Traffic congestion on a road segment typically begins as a small-scale spatiotemporal event that can then propagate throughout a road network and produce large-scale disruptions to a transportation system. In current techniques for the analysis of network flow, data is often aggregated to relatively large (e.g. 5 min) discrete time steps that obscure the small-scale spatiotemporal interactions that drive larger-scale dynamics. We propose a new method that handles fine-grained data to better capture those dynamics. Propagation patterns of traffic congestion are represented as spatiotemporally connected events. Each event is captured as a time series at the temporal resolution of the available trajectory data and at the spatial resolution of the network edge. The spatiotemporal propagation patterns of traffic congestion are captured using Dynamic Time Warping and represented as a set of directed acyclic graphs of spatiotemporal events. Results from this method are compared to an existing method using fine-grained data derived from an agent-based model of traffic simulation. Our method outperforms the existing method. Our method also successfully detects congestion propagation patterns that were reported by media news using sparse real-world data derived from taxis.

Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer's disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

Complexity of cell-type composition has created much skepticism surrounding the interpretation of bulk tissue transcriptomic studies. Recent studies have shown that deconvolution algorithms can be applied to computationally estimate cell-type proportions from gene expression data of bulk blood samples, but their performance when applied to brain tissue is unclear. Here, we have generated an immunohistochemistry (IHC) dataset for five major cell-types from brain tissue of 70 individuals, who also have bulk cortical gene expression data. With the IHC data as the benchmark, this resource enables quantitative assessment of deconvolution algorithms for brain tissue. We apply existing deconvolution algorithms to brain tissue by using marker sets derived from human brain single cell and cell-sorted RNA-seq data. We show that these algorithms can indeed produce informative estimates of constituent cell-type proportions. In fact, neuronal subpopulations can also be estimated from bulk brain tissue samples. Further, we show that including the cell-type proportion estimates as confounding factors is important for reducing false associations between Alzheimer's disease phenotypes and gene expression. Lastly, we demonstrate that using more accurate marker sets can substantially improve statistical power in detecting cell-type specific expression quantitative trait loci (eQTLs).

The computational approach of agent-based models (ABMs) supports the representation of interactions among spatially situated individuals as a decentralized process giving rise to space–time complexity in geographic systems. To cope with the computational complexity of these models, this article proposes a parallel approach that leverages the power of multicore systems, as these architectures have quickly become ubiquitous in high-performance and desktop computing. An ABM of individual-level spatial interaction that simulates information exchange, spatial diffusion of opinion development, and consensus building among decision makers is proposed to demonstrate the advantages of the parallel approach against its sequential counterpart. This study focuses on two key spatial properties of the interaction system of interest, the extent and range of interaction, and examines their influence on the computing performance of the proposed parallel model and the performance scalability of the model as more computing resources are added. Significant influence from these two properties is found and can be attributed to three possible sources of effects, namely the model level, the parallelization level, and the platform level. It is suggested that these effects should be taken into consideration when leveraging multicore computing resources for the development of parallel ABMs.

Agent-based models (ABM) allow for the bottom-up simulation of dynamics in complex adaptive spatial systems through the explicit representation of pattern–process interactions. This bottom-up simulation, however, has been identified as both data- and computing-intensive. While cyberinfrastrucutre provides such support for intensive computation, the appropriate management and use of cyberinfrastructure (CI)-enabled computing resources for ABM raise a challenging and intriguing issue. To gain insight into this issue, in this article we present a service-oriented simulation framework that supports spatially explicit agent-based modeling within a CI environment. This framework is designed at three levels: intermodel, intrasimulation, and individual. Functionalities at these levels are encapsulated into services, each of which is an assembly of new or existing services. Services at the intermodel and intrasimulation levels are suitable for generic ABM; individual-level services are designed specifically for modeling intelligent agents. The service-oriented simulation framework enables the integration of domain-specific functionalities for ABM and allows access to high-performance and distributed computing resources to perform simulation tasks that are often computationally intensive. We used a case study to investigate the utility of the framework in enabling agent-based modeling within a CI environment. We conducted experiments using supercomputing resources on the TeraGrid – a key element of the US CI. It is indicated that the service-oriented framework facilitates the leverage of CI-enabled resources for computationally intensive agent-based modeling.

The potential utility of agent‐based models of adaptive, spatially aware, and mobile entities in geographic and ecological research is considerable. Developing this potential, however, presents significant challenges to geographic information science. Modelling the spatio‐temporal behaviour of individuals requires new representational forms that capture how organisms store and use spatial information. New procedures must be developed that simulate how individuals produce bounded knowledge of geographical space through experiential learning, adapt this knowledge to continually changing environments, and apply it to spatial decision‐making processes. In this paper, we present a framework for the representation of adaptive, spatially aware, and mobile agents. To provide context to this research, a multiagent model is constructed to simulate the migratory behaviour of elk (Cervus elaphus) on Yellowstone's northern range. In this simulated environment, intelligent agents learn in ways that enable them to mimic real‐world behaviours and adapt to changing landscapes.

The goal of Spatial Decision Support Systems (SDSS) is to assist decision-makers as they generate alternative solutions to a variety of semi-structured geographical problems, and to evaluate these solutions with the help of applicable data and analytical models. Most existing SDSS, however, support only a limited number of decision-making environments, and are not designed to utilize web-accessible repositories of spatial data and models. These limitations are overcome through the use of ‘software agents’ within an agent-oriented modelling framework, called ‘Distributed Intelligent Geographical Modelling Environment (DIGME)’. The utility of this framework is demonstrated through the development of an SDSS to evaluate the ecological and economic impacts of agricultural policy for the Cache River watershed of southern Illinois.

Existing geoprocessing technologies lack the modelbase management capabilities needed to construct dynamic simulation models of geographical systems. A geoprocessing framework is presented here that incorporates the fundamental precepts of GIS, computer simulation, modelbase management, and SDSS into an integrated environment that supports the development and use of geographical models. To support this framework the field and object-based geographical data models are extended to include spatial processes and spatial relations. A prototype system was developed to test the utility of this framework and data model.

A distributed system of cooperating minicomputers is simulated by AIMER (Automatic Integration of Multiple Element Radars) to model and analyze the behavior of a radar tracking system. Simulation is applied in the AIMER project in an attempt to model a network of minicomputers to discover a maximally flexible network architecture. Because building the tracking system out of real hardware would not result in a flexible enough testbed system, the proposed configuration is represented by a software emulation. The instruction sets of the individual processors are emulated in order to allow separation of the measurement facilities from the execution of the system. The emulation is supported by a Nano-data QM-1 micro and nano-programmable host. Extensive performance monitoring hooks have been built into the emulation system which allow small performance perturbations to become visible.