Spatial-temporal Components Research Articles

Dynamic Spatial-Temporal Memory Augmentation Network for Traffic Prediction.

Traffic flow prediction plays a crucial role in the development of smart cities. However, existing studies face challenges in effectively capturing spatio-temporal contexts, handling hierarchical temporal features, and understanding spatial heterogeneity. To better manage the spatio-temporal correlations inherent in traffic flow, we present a novel model called Dynamic Spatio-Temporal Memory-Augmented Network (DSTMAN). Firstly, we design three spatial-temporal embeddings to capture dynamic spatial-temporal contexts and encode the unique characteristics of time units and spatial states. Secondly, these three spatial-temporal components are integrated to form a multi-scale spatial-temporal block, which effectively extracts hierarchical spatial-temporal dependencies. Finally, we introduce a meta-memory node bank to construct an adaptive neighborhood graph, implicitly representing spatial relationships and enhancing the learning of spatial heterogeneity through a secondary memory mechanism. Evaluation on four public datasets, including METR-LA and PEMS-BAY, demonstrates that the proposed model outperforms benchmark models such as MTGNN, DCRNN, and AGCRN. On the METR-LA dataset, our model reduces the MAE by 4% compared to MTGNN, 6.9% compared to DCRNN, and 5.8% compared to AGCRN, confirming its efficacy in traffic flow prediction.

Spatial-temporal drivers and incidence heterogeneity of hemorrhagic fever with renal syndrome transmission in Shandong Province, China, 2016–2022

BackgroundHemorrhagic fever with renal syndrome (HFRS) signals a recurring risk in Eurasia in recent years owing to its continued rise in case notifications and the extension of geographical distribution. This study was undertaken to investigate the spatiotemporal drivers and incidence heterogeneity of HFRS transmission in Shandong Province.MethodsThe epidemiological data for HFRS, meteorological data and socioeconomic data were obtained from China Information System for Disease Control and Prevention, China Meteorological Data Sharing Service System, and Shandong Statistical Yearbook, respectively. The spatial-temporal multicomponent model was employed to analyze the values of spatial-temporal components and the heterogeneity of HFRS transmission across distinct regions.ResultsThe total effect values of the autoregressive, epidemic, and endemic components were 0.451, 0.187, and 0.033, respectively, exhibiting significant heterogeneity across various cities. This suggested a pivotal role of the autoregressive component in propelling HFRS transmission in Shandong Province. The epidemic component of Qingdao, Weifang, Yantai, Weihai, and Jining declined sharply at the onset of 2020. The random effect identified distinct incidence levels associated with Qingdao and Weifang, signifying regional variations in HFRS occurrence.ConclusionsThe autoregressive component emerged as a significant driver in the transmission of HFRS in Shandong Province. Targeted preventive measures should be strategically implemented across various regions, taking into account the predominant component influencing the epidemic.

Analysis of the spatial-temporal components driving transmission of the severe fever with thrombocytopenia syndrome in Shandong Province, China, 2016-2018.

Existing models about the spatial-temporal distribution of the severe fever with thrombocytopenia syndrome (SFTS) entirely concentrate on aggregation, which provides limited knowledge to develop effective measures to control the epidemic of SFTS. This study aimed to identify the main spatial-temporal components and heterogeneity in different regions in Shandong Province, China. We applied the spatial-temporal multicomponent model to detect the spatial-temporal component values. A total of 2814 cases were reported from 2016 to 2018 in Shandong Province. The prevalence rate was 0.627 per 100,000, with an overall case fatality rate of 8.99%. SFTS cases were mostly clustered in central and eastern regions of Shandong Province. The total effect values of the autoregressive component, the spatiotemporal component and the endemic component were 0.586, 0.244 and 0.084, respectively, which demonstrated that the autoregressive component was the main factor driving the incidence of SFTS, followed by the spatiotemporal component. Gross domestic product per capita and weekly mean atmospheric pressure contributed to the incidence of SFTS with inverse effects. Obvious heterogeneity across regions for the autoregressive component and the spatiotemporal component was identified. In conclusion, the autoregressive and spatiotemporal components play a key role in driving the transmission of SFTS in Shandong Province. Based on the main component values, targeted measures should be formulated to control SFTS epidemics in different regions.

