To see the other types of publications on this topic, follow the link: Data Network Effects.
Journal articles on the topic 'Data Network Effects'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Data Network Effects.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Smith, Jeffrey A., and G. Robin Gauthier. "Estimating Contextual Effects from Ego Network Data." Sociological Methodology 50, no. 1 (June 2, 2020): 215–75. http://dx.doi.org/10.1177/0081175020922879.
Full textAbstract:
Network concepts are often used to characterize the features of a social context. For example, past work has asked if individuals in more socially cohesive neighborhoods have better mental health outcomes. Despite the ubiquity of use, it is relatively rare for contextual studies to use the methods of network analysis. This is the case, in part, because network data are difficult to collect, requiring information on all ties between all actors. In this article the authors ask whether it is possible to avoid such heavy data collection while still retaining the best features of a contextual-network study. The basic idea is to apply network sampling to the problem of contextual models, in which one uses sampled ego network data to infer the network features of each context and then uses the inferred network features as second-level predictors in a hierarchical linear model. The authors test the validity of this idea in the case of network cohesion. Using two complete data sets as a test, the authors find that ego network data are sufficient to capture the relationship between cohesion and important outcomes, such as attachment and deviance. The hope, going forward, is that researchers will find it easier to incorporate holistic network measures into traditional regression models.
2
De Giorgi, Giacomo, Anders Frederiksen, and Luigi Pistaferri. "Consumption Network Effects." Review of Economic Studies 87, no. 1 (May 6, 2019): 130–63. http://dx.doi.org/10.1093/restud/rdz026.
Full textAbstract:
Abstract In this article we study consumption network effects. Does the consumption of our peers affect our own consumption? How large is such effect? What are the economic mechanisms behind it? We use administrative panel data on Danish households to construct a measure of consumption based on tax records on income and assets. We combine tax record data with matched employer–employee data to identify peer groups based on workplace, which gives us a much tighter and credible definition of networks than used in previous literature. We use the non-overlapping network structure of one’s peers group, as well as firm-level shocks, to build valid instruments for peer consumption. We estimate non-negligible and statistically significant network effects, capable of generating sizable multiplier effect at the macro-level. We also investigate what mechanisms generate such effects, distinguishing between intertemporal and intratemporal consumption effects as well as a more traditional risk sharing view.
3
Jochmans, Koen, and Martin Weidner. "Fixed‐Effect Regressions on Network Data." Econometrica 87, no. 5 (2019): 1543–60. http://dx.doi.org/10.3982/ecta14605.
Full textAbstract:
This paper considers inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two‐way regression model. This is a workhorse technique in the analysis of matched data sets, such as employer–employee or student–teacher panel data. We formalize how the structure of the network affects the accuracy with which the fixed effects can be estimated. This allows us to derive sufficient conditions on the network for consistent estimation and asymptotically valid inference to be possible. Estimation of moments is also considered. We allow for general networks and our setup covers both the dense and the sparse case. We provide numerical results for the estimation of teacher value‐added models and regressions with occupational dummies.
4
Sewell, Daniel K. "Latent space models for network perception data." Network Science 7, no. 2 (April 15, 2019): 160–79. http://dx.doi.org/10.1017/nws.2019.1.
Full textAbstract:
AbstractSocial networks, wherein the edges represent nonbehavioral relations such as friendship, power, and influence, can be difficult to measure and model. A powerful tool to address this is cognitive social structures (Krackhardt, D. (1987). Cognitive social structures. Social Networks, 9(2), 109–134.), where the perception of the entire network is elicited from each actor. We provide a formal statistical framework to analyze informants’ perceptions of the network, implementing a latent space network model that can estimate, e.g., homophilic effects while accounting for informant error. Our model allows researchers to better understand why respondents’ perceptions differ. We also describe how to construct a meaningful single aggregated network that ameliorates potential respondent error. The proposed method provides a visualization method, an estimate of the informants’ biases and variances, and we describe a method for sidestepping forced-choice designs.
5
Welles, Brooke Foucault, and Noshir Contractor. "Individual Motivations and Network Effects." ANNALS of the American Academy of Political and Social Science 659, no. 1 (April 9, 2015): 180–90. http://dx.doi.org/10.1177/0002716214565755.
Full textAbstract:
This article explores the relative influence of individual and network-level effects on the emergence of online social relationships. Using network modeling and data drawn from logs of social behavior inside the virtual world Second Life, we combine individual- and network-level theories into an integrated model of online social relationship formation. Results reveal that time spent online and the network pressure toward balance (individuals tending to form relationships with others who have relationships in common) predict the emergence of online relationship ties, while gender, age, proximity, homophily (the tendency of individuals to form relationships among people with similar traits), and preferential attachment are not significant predictors within the observed networks. We discuss these results in light of existing research on online social relationships and describe how digital data and network analytics enable novel insights about the emergence of online social relationships.
6
Khamis, Azme, Zuhaimy Ismail ., Khalid Haron ., and Ahmad Tarmizi Mohamm . "The Effects of Outliers Data on Neural Network Performance." Journal of Applied Sciences 5, no. 8 (July 15, 2005): 1394–98. http://dx.doi.org/10.3923/jas.2005.1394.1398.
Full text
7
Rosin, Paul L., and Freddy Fierens. "The effects of data filtering on neural network learning." Neurocomputing 20, no. 1-3 (August 1998): 155–62. http://dx.doi.org/10.1016/s0925-2312(98)00008-3.
Full text
8
Maharjan, Pragya. "Effects of Social Network on Health Education." Shiksha Shastra Saurabh 21 (December 31, 2018): 31–36. http://dx.doi.org/10.3126/sss.v21i0.35087.
Full textAbstract:
Social networks have become an inseparable part of our modern life. Social network, if used properly, can be very beneficial for all the users in every respect. There are many researched applications of Social network in different fields, its application in Health Education was not mentioned anywhere properly, though. For the survey, the students of Bachelors’ levels, majoring in Health and Physical Education in constituent and affiliated colleges of T.U. of Kathmandu Valley were selected from the different colleges. Altogether 171 students, who were acquainted with Social network, were selected as respondents for the study. Questionnaires were used to collect quantitative data and interview guidelines were used to obtain qualitative data from teachers.
