Hierarchical random cellular neural networks for system-level brain-like signal processing.

PubMed

Kozma, Robert; Puljic, Marko

2013-09-01

Sensory information processing and cognition in brains are modeled using dynamic systems theory. The brain's dynamic state is described by a trajectory evolving in a high-dimensional state space. We introduce a hierarchy of random cellular automata as the mathematical tools to describe the spatio-temporal dynamics of the cortex. The corresponding brain model is called neuropercolation which has distinct advantages compared to traditional models using differential equations, especially in describing spatio-temporal discontinuities in the form of phase transitions. Phase transitions demarcate singularities in brain operations at critical conditions, which are viewed as hallmarks of higher cognition and awareness experience. The introduced Monte-Carlo simulations obtained by parallel computing point to the importance of computer implementations using very large-scale integration (VLSI) and analog platforms. Copyright © 2013 Elsevier Ltd. All rights reserved.

Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities

PubMed Central

Lindegren, Martin; Denker, Tim Spaanheden; Floeter, Jens; Fock, Heino O.; Sguotti, Camilla; Stäbler, Moritz; Otto, Saskia A.; Möllmann, Christian

2017-01-01

Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs. PMID:29136658

Spatio-Temporal Change Modeling of Lulc: a Semantic Kriging Approach

NASA Astrophysics Data System (ADS)

Bhattacharjee, S.; Ghosh, S. K.

2015-07-01

Spatio-temporal land-use/ land-cover (LULC) change modeling is important to forecast the future LULC distribution, which may facilitate natural resource management, urban planning, etc. The spatio-temporal change in LULC trend often exhibits non-linear behavior, due to various dynamic factors, such as, human intervention (e.g., urbanization), environmental factors, etc. Hence, proper forecasting of LULC distribution should involve the study and trend modeling of historical data. Existing literatures have reported that the meteorological attributes (e.g., NDVI, LST, MSI), are semantically related to the terrain. Being influenced by the terrestrial dynamics, the temporal changes of these attributes depend on the LULC properties. Hence, incorporating meteorological knowledge into the temporal prediction process may help in developing an accurate forecasting model. This work attempts to study the change in inter-annual LULC pattern and the distribution of different meteorological attributes of a region in Kolkata (a metropolitan city in India) during the years 2000-2010 and forecast the future spread of LULC using semantic kriging (SemK) approach. A new variant of time-series SemK is proposed, namely Rev-SemKts to capture the multivariate semantic associations between different attributes. From empirical analysis, it may be observed that the augmentation of semantic knowledge in spatio-temporal modeling of meteorological attributes facilitate more precise forecasting of LULC pattern.

Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty.

PubMed

Huang, Guowen; Lee, Duncan; Scott, E Marian

2018-03-30

The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation. The first stage is a multivariate spatio-temporal fusion model for predicting areal level average concentrations of multiple pollutants from both monitored and modelled pollution data. The second stage is a spatio-temporal model for estimating the health impact of multiple correlated pollutants simultaneously, which accounts for the uncertainty in the estimated pollution concentrations. The novel methodology is motivated by a new study of the impact of both particulate matter and nitrogen dioxide concentrations on respiratory hospital admissions in Scotland between 2007 and 2011, and the results suggest that both pollutants exhibit substantial and independent health effects. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

The Perception of Dynamic and Static Facial Expressions of Happiness and Disgust Investigated by ERPs and fMRI Constrained Source Analysis

PubMed Central

Trautmann-Lengsfeld, Sina Alexa; Domínguez-Borràs, Judith; Escera, Carles; Herrmann, Manfred; Fehr, Thorsten

2013-01-01

A recent functional magnetic resonance imaging (fMRI) study by our group demonstrated that dynamic emotional faces are more accurately recognized and evoked more widespread patterns of hemodynamic brain responses than static emotional faces. Based on this experimental design, the present study aimed at investigating the spatio-temporal processing of static and dynamic emotional facial expressions in 19 healthy women by means of multi-channel electroencephalography (EEG), event-related potentials (ERP) and fMRI-constrained regional source analyses. ERP analysis showed an increased amplitude of the LPP (late posterior positivity) over centro-parietal regions for static facial expressions of disgust compared to neutral faces. In addition, the LPP was more widespread and temporally prolonged for dynamic compared to static faces of disgust and happiness. fMRI constrained source analysis on static emotional face stimuli indicated the spatio-temporal modulation of predominantly posterior regional brain activation related to the visual processing stream for both emotional valences when compared to the neutral condition in the fusiform gyrus. The spatio-temporal processing of dynamic stimuli yielded enhanced source activity for emotional compared to neutral conditions in temporal (e.g., fusiform gyrus), and frontal regions (e.g., ventromedial prefrontal cortex, medial and inferior frontal cortex) in early and again in later time windows. The present data support the view that dynamic facial displays trigger more information reflected in complex neural networks, in particular because of their changing features potentially triggering sustained activation related to a continuing evaluation of those faces. A combined fMRI and EEG approach thus provides an advanced insight to the spatio-temporal characteristics of emotional face processing, by also revealing additional neural generators, not identifiable by the only use of an fMRI approach. PMID:23818974

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain.

PubMed

Higgins, Irina; Stringer, Simon; Schnupp, Jan

2017-01-01

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain

PubMed Central

Stringer, Simon

2017-01-01

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable. PMID:28797034

Assessing global vegetation activity using spatio-temporal Bayesian modelling

NASA Astrophysics Data System (ADS)

Mulder, Vera L.; van Eck, Christel M.; Friedlingstein, Pierre; Regnier, Pierre A. G.

2016-04-01

This work demonstrates the potential of modelling vegetation activity using a hierarchical Bayesian spatio-temporal model. This approach allows modelling changes in vegetation and climate simultaneous in space and time. Changes of vegetation activity such as phenology are modelled as a dynamic process depending on climate variability in both space and time. Additionally, differences in observed vegetation status can be contributed to other abiotic ecosystem properties, e.g. soil and terrain properties. Although these properties do not change in time, they do change in space and may provide valuable information in addition to the climate dynamics. The spatio-temporal Bayesian models were calibrated at a regional scale because the local trends in space and time can be better captured by the model. The regional subsets were defined according to the SREX segmentation, as defined by the IPCC. Each region is considered being relatively homogeneous in terms of large-scale climate and biomes, still capturing small-scale (grid-cell level) variability. Modelling within these regions is hence expected to be less uncertain due to the absence of these large-scale patterns, compared to a global approach. This overall modelling approach allows the comparison of model behavior for the different regions and may provide insights on the main dynamic processes driving the interaction between vegetation and climate within different regions. The data employed in this study encompasses the global datasets for soil properties (SoilGrids), terrain properties (Global Relief Model based on SRTM DEM and ETOPO), monthly time series of satellite-derived vegetation indices (GIMMS NDVI3g) and climate variables (Princeton Meteorological Forcing Dataset). The findings proved the potential of a spatio-temporal Bayesian modelling approach for assessing vegetation dynamics, at a regional scale. The observed interrelationships of the employed data and the different spatial and temporal trends support our hypothesis. That is, the change of vegetation in space and time may be better understood when modelling vegetation change as both a dynamic and multivariate process. Therefore, future research will focus on a multivariate dynamical spatio-temporal modelling approach. This ongoing research is performed within the context of the project "Global impacts of hydrological and climatic extremes on vegetation" (project acronym: SAT-EX) which is part of the Belgian research programme for Earth Observation Stereo III.

Spatio-temporal processing of tactile stimuli in autistic children

PubMed Central

Wada, Makoto; Suzuki, Mayuko; Takaki, Akiko; Miyao, Masutomo; Spence, Charles; Kansaku, Kenji

2014-01-01

Altered multisensory integration has been reported in autism; however, little is known concerning how the autistic brain processes spatio-temporal information concerning tactile stimuli. We report a study in which a crossed-hands illusion was investigated in autistic children. Neurotypical individuals often experience a subjective reversal of temporal order judgments when their hands are stimulated while crossed, and the illusion is known to be acquired in early childhood. However, under those conditions where the somatotopic representation is given priority over the actual spatial location of the hands, such reversals may not occur. Here, we showed that a significantly smaller illusory reversal was demonstrated in autistic children than in neurotypical children. Furthermore, in an additional experiment, the young boys who had higher Autism Spectrum Quotient (AQ) scores generally showed a smaller crossed hands deficit. These results suggest that rudimentary spatio-temporal processing of tactile stimuli exists in autistic children, and the altered processing may interfere with the development of an external frame of reference in real-life situations. PMID:25100146

Decoding-Accuracy-Based Sequential Dimensionality Reduction of Spatio-Temporal Neural Activities

NASA Astrophysics Data System (ADS)

Funamizu, Akihiro; Kanzaki, Ryohei; Takahashi, Hirokazu

Performance of a brain machine interface (BMI) critically depends on selection of input data because information embedded in the neural activities is highly redundant. In addition, properly selected input data with a reduced dimension leads to improvement of decoding generalization ability and decrease of computational efforts, both of which are significant advantages for the clinical applications. In the present paper, we propose an algorithm of sequential dimensionality reduction (SDR) that effectively extracts motor/sensory related spatio-temporal neural activities. The algorithm gradually reduces input data dimension by dropping neural data spatio-temporally so as not to undermine the decoding accuracy as far as possible. Support vector machine (SVM) was used as the decoder, and tone-induced neural activities in rat auditory cortices were decoded into the test tone frequencies. SDR reduced the input data dimension to a quarter and significantly improved the accuracy of decoding of novel data. Moreover, spatio-temporal neural activity patterns selected by SDR resulted in significantly higher accuracy than high spike rate patterns or conventionally used spatial patterns. These results suggest that the proposed algorithm can improve the generalization ability and decrease the computational effort of decoding.