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases.

Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.

Forecasting Spatio-Temporal Variation in Residential Burglary with the Integrated Laplace Approximation Framework: Effects of Crime Generators, Street Networks, and Prior Crimes

ObjectivesWe investigate the spatio-temporal variation of monthly residential burglary frequencies across neighborhoods as a function of crime generators, street network features and temporally and spatially lagged burglary frequencies. In addition, we evaluate the performance of the model as a forecasting tool.MethodsWe analyze 48 months of police-recorded residential burglaries across 20 neighborhoods in Amsterdam, the Netherlands, in combination with data on the locations of urban facilities (crime generators), frequencies of other crime types, and street network data. We apply the Integrated Laplace Approximation method, a Bayesian forecasting framework that is less computationally demanding than prior frameworks.ResultsThe local number of retail stores, the number of street robberies perpetrated and the closeness of the local street network are positively related to residential burglary. Inclusion of a general spatio-temporal interaction component significantly improves forecasting performance, but inclusion of spatial proximity or temporal recency components does not.DiscussionOur findings on crime generators and street network characteristics support evidence in the literature on environmental correlates of burglary. The significance of spatio-temporal interaction indicates that residential burglary is spatio-temporally concentrated. Our finding that recency and proximity of prior burglaries do not contribute to the performance of the forecast, probably indicates that relevant spatio-temporal interaction is limited to fine-grained spatial and temporal units of analysis, such as days and street blocks.

Taste coding strategies in insular cortex.

While the cortical representation of sensory stimuli is well described for some sensory systems, a clear understanding of the cortical representation of taste stimuli remains elusive. Recent investigations have focused on both spatial and temporal organization of taste responses in the putative taste region of insular cortex. This review highlights recent literature focused on spatiotemporal coding strategies in insular cortex. These studies are examined in the context of the organization and function of the entire insular cortex, rather than a specific gustatory region of insular cortex. In regard to a taste quality-specific map, imaging studies have reported conflicting results, whereas electrophysiology studies have described a broad distribution of taste-responsive neurons found throughout insular cortex with no spatial organization. The current collection of evidence suggests that insular cortex may be organized into a hedonic or “viscerotopic” map, rather than one ordered according to taste quality. Further, it has been proposed that cortical taste responses can be separated into temporal “epochs” representing stimulus identity and palatability. This coding strategy presents a potential framework, whereby the coordinated activity of a population of neurons allows for the same neurons to respond to multiple taste stimuli or even other sensory modalities, a well-documented phenomenon in insular cortex neurons. However, these representations may not be static, as several studies have demonstrated that both spatial representation and temporal dynamics of taste coding change with experience. Collectively, these studies suggest that cortical taste representation is not organized in a spatially discrete map, but rather is plastic and spatially dispersed, using temporal information to encode multiple types of information about ingested stimuli. Impact statement The organization of taste coding in insular cortex is widely debated. While early work has focused on whether taste quality is encoded via labeled line or ensemble mechanisms, recent work has attempted to delineate the spatial organization and temporal components of taste processing in insular cortex. Recent imaging and electrophysiology studies have reported conflicting results in regard to the spatial organization of cortical taste responses, and many studies ignore potentially important temporal dynamics when investigating taste processing. This review highlights the latest research in these areas and examines them in the context of the anatomy and physiology of the insular cortex in general to provide a more comprehensive description of taste coding in insular cortex.

Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction

Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However, due to the complex spatial-temporal correlations of traffic data, it is very challenging to predict traffic speed timely and accurately. The traffic speed renders not only short-term neighboring and multiple long-term periodic dependencies in the temporal dimension but also local and global dependencies in the spatial dimension. To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction. The model consists of three spatial-temporal components with the same structure and an external component. The three spatial-temporal components are used to model the recent, daily-periodic, and weekly-periodic spatial-temporal correlations of the traffic data, respectively. More specifically, each spatial-temporal component consists of a dynamic temporal module and a global correlated spatial module. The former contains multiple residual blocks which are stacked by dilated casual convolutions, while the latter contains a localized graph convolution and a global correlated mechanism. The external component is used to extract the effect of external factors, such as holidays and weather conditions, on the traffic speed. Experimental results on two real-world traffic datasets have demonstrated that the proposed GSTGCN outperforms the state-of-the-art baselines.

