scholarly journals Dose–response functions and surrogate models for exploring social contagion in the Copenhagen Networks Study

2021 ◽  
Author(s):  
Jonathan F. Donges ◽  
Jakob H. Lochner ◽  
Niklas H. Kitzmann ◽  
Jobst Heitzig ◽  
Sune Lehmann ◽  
...  
Keyword(s):  
Dose Response ◽  
Surrogate Data ◽  
Data Models ◽  
Network Data ◽  
Temporal Network ◽  
Response Functions ◽  
Data Set ◽  
Significant Evidence ◽  
Spreading Dynamics ◽  
Spreading Processes

AbstractSpreading dynamics and complex contagion processes on networks are important mechanisms underlying the emergence of critical transitions, tipping points and other non-linear phenomena in complex human and natural systems. Increasing amounts of temporal network data are now becoming available to study such spreading processes of behaviours, opinions, ideas, diseases and innovations to test hypotheses regarding their specific properties. To this end, we here present a methodology based on dose–response functions and hypothesis testing using surrogate data models that randomise most aspects of the empirical data while conserving certain structures relevant to contagion, group or homophily dynamics. We demonstrate this methodology for synthetic temporal network data of spreading processes generated by the adaptive voter model. Furthermore, we apply it to empirical temporal network data from the Copenhagen Networks Study. This data set provides a physically-close-contact network between several hundreds of university students participating in the study over the course of 3 months. We study the potential spreading dynamics of the health-related behaviour “regularly going to the fitness studio” on this network. Based on a hierarchy of surrogate data models, we find that our method neither provides significant evidence for an influence of a dose–response-type network spreading process in this data set, nor significant evidence for homophily. The empirical dynamics in exercise behaviour are likely better described by individual features such as the disposition towards the behaviour, and the persistence to maintain it, as well as external influences affecting the whole group, and the non-trivial network structure. The proposed methodology is generic and promising also for applications to other temporal network data sets and traits of interest.

scholarly journals From temporal network data to the dynamics of social relationships

2021 ◽  
Author(s):  
Valeria Gelardi ◽  
Alain Barrat ◽  
Nicolas Claidière
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Time Windows ◽  
Social Systems ◽  
Network Evolution ◽  
Temporal Organization ◽  
Network Data ◽  
Temporal Network ◽  
Data Set ◽  
Wide Range

Networks are well-established representations of social systems, and temporal networks are widely used to study their dynamics. Temporal network data often consist in a succession of static networks over consecutive time windows whose length, however, is arbitrary, not necessarily corresponding to any intrinsic timescale of the system. Moreover, the resulting view of social network evolution is unsatisfactory: short time windows contain little information, whereas aggregating over large time windows blurs the dynamics. Going from a temporal network to a meaningful evolving representation of a social network therefore remains a challenge. Here we introduce a framework to that purpose: transforming temporal network data into an evolving weighted network where the weights of the links between individuals are updated at every interaction. Most importantly, this transformation takes into account the interdependence of social relationships due to the finite attention capacities of individuals: each interaction between two individuals not only reinforces their mutual relationship but also weakens their relationships with others. We study a concrete example of such a transformation and apply it to several data sets of social interactions. Using temporal contact data collected in schools, we show how our framework highlights specificities in their structure and temporal organization. We then introduce a synthetic perturbation into a data set of interactions in a group of baboons to show that it is possible to detect a perturbation in a social group on a wide range of timescales and parameters. Our framework brings new perspectives to the analysis of temporal social networks.

scholarly journals Development of dose-response functions for historic paper degradation using exposure to natural conditions and multivariate regression

2019 ◽  
Vol 168 ◽  
pp. 108944 ◽  
Author(s):  
Gianluca Pastorelli ◽  
Shuo Cao ◽  
Irena Kralj Cigić ◽  
Costanza Cucci ◽  
Abdelrazek Elnaggar ◽  
...  
Keyword(s):  
Dose Response ◽  
Multivariate Regression ◽  
Response Functions ◽  
Natural Conditions ◽  
Paper Degradation ◽  
Historic Paper

scholarly journals The Murrumbidgee soil moisture monitoring network data set

2012 ◽  
Vol 48 (7) ◽  
Author(s):  
A. B. Smith ◽  
J. P. Walker ◽  
A. W. Western ◽  
R. I. Young ◽  
K. M. Ellett ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Monitoring Network ◽  
Network Data ◽  
Data Set ◽  
Soil Moisture Monitoring

Magnetic field influences on the lateral dose response functions of photon-beam detectors: MC study of wall-less water-filled detectors with various densities

2017 ◽  
Vol 62 (12) ◽  
pp. 5131-5148 ◽  
Author(s):  
Hui Khee Looe ◽  
Björn Delfs ◽  
Daniela Poppinga ◽  
Dietrich Harder ◽  
Björn Poppe
Keyword(s):  
Magnetic Field ◽  
Dose Response ◽  
Photon Beam ◽  
Response Functions