Analyzing the evolution of young people's brain cancer mortality in Spanish provinces.

PubMed

Ugarte, M D; Adin, A; Goicoa, T; López-Abente, G

2015-06-01

To analyze the spatio-temporal evolution of brain cancer relative mortality risks in young population (under 20 years of age) in Spanish provinces during the period 1986-2010. A new and flexible conditional autoregressive spatio-temporal model with two levels of spatial aggregation was used. Brain cancer relative mortality risks in young population in Spanish provinces decreased during the last years, although a clear increase was observed during the 1990s. The global geographical pattern emphasized a high relative mortality risk in Navarre and a low relative mortality risk in Madrid. Although there is a specific Autonomous Region-time interaction effect on the relative mortality risks this effect is weak in the final estimates when compared to the global spatial and temporal effects. Differences in mortality between regions and over time may be caused by the increase in survival rates, the differences in treatment or the availability of diagnostic tools. The increase in relative risks observed in the 1990s was probably due to improved diagnostics with computerized axial tomography and magnetic resonance imaging techniques. Copyright © 2015 Elsevier Ltd. All rights reserved.

Possible Quantum Absorber Effects in Cortical Synchronization

NASA Astrophysics Data System (ADS)

Kämpf, Uwe

The Wheeler-Feynman transactional "absorber" approach was proposed originally to account for anomalous resonance coupling between spatio-temporally distant measurement partners in entangled quantum states of so-called Einstein-Podolsky-Rosen paradoxes, e.g. of spatio-temporal non-locality, quantum teleportation, etc. Applied to quantum brain dynamics, however, this view provides an anticipative resonance coupling model for aspects of cortical synchronization and recurrent visual action control. It is proposed to consider the registered activation patterns of neuronal loops in so-called synfire chains not as a result of retarded brain communication processes, but rather as surface effects of a system of standing waves generated in the depth of visual processing. According to this view, they arise from a counterbalance between the actual input's delayed bottom-up data streams and top-down recurrent information-processing of advanced anticipative signals in a Wheeler-Feynman-type absorber mode. In the framework of a "time-loop" model, findings about mirror neurons in the brain cortex are suggested to be at least partially associated with temporal rather than spatial mirror functions of visual processing, similar to phase conjugate adaptive resonance-coupling in nonlinear optics.

Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol

PubMed Central

Won, Dong-Ok; Chi, Seong In; Seo, Kwang-Suk; Kim, Hyun Jeong; Müller, Klaus-Robert; Lee, Seong-Whan

2017-01-01

On sedation motivated by the clinical needs for safety and reliability, recent studies have attempted to identify brain-specific signatures for tracking patient transition into and out of consciousness, but the differences in neurophysiological effects between 1) the sedative types and 2) the presence/absence of surgical stimulations still remain unclear. Here we used multimodal electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) measurements to observe electrical and hemodynamic responses during sedation simultaneously. Forty healthy volunteers were instructed to push the button to administer sedatives in response to auditory stimuli every 9–11 s. To generally illustrate brain activity at repetitive transition points at the loss of consciousness (LOC) and the recovery of consciousness (ROC), patient-controlled sedation was performed using two different sedatives (midazolam (MDZ) and propofol (PPF)) under two surgical conditions. Once consciousness was lost via sedatives, we observed gradually increasing EEG power at lower frequencies (<15 Hz) and decreasing power at higher frequencies (>15 Hz), as well as spatially increased EEG powers in the delta and lower alpha bands, and particularly also in the upper alpha rhythm, at the frontal and parieto-occipital areas over time. During ROC from unconsciousness, these spatio-temporal changes were reversed. Interestingly, the level of consciousness was switched on/off at significantly higher effect-site concentrations of sedatives in the brain according to the use of surgical stimuli, but the spatio-temporal EEG patterns were similar, regardless of the sedative used. We also observed sudden phase shifts in fronto-parietal connectivity at the LOC and the ROC as critical points. fNIRS measurement also revealed mild hemodynamic fluctuations. Compared with general anesthesia, our results provide insights into critical hallmarks of sedative-induced (un)consciousness, which have similar spatio-temporal EEG-fNIRS patterns regardless of the stage and the sedative used. PMID:29121108

Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol.

PubMed

Yeom, Seul-Ki; Won, Dong-Ok; Chi, Seong In; Seo, Kwang-Suk; Kim, Hyun Jeong; Müller, Klaus-Robert; Lee, Seong-Whan

2017-01-01

On sedation motivated by the clinical needs for safety and reliability, recent studies have attempted to identify brain-specific signatures for tracking patient transition into and out of consciousness, but the differences in neurophysiological effects between 1) the sedative types and 2) the presence/absence of surgical stimulations still remain unclear. Here we used multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) measurements to observe electrical and hemodynamic responses during sedation simultaneously. Forty healthy volunteers were instructed to push the button to administer sedatives in response to auditory stimuli every 9-11 s. To generally illustrate brain activity at repetitive transition points at the loss of consciousness (LOC) and the recovery of consciousness (ROC), patient-controlled sedation was performed using two different sedatives (midazolam (MDZ) and propofol (PPF)) under two surgical conditions. Once consciousness was lost via sedatives, we observed gradually increasing EEG power at lower frequencies (<15 Hz) and decreasing power at higher frequencies (>15 Hz), as well as spatially increased EEG powers in the delta and lower alpha bands, and particularly also in the upper alpha rhythm, at the frontal and parieto-occipital areas over time. During ROC from unconsciousness, these spatio-temporal changes were reversed. Interestingly, the level of consciousness was switched on/off at significantly higher effect-site concentrations of sedatives in the brain according to the use of surgical stimuli, but the spatio-temporal EEG patterns were similar, regardless of the sedative used. We also observed sudden phase shifts in fronto-parietal connectivity at the LOC and the ROC as critical points. fNIRS measurement also revealed mild hemodynamic fluctuations. Compared with general anesthesia, our results provide insights into critical hallmarks of sedative-induced (un)consciousness, which have similar spatio-temporal EEG-fNIRS patterns regardless of the stage and the sedative used.

Spatio-temporal variability of hydro-chemical characteristics of coastal waters of Gulf of Mannar Marine Biosphere Reserve (GoMMBR), South India

NASA Astrophysics Data System (ADS)

Kathiravan, K.; Natesan, Usha; Vishnunath, R.

2017-03-01

The intention of this study was to appraise the spatial and temporal variations in the physico-chemical parameters of coastal waters of Rameswaram Island, Gulf of Mannar Marine Biosphere Reserve, south India, using multivariate statistical techniques, such as cluster analysis, factor analysis and principal component analysis. Spatio-temporal variations among the physico-chemical parameters are observed in the coastal waters of Gulf of Mannar, especially during northeast and post monsoon seasons. It is inferred that the high loadings of pH, temperature, suspended particulate matter, salinity, dissolved oxygen, biochemical oxygen demand, chlorophyll a, nutrient species of nitrogen and phosphorus strongly determine the discrimination of coastal water quality. Results highlight the important role of monsoonal variations to determine the coastal water quality around Rameswaram Island.

Application research on temporal GIS in the transportation information management system

NASA Astrophysics Data System (ADS)

Wang, Wei; Qin, Qianqing; Wang, Chao

2006-10-01

The application, development and key matters of applying spatio-temporal GIS to traffic information management system are discussed in this paper by introducing the development of spatio-temporal database, current models of spatio-temporal data, traits of traffic information management system. This paper proposes a method of organizing spatio-temporal data taking road object changes into consideration, and describes its data structure in 3 aspects, including structure of spatio-temporal object, organizing method spatio-temporal data and storage means of spatio-temporal data. Trying to manage types of spatio-temporal data involved in traffic system, such as road information, river information, railway information, social and economical data, and etc, uniformly, efficiently and with low redundancy.

Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template

PubMed Central

Guizard, Nicolas; Fonov, Vladimir S.; García-Lorenzo, Daniel; Nakamura, Kunio; Aubert-Broche, Bérengère; Collins, D. Louis

2015-01-01

Neurodegenerative diseases such as Alzheimer's disease present subtle anatomical brain changes before the appearance of clinical symptoms. Manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each time-point is analyzed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scanners and other physiological effects may also introduce variability in the measurement. We propose to use 4D non-linear registration with spatio-temporal regularization to correct for potential longitudinal inconsistencies in the context of structure segmentation. The major contribution of this article is the use of individual template creation with spatio-temporal regularization of the deformation fields for each subject. We validate our method with different sets of real MRI data, compare it to available longitudinal methods such as FreeSurfer, SPM12, QUARC, TBM, and KNBSI, and demonstrate that spatially local temporal regularization yields more consistent rates of change of global structures resulting in better statistical power to detect significant changes over time and between populations. PMID:26301716

Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Su, Shiliang; Zhi, Junjun; Lou, Liping; Huang, Fang; Chen, Xia; Wu, Jiaping

Characterizing the spatio-temporal patterns and apportioning the pollution sources of water bodies are important for the management and protection of water resources. The main objective of this study is to describe the dynamics of water quality and provide references for improving river pollution control practices. Comprehensive application of neural-based modeling and different multivariate methods was used to evaluate the spatio-temporal patterns and source apportionment of pollution in Qiantang River, China. Measurement data were obtained and pretreated for 13 variables from 41 monitoring sites for the period of 2001-2004. A self-organizing map classified the 41 monitoring sites into three groups (Group A, B and C), representing different pollution characteristics. Four significant parameters (dissolved oxygen, biochemical oxygen demand, total phosphorus and total lead) were identified by discriminant analysis for distinguishing variations of different years, with about 80% correct assignment for temporal variation. Rotated principal component analysis (PCA) identified four potential pollution sources for Group A (domestic sewage and agricultural pollution, industrial wastewater pollution, mineral weathering, vehicle exhaust and sand mining), five for Group B (heavy metal pollution, agricultural runoff, vehicle exhaust and sand mining, mineral weathering, chemical plants discharge) and another five for Group C (vehicle exhaust and sand mining, chemical plants discharge, soil weathering, biochemical pollution, mineral weathering). The identified potential pollution sources explained 75.6% of the total variances for Group A, 75.0% for Group B and 80.0% for Group C, respectively. Receptor-based source apportionment was applied to further estimate source contributions for each pollution variable in the three groups, which facilitated and supported the PCA results. These results could assist managers to develop optimal strategies and determine priorities for river pollution control and effective water resources management.

A neuronal model of a global workspace in effortful cognitive tasks.

PubMed

Dehaene, S; Kerszberg, M; Changeux, J P

1998-11-24

A minimal hypothesis is proposed concerning the brain processes underlying effortful tasks. It distinguishes two main computational spaces: a unique global workspace composed of distributed and heavily interconnected neurons with long-range axons, and a set of specialized and modular perceptual, motor, memory, evaluative, and attentional processors. Workspace neurons are mobilized in effortful tasks for which the specialized processors do not suffice. They selectively mobilize or suppress, through descending connections, the contribution of specific processor neurons. In the course of task performance, workspace neurons become spontaneously coactivated, forming discrete though variable spatio-temporal patterns subject to modulation by vigilance signals and to selection by reward signals. A computer simulation of the Stroop task shows workspace activation to increase during acquisition of a novel task, effortful execution, and after errors. We outline predictions for spatio-temporal activation patterns during brain imaging, particularly about the contribution of dorsolateral prefrontal cortex and anterior cingulate to the workspace.

Fiber-array based optogenetic prosthetic system for stimulation therapy

NASA Astrophysics Data System (ADS)

Gu, Ling; Cote, Chris; Tejeda, Hector; Mohanty, Samarendra

2012-02-01

Recent advent of optogenetics has enabled activation of genetically-targeted neuronal cells using low intensity blue light with high temporal precision. Since blue light is attenuated rapidly due to scattering and absorption in neural tissue, optogenetic treatment of neurological disorders may require stimulation of specific cell types in multiple regions of the brain. Further, restoration of certain neural functions (vision, and auditory etc) requires accurate spatio-temporal stimulation patterns rather than just precise temporal stimulation. In order to activate multiple regions of the central nervous system in 3D, here, we report development of an optogenetic prosthetic comprising of array of fibers coupled to independently-controllable LEDs. This design avoids direct contact of LEDs with the brain tissue and thus does not require electrical and heat isolation, which can non-specifically stimulate and damage the local brain regions. The intensity, frequency, and duty cycle of light pulses from each fiber in the array was controlled independently using an inhouse developed LabView based program interfaced with a microcontroller driving the individual LEDs. While the temporal profile of the light pulses was controlled by varying the current driving the LED, the beam profile emanating from each fiber tip could be sculpted by microfabrication of the fiber tip. The fiber array was used to stimulate neurons, expressing channelrhodopsin-2, in different locations within the brain or retina. Control of neural activity in the mice cortex, using the fiber-array based prosthetic, is evaluated from recordings made with multi-electrode array (MEA). We also report construction of a μLED array based prosthetic for spatio-temporal stimulation of cortex.

Resting state networks in empirical and simulated dynamic functional connectivity.

PubMed

Glomb, Katharina; Ponce-Alvarez, Adrián; Gilson, Matthieu; Ritter, Petra; Deco, Gustavo

2017-10-01

It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions ("communities") that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations. Copyright © 2017 Elsevier Inc. All rights reserved.

Investigation on the dominant factors controlling the spatio-temporal distribution of soil moisture in experimental grasslands

NASA Astrophysics Data System (ADS)

Schwichtenberg, G.; Hildebrandt, A.; Samaniego-Eguiguren, L.; Kreutziger, Y.; Attinger, S.

2009-04-01

The spatio-temporal distribution of soil moisture in the unsaturated zone influences the vegetation growth, governs the runoff generation processes as well as the energy balance at the interface between biosphere and the atmosphere, by influencing evapotranspiration. A better understanding of the spatio-temporal variability and dependence of soil moisture on living versus abiotic environment would lead to an improved representation of the soil-vegetation-atmosphere processes in hydrological and climate models. The Jena Experiment site (Germany) was established October 2001 in order to analyse the interaction between plant diversity and ecosystem processes. The main experiment covers 92 plots of 20 x 20 m arranged into a grid, on which a mixture of up to 60 grassland species and of one to four plant functional groups have been seeded. Each of these plots is equipped with at least one measurement tube for soil moisture. Measurements have been conducted weekly for four growing seasons (SSF). Here, we use geostatistical methods, like variograms and multivariate regressions, to investigate in how far abiotic environment and ecosystem explain the spatial and temporal variation of soil moisture at the Jena Experiment site. We test the influence of the soil environment, biodiversity, leaf area index and groundwater table. The poster will present the results of this analysis.

Nonlinear analyses of interictal EEG map the brain interdependences in human focal epilepsy

NASA Astrophysics Data System (ADS)

Quyen, Michel Le Van; Martinerie, Jacques; Adam, Claude; Varela, Francisco J.

1999-03-01

The degree of interdependence between intracranial electroencephalographic (EEG) channels was investigated in epileptic patients with temporal lobe seizures during interictal (between seizures) periods. With a novel method to characterize nonlinear cross-predictability, that is, the predictability of one channel using another channel as data base, we demonstrated here a possibility to extract information on the spatio-temporal organization of interactions between multichannel recording sites. This method determines whether two channels contain common activity, and often, whether one channel contains activity induced by the activity of the other channel. In particular, the technique and the comparison with surrogate data demonstrated that transient large-scale nonlinear entrainments by the epileptogenic region can be identified, this with or without epileptic activity. Furthermore, these recurrent activities related with the epileptic foci occurred in well-defined spatio-temporal patterns. This suggests that the epileptogenic region can exhibit very subtle influences on other brain regions during an interictal period and raises the possibility that the cross-predictability analysis of interictal data may be used as a significant aid in locating epileptogenic foci.

Non-contact time-domain imaging of functional brain activation and heterogeneity of superficial signals

NASA Astrophysics Data System (ADS)

Wabnitz, H.; Mazurenka, M.; Di Sieno, L.; Contini, D.; Dalla Mora, A.; Farina, A.; Hoshi, Y.; Kirilina, E.; Macdonald, R.; Pifferi, A.

2017-07-01

Non-contact scanning at small source-detector separation enables imaging of cerebral and extracranial signals at high spatial resolution and their separation based on early and late photons accounting for the related spatio-temporal characteristics.

Portable non-invasive brain-computer interface: challenges and opportunities of optical modalities

NASA Astrophysics Data System (ADS)

Scholl, Clara A.; Hendrickson, Scott M.; Swett, Bruce A.; Fitch, Michael J.; Walter, Erich C.; McLoughlin, Michael P.; Chevillet, Mark A.; Blodgett, David W.; Hwang, Grace M.

2017-05-01

The development of portable non-invasive brain computer interface technologies with higher spatio-temporal resolution has been motivated by the tremendous success seen with implanted devices. This talk will discuss efforts to overcome several major obstacles to viability including approaches that promise to improve spatial and temporal resolution. Optical approaches in particular will be highlighted and the potential benefits of both Blood-Oxygen Level Dependent (BOLD) and Fast Optical Signal (FOS) will be discussed. Early-stage research into the correlations between neural activity and FOS will be explored.