Muscle Synergy Analysis of a Hand-Grasp Dataset: A Limited Subset of Motor Modules May Underlie a Large Variety of Grasps.

Background: Kinematic and muscle patterns underlying hand grasps have been widely investigated in the literature. However, the identification of a reduced set of motor modules, generalizing across subjects and grasps, may be valuable for increasing the knowledge of hand motor control, and provide methods to be exploited in prosthesis control and hand rehabilitation.Methods: Motor muscle synergies were extracted from a publicly available database including 28 subjects, executing 20 hand grasps selected for daily-life activities. The spatial synergies and temporal components were analyzed with a clustering algorithm to characterize the patterns underlying hand-grasps.Results: Motor synergies were successfully extracted on all 28 subjects. Clustering orders ranging from 2 to 50 were tested. A subset of ten clusters, each one represented by a spatial motor module, approximates the original dataset with a mean maximum error of 5% on reconstructed modules; however, each spatial synergy might be employed with different timing and recruited at different grasp stages. Two temporal activation patterns are often recognized, corresponding to the grasp/hold phase, and to the pre-shaping and release phase.Conclusions: This paper presents one of the biggest analysis of muscle synergies of hand grasps currently available. The results of 28 subjects performing 20 different grasps suggest that a limited number of time dependent motor modules (shared among subjects), correctly elicited by a control activation signal, may underlie the execution of a large variety of hand grasps. However, spatial synergies are not strongly related to specific motor functions but may be recruited at different stages, depending on subject and grasp. This result can lead to applications in rehabilitation and assistive robotics.

Robotic Assistance for Upper Limbs May Induce Slight Changes in Motor Modules Compared With Free Movements in Stroke Survivors: A Cluster-Based Muscle Synergy Analysis.

Background: The efficacy of robot-assisted rehabilitation as a technique for achieving motor recovery is still being debated. The effects of robotic assistance are generally measured using standard clinical assessments. Few studies have investigated the value of human-centered instrumental analysis, taking the modular organization of the human neuromotor system into account in assessing how stroke survivors interact with robotic set-ups. In this paper, muscle synergy analysis was coupled with clustering procedures to elucidate the effect of human-robot interaction on the spatial and temporal features, and directional tuning of motor modules during robot-assisted movements.Methods: Twenty-two stroke survivors completed a session comprising a series of hand-to-mouth movements with and without robotic assistance. Patients were assessed instrumentally, recording kinematic, and electromyographic data to extract spatial muscle synergies and their temporal components. Patients' spatial synergies were grouped by means of a cluster analysis, matched pairwise across conditions (free and robot-assisted movement), and compared in terms of their spatial and temporal features, and directional tuning, to examine how robotic assistance altered their motor modules.Results: Motor synergies were successfully extracted for all 22 patients in both conditions. Seven clusters (spatial synergies) could describe the original datasets, in both free and robot-assisted movements. Interacting with the robot slightly altered the spatial synergies' features (to a variable extent), as well as their temporal components and directional tuning.Conclusions: Slight differences were identified in the characteristics of spatial synergies, temporal components and directional tuning of the motor modules of stroke survivors engaging in free and robot-assisted movements. Such effects are worth investigating in the framework of a modular description of the neuromusculoskeletal system to shed more light on human-robot interaction, and the effects of robotic assistance and rehabilitation.

Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification.