The students and teachers profoundly used Social network still they should use it for educational purpose. The communication gap between teachers and students were clearly perceived.
9
Oană, Iulian. "Network Effects on Rhythms of Scientific Publications." International Review of Social Research 8, no. 2 (December 1, 2018): 143–55. http://dx.doi.org/10.2478/irsr-2018-0016.
Full textAbstract:
Abstract Studies based on bibliometric records have introduced the idea of ‘rhythmicity’ when it comes to the publication of research articles. However, the main approach of this particular topic was to analyze journal specific data on rates of manuscript and review submissions. This study takes another path, by analyzing aspects of publication rhythmicity based not on individual, attribute data, but taking into account the fact that publication of research results and the efforts leading to a certain manuscript are often collective endeavors. Thus, co-authorship ego networks are interpreted through the theoretical lenses of ‘social time’ (for temporality), and ‘homophily’ and ‘preferential attachment’ (for network characteristics). For this article, the same data analyzed by M.-G. Hâncean and M. Perc in their 2016 article, Homophily in coauthorship networks of East European sociologists, were used. The data was based on Web of Science bibliometric records for three populations of academic sociologists, from Poland, Romania and Slovenia, and their co-authors. The purpose was to see if the publishing rhythm of an author (i.e., ego) is influenced by the publishing rhythm of her co-authors (i.e., alters) and by the structural characteristics of her ego-network. Rhythmicity was measured as the sum of standard deviations from the mean for the number of articles published between 2006 and 2016, resulting in a score which characterizes egos and alters as constant or irregular in their publishing activity. Results suggest that the structural features of the co-authorship networks can give us certain insights for the rhythmicity of publications. Mainly, structural features of network size, density and node betweenness explain more the variation of egos’ constancy or irregularity in (non)publication than the rhythmicity of their co-authors.
10
McPherson, Miller, and Jeffrey A. Smith. "Network Effects in Blau Space: Imputing Social Context from Survey Data." Socius: Sociological Research for a Dynamic World 5 (January 2019): 237802311986859. http://dx.doi.org/10.1177/2378023119868591.
Full textAbstract:
We develop a method of imputing ego network characteristics for respondents in probability samples of individuals. This imputed network uses the homophily principle to estimate certain properties of a respondent’s core discussion network in the absence of actual network data. These properties measure the potential exposure of respondents to the attitudes, values, beliefs, and so on of their (likely) network alters. We use American National Election Study data to demonstrate that the imputed network features show substantial effects on individual-level measures, such as political attitudes and beliefs. In some cases, the imputed network variable substantially reduces the effects of standard sociodemographic variables, like age and education. We argue that the imputed network variable captures many of the aspects of social context that have been at the core of sociological analysis for decades.
11
Li, Cheng-Wei, and Bor-Sen Chen. "Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived from Next-Generation Sequencing Data." Disease Markers 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/4149608.
Full textAbstract:
Epigenetic and microRNA (miRNA) regulation are associated with carcinogenesis and the development of cancer. By using the available omics data, including those from next-generation sequencing (NGS), genome-wide methylation profiling, candidate integrated genetic and epigenetic network (IGEN) analysis, and drug response genome-wide microarray analysis, we constructed an IGEN system based on three coupling regression models that characterize protein-protein interaction networks (PPINs), gene regulatory networks (GRNs), miRNA regulatory networks (MRNs), and epigenetic regulatory networks (ERNs). By applying system identification method and principal genome-wide network projection (PGNP) to IGEN analysis, we identified the core network biomarkers to investigate bladder carcinogenic mechanisms and design multiple drug combinations for treating bladder cancer with minimal side-effects. The progression of DNA repair and cell proliferation in stage 1 bladder cancer ultimately results not only in the derepression of miR-200a and miR-200b but also in the regulation of the TNF pathway to metastasis-related genes or proteins, cell proliferation, and DNA repair in stage 4 bladder cancer. We designed a multiple drug combination comprising gefitinib, estradiol, yohimbine, and fulvestrant for treating stage 1 bladder cancer with minimal side-effects, and another multiple drug combination comprising gefitinib, estradiol, chlorpromazine, and LY294002 for treating stage 4 bladder cancer with minimal side-effects.
12
Tiuryn, Jerzy, and Ewa Szczurek. "Learning signaling networks from combinatorial perturbations by exploiting siRNA off-target effects." Bioinformatics 35, no. 14 (July 2019): i605—i614. http://dx.doi.org/10.1093/bioinformatics/btz334.
Full textAbstract:
Abstract Motivation Perturbation experiments constitute the central means to study cellular networks. Several confounding factors complicate computational modeling of signaling networks from this data. First, the technique of RNA interference (RNAi), designed and commonly used to knock-down specific genes, suffers from off-target effects. As a result, each experiment is a combinatorial perturbation of multiple genes. Second, the perturbations propagate along unknown connections in the signaling network. Once the signal is blocked by perturbation, proteins downstream of the targeted proteins also become inactivated. Finally, all perturbed network members, either directly targeted by the experiment, or by propagation in the network, contribute to the observed effect, either in a positive or negative manner. One of the key questions of computational inference of signaling networks from such data are, how many and what combinations of perturbations are required to uniquely and accurately infer the model? Results Here, we introduce an enhanced version of linear effects models (LEMs), which extends the original by accounting for both negative and positive contributions of the perturbed network proteins to the observed phenotype. We prove that the enhanced LEMs are identified from data measured under perturbations of all single, pairs and triplets of network proteins. For small networks of up to five nodes, only perturbations of single and pairs of proteins are required for identifiability. Extensive simulations demonstrate that enhanced LEMs achieve excellent accuracy of parameter estimation and network structure learning, outperforming the previous version on realistic data. LEMs applied to Bartonella henselae infection RNAi screening data identified known interactions between eight nodes of the infection network, confirming high specificity of our model and suggested one new interaction. Availability and implementation https://github.com/EwaSzczurek/LEM Supplementary information Supplementary data are available at Bioinformatics online.