Exploring differences between left and right hand motor imagery via spatio-temporal EEG microstate.

PubMed

Liu, Weifeng; Liu, Xiaoming; Dai, Ruomeng; Tang, Xiaoying

2017-12-01

EEG-based motor imagery is very useful in brain-computer interface. How to identify the imaging movement is still being researched. Electroencephalography (EEG) microstates reflect the spatial configuration of quasi-stable electrical potential topographies. Different microstates represent different brain functions. In this paper, microstate method was used to process the EEG-based motor imagery to obtain microstate. The single-trial EEG microstate sequences differences between two motor imagery tasks - imagination of left and right hand movement were investigated. The microstate parameters - duration, time coverage and occurrence per second as well as the transition probability of the microstate sequences were obtained with spatio-temporal microstate analysis. The results were shown significant differences (P < 0.05) with paired t-test between the two tasks. Then these microstate parameters were used as features and a linear support vector machine (SVM) was utilized to classify the two tasks with mean accuracy 89.17%, superior performance compared to the other methods. These indicate that the microstate can be a promising feature to improve the performance of the brain-computer interface classification.

An event-version-based spatio-temporal modeling approach and its application in the cadastral management

NASA Astrophysics Data System (ADS)

Li, Yangdong; Han, Zhen; Liao, Zhongping

2009-10-01

Spatiality, temporality, legality, accuracy and continuality are characteristic of cadastral information, and the cadastral management demands that the cadastral data should be accurate, integrated and updated timely. It's a good idea to build an effective GIS management system to manage the cadastral data which are characterized by spatiality and temporality. Because no sound spatio-temporal data models have been adopted, however, the spatio-temporal characteristics of cadastral data are not well expressed in the existing cadastral management systems. An event-version-based spatio-temporal modeling approach is first proposed from the angle of event and version. Then with the help of it, an event-version-based spatio-temporal cadastral data model is built to represent spatio-temporal cadastral data. At last, the previous model is used in the design and implementation of a spatio-temporal cadastral management system. The result of the application of the system shows that the event-version-based spatio-temporal data model is very suitable for the representation and organization of cadastral data.

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences

PubMed Central

Jung, Minju; Hwang, Jungsik; Tani, Jun

2015-01-01

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns. PMID:26147887

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences.

PubMed

Jung, Minju; Hwang, Jungsik; Tani, Jun

2015-01-01

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.

NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data.

PubMed

Kasabov, Nikola K

2014-04-01

The brain functions as a spatio-temporal information processing machine. Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli. An enormous amount of such data has been already collected, including brain structural and functional data under different conditions, molecular and genetic data, in an attempt to make a progress in medicine, health, cognitive science, engineering, education, neuro-economics, Brain-Computer Interfaces (BCI), and games. Yet, there is no unifying computational framework to deal with all these types of data in order to better understand this data and the processes that generated it. Standard machine learning techniques only partially succeeded and they were not designed in the first instance to deal with such complex data. Therefore, there is a need for a new paradigm to deal with STBD. This paper reviews some methods of spiking neural networks (SNN) and argues that SNN are suitable for the creation of a unifying computational framework for learning and understanding of various STBD, such as EEG, fMRI, genetic, DTI, MEG, and NIRS, in their integration and interaction. One of the reasons is that SNN use the same computational principle that generates STBD, namely spiking information processing. This paper introduces a new SNN architecture, called NeuCube, for the creation of concrete models to map, learn and understand STBD. A NeuCube model is based on a 3D evolving SNN that is an approximate map of structural and functional areas of interest of the brain related to the modeling STBD. Gene information is included optionally in the form of gene regulatory networks (GRN) if this is relevant to the problem and the data. A NeuCube model learns from STBD and creates connections between clusters of neurons that manifest chains (trajectories) of neuronal activity. Once learning is applied, a NeuCube model can reproduce these trajectories, even if only part of the input STBD or the stimuli data is presented, thus acting as an associative memory. The NeuCube framework can be used not only to discover functional pathways from data, but also as a predictive system of brain activities, to predict and possibly, prevent certain events. Analysis of the internal structure of a model after training can reveal important spatio-temporal relationships 'hidden' in the data. NeuCube will allow the integration in one model of various brain data, information and knowledge, related to a single subject (personalized modeling) or to a population of subjects. The use of NeuCube for classification of STBD is illustrated in a case study problem of EEG data. NeuCube models result in a better accuracy of STBD classification than standard machine learning techniques. They are robust to noise (so typical in brain data) and facilitate a better interpretation of the results and understanding of the STBD and the brain conditions under which data was collected. Future directions for the use of SNN for STBD are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting BCI subject performance using probabilistic spatio-temporal filters.

PubMed

Suk, Heung-Il; Fazli, Siamac; Mehnert, Jan; Müller, Klaus-Robert; Lee, Seong-Whan

2014-01-01

Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian-and thereby probabilistic-framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the subject population. As expected, large variability of brain rhythms is observed between subjects. We have clustered subjects according to similarities in their corresponding spectral characteristics from the BSSFO model, which is found to reflect their BCI performances well. In BCI, a considerable percentage of subjects is unable to use a BCI for communication, due to their missing ability to modulate their brain rhythms-a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects' performance preceding the actual, time-consuming BCI-experiment enhances the usage of BCIs, e.g., by detecting users with BCI inability. This work additionally contributes by using the novel BSSFO method to predict the BCI-performance using only 2 minutes and 3 channels of resting-state EEG data recorded before the actual BCI-experiment. Specifically, by grouping the individual frequency characteristics we have nicely classified them into the subject 'prototypes' (like μ - or β -rhythm type subjects) or users without ability to communicate with a BCI, and then by further building a linear regression model based on the grouping we could predict subjects' performance with the maximum correlation coefficient of 0.581 with the performance later seen in the actual BCI session.

Spatio-temporal variations in the diversity and abundance of commercially important Decapoda and Stomatopoda in subtropical Hong Kong waters

NASA Astrophysics Data System (ADS)

Lui, Karen K. Y.; Ng, Jasmine S. S.; Leung, Kenneth M. Y.

2007-05-01

In subtropical Hong Kong, western waters (WW) are strongly influenced by the freshwater input from the Pearl River estuary, especially during summer monsoon, whereas eastern waters (EW) are predominantly influenced by oceanic currents throughout the year. Such hydrographical differences may lead to spatio-temporal differences in biodiversity of benthic communities. This study investigated the diversity and abundance of commercially important decapods and stomatopods in EW (i.e. Tolo Harbour and Channel) and WW (i.e. Tuen Mun and Lantau Island) of Hong Kong using monthly trawl surveys (August 2003-May 2005). In total, 22 decapod and nine stomatopod species were recorded. The penaeid Metapenaeopsis sp. and stomatopod Oratosquillina interrupta were the most abundant and dominant crustaceans in EW and WW, respectively. Both univariate and multivariate analyses showed that WW supported significantly higher abundance, biomass and diversity of crustaceans than EW, although there were significant between-site and within-site variations in community structure. Higher abundance and biomass of crustaceans were recorded in summer than winter. Such spatio-temporal variations could be explained by differences in the hydrography, environmental conditions and anthropogenic impacts between the two areas. Temporal patterns in the abundance-biomass comparison curves and negative W-statistics suggest that the communities have been highly disturbed in both areas, probably due to anthropogenic activities such as bottom trawling and marine pollution.

Neurovision processor for designing intelligent sensors

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1992-03-01

A programmable multi-task neuro-vision processor, called the Positive-Negative (PN) neural processor, is proposed as a plausible hardware mechanism for constructing robust multi-task vision sensors. The computational operations performed by the PN neural processor are loosely based on the neural activity fields exhibited by certain nervous tissue layers situated in the brain. The neuro-vision processor can be programmed to generate diverse dynamic behavior that may be used for spatio-temporal stabilization (STS), short-term visual memory (STVM), spatio-temporal filtering (STF) and pulse frequency modulation (PFM). A multi- functional vision sensor that performs a variety of information processing operations on time- varying two-dimensional sensory images can be constructed from a parallel and hierarchical structure of numerous individually programmed PN neural processors.

Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise

PubMed Central

Zammit-Mangion, Andrew; Rougier, Jonathan; Schön, Nana; Lindgren, Finn; Bamber, Jonathan

2015-01-01

Antarctica is the world's largest fresh-water reservoir, with the potential to raise sea levels by about 60 m. An ice sheet contributes to sea-level rise (SLR) when its rate of ice discharge and/or surface melting exceeds accumulation through snowfall. Constraining the contribution of the ice sheets to present-day SLR is vital both for coastal development and planning, and climate projections. Information on various ice sheet processes is available from several remote sensing data sets, as well as in situ data such as global positioning system data. These data have differing coverage, spatial support, temporal sampling and sensing characteristics, and thus, it is advantageous to combine them all in a single framework for estimation of the SLR contribution and the assessment of processes controlling mass exchange with the ocean. In this paper, we predict the rate of height change due to salient geophysical processes in Antarctica and use these to provide estimates of SLR contribution with associated uncertainties. We employ a multivariate spatio-temporal model, approximated as a Gaussian Markov random field, to take advantage of differing spatio-temporal properties of the processes to separate the causes of the observed change. The process parameters are estimated from geophysical models, while the remaining parameters are estimated using a Markov chain Monte Carlo scheme, designed to operate in a high-performance computing environment across multiple nodes. We validate our methods against a separate data set and compare the results to those from studies that invariably employ numerical model outputs directly. We conclude that it is possible, and insightful, to assess Antarctica's contribution without explicit use of numerical models. Further, the results obtained here can be used to test the geophysical numerical models for which in situ data are hard to obtain. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd. PMID:25937792

Spatio-temporal Organization During Ventricular Fibrillation in the Human Heart.