The event-related potential (ERP) is the brain response measured in electroencephalography (EEG), which reflects the process of human cognitive activity. ERP has been introduced into brain computer interfaces (BCIs) to communicate the computer with the subject's intention. Due to the low signal-to-noise ratio of EEG, most ERP studies are based on grand-averaging over many trials. Recently single-trial ERP detection attracts more attention, which enables real time processing tasks as rapid face identification. All the targets needed to be retrieved may appear only once, and there is no knowledge of target label for averaging. More interestingly, how the features contribute temporally and spatially to single-trial ERP detection has not been fully investigated. In this paper, we propose to implement a local-learning-based (LLB) feature extraction method to investigate the importance of spatial-temporal components of ERP in a task of rapid face identification using single-trial detection. Comparing to previous methods, LLB method preserves the nonlinear structure of EEG signal distribution, and analyze the importance of original spatial-temporal components via optimization in feature space. As a data-driven methods, the weighting of the spatial-temporal component does not depend on the ERP detection method. The importance weights are optimized by making the targets more different from non-targets in feature space, and regularization penalty is introduced in optimization for sparse weights. This spatial-temporal feature extraction method is evaluated on the EEG data of 15 participants in performing a face identification task using rapid serial visual presentation paradigm. Comparing with other methods, the proposed spatial-temporal analysis method uses sparser (only 10% of the total) features, and could achieve comparable performance (98%) of single-trial ERP detection as the whole features across different detection methods. The interesting finding is that the N250 is the earliest temporal component that contributes to single-trial ERP detection in face identification. And the importance of N250 components is more laterally distributed toward the left hemisphere. We show that using only the left N250 component over-performs the right N250 in the face identification task using single-trial ERP detection. The finding is also important in building a fast and efficient (fewer electrodes) BCI system for rapid face identification.

Using Variance Structure to Quantify Responses to Perturbation in Fish Catches

AbstractWe present a case study evaluation of gill‐net catches of Walleye Sander vitreus to assess potential effects of large‐scale changes in Oneida Lake, New York, including the disruption of trophic interactions by double‐crested cormorants Phalacrocorax auritus and invasive dreissenid mussels. We used the empirical long‐term gill‐net time series and a negative binomial linear mixed model to partition the variability in catches into spatial and coherent temporal variance components, hypothesizing that variance partitioning can help quantify spatiotemporal variability and determine whether variance structure differs before and after large‐scale perturbations. We found that the mean catch and the total variability of catches decreased following perturbation but that not all sampling locations responded in a consistent manner. There was also evidence of some spatial homogenization concurrent with a restructuring of the relative productivity of individual sites. Specifically, offshore sites generally became more productive following the estimated break point in the gill‐net time series. These results provide support for the idea that variance structure is responsive to large‐scale perturbations; therefore, variance components have potential utility as statistical indicators of response to a changing environment more broadly. The modeling approach described herein is flexible and would be transferable to other systems and metrics. For example, variance partitioning could be used to examine responses to alternative management regimes, to compare variability across physiographic regions, and to describe differences among climate zones. Understanding how individual variance components respond to perturbation may yield finer‐scale insights into ecological shifts than focusing on patterns in the mean responses or total variability alone.Received July 26, 2016; accepted February 28, 2017 Published online April 25, 2017

Classification of visual and linguistic tasks using eye-movement features

The role of the task has received special attention in visual-cognition research because it can provide causal explanations of goal-directed eye-movement responses. The dependency between visual attention and task suggests that eye movements can be used to classify the task being performed. A recent study by Greene, Liu, and Wolfe (2012), however, fails to achieve accurate classification of visual tasks based on eye-movement features. In the present study, we hypothesize that tasks can be successfully classified when they differ with respect to the involvement of other cognitive domains, such as language processing. We extract the eye-movement features used by Greene et al. as well as additional features from the data of three different tasks: visual search, object naming, and scene description. First, we demonstrated that eye-movement responses make it possible to characterize the goals of these tasks. Then, we trained three different types of classifiers and predicted the task participants performed with an accuracy well above chance (a maximum of 88% for visual search). An analysis of the relative importance of features for classification accuracy reveals that just one feature, i.e., initiation time, is sufficient for above-chance performance (a maximum of 79% accuracy in object naming). Crucially, this feature is independent of task duration, which differs systematically across the three tasks we investigated. Overall, the best task classification performance was obtained with a set of seven features that included both spatial information (e.g., entropy of attention allocation) and temporal components (e.g., total fixation on objects) of the eye-movement record. This result confirms the task-dependent allocation of visual attention and extends previous work by showing that task classification is possible when tasks differ in the cognitive processes involved (purely visual tasks such as search vs. communicative tasks such as scene description).