13
Changalvala, Raghu, Brandon Fedoruk, and Hafiz Malik. "Radar Data Integrity Verification Using 2D QIM-Based Data Hiding." Sensors 20, no. 19 (September 27, 2020): 5530. http://dx.doi.org/10.3390/s20195530.
Full textAbstract:
The modern-day vehicle is evolved in a cyber-physical system with internal networks (controller area network (CAN), Ethernet, etc.) connecting hundreds of micro-controllers. From the traditional core vehicle functions, such as vehicle controls, infotainment, and power-train management, to the latest developments, such as advanced driver assistance systems (ADAS) and automated driving features, each one of them uses CAN as their communication network backbone. Automated driving and ADAS features rely on data transferred over the CAN network from multiple sensors mounted on the vehicle. Verifying the integrity of the sensor data is essential for the safety and security of occupants and the proper functionality of these applications. Though the CAN interface ensures reliable data transfer, it lacks basic security features, including message authentication, which makes it vulnerable to a wide array of attacks, including spoofing, replay, DoS, etc. Using traditional cryptography-based methods to verify the integrity of data transmitted over CAN interfaces is expected to increase the computational complexity, latency, and overall cost of the system. In this paper, we propose a light-weight alternative to verify the sensor data’s integrity for vehicle applications that use CAN networks for data transfers. To this end, a framework for 2-dimensional quantization index modulation (2D QIM)-based data hiding is proposed to achieve this goal. Using a typical radar sensor data transmission scenario in an autonomous vehicle application, we analyzed the performance of the proposed framework regarding detecting and localizing the sensor data tampering. The effects of embedding-induced distortion on the applications using the radar data were studied through a sensor fusion algorithm. It was observed that the proposed framework offers the much-needed data integrity verification without compromising on the quality of sensor fusion data and is implemented with low overall design complexity. This proposed framework can also be used on any physical network interface other than CAN, and it offers traceability to in-vehicle data beyond the scope of the in-vehicle applications.
14
Stöhlker, U., M. Bleher, H. Doll, H. Dombrowski, W. Harms, I. Hellmann, R. Luff, B. Prommer, S. Seifert, and F. Weiler. "THE GERMAN DOSE RATE MONITORING NETWORK AND IMPLEMENTED DATA HARMONIZATION TECHNIQUES." Radiation Protection Dosimetry 183, no. 4 (January 10, 2018): 405–17. http://dx.doi.org/10.1093/rpd/ncy154.
Full textAbstract:
Abstract Environmental radiation monitoring networks have been established in Europe and world-wide for the purpose of protecting population and environment against ionizing radiation. Some of these networks had been established during the cold war period and were improved after the Chernobyl accident in 1986. Today, the German Federal Office for Radiation Protection (BfS) operates an early warning network with roughly 1800 ambient dose equivalent rate (ADER) stations equally distributed over the German territory. The hardware and software of all network components are developed in-house allowing the continuous optimization of all relevant components. A probe characterization and quality assurance and control program are in place. Operational and technical aspects of the network and data harmonization techniques are described. The latter allows for calculating of the terrestrial and net ADER combined with uncertainties mainly from site specific effects. Harmonized data are finally used as input to the German emergency management system and the European radiological data exchange platform.
15
Triana, Ana María, Enrico Glerean, Jari Saramäki, and Onerva Korhonen. "Effects of spatial smoothing on group-level differences in functional brain networks." Network Neuroscience 4, no. 3 (January 2020): 556–74. http://dx.doi.org/10.1162/netn_a_00132.
Full textAbstract:
Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum disorder and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0–32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different. For thresholded networks, larger smoothing kernels lead to more similar networks, although this depends on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the region of interest (ROI) size, but varies with link length. The effects of spatial smoothing are diverse, nontrivial, and difficult to predict. This has important consequences: The choice of smoothing kernel affects the observed network differences.
16
DUKE, JAMES B. "Estimation of the Network Effects Model in a Large Data Set." Sociological Methods & Research 21, no. 4 (May 1993): 465–81. http://dx.doi.org/10.1177/0049124193021004003.
Full text
17
Van Leemput, Koenraad, Tim Van den Bulcke, Thomas Dhollander, Bart De Moor, Kathleen Marchal, and Piet van Remortel. "Exploring the Operational Characteristics of Inference Algorithms for Transcriptional Networks by Means of Synthetic Data." Artificial Life 14, no. 1 (January 2008): 49–63. http://dx.doi.org/10.1162/artl.2008.14.1.49.
Full textAbstract:
The development of structure-learning algorithms for gene regulatory networks depends heavily on the availability of synthetic data sets that contain both the original network and associated expression data. This article reports the application of SynTReN, an existing network generator that samples topologies from existing biological networks and uses Michaelis-Menten and Hill enzyme kinetics to simulate gene interactions. We illustrate the effects of different aspects of the expression data on the quality of the inferred network. The tested expression data parameters are network size, network topology, type and degree of noise, quantity of expression data, and interaction types between genes. This is done by applying three well-known inference algorithms to SynTReN data sets. The results show the power of synthetic data in revealing operational characteristics of inference algorithms that are unlikely to be discovered by means of biological microarray data only.
18
Tang, Xiaolan, Zhi Geng, Wenlong Chen, and Mojtaba Moharrer. "Data Dissemination Based on Fuzzy Logic and Network Coding in Vehicular Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/6834053.
Full textAbstract:
Vehicular networks, as a significant technology in intelligent transportation systems, improve the convenience, efficiency, and safety of driving in smart cities. However, because of the high velocity, the frequent topology change, and the limited bandwidth, it is difficult to efficiently propagate data in vehicular networks. This paper proposes a data dissemination scheme based on fuzzy logic and network coding for vehicular networks, named SFN. It uses fuzzy logic to compute a transmission ability for each vehicle by comprehensively considering the effects of three factors: the velocity change rate, the velocity optimization degree, and the channel quality. Then, two nodes with high abilities are selected as primary backbone and slave backbone in every road segment, which propagate data to other vehicles in this segment and forward them to the backbones in the next segment. The backbone network helps to increase the delivery ratio and avoid invalid transmissions. Additionally, network coding is utilized to reduce transmission overhead and accelerate data retransmission in interbackbone forwarding and intrasegment broadcasting. Experiments show that, compared with existing schemes, SFN has a high delivery ratio and a short dissemination delay, while the backbone network keeps high reliability.