PubMed

Robson, Jinny; Aram, Parham; Nash, Martyn P; Bradley, Chris P; Hayward, Martin; Paterson, David J; Taggart, Peter; Clayton, Richard H; Kadirkamanathan, Visakan

2018-06-01

In this paper, we present a novel approach to quantify the spatio-temporal organization of electrical activation during human ventricular fibrillation (VF). We propose three different methods based on correlation analysis, graph theoretical measures and hierarchical clustering. Using the proposed approach, we quantified the level of spatio-temporal organization during three episodes of VF in ten patients, recorded using multi-electrode epicardial recordings with 30 s coronary perfusion, 150 s global myocardial ischaemia and 30 s reflow. Our findings show a steady decline in spatio-temporal organization from the onset of VF with coronary perfusion. We observed transient increases in spatio-temporal organization during global myocardial ischaemia. However, the decline in spatio-temporal organization continued during reflow. Our results were consistent across all patients, and were consistent with the numbers of phase singularities. Our findings show that the complex spatio-temporal patterns can be studied using complex network analysis.

The choice of the source space and the Laplacian matrix in LORETA and the spatio-temporal Kalman filter EEG inverse methods.

PubMed

Habboush, Nawar; Hamid, Laith; Japaridze, Natia; Wiegand, Gert; Heute, Ulrich; Stephani, Ulrich; Galka, Andreas; Siniatchkin, Michael

2015-08-01

The discretization of the brain and the definition of the Laplacian matrix influence the results of methods based on spatial and spatio-temporal smoothness, since the Laplacian operator is used to define the smoothness based on the neighborhood of each grid point. In this paper, the results of low resolution electromagnetic tomography (LORETA) and the spatiotemporal Kalman filter (STKF) are computed using, first, a greymatter source space with the standard definition of the Laplacian matrix and, second, using a whole-brain source space and a modified definition of the Laplacian matrix. Electroencephalographic (EEG) source imaging results of five inter-ictal spikes from a pre-surgical patient with epilepsy are used to validate the two aforementioned approaches. The results using the whole-brain source space and the modified definition of the Laplacian matrix were concentrated in a single source activation, stable, and concordant with the location of the focal cortical dysplasia (FCD) in the patient's brain compared with the results which use a grey-matter grid and the classical definition of the Laplacian matrix. This proof-of-concept study demonstrates a substantial improvement of source localization with both LORETA and STKF and constitutes a basis for further research in a large population of patients with epilepsy.

Complex, Dynamic Combination of Physical, Chemical and Nutritional Variables Controls Spatio-Temporal Variation of Sandy Beach Community Structure

PubMed Central

Ortega Cisneros, Kelly; Smit, Albertus J.; Laudien, Jürgen; Schoeman, David S.

2011-01-01

Sandy beach ecological theory states that physical features of the beach control macrobenthic community structure on all but the most dissipative beaches. However, few studies have simultaneously evaluated the relative importance of physical, chemical and biological factors as potential explanatory variables for meso-scale spatio-temporal patterns of intertidal community structure in these systems. Here, we investigate macroinfaunal community structure of a micro-tidal sandy beach that is located on an oligotrophic subtropical coast and is influenced by seasonal estuarine input. We repeatedly sampled biological and environmental variables at a series of beach transects arranged at increasing distances from the estuary mouth. Sampling took place over a period of five months, corresponding with the transition between the dry and wet season. This allowed assessment of biological-physical relationships across chemical and nutritional gradients associated with a range of estuarine inputs. Physical, chemical, and biological response variables, as well as measures of community structure, showed significant spatio-temporal patterns. In general, bivariate relationships between biological and environmental variables were rare and weak. However, multivariate correlation approaches identified a variety of environmental variables (i.e., sampling session, the C∶N ratio of particulate organic matter, dissolved inorganic nutrient concentrations, various size fractions of photopigment concentrations, salinity and, to a lesser extent, beach width and sediment kurtosis) that either alone or combined provided significant explanatory power for spatio-temporal patterns of macroinfaunal community structure. Overall, these results showed that the macrobenthic community on Mtunzini Beach was not structured primarily by physical factors, but instead by a complex and dynamic blend of nutritional, chemical and physical drivers. This emphasises the need to recognise ocean-exposed sandy beaches as functional ecosystems in their own right. PMID:21858213

Complex, dynamic combination of physical, chemical and nutritional variables controls spatio-temporal variation of sandy beach community structure.

PubMed

Ortega Cisneros, Kelly; Smit, Albertus J; Laudien, Jürgen; Schoeman, David S

2011-01-01

Sandy beach ecological theory states that physical features of the beach control macrobenthic community structure on all but the most dissipative beaches. However, few studies have simultaneously evaluated the relative importance of physical, chemical and biological factors as potential explanatory variables for meso-scale spatio-temporal patterns of intertidal community structure in these systems. Here, we investigate macroinfaunal community structure of a micro-tidal sandy beach that is located on an oligotrophic subtropical coast and is influenced by seasonal estuarine input. We repeatedly sampled biological and environmental variables at a series of beach transects arranged at increasing distances from the estuary mouth. Sampling took place over a period of five months, corresponding with the transition between the dry and wet season. This allowed assessment of biological-physical relationships across chemical and nutritional gradients associated with a range of estuarine inputs. Physical, chemical, and biological response variables, as well as measures of community structure, showed significant spatio-temporal patterns. In general, bivariate relationships between biological and environmental variables were rare and weak. However, multivariate correlation approaches identified a variety of environmental variables (i.e., sampling session, the C∶N ratio of particulate organic matter, dissolved inorganic nutrient concentrations, various size fractions of photopigment concentrations, salinity and, to a lesser extent, beach width and sediment kurtosis) that either alone or combined provided significant explanatory power for spatio-temporal patterns of macroinfaunal community structure. Overall, these results showed that the macrobenthic community on Mtunzini Beach was not structured primarily by physical factors, but instead by a complex and dynamic blend of nutritional, chemical and physical drivers. This emphasises the need to recognise ocean-exposed sandy beaches as functional ecosystems in their own right.

Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

NASA Astrophysics Data System (ADS)

Gollas, Frank; Tetzlaff, Ronald

2009-05-01

Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio-temporal autoregressive filter models are considered, for a prediction of EEG signal values. Thus Signal features values for successive, short, quasi stationary segments of brain electrical activity can be obtained, with the objective of detecting distinct changes prior to impending epileptic seizures. Furthermore long term recordings gained during presurgical diagnostics in temporal lobe epilepsy are analyzed and the predictive performance of the extracted features is evaluated statistically. Therefore a Receiver Operating Characteristic analysis is considered, assessing the distinguishability between distributions of supposed preictal and interictal periods.

Does my brain want what my eyes like? - How food liking and choice influence spatio-temporal brain dynamics of food viewing.

PubMed

Bielser, Marie-Laure; Crézé, Camille; Murray, Micah M; Toepel, Ulrike

2016-12-01

How food valuation and decision-making influence the perception of food is of major interest to better understand food intake behavior and, by extension, body weight management. Our study investigated behavioral responses and spatio-temporal brain dynamics by means of visual evoked potentials (VEPs) in twenty-two normal-weight participants when viewing pairs of food photographs. Participants rated how much they liked each food item (valuation) and subsequently chose between the two alternative food images. Unsurprisingly, strongly liked foods were also chosen most often. Foods were rated faster as strongly liked than as mildly liked or disliked irrespective of whether they were subsequently chosen over an alternative. Moreover, strongly liked foods were subsequently also chosen faster than the less liked alternatives. Response times during valuation and choice were positively correlated, but only when foods were liked; the faster participants rated foods as strongly liked, the faster they were in choosing the food item over an alternative. VEP modulations by the level of liking attributed as well as the subsequent choice were found as early as 135-180ms after food image onset. Analyses of neural source activity patterns over this time interval revealed an interaction between liking and the subsequent choice within the insula, dorsal frontal and superior parietal regions. The neural responses to food viewing were found to be modulated by the attributed level of liking only when foods were chosen, not when they were dismissed for an alternative. Therein, the responses to disliked foods were generally greater than those to foods that were liked more. Moreover, the responses to disliked but chosen foods were greater than responses to disliked foods which were subsequently dismissed for an alternative offer. Our findings show that the spatio-temporal brain dynamics to food viewing are immediately influenced both by how much foods are liked and by choices taken on them. These valuation and choice processes are subserved by brain regions involved in salience and reward attribution as well as in decision-making processes, which are likely to influence prospective dietary choices in everyday life. Copyright © 2015 Elsevier Inc. All rights reserved.