Dissociable networks involved in spatial and temporal order source retrieval

Space and time are important components of our episodic memories. Without this information, we cannot determine the “where and when” of our recent memories, rendering it difficult to disambiguate individual episodes from each other. The neural underpinnings of spatial and temporal order memory in humans remain unclear, in part because of difficulties in disentangling the contributions of these two types of source information. To address this issue, we conducted an experiment in which participants first navigated a virtual city, experiencing unique routes in a specific temporal order and learning about the spatial layout of the city. Spatial and temporal order information were dissociated in our task such that learning one type of information did not facilitate the other behaviorally. This allowed us to then address the extent to which the two types of information involved functionally distinct or overlapping brain areas. During functional magnetic resonance imaging (fMRI), participants retrieved information about the relative distance of stores within the city (spatial task) and the temporal order of stores from each other (temporal task). Comparable hippocampal activity was observed during these two tasks, but greater prefrontal activity was seen during temporal order retrieval whereas greater parahippocampal activity was seen during spatial retrieval. We suggest that while the brain possesses dissociable networks for maintaining and representing spatial layout and temporal order components of episodic memory, this information may converge into a common representation for source memory in areas such as the hippocampus.

A Multi-Scale Analysis to set the Default Mode Network in noisy fMRI data

Event Abstract Back to Event A Multi-Scale Analysis to set the Default Mode Network in noisy fMRI data Katherine Baquero1*, Francisco Gómez2, Andrea Soddu2, Audrey Vanhaudenhuyse2, Athena Demertzi2, Jean-Flory Tshibanda3, Olivia Gosseries2, Quentin Noirhomme2, Steven Laureys2 and Eduardo Romero1* 1 National University of Colombia, Biomedical Engineering, Colombia 2 University of Liege, Cyclotron Research Centre and Neurology Department, Belgium 3 University of Liege, CHU Sart Tilman Hospital, Belgium The Default Mode Network (DMN) is presently defined by those brain zones involved in maintaining a baseline brain activation. This network is usually revealed using Independent Component Analysis upon the fMRI data. However, a number of factors can easily perturb the acquired data, in particular a large head motion. Yet this problem has been partially overcome by registering the acquired brain volume, registration is still very limited in case of large head movements, a frequent scenario in patients with disorders of consciousness. This article presents a multiscale analysis of the fMRI data which improves the robustness with which the DMN is detected in subjects that move the head during the acquisition process. Initially, the method obtains multiple scales by filtering the original volumes out with a sequence of gaussian filters. Each fMRI scale is preprocessed by registering, normalizing, smoothing and coregistering, as described elsewhere, using the SPM software. These preprocessed data are then decomposed into the spatial-temporal components with the FastICA algorithm. The DMN is then selected with the Goodness of Fit approach (GoF), for each of the different scales. Finally, the components of the DMN at different scales are summed up, ruling out the original volume. The approach was validated by perturbing the acquired sequence of five healthy subjects with the six rigid-body movement series obtained from 15 subjects (5 Control, 3 Minimally Conscious State, 2 Locked in Syndrome and 5 Vegetative State), with head motion from 1mm up to 15 mm. In summary, the proposed method was applied to 18 disturbed data that lost the DMN, from which 11 fMRI data recovered the DMN. The mean of the Goodness of fit of the DMN component increased from 0.1 to 0.4. This multiscale analysis improves the robustness of the DMN detection in case of large head movements. Keywords: Computational Neurosciences, Default Mode Network, fMRI, High head motions, multiscale analysis Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Abstract Topic: neurons, networks and dynamical systems (please use "neurons, networks and dynamical systems" as keywords) Citation: Baquero K, Gómez F, Soddu A, Vanhaudenhuyse A, Demertzi A, Tshibanda J, Gosseries O, Noirhomme Q, Laureys S and Romero E (2011). A Multi-Scale Analysis to set the Default Mode Network in noisy fMRI data. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00067 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Aug 2011; Published Online: 04 Oct 2011. * Correspondence: Miss. Katherine Baquero, National University of Colombia, Biomedical Engineering, Bogotá, Bogota DC, 11001000, Colombia, kabaquero@uliege.be Prof. Eduardo Romero, National University of Colombia, Biomedical Engineering, Bogotá, Bogota DC, 11001000, Colombia, edromero@unal.edu.co Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Katherine Baquero Francisco Gómez Andrea Soddu Audrey Vanhaudenhuyse Athena Demertzi Jean-Flory Tshibanda Olivia Gosseries Quentin Noirhomme Steven Laureys Eduardo Romero Google Katherine Baquero Francisco Gómez Andrea Soddu Audrey Vanhaudenhuyse Athena Demertzi Jean-Flory Tshibanda Olivia Gosseries Quentin Noirhomme Steven Laureys Eduardo Romero Google Scholar Katherine Baquero Francisco Gómez Andrea Soddu Audrey Vanhaudenhuyse Athena Demertzi Jean-Flory Tshibanda Olivia Gosseries Quentin Noirhomme Steven Laureys Eduardo Romero PubMed Katherine Baquero Francisco Gómez Andrea Soddu Audrey Vanhaudenhuyse Athena Demertzi Jean-Flory Tshibanda Olivia Gosseries Quentin Noirhomme Steven Laureys Eduardo Romero Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Separation of drifting pulsating structures in a complex radio spectrum of the 2001 April 11 event