19
Chaters, G. L., P. C. D. Johnson, S. Cleaveland, J. Crispell, W. A. de Glanville, T. Doherty, L. Matthews, et al. "Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1776 (May 20, 2019): 20180264. http://dx.doi.org/10.1098/rstb.2018.0264.
Full textAbstract:
Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ ( R 0 = 3) and ‘slow’ ( R 0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
20
Bramoullé, Yann, Habiba Djebbari, and Bernard Fortin. "Peer Effects in Networks: A Survey." Annual Review of Economics 12, no. 1 (August 2, 2020): 603–29. http://dx.doi.org/10.1146/annurev-economics-020320-033926.
Full textAbstract:
We survey the recent, fast-growing literature on peer effects in networks. An important recurring theme is that the causal identification of peer effects depends on the structure of the network itself. In the absence of correlated effects, the reflection problem is generally solved by network interactions even in nonlinear, heterogeneous models. By contrast, microfoundations are generally not identified. We discuss and assess the various approaches developed by economists to account for correlated effects and network endogeneity in particular. We classify these approaches in four broad categories: random peers, random shocks, structural endogeneity, and panel data. We review an emerging literature relaxing the assumption that the network is perfectly known. Throughout, we provide a critical reading of the existing literature and identify important gaps and directions for future research.
21
Krivchenkov, Aleksandr, and Dmitry Sedykh. "Analysis Of Packets Delay In Wireless Data Networks." Transport and Telecommunication Journal 16, no. 4 (December 1, 2015): 330–40. http://dx.doi.org/10.1515/ttj-2015-0030.
Full textAbstract:
Abstract The networks with wireless links for automation control applications traffic transmission when packets have small size and application payload is predictable are under consideration. Analytical model for packets delay on their propagation path through the network is proposed. Estimations for network architectures based on WiFi and Bluetooth wireless technologies are made. The specifications for physical layer 802.11 a/b/g/n and 802.15.1 are under consideration. Analytical and experimental results for delivered network bandwidth for different network architecture, traffic structure and wireless technologies were compared to validate that basic mechanisms are correctly taken into account in the model. It is shown that basic effects are taken into account and further accuracy “improvement” of the model will give not more than 5%. As a result that is important for automation control applications we have reliably received the lowest possible level for packets delay in one wireless link. For 802.11 it is of order of 0.2 ms, for 802.15.1 it is 1.25 ms and is true when application packet can be transferred by one data frame.
22
Rao, D. Srinivasa, and G. B. S. R. Naidu. "A Study of the Effects on QoS in WiFi-Cellular Offloading Scenarios." International Journal of Mathematical, Engineering and Management Sciences 4, no. 3 (June 1, 2019): 795–802. http://dx.doi.org/10.33889/ijmems.2019.4.3-062.
Full textAbstract:
Nowadays the mobile data usage has been significantly increased by an unprecedented amount with the wide spread of smart devices, which is known as the explosion of data traffic. The rapid growth in mobile data traffic leads to a deficiency of cellular network capacity. To solve this problem, readily available Wi-Fi networks are used to offload the data traffic from cellular networks. The Wi-Fi offloading must ensure guaranteed throughput and delay performance for the users. However, if the user doesn’t meet any Wi-Fi network during the download period, the quality of experience gets degraded. Quality of experience can be improved with the help of various techniques such as resource allocation, scheduling, and handoff schemes. To know the effect of the offloading process, some key parameters are identified in this paper and the effect of offloading on these parameters is studied. Here, in this paper a study of various parameters like download time, number of users, data size on the throughput, delay and packet loss is done in the cellular network -WiFi offloading scenarios. This study highlights the need for an efficient QoS mechanism in future heterogeneous networks. It can be considered as a research aspect in upcoming integrated networks.
23
Han, A. Reum, Dong-Soo Kahng, Tae Woo Ryu, Hyun S. Moon, Kwang Hyung Lee, and Doheon Lee. "Extraction of Developmentally Important Genes from Microarray Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (May 20, 2005): 277–81. http://dx.doi.org/10.20965/jaciii.2005.p0277.
Full textAbstract:
Using microarray data on 4,028 genes during the lifecycle of <I>Drosophila melanogaster</I>, we constructed gene expression networks for 4 developmental stages of the fruitfly: egg and early embryo, embryo, larva, and pupa. The network for each stage showed a scale-free property with 0.85 < γ < 1.85 showing one or two giant clusters and many small clusters. Since hubs are thought to bring relatively dramatic changes in a network, we assumed hubs can be effective targets of medical treatments and/or drugs. Accordingly, we analyzed hub genes with a high degree for all networks. We found many previously studied genes that play crucial roles in each stage. We also assigned the biological process of gene ontology (GO) to genes in local dense regions of a cluster centered on hubs and found several enriched functions that are the keys to understand the effects of hubs.
24
Ramanna, Karthik, and Ewa Sletten. "Network Effects in Countries' Adoption of IFRS." Accounting Review 89, no. 4 (January 1, 2014): 1517–43. http://dx.doi.org/10.2308/accr-50717.
Full textAbstract:
ABSTRACT: If the differences in accounting standards across countries reflect relatively stable institutional differences, why did several countries rapidly adopt IFRS in the 2003–2008 period? We test the hypothesis that perceived network benefits from the extant worldwide adoption of IFRS can explain part of a country's shift away from local accounting standards. We find that perceived network benefits increase the degree of IFRS harmonization among countries and that smaller countries have a differentially higher response to these benefits. Further, economic ties with the European Union are a particularly important source of network effects. The results, robust to numerous alternative hypotheses and specifications, suggest IFRS adoption was self-reinforcing during the sample period, which, in turn, has implications for the consequences of IFRS adoption. Data Availability: Most data are available from public sources identified in the text; hand-collected data are available upon request.