Niveaux d'étude du cerveau, et sagesse physique

NASA Astrophysics Data System (ADS)

Toulouse, Gérard

1993-02-01

The brain is a complex spatio-temporal affair. Several brain theories propose the definition of three superposed levels of study. But physics, though the experience of condensed matter physics, suggests that it is unwise to enforce onto brain theories a unified hierarchical scheme, the inspiration for which seems to come from the realm of sub-molecular physics. Le cerveau est une affaire spatio-temporelle complexe. Plusieurs théories du cerveau proposent de définir trois niveaux d'études superposés. Mais la physique, à travers l'expérience de la physique de la matière condensée, suggère qu'il n'est pas sage d'imposer sur les théories du cerveau un schéma hiérarchique unifié, dont l'inspiration semble provenir du domaine de la physique sub-moléculaire.

Multivariate Statistical Postprocessing of Ensemble Forcasts of Precipitation and Temperature over four River Basins in California

NASA Astrophysics Data System (ADS)

Scheuerer, Michael; Hamill, Thomas M.; Whitin, Brett; He, Minxue; Henkel, Arthur

2017-04-01

Hydrological forecasts strongly rely on predictions of precipitation amounts and temperature as meteorological inputs to hydrological models. Ensemble weather predictions provide a number of different scenarios that reflect the uncertainty about these meteorological inputs, but are often biased and underdispersive, and therefore require statistical postprocessing. In hydrological applications it is crucial that spatial and temporal (i.e. between different forecast lead times) dependencies as well as dependence between the two weather variables is adequately represented by the recalibrated forecasts. We present a study with temperature and precipitation forecasts over four river basins over California that are postprocessed with a variant of the nonhomogeneous Gaussian regression method (Gneiting et al., 2005) and the censored, shifted gamma distribution approach (Scheuerer and Hamill, 2015) respectively. For modelling spatial, temporal and inter-variable dependence we propose a variant of the Schaake Shuffle (Clark et al., 2005) that uses spatio-temporal trajectories of observed temperture and precipitation as a dependence template, and chooses the historic dates in such a way that the divergence between the marginal distributions of these trajectories and the univariate forecast distributions is minimized. For the four river basins considered in our study, this new multivariate modelling technique consistently improves upon the Schaake Shuffle and yields reliable spatio-temporal forecast trajectories of temperature and precipitation that can be used to force hydrological forecast systems. References: Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., Wilby, R., 2004. The Schaake Shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology, 5, pp.243-262. Gneiting, T., Raftery, A.E., Westveld, A.H., Goldman, T., 2005. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS. Monthly Weather Review, 133, pp.1098-1118. Scheuerer, M., Hamill, T.M., 2015. Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Monthly Weather Review, 143, pp.4578-4596. Scheuerer, M., Hamill, T.M., Whitin, B., He, M., and Henkel, A., 2016: A method for preferential selection of dates in the Schaake shuffle approach to constructing spatio-temporal forecast fields of temperature and precipitation. Water Resources Research, submitted.

Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing.

PubMed

Ciullo, Valentina; Vecchio, Daniela; Gili, Tommaso; Spalletta, Gianfranco; Piras, Federica

2018-01-01

The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still debated. Using graph theoretical analysis applied to structural connectivity data, we tested the extent of brain connectivity properties associated with spatio-temporal predictive performance across 29 healthy subjects. Participants detected visual targets appearing at one out of three locations after one out of three intervals; expectations about stimulus location (spatial condition) or onset (temporal condition) were induced by valid or invalid symbolic cues. Connectivity matrices and centrality/segregation measures, expressing the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation, were calculated from whole-brain tractography and cortico-subcortical parcellation. Results: Response preparedness to cued stimuli relied on different structural connectivity networks for the temporal and spatial domains. Significant covariance was observed between centrality measures of regions within a subcortical-fronto-parietal-occipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit. When the potential for functional segregation was tested, we found highly clustered structural connectivity across the right superior, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting. Conversely, when the interaction between explicit and implicit temporal orienting processes was considered at the long interval, we found that explicit processes were related to centrality measures of the bilateral inferior parietal lobule. Degree centrality of the same region in the left hemisphere covaried with behavioral measures indexing the process of attentional re-orienting. These results represent a crucial step forward the ordinary predictive processing description, as we identified the patterns of connectivity characterizing the brain organization associated with the ability to generate and update temporal expectancies in case of contextual violations.

Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data

NASA Astrophysics Data System (ADS)

Garg, Rahul; Cecchi, Guillermo A.; Rao, A. Ravishankar

2011-03-01

Functional neuroimaging research is moving from the study of "activations" to the study of "interactions" among brain regions. Granger causality analysis provides a powerful technique to model spatio-temporal interactions among brain regions. We apply this technique to full-brain fMRI data without aggregating any voxel data into regions of interest (ROIs). We circumvent the problem of dimensionality using sparse regression from machine learning. On a simple finger-tapping experiment we found that (1) a small number of voxels in the brain have very high prediction power, explaining the future time course of other voxels in the brain; (2) these voxels occur in small sized clusters (of size 1-4 voxels) distributed throughout the brain; (3) albeit small, these clusters overlap with most of the clusters identified with the non-temporal General Linear Model (GLM); and (4) the method identifies clusters which, while not determined by the task and not detectable by GLM, still influence brain activity.

Distinct spatio-temporal profiles of beta-oscillations within visual and sensorimotor areas during action recognition as revealed by MEG.

PubMed

Pavlidou, Anastasia; Schnitzler, Alfons; Lange, Joachim

2014-05-01

The neural correlates of action recognition have been widely studied in visual and sensorimotor areas of the human brain. However, the role of neuronal oscillations involved during the process of action recognition remains unclear. Here, we were interested in how the plausibility of an action modulates neuronal oscillations in visual and sensorimotor areas. Subjects viewed point-light displays (PLDs) of biomechanically plausible and implausible versions of the same actions. Using magnetoencephalography (MEG), we examined dynamic changes of oscillatory activity during these action recognition processes. While both actions elicited oscillatory activity in visual and sensorimotor areas in several frequency bands, a significant difference was confined to the beta-band (∼20 Hz). An increase of power for plausible actions was observed in left temporal, parieto-occipital and sensorimotor areas of the brain, in the beta-band in successive order between 1650 and 2650 msec. These distinct spatio-temporal beta-band profiles suggest that the action recognition process is modulated by the degree of biomechanical plausibility of the action, and that spectral power in the beta-band may provide a functional interaction between visual and sensorimotor areas in humans. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

PubMed Central

Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting

2014-01-01

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type. PMID:25051028

A hybrid spatio-temporal data indexing method for trajectory databases.

PubMed

Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting

2014-07-21

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

Research on spatio-temporal database techniques for spatial information service

NASA Astrophysics Data System (ADS)

Zhao, Rong; Wang, Liang; Li, Yuxiang; Fan, Rongshuang; Liu, Ping; Li, Qingyuan

2007-06-01

Geographic data should be described by spatial, temporal and attribute components, but the spatio-temporal queries are difficult to be answered within current GIS. This paper describes research into the development and application of spatio-temporal data management system based upon GeoWindows GIS software platform which was developed by Chinese Academy of Surveying and Mapping (CASM). Faced the current and practical requirements of spatial information application, and based on existing GIS platform, one kind of spatio-temporal data model which integrates vector and grid data together was established firstly. Secondly, we solved out the key technique of building temporal data topology, successfully developed a suit of spatio-temporal database management system adopting object-oriented methods. The system provides the temporal data collection, data storage, data management and data display and query functions. Finally, as a case study, we explored the application of spatio-temporal data management system with the administrative region data of multi-history periods of China as the basic data. With all the efforts above, the GIS capacity of management and manipulation in aspect of time and attribute of GIS has been enhanced, and technical reference has been provided for the further development of temporal geographic information system (TGIS).

The influence of natural factors on the spatio-temporal distribution of Oncomelania hupensis.