Aims. We present new method of separating a complex radio spectrum into single radio bursts. The method is used in the analysis of the 0.8–2.0 GHz radio spectrum of the 2001 April 11 event, which was rich in drifting pulsating structures. Methods. The method is based on the wavelet analysis technique, which separates different spatial-temporal components (radio bursts) that are difficult to recognize in the original radio spectrum. Results. We show with this method that the complex radio spectrum observed during the 2001 April 11 event consists of at least four drifting pulsating structures (DPSs). These structures were separated with respect to their different frequency drifts. The DPSs indicate at least four plasmoids that are supposed to be formed in a flaring current sheet.

Noen statistiske modeller innen romlig epidemiologi

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The coupled dipole model: an integrated model for multiple MEG/EEG data sets

Often MEG/EEG is measured in a few slightly different conditions to investigate the functionality of the human brain. This kind of data sets show similarities, though are different for each condition. When solving the inverse problem (IP), performing the source localization, one encounters the problem that this IP is ill-posed: constraints are necessary to solve and stabilize the solution to the IP. Moreover, a substantial amount of data is needed to avoid a signal to noise ratio (SNR) that is too poor for source localizations. In the case of similar conditions, this common information can be exploited by analyzing the data sets simultaneously. The here proposed coupled dipole model (CDM) provides an integrated method in which these similarities between conditions are used to solve and stabilize the inverse problem. The coupled dipole model is applicable when data sets contain common sources or common source time functions. The coupled dipole model uses a set of common sources and a set of common source time functions (STFs) to model all conditions in one single model. The data of each condition are mathematically described as a linear combination of these common spatial and common temporal components. This linear combination is specified in a coupling matrix for each data set. The coupled dipole model was applied in two simulation studies and in one experimental study. The simulations show that the errors in the estimated spatial and temporal parameters decrease compared to the standard separate analyses. A decrease in position error of a factor of 10 was shown for the localization of two nearby sources. In the experimental application, the coupled dipole model was shown to be necessary to obtain a plausible solution in at least 3 of 15 conditions investigated. Moreover, using the CDM, a direct comparison between parameters in different conditions is possible, whereas in separate models, the scaling of the amplitude parameters varies in general from data set to data set.