25
Jacobsen, Dag Ingvar. "Network Context, Trust and Success. Evidence from Regional Governance Networks in Norway." Lex localis - Journal of Local Self-Government 11, no. 4 (September 19, 2013): 851–69. http://dx.doi.org/10.4335/11.4.851-869(2013).
Full textAbstract:
While our knowledge on the form and functioning of networks increases, we have little knowledge on the effects of network context on the impact of governance networks. This study focuses on contextual elements and their effect on perceived network outcomes on three dimensions – service provision, solving “wicked” problems, and exerting external influence - controlling for trust and consensus. Data from 11 governance networks in Norway are combined with individual data on trust, consensus and outcomes. Results indicate that contextual factors have significant effects on network outcomes, particularly on the ability to solve complex problems and on external influence. Possible mechanisms are discussed, and implications for future studies of governance networks are outlined.
26
Zhang, Yuanyuan, and Marine Minier. "Selective Forwarding Attacks against Data and ACK Flows in Network Coding and Countermeasures." Journal of Computer Networks and Communications 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/184783.
Full textAbstract:
Network coding has attracted the attention of many researchers in security and cryptography. In this paper, a well-known attackselective forwarding attackwill be studied in network coding systems. While most of the works have been dedicated to the countermeasures against pollution attacks where an attacker modifies intermediate packets, only few works concern selective forwarding attacks on data or acknowledgment (ACK) packets; those last ones are required in network coding. However, selective forwarding attacks stay a real threat in resource constraint networks such as wireless sensor networks, especially when selective forwarding attacks target the acknowledgment (ACK) messages, referred to asflooding attack. In the latter model, an adversary can easily create congestion in the network and exhaust all the resources available. The degradation of the QoS (delay, energy) goes beyond the capabilities of cryptographic solutions. In this paper, we first simulate and analyze the effects of selective forwarding attacks on both data flows and ACK flows. We then investigate the security capabilities of multipath acknowledgment in more details than in our original proposal (Zhang et al., 2011).
27
Fan, Ya Qin, Yu Ding, Mei Lin Liu, and Xin Zhang. "Research on the Wireless Sensor Network Data Fusion Technology." Advanced Materials Research 756-759 (September 2013): 751–55. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.751.
Full textAbstract:
Wireless sensor network ( WSN ) is a kind of energy constrained network, by using data fusion technology, the elimination of redundant data, can save energy, prolong the network life purpose. Data fusion in wireless sensor network can realize different protocol layers, Based on the introduction of wireless sensor network and data fusion related knowledge, prove that the arithmetic mean method is effective, and use OPNET software tool for network simulation, finally, analysis results and conclude, verify effects of the arithmetic average fusion algorithm for wireless sensor network.
28
Lee, UnCheol, Markus Müller, Gyu-Jeong Noh, ByungMoon Choi, and George A. Mashour. "Dissociable Network Properties of Anesthetic State Transitions." Anesthesiology 114, no. 4 (April 1, 2011): 872–81. http://dx.doi.org/10.1097/aln.0b013e31821102c9.
Full textAbstract:
Background It is still unknown whether anesthetic state transitions are continuous or binary. Mathematical graph theory is one method by which to assess whether brain networks change gradually or abruptly upon anesthetic induction and emergence. Methods Twenty healthy males were anesthetized with an induction dose of propofol, with continuous measurement of 21-channel electroencephalogram at baseline, during anesthesia, and during recovery. From these electroencephalographic data a "genuine network" was reconstructed based on the surrogate data method. The effects of topologic structure and connection strength on information transfer through the network were measured independently across different states. Results Loss of consciousness was consistently associated with a disruption of network topology. However, recovery of consciousness was associated with complex patterns of altered connection strength after the initial topologic structure had slowly recovered. In one group of subjects, there was a precipitous increase of connection strength that was associated with reduced variability of emergence time. Analysis of regional effects on brain networks demonstrated that the parietal network was significantly disrupted, whereas the frontal network was minimally affected. Conclusions By dissociating the effects of network structure and connection strength, both continuous and discrete elements of anesthetic state transitions were identified. The study also supports a critical role of parietal networks as a target of general anesthetics.
29
Carnegie, Nicole Bohme. "Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure." Statistics in Medicine 37, no. 2 (February 13, 2017): 236–48. http://dx.doi.org/10.1002/sim.7259.
Full text
30
Josten, Michael, and Mark Trappmann. "Interviewer Effects on a Network-Size Filter Question." Journal of Official Statistics 32, no. 2 (June 1, 2016): 349–73. http://dx.doi.org/10.1515/jos-2016-0020.
Full textAbstract:
Abstract There is evidence that survey interviewers may be tempted to manipulate answers to filter questions in a way that minimizes the number of follow-up questions. This becomes relevant when ego-centered network data are collected. The reported network size has a huge impact on interview duration if multiple questions on each alter are triggered. We analyze interviewer effects on a network-size question in the mixed-mode survey “Panel Study ‘Labour Market and Social Security’” (PASS), where interviewers could skip up to 15 follow-up questions by generating small networks. Applying multilevel models, we find almost no interviewer effects in CATI mode, where interviewers are paid by the hour and frequently supervised. In CAPI, however, where interviewers are paid by case and no close supervision is possible, we find strong interviewer effects on network size. As the area-specific network size is known from telephone mode, where allocation to interviewers is random, interviewer and area effects can be separated. Furthermore, a difference-in-difference analysis reveals the negative effect of introducing the follow-up questions in Wave 3 on CAPI network size. Attempting to explain interviewer effects we neither find significant main effects of experience within a wave, nor significantly different slopes between interviewers.
31
Wang, Lei, Jia Li, and Chen Gen Wang. "Application of Data Mining Technology in Structural Strength Design." Key Engineering Materials 353-358 (September 2007): 2770–73. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.2770.