PubMed

Cheng, Gong; Li, Dan; Zhuang, Dafang; Wang, Yong

2016-12-01

We analyzed the influence of natural factors, such as temperature, rainfall, vegetation and hydrology, on the spatio-temporal distribution of Oncomelania hupensis and explored the leading factors influencing these parameters. The results will provide reference methods and theoretical a basis for the schistosomiasis control. GIS (Geographic Information System) spatial display and analysis were used to describe the spatio-temporal distribution of Oncomelania hupensis in the study area (Dongting Lake in Hunan Province) from 2004 to 2011. Correlation analysis was used to detect the natural factors associated with the spatio-temporal distribution of O. hupensis. Spatial regression analysis was used to quantitatively analyze the effects of related natural factors on the spatio-temporal distribution of snails and explore the dominant factors influencing this parameter. (1) Overall, the spatio-temporal distribution of O. hupensis was governed by the comprehensive effects of natural factors. In the study area, the average density of living snails showed a downward trend, with the exception of a slight rebound in 2009. The density of living snails showed significant spatial clustering, and the degree of aggregation was initially weak but enhanced later. Regions with high snail density and towns with an HH distribution pattern were mostly distributed in the plain areas in the northwestern and inlet and outlet of the lake. (2) There were space-time differences in the influence of natural factors on the spatio-temporal distribution of O. hupensis. Temporally, the comprehensive influence of natural factors on snail distribution increased first and then decreased. Natural factors played an important role in snail distribution in 2005, 2006, 2010 and 2011. Spatially, it decreased from the northeast to the southwest. Snail distributions in more than 20 towns located along the Yuanshui River and on the west side of the Lishui River were less affected by natural factors, whereas relatively larger in areas around the outlet of the lake (Chenglingji) were more affected. (3) The effects of natural factors on the spatio-temporal distribution of O. hupensis were spatio-temporally heterogeneous. Rainfall, land surface temperature, NDVI, and distance from water sources all played an important role in the spatio-temporal distribution of O. hupensis. In addition, due to the effects of the local geographical environment, the direction of the influences the average annual rainfall, land surface temperature, and NDVI had on the spatio-temporal distribution of O. hupensis were all spatio-temporally heterogeneous, and both the distance from water sources and the history of snail distribution always had positive effects on the distribution O. hupensis, but the direction of the influence was spatio-temporally heterogeneous. (4) Of all the natural factors, the leading factors influencing the spatio-temporal distribution of O. hupensis were rainfall and vegetation (NDVI), and the primary factor alternated between these two. The leading role of rainfall decreased year by year, while that of vegetation (NDVI) increased from 2004 to 2011. The spatio-temporal distribution of O. hupensis was significantly influenced by natural factors, and the influences were heterogeneous across space and time. Additionally, the variation in the spatial-temporal distribution of O. hupensis was mainly affected by rainfall and vegetation. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Spatio-temporal networks: reachability, centrality and robustness.

PubMed

Williams, Matthew J; Musolesi, Mirco

2016-06-01

Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks.

Visualization of Spatio-Temporal Relations in Movement Event Using Multi-View

NASA Astrophysics Data System (ADS)

Zheng, K.; Gu, D.; Fang, F.; Wang, Y.; Liu, H.; Zhao, W.; Zhang, M.; Li, Q.

2017-09-01

Spatio-temporal relations among movement events extracted from temporally varying trajectory data can provide useful information about the evolution of individual or collective movers, as well as their interactions with their spatial and temporal contexts. However, the pure statistical tools commonly used by analysts pose many difficulties, due to the large number of attributes embedded in multi-scale and multi-semantic trajectory data. The need for models that operate at multiple scales to search for relations at different locations within time and space, as well as intuitively interpret what these relations mean, also presents challenges. Since analysts do not know where or when these relevant spatio-temporal relations might emerge, these models must compute statistical summaries of multiple attributes at different granularities. In this paper, we propose a multi-view approach to visualize the spatio-temporal relations among movement events. We describe a method for visualizing movement events and spatio-temporal relations that uses multiple displays. A visual interface is presented, and the user can interactively select or filter spatial and temporal extents to guide the knowledge discovery process. We also demonstrate how this approach can help analysts to derive and explain the spatio-temporal relations of movement events from taxi trajectory data.

Working memory load-dependent spatio-temporal activity of single-trial P3 response detected with an adaptive wavelet denoiser.

PubMed

Zhang, Qiushi; Yang, Xueqian; Yao, Li; Zhao, Xiaojie

2017-03-27

Working memory (WM) refers to the holding and manipulation of information during cognitive tasks. Its underlying neural mechanisms have been explored through both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Trial-by-trial coupling of simultaneously collected EEG and fMRI signals has become an important and promising approach to study the spatio-temporal dynamics of such cognitive processes. Previous studies have demonstrated a modulation effect of the WM load on both the BOLD response in certain brain areas and the amplitude of P3. However, much remains to be explored regarding the WM load-dependent relationship between the amplitude of ERP components and cortical activities, and the low signal-to-noise ratio (SNR) of the EEG signal still poses a challenge to performing single-trial analyses. In this paper, we investigated the spatio-temporal activities of P3 during an n-back verbal WM task by introducing an adaptive wavelet denoiser into the extraction of single-trial P3 features and using general linear model (GLM) to integrate simultaneously collected EEG and fMRI data. Our results replicated the modulation effect of the WM load on the P3 amplitude. Additionally, the activation of single-trial P3 amplitudes was detected in multiple brain regions, including the insula, the cuneus, the lingual gyrus (LG), and the middle occipital gyrus (MOG). Moreover, we found significant correlations between P3 features and behavioral performance. These findings suggest that the single-trial integration of simultaneous EEG and fMRI signals may provide new insights into classical cognitive functions. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Spatio-temporal Bayesian model selection for disease mapping

PubMed Central

Carroll, R; Lawson, AB; Faes, C; Kirby, RS; Aregay, M; Watjou, K

2016-01-01

Spatio-temporal analysis of small area health data often involves choosing a fixed set of predictors prior to the final model fit. In this paper, we propose a spatio-temporal approach of Bayesian model selection to implement model selection for certain areas of the study region as well as certain years in the study time line. Here, we examine the usefulness of this approach by way of a large-scale simulation study accompanied by a case study. Our results suggest that a special case of the model selection methods, a mixture model allowing a weight parameter to indicate if the appropriate linear predictor is spatial, spatio-temporal, or a mixture of the two, offers the best option to fitting these spatio-temporal models. In addition, the case study illustrates the effectiveness of this mixture model within the model selection setting by easily accommodating lifestyle, socio-economic, and physical environmental variables to select a predominantly spatio-temporal linear predictor. PMID:28070156

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior.

PubMed

Hansen, Sofie Therese; Hansen, Lars Kai

2017-03-01

Electroencephalography (EEG) can capture brain dynamics in high temporal resolution. By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging connections exist in the brain than long ranging, arguing for spatially focal sources. Additionally, recent work (Delorme et al., 2012) argues that EEG can be decomposed into components having sparse source distributions. On the temporal side both short and long term stationarity of brain activation are seen. We summarize these insights in an inverse solver, the so-called "Variational Garrote" (Kappen and Gómez, 2013). Using a Markov prior we can incorporate flexible degrees of temporal stationarity. Through spatial basis functions spatially smooth distributions are obtained. Sparsity of these are inherent to the Variational Garrote solver. We name our method the MarkoVG and demonstrate its ability to adapt to the temporal smoothness and spatial sparsity in simulated EEG data. Finally a benchmark EEG dataset is used to demonstrate MarkoVG's ability to recover non-stationary brain dynamics. Copyright © 2016 Elsevier Inc. All rights reserved.

Spatio-temporal Hotelling observer for signal detection from image sequences

PubMed Central

Caucci, Luca; Barrett, Harrison H.; Rodríguez, Jeffrey J.

2010-01-01

Detection of signals in noisy images is necessary in many applications, including astronomy and medical imaging. The optimal linear observer for performing a detection task, called the Hotelling observer in the medical literature, can be regarded as a generalization of the familiar prewhitening matched filter. Performance on the detection task is limited by randomness in the image data, which stems from randomness in the object, randomness in the imaging system, and randomness in the detector outputs due to photon and readout noise, and the Hotelling observer accounts for all of these effects in an optimal way. If multiple temporal frames of images are acquired, the resulting data set is a spatio-temporal random process, and the Hotelling observer becomes a spatio-temporal linear operator. This paper discusses the theory of the spatio-temporal Hotelling observer and estimation of the required spatio-temporal covariance matrices. It also presents a parallel implementation of the observer on a cluster of Sony PLAYSTATION 3 gaming consoles. As an example, we consider the use of the spatio-temporal Hotelling observer for exoplanet detection. PMID:19550494

Spatio-temporal Hotelling observer for signal detection from image sequences.

PubMed

Caucci, Luca; Barrett, Harrison H; Rodriguez, Jeffrey J

2009-06-22

Detection of signals in noisy images is necessary in many applications, including astronomy and medical imaging. The optimal linear observer for performing a detection task, called the Hotelling observer in the medical literature, can be regarded as a generalization of the familiar prewhitening matched filter. Performance on the detection task is limited by randomness in the image data, which stems from randomness in the object, randomness in the imaging system, and randomness in the detector outputs due to photon and readout noise, and the Hotelling observer accounts for all of these effects in an optimal way. If multiple temporal frames of images are acquired, the resulting data set is a spatio-temporal random process, and the Hotelling observer becomes a spatio-temporal linear operator. This paper discusses the theory of the spatio-temporal Hotelling observer and estimation of the required spatio-temporal covariance matrices. It also presents a parallel implementation of the observer on a cluster of Sony PLAYSTATION 3 gaming consoles. As an example, we consider the use of the spatio-temporal Hotelling observer for exoplanet detection.

