Dynamic Interaction Patterns Research Articles

Methods for Quantifying Patterns of Dynamic Interactions in Dyads.

Individuals in both clinical and research settings increasingly provide data across numerous time points. Examples include measurements collected from wearable technology (e.g., accelerometers), psychophysiological measures, coded observations, social media behaviors, and daily diary data. When numerous observations are available for each individual, the data fall under the class of time series data and can be examined from within a dynamic systems perspective. We provide a broad overview of current analytic methods for quantifying relations among dyads using this data type. The techniques include those from within a linear modeling framework, approaches that include a measurement model, and methods for examining cyclical relations. We also discuss some special topics, such as methods that allow for models to shift across time and that accommodate heterogeneity across individuals. Finally, methods that account for similar shapes of nonlinear curves across time are described. From this breadth of options, we hope to help guide practitioners, clinicians, and researchers in choosing the optimal method for their data and line of questions. To further aid in this choice, we indicate programs available for each technique. Example dyads presented here range from mother-infant, patient-caretaker, and husband-wife; however, the analytic methods can be applied to any type of dyad.

A Functional Connectivity Analysis Toolbox for Multiple Spike Trains Data: �ToolConnect�

A Functional Connectivity Analysis Toolbox for Multiple Spike Trains Data: �ToolConnect�

Diet-Dependent Modular Dynamic Interactions of the Equine Cecal Microbiota.

Knowledge on dynamic interactions in microbiota is pivotal for understanding the role of bacteria in the gut. We herein present comprehensive dynamic models of the horse cecal microbiota, which include short-chained fatty acids, carbohydrate metabolic networks, and taxonomy. Dynamic models were derived from time-series data in a crossover experiment in which four cecum-cannulated horses were fed a starch-rich diet of hay supplemented with barley (starch intake 2 g kg−1 body weight per day) and a fiber-rich diet of only hay. Cecal contents were sampled via the cannula each h for 24 h for both diets. We observed marked differences in the microbial dynamic interaction patterns for Fibrobacter succinogenes, Lachnospiraceae, Streptococcus, Treponema, Anaerostipes, and Anaerovibrio between the two diet groups. Fluctuations and microbiota interactions were the most pronounced for the starch rich diet, with Streptococcus spp. and Anaerovibrio spp. showing the largest fluctuations. Shotgun metagenome sequencing revealed that diet differences may be explained by modular switches in metabolic cross-feeding between microbial consortia in which fermentation is linked to sugar alcohols and amino sugars for the starch-rich diet and monosaccharides for the fiber-rich diet. In conclusion, diet may not only affect the composition of the cecal microbiota, but also dynamic interactions and metabolic cross-feeding.

Network-Level Connectivity Dynamics of Movie Watching in 6-Year-Old Children.

Better understanding of the developing brain’s functional mechanisms is critical for improving diagnosis and treatment of different developmental disorders. Particularly, characterizing how the developing brain dynamically reorganizes during different cognitive states may offer novel insight into the neuronal mechanisms of cognitive deficits. Imaging the brain during naturalistic conditions, like movie watching, provides a highly practical way to study young children’s developing functional brain systems. In this study we compared the network-level functional organization of 6-year-old children while they were at rest with their functional connectivity as they watched short video clips. We employed both a data-driven independent component analysis (ICA) approach and a hypothesis-driven seed-based analysis to identify changes in network-level functional interactions during the shift from resting to video watching. Our ICA results showed that naturally watching a movie elicits significant changes in the functional connectivity between the visual system and the dorsal attention network when compared to rest (t(32) = 5.02, p = 0.0001). More interestingly, children showed an immature, but qualitatively adult-like, pattern of reorganization among three of the brain’s higher-order networks (frontal control, default-mode and dorsal attention). For both ICA and seed-based approaches, we observed a decrease in the frontal network’s correlation with the dorsal attention network (ICA: t(32) = −2.46, p = 0.02; Seed-based: t(32) = −1.62, p =0.12) and an increase in its connectivity with the default mode network (ICA: t(32) = 2.84, p = 0.008; Seed-based: t(32) = 2.28, p =0.03), which is highly consistent with the pattern observed in adults. These results offer improved understanding of the developing brain’s dynamic network-level interaction patterns during the transition between different brain states and call for further studies to examine potential alterations to such dynamic patterns in different developmental disorders.