Full textAbstract:
Based on neural networks, the present paper gives an engineering application of data mining. The back propagation (BP) neural network is used as the algorithm of data mining. Then the effects of structural technologic parameters on stress in the weld region of the shield engine rotor in a submarine are analyzed. The mined data come from the numerical simulations of the finite element method. The effects of different parameters on the stress in the weld region are achieved from the results of the data mining. The discovered knowledge is beneficial to the security improvement of structural strength design for the engine rotor.
32
Zhang, Jing Wei, Sheng Bo Ye, Hai Liu, Li Yi, and Guang You Fang. "Filtering out Antenna Effects From GPR Data by an RBF Neural Network." IEEE Geoscience and Remote Sensing Letters 16, no. 9 (September 2019): 1378–82. http://dx.doi.org/10.1109/lgrs.2019.2899896.
Full text
33
Abolade, Olatilewa, Alexander Okandeji, Alice Oke, Martins Osifeko, and Ajibola Oyedeji. "Overhead effects of data encryption on TCP throughput across IPSEC secured network." Scientific African 13 (September 2021): e00855. http://dx.doi.org/10.1016/j.sciaf.2021.e00855.
Full text
34
Sinha, Meghamala, Prasad Tadepalli, and Stephen A. Ramsey. "Voting-based integration algorithm improves causal network learning from interventional and observational data: An application to cell signaling network inference." PLOS ONE 16, no. 2 (February 8, 2021): e0245776. http://dx.doi.org/10.1371/journal.pone.0245776.
Full textAbstract:
In order to increase statistical power for learning a causal network, data are often pooled from multiple observational and interventional experiments. However, if the direct effects of interventions are uncertain, multi-experiment data pooling can result in false causal discoveries. We present a new method, “Learn and Vote,” for inferring causal interactions from multi-experiment datasets. In our method, experiment-specific networks are learned from the data and then combined by weighted averaging to construct a consensus network. Through empirical studies on synthetic and real-world datasets, we found that for most of the larger-sized network datasets that we analyzed, our method is more accurate than state-of-the-art network inference approaches.
35
Wang, Shaocheng, Wei Ren, and Ubaid M. Al-Saggaf. "Effects of Switching Network Topologies on Stealthy False Data Injection Attacks Against State Estimation in Power Networks." IEEE Systems Journal 11, no. 4 (December 2017): 2640–51. http://dx.doi.org/10.1109/jsyst.2015.2494521.
Full text
36
Klaster, Esther, Celeste Wilderom, and Dennis Muntslag. "Beyond the Network Border: The Emergence of Regional “Meta-Networks” and Their Effects on Dutch Public-Policy Projects." Project Management Journal 49, no. 2 (April 2018): 42–55. http://dx.doi.org/10.1177/875697281804900203.
Full textAbstract:
Although network research typically examines whole networks, interorganizational networks are not isolated entities. This study focuses on overlapping regional networks that emerge as an unintentional result of an amalgamation of central governmental projects. We use archival, interview, and social network data and visualize the normally imperceptible meta-networks. Large and centralized meta-networks are found to stimulate goal attainment, while smaller, decentralized meta-networks have higher-quality relations. Both funders and network members who co-implement public projects should make good use of regional meta-networks. Therefore, future research on the determinants of successful decentralized projects should include the dynamics and effects of meta-networks.
37
INDLEKOFER, NATALIE, and ULRIK BRANDES. "Relative importance of effects in stochastic actor-oriented models." Network Science 1, no. 3 (December 2013): 278–304. http://dx.doi.org/10.1017/nws.2013.21.
Full textAbstract:
AbstractA measure of relative importance of network effects in the stochastic actor-oriented model (SAOM) is proposed. The SAOM is a parametric model for statistical inference in longitudinal social networks. The complexity of the model makes the interpretation of inferred results difficult. So far, the focus is on significance tests while the relative importance of effects is usually ignored. Indeed, there is no established measure to determine the relative importance of an effect in a SAOM. We introduce such a measure based on the influence effects have on decisions of individual actors in the network. We demonstrate its utility on empirical data by analyzing an evolving friendship network of university freshmen.
38
Oscar CLAVERIA, Oscar CLAVERIA, Enric MONTE, and Salvador TORRA. "DATA PRE-PROCESSING FOR NEURAL NETWORK-BASED FORECASTING: DOES IT REALLY MATTER?" Technological and Economic Development of Economy 23, no. 5 (November 4, 2015): 709–25. http://dx.doi.org/10.3846/20294913.2015.1070772.
Full textAbstract:
This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and the Elman neural networks. The structure of the networks is based on a multiple-input multiple-output (MIMO) approach. We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.
39
Luo, Yi, and Da Lin Qian. "Effects of Bus Lane Operation on Transportation Networks." Advanced Materials Research 1030-1032 (September 2014): 2019–23. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.2019.
Full textAbstract:
In this paper, we proposed a network efficiency measure for congested networks based on the user equilibrium, travel costs, demand and road resources being occupied. Compares the network efficiency on expressway before and after the bus lane operation, the results show that the exclusive bus lane stimulates demand for mass transportation, which greatly improves the efficiency of transit operation. Finally, combined with the survey data, we are clear that how to improve the level of services of public transportation and how to attract more travelers to use buses for commuting.
40
Liu, Fang Tsung, Ceweng Erh Weng, Chien Ming Huang, Chang Yan Yang, and Huang Chu Huang. "The Effects of ZigBee Position with Different Hidden Layers in Back-Propagation Neural Network." Advanced Materials Research 189-193 (February 2011): 1761–67. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.1761.
Full textAbstract:
In this paper, the research topic is that the expert experience is established by the size of the measured signal strength of wireless sensor networks and put the strength of the actual collection of historical data into the neural network model. In order to get the minimize error we use the errors to modify the weights and threshold of the neural network links. We compare the differences of hidden layer neural network and the experimental results. We set up a wireless sensor networks environment to collect the measurement values of signal strength (RSSI) and develop an indoor positioning system.