Habitat-specific Signal Structure for Olfaction: An Example from Artificial Streams

Many animals use chemical signals to acquire information about their habitats. The structure of this information is dependent upon specific features within a habitat, and the information in signals can be habitat-specific. We quantified the spatial and temporal information in an aquatic odor plume in three different artificial stream habitats with different substrate types by measuring turbulent odor plumes with an electrochemical detection system. Streams had one of three substrate types that correlated with typical aquatic habitats: sand (≈4.2 × 10−2 cm diameter), gravel (≈2.5 cm), and cobble (≈4.5 cm). As predicted from the hydrodynamics, the spatial and temporal structures of the signals were different on different substrates. Spectral analysis showed that the sand and cobble substrates had signals that were dominated by lower frequency fluctuations, whereas gravel had the highest and broadest range of signal fluctuations. Cross- and autocorrelations showed that signals on the gravel substrate had the largest spatial and shortest temporal components. Our results imply that the information obtained from chemical signals may be limited in some habitats. These constraints on information may affect how organisms perform chemically mediated behaviors.

Reproductive Synchrony in Brucellosis-Exposed Bison in the Southern Greater Yellowstone Ecosystem and in Noninfected Populations

Shooting of bison (Bison bison) in the Greater Yellowstone Ecosystem is a highly polarized, emo- tional issue because native ungulates exposed to brucellosis (Brucella abortus) may infect cattle when they dis- perse or migrate beyond protected reserves. Both bison and elk (Cervus elaphus) carry brucellosis, a disease that causes abortion in livestock and is often transmitted through contact with expelled fetuses or birth mem- branes andfluids. If Brucella-infected bison experience a prolongedperiod of birth, cattle in areas of sympatry will have increased susceptibility to disease. We tested the hypothesis that reproductive synchrony differs be- tween Brucella-free and Brucella-infected bison by contrasting patterns between the Brucella-exposed bison population (in Jackson Hole, Wyoming) within the southern Greater Yellowstone Ecosystem with three non- exposed populations (National Bison Range in Montana, and Wind Cave and Badlands National Parks in South Dakota) that inhabit prairies. Populations averaged 42 births per site per year. Fifty percent of all the births occurred within 1 month, and 95% within 61 days at three sites. It took 89 days for 95% of the births to occur at the noninfected Badlands site. Multiple comparison of regression slopes of the relationship between birth synchrony and the cumulative proportion of total births revealed interpopulation differences (p < 0.01), but the brucellosis-exposed population did not exhibit a parturition period different from that of the nondiseased populations. These among-population similarities suggest that reproductive synchrony is not protracted in Brucella-infected bison. Although Brucella transmission from bison to cattle occurs experimen- tally, captive densities during testing exceeded the mean for the Greater Yellowstone Ecosystem by about 1500 times, and that in 10 noninfected populations by more than 100 times. Although mean density can be mis- leading as a surrogate for the potential transmission of disease from bison to cattle, bison densities have not approached those reportedfor disease transmission. Among the ecologicalfactors that may affect the spread of brucellosisfrom bison to cattle in the southern Yellowstone region, two stand out: (1) access to high-quality food, perhaps because animals in superior physiological condition tend to exhibit tighter reproductive syn- chrony and (2) incidence of disease in elk. The timing of parturition in bison is neither a simnple process nor is it likely to be based on responses to a single variable. A potentially fertile area for future investigation will concern relationships among food, reproductive synchrony, and spatial-temporal components of bison, elk, and cattle distribution.

Low-frequency oscillations in the Northern Hemisphere

Multichannel singular spectrum analysis (MSSA) has been applied to 5-day mean grid point data of 500 hPa geopotential heights over the Northern Hemisphere, to explore low frequency oscillatory variabilities over the region. Two major low-frequency oscillations with periods of 60–70 and 30–40 days were found. These two quasi-periodicities are significantly (at the 99% confidence level) different from those found in the corresponding pure red noise process. Also, they were reproducible when using different window lengths for MSSA and when applying MSSA to two equal-length subsets of the basic data series. In addition, when one subset (first subset) is projected onto the patterns of the other subset, the resulting (projection) spatial-temporal principal components corresponding to the two oscillations are highly correlated with those ST-PCs directly derived from the first subset by MSSA, indicating that the oscillatory components obtained from one subset explain a significant amount of variance in the other subset. Some reliable information about the amplitudes of the oscillations has been also obtained by using composite analysis.