Atomic dynamic functional interaction patterns for characterization of ADHD.

Modeling abnormal temporal dynamics of functional interactions in psychiatric disorders has been of great interest in the neuroimaging field, and thus a variety of methods have been proposed so far. However, the temporal dynamics and disease-related abnormalities of functional interactions within specific data-driven discovered subnetworks have been rarely explored yet. In this work, we propose a novel computational framework composed of an effective Bayesian connectivity change point model for modeling functional brain interactions and their dynamics simultaneously and an effective variant of nonnegative matrix factorization for assessing the functional interaction abnormalities within subnetworks. This framework has been applied on the resting state fmagnetic resonance imaging (fMRI) datasets of 23 children with attention-deficit/hyperactivity disorder (ADHD) and 45 normal control (NC) children, and has revealed two atomic functional interaction patterns (AFIPs) discovered for ADHD and another two AFIPs derived for NC. Together, these four AFIPs could be grouped into two pairs, one common pair representing the common AFIPs in ADHD and NC, and the other abnormal pair representing the abnormal AFIPs in ADHD. Interestingly, by comparing the abnormal AFIP pair, two data-driven abnormal functional subnetworks are derived. Strikingly, by evaluating the approximation based on the four AFIPs, all of the ADHD children were successfully differentiated from NCs without any false positive.

A critical examination of indices of dynamic interaction for wildlife telemetry studies

Wildlife scientists continue to be interested in studying ways to quantify how the movements of animals are interdependent - dynamic interaction. While a number of applied studies of dynamic interaction exist, little is known about the comparative effectiveness and applicability of available methods used for quantifying interactions between animals. We highlight the formulation, implementation and interpretation of a suite of eight currently available indices of dynamic interaction. Point- and path-based approaches are contrasted to demonstrate differences between methods and underlying assumptions on telemetry data. Correlated and biased correlated random walks were simulated at a range of sampling resolutions to generate scenarios with dynamic interaction present and absent. We evaluate the effectiveness of each index at identifying different types of interactive behaviour at each sampling resolution. Each index is then applied to an empirical telemetry data set of three white-tailed deer (Odocoileus virginianus) dyads. Results from the simulated data show that three indices of dynamic interaction reliant on statistical testing procedures are susceptible to Type I error, which increases at fine sampling resolutions. In the white-tailed deer examples, a recently developed index for quantifying local-level cohesive movement behaviour (the di index) provides revealing information on the presence of infrequent and varying interactions in space and time. Point-based approaches implemented with finely sampled telemetry data overestimate the presence of interactions (Type I errors). Indices producing only a single global statistic (7 of the 8 indices) are unable to quantify infrequent and varying interactions through time. The quantification of infrequent and variable interactive behaviour has important implications for the spread of disease and the prevalence of social behaviour in wildlife. Guidelines are presented to inform researchers wishing to study dynamic interaction patterns in their own telemetry data sets. Finally, we make our code openly available, in the statistical software R, for computing each index of dynamic interaction presented herein.

Sparse time series chain graphical models for reconstructing genetic networks

We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of contemporaneous and dynamic interactions by efficiently combining Gaussian graphical models and Bayesian dynamic networks. We use penalized likelihood inference with a smoothly clipped absolute deviation penalty to explore the relationships among the observed time course gene expressions. The method is illustrated on simulated data and on real data examples from Arabidopsis thaliana and mammary gland time course microarray gene expressions.