41
YU, CHANGHUA, MICHAEL T. MANRY, and JIANG LI. "EFFECTS OF NONSINGULAR PREPROCESSING ON FEEDFORWARD NETWORK TRAINING." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 02 (March 2005): 217–47. http://dx.doi.org/10.1142/s0218001405004022.
Full textAbstract:
In the neural network literature, many preprocessing techniques, such as feature de-correlation, input unbiasing and normalization, are suggested to accelerate multilayer perceptron training. In this paper, we show that a network trained with an original data set and one trained with a linear transformation of the original data will go through the same training dynamics, as long as they start from equivalent states. Thus preprocessing techniques may not be helpful and are merely equivalent to using a different weight set to initialize the network. Theoretical analyses of such preprocessing approaches are given for conjugate gradient, back propagation and the Newton method. In addition, an efficient Newton-like training algorithm is proposed for hidden layer training. Experiments on various data sets confirm the theoretical analyses and verify the improvement of the new algorithm.
42
Schneider, Henrique. "The Institutions of Privacy: Data Protection Versus Property Rights to Data." SATS 22, no. 1 (July 1, 2021): 111–29. http://dx.doi.org/10.1515/sats-2020-0004.
Full textAbstract:
Abstract This paper investigates the conceptual possibility for, and the institutions relating to a positive right of private property to data. To do so, it distinguishes between structured data, as a designator, and datapoints, which are data embedded in the timeline. The reasoning being explored here is: the agents generating datapoints – he source of the data – have a right to private property to the datapoints they generate. The agents, then, can choose to retain the datapoints or to sell them to data-users, aggregators, etc. Once these data-users render property of data themselves, they can further market it. There are, however, challenges to this view. One is the relative high cost of managing private property to data versus the relative low cost of misappropriating data and datapoints. The other is network effects: more precisely, data created or enriched in networks.
43
Xia, Hanjue, Johannes Horn, Monika J. Piotrowska, Konrad Sakowski, André Karch, Hannan Tahir, Mirjam Kretzschmar, and Rafael Mikolajczyk. "Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections." PLOS Computational Biology 17, no. 5 (May 6, 2021): e1008941. http://dx.doi.org/10.1371/journal.pcbi.1008941.
Full textAbstract:
In the year 2020, there were 105 different statutory insurance companies in Germany with heterogeneous regional coverage. Obtaining data from all insurance companies is challenging, so that it is likely that projects will have to rely on data not covering the whole population. Consequently, the study of epidemic spread in hospital referral networks using data-driven models may be biased. We studied this bias using data from three German regional insurance companies covering four federal states: AOK (historically “general local health insurance company”, but currently only the abbreviation is used) Lower Saxony (in Federal State of Lower Saxony), AOK Bavaria (in Bavaria), and AOK PLUS (in Thuringia and Saxony). To understand how incomplete data influence network characteristics and related epidemic simulations, we created sampled datasets by randomly dropping a proportion of patients from the full datasets and replacing them with random copies of the remaining patients to obtain scale-up datasets to the original size. For the sampled and scale-up datasets, we calculated several commonly used network measures, and compared them to those derived from the original data. We found that the network measures (degree, strength and closeness) were rather sensitive to incompleteness. Infection prevalence as an outcome from the applied susceptible-infectious-susceptible (SIS) model was fairly robust against incompleteness. At incompleteness levels as high as 90% of the original datasets the prevalence estimation bias was below 5% in scale-up datasets. Consequently, a coverage as low as 10% of the local population of the federal state population was sufficient to maintain the relative bias in prevalence below 10% for a wide range of transmission parameters as encountered in clinical settings. Our findings are reassuring that despite incomplete coverage of the population, German health insurance data can be used to study effects of patient traffic between institutions on the spread of pathogens within healthcare networks.
44
Lehr, Christian, and Gunnar Lischeid. "Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors." Hydrology and Earth System Sciences 24, no. 2 (February 3, 2020): 501–13. http://dx.doi.org/10.5194/hess-24-501-2020.
Full textAbstract:
Abstract. Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected “normal” behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the “stable” principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.
45
Cheung, Ethan Siu Leung, and Kedong Ding. "EFFECTS OF SOCIAL SUPPORT NETWORK ON AMERICAN OLDER ADULTS’ MEMORY FUNCTIONS." Innovation in Aging 3, Supplement_1 (November 2019): S929—S930. http://dx.doi.org/10.1093/geroni/igz038.3383.
Full textAbstract:
Abstract Background: Previous studies have found older adults’ cognitive functions are strongly associated with their social networks, including memory. Yet, few studies have explored the influences of specific social network members, such as siblings and children. Further, little studies examined the impact of the size of older adults’ social networks. Hence, this study aimed to investigate how older adults’ relationships with their spouses, siblings, and children, as well as the size of their social networks, affect American older adults’ memory functions. Methods: Using the 2018 data from NHATS, 5547 samples were included. We adopted a multiple logistic regression model to test the impact of social support network sizes, and how associations of social support networks varied between spouses, siblings, and children. All models were calibrated for age, gender, education, income, and race/ethnicity. Results: Analysis showed that higher socioeconomic status (more education and without Medicaid), being female, and younger age were associated with increased odds of having good self-rated memory functions. Older adults with larger social support networks (>=3 individuals) were more likely to have better self-rated memory function (adjusted odds ratio, 1.182, p<0.05), while holding other variables. Having a spouse also increased odds of higher self-rating memory function, in contrast to having children. Conclusion: This study highlighted the importance of having a larger social network size for older adult’s memory function and indicated the necessity of developing intervention programs to expand older adults' social network size, especially for those with lower socioeconomic status.
46
Pasquini, Lorenzo, Fernanda Palhano-Fontes, and Draulio B. Araujo. "Subacute effects of the psychedelic ayahuasca on the salience and default mode networks." Journal of Psychopharmacology 34, no. 6 (April 7, 2020): 623–35. http://dx.doi.org/10.1177/0269881120909409.
