deepak Prashar ◽

Nishant Jha ◽

Muhammad Shafiq ◽

Nazir Ahmad ◽

Mamoon Rashid ◽

...

Keyword(s):

Network Topology ◽

Network Inference ◽

Partial Information ◽

Storage Systems ◽

Automated System ◽

Inference Algorithm ◽

Data Control ◽

Packet Tracer ◽

Major Factors ◽

And Storage

Network topology is one of the major factors in defining the behavior of a network. In the present scenario, the demand for network security has increased due to an increase in the possibility of attacks by malicious users. In this paper, a blockchain-based system is suggested for securely discovering and storing networks. Techniques such as cloud-based storage systems are not efficient and are lacking in trust, privacy, security, and data control. The blockchain-based technique suggested in this paper is capable of resolving these challenges. Experiments were performed using Mininet, Cisco Packet Tracer, and Ethereum blockchain with the network inference algorithm. This algorithm is capable of inferring the network topology even when only partial information regarding the network is available. The results obtained clearly show that the network is resistant to malicious users and various external attacks, making the network robust.

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Electron Diffraction of Wet Biological Membranes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100050809 ◽

1974 ◽

Vol 32 ◽

pp. 158-159

Author(s):

S.W. Hui ◽

D.F. Parsons

Keyword(s):

Electron Diffraction ◽

Data Collection ◽

Biological Membranes ◽

Small Areas ◽

Electron Diffraction Technique ◽

Electron Microscopes ◽

Collection Time ◽

Camera Length

The development of the hydration stages for electron microscopes has opened up the application of electron diffraction in the study of biological membranes. Membrane specimen can now be observed without the artifacts introduced during drying, fixation and staining. The advantages of the electron diffraction technique, such as the abilities to observe small areas and thin specimens, to image and to screen impurities, to vary the camera length, and to reduce data collection time are fully utilized. Here we report our pioneering work in this area.

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Automated electron tomography: from data collection to image processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100136507 ◽

1995 ◽

Vol 53 ◽

pp. 26-27

Author(s):

Weiping Liu ◽

Jennifer Fung ◽

W.J. de Ruijter ◽

Hans Chen ◽

John W. Sedat ◽

...

Keyword(s):

Image Processing ◽

Data Collection ◽

Structural Information ◽

Imaging Technique ◽

Electron Tomography ◽

Ccd Camera ◽

Automated Data Collection ◽

Full Control ◽

Transmission Electron ◽

Amorphous Structures

Electron tomography is a technique where many projections of an object are collected from the transmission electron microscope (TEM), and are then used to reconstruct the object in its entirety, allowing internal structure to be viewed. As vital as is the 3-D structural information and with no other 3-D imaging technique to compete in its resolution range, electron tomography of amorphous structures has been exercised only sporadically over the last ten years. Its general lack of popularity can be attributed to the tediousness of the entire process starting from the data collection, image processing for reconstruction, and extending to the 3-D image analysis. We have been investing effort to automate all aspects of electron tomography. Our systems of data collection and tomographic image processing will be briefly described.To date, we have developed a second generation automated data collection system based on an SGI workstation (Fig. 1) (The previous version used a micro VAX). The computer takes full control of the microscope operations with its graphical menu driven environment. This is made possible by the direct digital recording of images using the CCD camera.

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