A capability-based security approach to manage access control in the Internet of Things

Resource and information protection plays a relevant role in distributed systems like the ones present in the Internet of Things (IoT). Authorization frameworks like RBAC and ABAC do not provide scalable, manageable, effective, and efficient mechanisms to support distributed systems with many interacting services and are not able to effectively support the dynamicity and scaling needs of IoT contexts that envisage a potentially unbound number of sensors, actuators and related resources, services and subjects, as well as a more relevance of short-lived, unplanned and dynamic interaction patterns. Furthermore, as more end-users start using smart devices (e.g. smart phones, smart home appliances, etc.) the need to have more scalable, manageable, understandable and easy to use access control mechanisms increases. This paper describes a capability based access control system that enterprises, or even individuals, can use to manage their own access control processes to services and information. The proposed mechanism supports rights delegation and a more sophisticated access control customization. The proposed approach is being developed within the European FP7 IoT@Work project to manage access control to some of the project’s services deployed in the shop floor.

The plate contact geometry investigation based on earthquake source parameters at the Burma arc subduction zone

Accurately characterizing the three-dimensional geometric contacts between the crust of the Chinese mainland and adjacent regions is important for understanding the dynamics of this part of Asia from the viewpoint of global plate systems. In this paper, a method is introduced to investigate the geometric contacts between the Eurasian and Indian plates at the Burma arc subduction zone using earthquake source parameters based on the Slab1.0 model of Hayes et al. (2009, 2010). The distribution of earthquake focus depths positioned in 166 sections along the Burma Arc subduction zone boundary has been investigated. Linear plane fitting and curved surface fitting has been performed on each section. Three-dimensional geometric contacts and the extent of subduction are defined quantitatively. Finally, the focal depth distribution is outlined for six typical sections along the Burma arc subduction zone, combining focal mechanisms with background knowledge of geologic structure. Possible dynamic interaction patterns are presented and discussed. This paper provides an elementary method for studying the geometric contact of the Chinese mainland crust with adjacent plates and serves as a global reference for dynamic interactions between plates and related geodynamic investigations.

DYNAMIC INTERACTION NETWORKS VERSUS LOCAL TREND MODELS FOR MULTIPLE TIME-SERIES PREDICTION

Time-series modeling and prediction have been very well researched by both the statistical and data mining communities. However, the multiple time-series problem of modeling and predicting simultaneous movements of a collection of time-sensitive variables that are related to each other has received much less attention. Strong relationships between variables suggest that trajectories of given variables involved in the relationships can be improved by including the nature and strength of these relationships in a prediction model. The key challenge is to capture the dynamics of the relationships to reflect changes that take place continuously over time. This research presents a global model to capture inclusive patterns of dynamic interactions between multiple time-series and a local trend model to extract localized profiles of relationships and recurring trends in multiple time-series. Our experimentation revealed that the global and local models specially developed for multiple time-series prediction outperformed methods such as multiple linear regression and the multilayer perceptron that were developed for predicting single time-series.

Dynamics of RpaB–promoter interaction during high light stress, revealed by chromatin immunoprecipitation (ChIP) analysis in Synechococcus elongatus PCC 7942

In cyanobacteria, a series of genes are induced by, and cause tolerance to, high light stress conditions. Some of these genes share a short, repeated sequence motif known as a high light regulatory 1 (HLR1) element in their promoter regions. Previously, RpaB, a two-component response regulator, was shown to interact with the HLR1 element of several high light-responsive promoters in vitro. However, how RpaB regulates target promoters in vivo remained elusive. In this study, we analyzed the role of RpaB in transcriptional regulation of high light-responsive genes by chromatin immunoprecipitation (ChIP) analysis, which has been recently developed and utilized to study in vivo interactions between DNA-binding proteins and the relevant target DNA. One of the advantages of this method is the ability to detect dynamic interaction patterns in response to various growth and/or environmental conditions instantaneously at the time of the analysis. Here we examined the binding patterns of RpaB under various light conditions using ChIP assays. We found that strong interactions of RpaB with target promoters were weakened in a high light-dependent manner, and that the lower binding level of RpaB continued as long as the high light conditions were maintained. Thus, in regulation of high light-inducible genes, we suggest that RpaB functions as a repressor under normal light conditions, and that high light conditions result in release of the repression.