Testing for Trends in Dose-Response Microarray Experiments: A Comparison of Several Testing Procedures, Multiplicity and Resampling-Based Inference

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Dan Lin ◽  
Ziv Shkedy ◽  
Dani Yekutieli ◽  
Tomasz Burzykowski ◽  
Hinrich W.H. Göhlmann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dose Response ◽  
Multiple Testing ◽  
Pharmaceutical Research ◽  
Ratio Test ◽  
Familywise Error Rate ◽  
Response Level ◽  
Data Set ◽  
Monotonic Trend ◽  
Testing Procedures

Dose-response studies are commonly used in experiments in pharmaceutical research in order to investigate the dependence of the response on dose, i.e., a trend of the response level toxicity with respect to dose. In this paper we focus on dose-response experiments within a microarray setting in which several microarrays are available for a sequence of increasing dose levels. A gene is called differentially expressed if there is a monotonic trend (with respect to dose) in the gene expression. We review several testing procedures which can be used in order to test equality among the gene expression means against ordered alternatives with respect to dose, namely Williams' (Williams 1971 and 1972), Marcus' (Marcus 1976), global likelihood ratio test (Bartholomew 1961, Barlow et al. 1972, and Robertson et al. 1988), and M (Hu et al. 2005) statistics. Additionally we introduce a modification to the standard error of the M statistic. We compare the performance of these five test statistics. Moreover, we discuss the issue of one-sided versus two-sided testing procedures. False Discovery Rate (Benjamni and Hochberg 1995, Ge et al. 2003), and resampling-based Familywise Error Rate (Westfall and Young 1993) are used to handle the multiple testing issue. The methods above are applied to a data set with 4 doses (3 arrays per dose) and 16,998 genes. Results on the number of significant genes from each statistic are discussed. A simulation study is conducted to investigate the power of each statistic. A R library IsoGene implementing the methods is available from the first author.

Creation Method of Priori Neural Network Data Set for Processing Digital Aerial Photographs in Automatic Mode

2021 ◽  
Author(s):  
Serhiy Kovbasiuk ◽  
Leonid Kanevskyy ◽  
Sergiy Chernyshuk ◽  
Leonid Naumchak ◽  
Mykola Romanchuk
Keyword(s):  
Neural Network ◽  
Aerial Photographs ◽  
Network Data ◽  
Data Set ◽  
Automatic Mode

scholarly journals Pharmacological curve fitting to analyze cutaneous adrenergic responses

2011 ◽  
Vol 111 (6) ◽  
pp. 1703-1709 ◽  
Author(s):  
Megan M. Wenner ◽  
Thad E. Wilson ◽  
Scott L. Davis ◽  
Nina S. Stachenfeld
Keyword(s):  
Dose Response ◽  
Curve Fitting ◽  
Repeated Measures ◽  
Nonlinear Modeling ◽  
Response Curves ◽  
Data Set ◽  
Sigmoidal Model ◽  
Dose Response Curves ◽  
Cutaneous Vasoconstriction

Although dose-response curves are commonly used to describe in vivo cutaneous α-adrenergic responses, modeling parameters and analyses methods are not consistent across studies. The goal of the present investigation was to compare three analysis methods for in vivo cutaneous vasoconstriction studies using one reference data set. Eight women (22 ± 1 yr, 24 ± 1 kg/m2) were instrumented with three cutaneous microdialysis probes for progressive norepinephrine (NE) infusions (1 × 10−8, 1 × 10−6, 1 × 10−5, 1 × 10−4, and 1 × 10−3 logM). NE was infused alone, co-infused with NG-monomethyl-l-arginine (l-NMMA, 10 mM) or Ketorolac tromethamine (KETO, 10 mM). For each probe, dose-response curves were generated using three commonly reported analyses methods: 1) nonlinear modeling without data manipulation, 2) nonlinear modeling with data normalization and constraints, and 3) percent change from baseline without modeling. Not all data conformed to sigmoidal dose-response curves using analysis 1, whereas all subjects' curves were modeled using analysis 2. When analyzing only curves that fit the sigmoidal model, NE + KETO induced a leftward shift in ED50 compared with NE alone with analyses 1 and 2 ( F test, P < 0.05) but only tended to shift the response leftward with analysis 3 (repeated-measures ANOVA, P = 0.08). Neither maximal vasoconstrictor capacity (Emax) in analysis 1 nor %change CVC change from baseline in analysis 3 were altered by blocking agents. In conclusion, although the overall detection of curve shifts and interpretation was similar between the two modeling methods of curve fitting, analysis 2 produced more sigmoidal curves.

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