Full textAbstract:
Background: Neuroimaging studies have just begun to explore the acute effects of psychedelics on large-scale brain networks’ functional organization. Even less is known about the neural correlates of subacute effects taking place days after the psychedelic experience. This study explores the subacute changes of primary sensory brain networks and networks supporting higher-order affective and self-referential functions 24 hours after a single session with the psychedelic ayahuasca. Methods: We leveraged task-free functional magnetic resonance imaging data 1 day before and 1 day after a randomized placebo-controlled trial exploring the effects of ayahuasca in naïve healthy participants (21 placebo/22 ayahuasca). We derived intra- and inter-network functional connectivity of the salience, default mode, visual, and sensorimotor networks, and assessed post-session connectivity changes between the ayahuasca and placebo groups. Connectivity changes were associated with Hallucinogen Rating Scale scores assessed during the acute effects. Results: Our findings revealed increased anterior cingulate cortex connectivity within the salience network, decreased posterior cingulate cortex connectivity within the default mode network, and increased connectivity between the salience and default mode networks 1 day after the session in the ayahuasca group compared to placebo. Connectivity of primary sensory networks did not differ between groups. Salience network connectivity increases correlated with altered somesthesia scores, decreased default mode network connectivity correlated with altered volition scores, and increased salience default mode network connectivity correlated with altered affect scores. Conclusion: These findings provide preliminary evidence for subacute functional changes induced by the psychedelic ayahuasca on higher-order cognitive brain networks that support interoceptive, affective, and self-referential functions.
47
Meryet-Figuiere, Matthieu, Mégane Vernon, Mamy Andrianteranagna, Bernard Lambert, Célia Brochen, Jean-Paul Issartel, Audrey Guttin, et al. "Network-Based Integration of Multi-Omics Data Identifies the Determinants of miR-491-5p Effects." Cancers 13, no. 16 (August 5, 2021): 3970. http://dx.doi.org/10.3390/cancers13163970.
Full textAbstract:
The identification of miRNAs’ targets and associated regulatory networks might allow the definition of new strategies using drugs whose association mimics a given miRNA’s effects. Based on this assumption we devised a multi-omics approach to precisely characterize miRNAs’ effects. We combined miR-491-5p target affinity purification, RNA microarray, and mass spectrometry to perform an integrated analysis in ovarian cancer cell lines. We thus constructed an interaction network that highlighted highly connected hubs being either direct or indirect targets of miR-491-5p effects: the already known EGFR and BCL2L1 but also EP300, CTNNB1 and several small-GTPases. By using different combinations of specific inhibitors of these hubs, we could greatly enhance their respective cytotoxicity and mimic the miR-491-5p-induced phenotype. Our methodology thus constitutes an interesting strategy to comprehensively study the effects of a given miRNA. Moreover, we identified targets for which pharmacological inhibitors are already available for a clinical use or in clinical trials. This study might thus enable innovative therapeutic options for ovarian cancer, which remains the leading cause of death from gynecological malignancies in developed countries.
48
Neyland, Blake R., Christina E. Hugenschmidt, Robert G. Lyday, Jonathan H. Burdette, Laura D. Baker, W. Jack Rejeski, Michael E. Miller, Stephen B. Kritchevsky, and Paul J. Laurienti. "Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study." Brain Sciences 11, no. 1 (January 17, 2021): 118. http://dx.doi.org/10.3390/brainsci11010118.
Full textAbstract:
Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.
49
Remuzzi, A., and W. M. Deen. "Theoretical effects of network structure on glomerular filtration of macromolecules." American Journal of Physiology-Renal Physiology 257, no. 1 (July 1, 1989): F152—F158. http://dx.doi.org/10.1152/ajprenal.1989.257.1.f152.
Full textAbstract:
A parallel network model was developed to examine the effects of a distribution of capillary lengths on the filtration of macromolecules by the glomerulus. When networks having different distributions of capillary lengths (but similar values of single nephron glomerular filtration rate) were compared, the filtrate-to-plasma concentration ratio (theta s) for neutral macromolecules was found to increase as the vessel lengths became less uniform. Because anatomical studies have demonstrated that the glomerulus is in fact a heterogeneous network, this implies that the conventional modeling assumption of identical capillaries in parallel leads to an overestimation of effective pore sizes. However, simulations employing various pore-size distributions demonstrated that the expected errors in estimating membrane-pore parameters are generally negligible. Furthermore, the dependence of theta s on hemodynamic inputs such as glomerular plasma flow rate and transmembrane hydraulic pressure difference was insensitive to the assumed distribution of capillary lengths. We conclude that models based on dimensionally uniform capillary networks remain valid for interpreting clearance data for macromolecules.
50
Cardner, Mathias, Nathalie Meyer-Schaller, Gerhard Christofori, and Niko Beerenwinkel. "Inferring signalling dynamics by integrating interventional with observational data." Bioinformatics 35, no. 14 (July 2019): i577—i585. http://dx.doi.org/10.1093/bioinformatics/btz325.
Full textAbstract:
Abstract Motivation In order to infer a cell signalling network, we generally need interventional data from perturbation experiments. If the perturbation experiments are time-resolved, then signal progression through the network can be inferred. However, such designs are infeasible for large signalling networks, where it is more common to have steady-state perturbation data on the one hand, and a non-interventional time series on the other. Such was the design in a recent experiment investigating the coordination of epithelial–mesenchymal transition (EMT) in murine mammary gland cells. We aimed to infer the underlying signalling network of transcription factors and microRNAs coordinating EMT, as well as the signal progression during EMT. Results In the context of nested effects models, we developed a method for integrating perturbation data with a non-interventional time series. We applied the model to RNA sequencing data obtained from an EMT experiment. Part of the network inferred from RNA interference was validated experimentally using luciferase reporter assays. Our model extension is formulated as an integer linear programme, which can be solved efficiently using heuristic algorithms. This extension allowed us to infer the signal progression through the network during an EMT time course, and thereby assess when each regulator is necessary for EMT to advance. Availability and implementation R package at https://github.com/cbg-ethz/timeseriesNEM. The RNA sequencing data and microscopy images can be explored through a Shiny app at https://emt.bsse.ethz.ch. Supplementary information Supplementary data are available at Bioinformatics online.