Mapping Dynamic Interaction Patterns in Work Groups

The purpose of this exploratory study is to visually map the dynamics of work groups using a method that emerged from chaos/complexity research. The verbal statements of members of 16 work groups were categorized using the Group Development Observation System. A Wavelet Transform Test was used to produce visual representations of the dynamic interaction patterns in each group's data string. Overall, the patterns that emerged in all 16 groups were similar in shape but different with regard to color, overall tone, and visual clarity. The groups were divided into three tiers based on these visual differences. Significant differences were noted among the tiers. The results support the concept that groups are complex adaptive systems and that the generated images provide a broader perspective of these systems, which may help researchers to explore human groups in more depth.

Coupled nucleotide covariations reveal dynamic RNA interaction patterns.

Evolutionarily conserved structures in related RNA molecules contain coordinated variations (covariations) of paired nucleotides. Analysis of covariations is a very powerful approach to deduce phylogenetically conserved (i.e., functional) conformations, including tertiary interactions. Here we discuss conserved RNA folding pathways that are revealed by covariation patterns. In such pathways, structural requirements for alternative pairings cause some nucleotides to covary with two different partners. Such "coupled" covariations between three or more nucleotides were found in various types of RNAs. The analysis of coupled covariations can unravel important features of RNA folding dynamics and improve phylogeny reconstruction in some cases. Importantly, it is necessary to distinguish between multiple covariations determined by mutually exclusive structures and those determined by tertiary contacts.

Alternative Time Patterns of Decisions and Dynamic Strategic Interactions

Alternative Time Patterns of Decisions and Dynamic Strategic Interactions

Alternative Time Patterns of Decisions and Dynamic Strategic Interactions

This paper offers a simple approach to study steady-state Markov perfect equilibria arising in dynamic games when players (are obliged to) commit their actions for several periods. It highlights the importance of spillovers and dynamic strategic interactions when assessing the various time patterns conceivable in such games. The obtained results are then applied to various, either micro-or macroeconomic, issues where time patterns of decisions matter.

The GKS Input Model in Manifold

This paper describes the specification of the GKS input model in Manifold. The aim of the work reported in this paper was two‐fold: first, to review the communication patterns implied by the GKS input model, and second, to evaluate the suitability of the Manifold language as a tool for defining complex dynamic interaction patterns that are common in non‐trivial user interfaces.The GKS input model is also adopted by all more recent ISO graphics standard documents. A more formal scrutiny of the inter‐communication of the components of this model, excluding the implementation details of their functionality, is instructive in itself. It can reveal directions for improvement of its shortcomings and for generalization of its strengths for the ongoing effort to define the functionality of future graphics packages.Manifold is a language for describing inter‐process communications. Processes in Manifold communicate by means of buffered communication links called streams and by reacting to events raised asynchronously by other processes. Our experience shows that Manifold is a promising tool for describing systems of cooperating parallel processes. Our Manifold specification of the GKS input model offers a very flexible way to structure user defined logical input devices. Furthermore, it is simple and modular enough to allow easy extensions to include more functionality by local modifications. As such, it can serve as a basis for possible extensions and enhancements envisioned for future graphics packages. 1987 CR Categories: C.1.2, C.1.3, C.2.m, D.1.3, F.1.2, I.1.3, I.3.6, I.3.4. 1885 Mathematical Subject Classification: 68N99, 68Q10,68U05.

Dynamic distributed systems

AbstractThis paper introduces a new methodology for building flexible and programmable multiprocessor systems. It is based on a distributed implementation of monitors which is supported by a system‐wide address relocation mechanism. This provides the dynamic reallocation of processes and monitors within the system.Methods of implementing the communications subsystem based on a number of common network architectures are presented.An algorithm which effects dynamic resource allocation is presented. Its effect is that groups of processes which are interacting through a monitor will tend to accumulate at various sites in the system in a manner reflecting the dynamic interaction patterns of the program. This is accomplished without there being any centralized control, or even knowledge of the overall program interactions.The feasibility of the system has been investigated using a model system running on a loosely connected dual processor system.