A Spatio-Temporally Explicit Random Encounter Model for Large-Scale Population Surveys

PubMed Central

Jousimo, Jussi; Ovaskainen, Otso

2016-01-01

Random encounter models can be used to estimate population abundance from indirect data collected by non-invasive sampling methods, such as track counts or camera-trap data. The classical Formozov–Malyshev–Pereleshin (FMP) estimator converts track counts into an estimate of mean population density, assuming that data on the daily movement distances of the animals are available. We utilize generalized linear models with spatio-temporal error structures to extend the FMP estimator into a flexible Bayesian modelling approach that estimates not only total population size, but also spatio-temporal variation in population density. We also introduce a weighting scheme to estimate density on habitats that are not covered by survey transects, assuming that movement data on a subset of individuals is available. We test the performance of spatio-temporal and temporal approaches by a simulation study mimicking the Finnish winter track count survey. The results illustrate how the spatio-temporal modelling approach is able to borrow information from observations made on neighboring locations and times when estimating population density, and that spatio-temporal and temporal smoothing models can provide improved estimates of total population size compared to the FMP method. PMID:27611683

Detecting changes resulting from human pressure in a naturally quick-changing and heterogeneous environment: Spatial and temporal scales of variability in coastal lagoons

NASA Astrophysics Data System (ADS)

Pérez-Ruzafa, A.; Marcos, C.; Pérez-Ruzafa, I. M.; Barcala, E.; Hegazi, M. I.; Quispe, J.

2007-10-01

To detect changes in ecosystems due to human impact, experimental designs must include replicates at the appropriate scale to avoid pseudoreplication. Although coastal lagoons, with their highly variable environmental factors and biological assemblages, are relatively well-studied systems, very little is known about their natural scales of variation. In this study, we investigate the spatio-temporal scales of variability in the Mar Menor coastal lagoon (SE Spain) using structured hierarchical sampling designs, mixed and permutational multi-variate analyses of variance, and ordination multi-variate analyses applied to hydrographical parameters, nutrients, chlorophyll a and ichthyoplankton in the water column, and to macrophyte and fish benthic assemblages. Lagoon processes in the Mar Menor show heterogeneous patterns at different temporal and spatial scales. The water column characteristics (including nutrient concentration) showed small-scale spatio-temporal variability, from 10 0 to 10 1 km and from fortnightly to seasonally. Biological features (chlorophyll a concentration and ichthyoplankton assemblage descriptors) showed monthly changes and spatial patterns at the scale of 10 0 (chlorophyll a) - 10 1 km (ichthyoplankton). Benthic assemblages (macrophytes and fishes) showed significant differences between types of substrates in the same locality and between localities, according to horizontal gradients related with confinement in the lagoon, at the scale of 10 0-10 1 km. The vertical zonation of macrophyte assemblages (at scales of 10 1-10 2 cm) overlaps changes in substrata and horizontal gradients. Seasonal patterns in vegetation biomass were not significant, but the significant interaction between Locality and Season indicated that the seasons of maximum and minimum biomass depend on local environmental conditions. Benthic fish assemblages showed no significant patterns at the monthly scale but did show seasonal patterns.

Spatio-temporal response of maize yield to edaphic and meteorological conditions in a saline farmland

USDA-ARS?s Scientific Manuscript database

Spatio-temporal variability of crop production strongly depends on soil heterogeneity, meteorological conditions, and their interaction. Canopy reflectance can be used to describe crop status and yield spatial variability. The objectives of this work were to understand the spatio-temporal variabilit...

Incorporating time and spatial-temporal reasoning into situation management

NASA Astrophysics Data System (ADS)

Jakobson, Gabriel

2010-04-01

Spatio-temporal reasoning plays a significant role in situation management that is performed by intelligent agents (human or machine) by affecting how the situations are recognized, interpreted, acted upon or predicted. Many definitions and formalisms for the notion of spatio-temporal reasoning have emerged in various research fields including psychology, economics and computer science (computational linguistics, data management, control theory, artificial intelligence and others). In this paper we examine the role of spatio-temporal reasoning in situation management, particularly how to resolve situations that are described by using spatio-temporal relations among events and situations. We discuss a model for describing context sensitive temporal relations and show have the model can be extended for spatial relations.

Spatio-Temporal Video Segmentation with Shape Growth or Shrinkage Constraint

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Charpiat, Guillaume; Brucker, Ludovic; Menze, Bjoern H.

2014-01-01

We propose a new method for joint segmentation of monotonously growing or shrinking shapes in a time sequence of noisy images. The task of segmenting the image time series is expressed as an optimization problem using the spatio-temporal graph of pixels, in which we are able to impose the constraint of shape growth or of shrinkage by introducing monodirectional infinite links connecting pixels at the same spatial locations in successive image frames. The globally optimal solution is computed with a graph cut. The performance of the proposed method is validated on three applications: segmentation of melting sea ice floes and of growing burned areas from time series of 2D satellite images, and segmentation of a growing brain tumor from sequences of 3D medical scans. In the latter application, we impose an additional intersequences inclusion constraint by adding directed infinite links between pixels of dependent image structures.

Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

NASA Astrophysics Data System (ADS)

Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

2016-04-01

The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu/index.php/ and http://earthsystemdatacube.net/. Known anomalies such as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.

Spatio-temporal scaling of channels in braided streams.

Treesearch

A.G. Hunt; G.E. Grant; V.K. Gupta

2006-01-01

The spatio-temporal scaling relationship for individual channels in braided streams is shown to be identical to the spatio-temporal scaling associated with constant Froude number, e.g., Fr = l. A means to derive this relationship is developed from a new theory of sediment transport. The mechanism by which the Fr = l condition apparently governs the scaling seems to...

Neural source dynamics of brain responses to continuous stimuli: Speech processing from acoustics to comprehension.

PubMed

Brodbeck, Christian; Presacco, Alessandro; Simon, Jonathan Z

2018-05-15

Human experience often involves continuous sensory information that unfolds over time. This is true in particular for speech comprehension, where continuous acoustic signals are processed over seconds or even minutes. We show that brain responses to such continuous stimuli can be investigated in detail, for magnetoencephalography (MEG) data, by combining linear kernel estimation with minimum norm source localization. Previous research has shown that the requirement to average data over many trials can be overcome by modeling the brain response as a linear convolution of the stimulus and a kernel, or response function, and estimating a kernel that predicts the response from the stimulus. However, such analysis has been typically restricted to sensor space. Here we demonstrate that this analysis can also be performed in neural source space. We first computed distributed minimum norm current source estimates for continuous MEG recordings, and then computed response functions for the current estimate at each source element, using the boosting algorithm with cross-validation. Permutation tests can then assess the significance of individual predictor variables, as well as features of the corresponding spatio-temporal response functions. We demonstrate the viability of this technique by computing spatio-temporal response functions for speech stimuli, using predictor variables reflecting acoustic, lexical and semantic processing. Results indicate that processes related to comprehension of continuous speech can be differentiated anatomically as well as temporally: acoustic information engaged auditory cortex at short latencies, followed by responses over the central sulcus and inferior frontal gyrus, possibly related to somatosensory/motor cortex involvement in speech perception; lexical frequency was associated with a left-lateralized response in auditory cortex and subsequent bilateral frontal activity; and semantic composition was associated with bilateral temporal and frontal brain activity. We conclude that this technique can be used to study the neural processing of continuous stimuli in time and anatomical space with the millisecond temporal resolution of MEG. This suggests new avenues for analyzing neural processing of naturalistic stimuli, without the necessity of averaging over artificially short or truncated stimuli. Copyright © 2018 Elsevier Inc. All rights reserved.

The Voronoi spatio-temporal data structure

NASA Astrophysics Data System (ADS)

Mioc, Darka

2002-04-01

Current GIS models cannot integrate the temporal dimension of spatial data easily. Indeed, current GISs do not support incremental (local) addition and deletion of spatial objects, and they can not support the temporal evolution of spatial data. Spatio-temporal facilities would be very useful in many GIS applications: harvesting and forest planning, cadastre, urban and regional planning, and emergency planning. The spatio-temporal model that can overcome these problems is based on a topological model---the Voronoi data structure. Voronoi diagrams are irregular tessellations of space, that adapt to spatial objects and therefore they are a synthesis of raster and vector spatial data models. The main advantage of the Voronoi data structure is its local and sequential map updates, which allows us to automatically record each event and performed map updates within the system. These map updates are executed through map construction commands that are composed of atomic actions (geometric algorithms for addition, deletion, and motion of spatial objects) on the dynamic Voronoi data structure. The formalization of map commands led to the development of a spatial language comprising a set of atomic operations or constructs on spatial primitives (points and lines), powerful enough to define the complex operations. This resulted in a new formal model for spatio-temporal change representation, where each update is uniquely characterized by the numbers of newly created and inactivated Voronoi regions. This is used for the extension of the model towards the hierarchical Voronoi data structure. In this model, spatio-temporal changes induced by map updates are preserved in a hierarchical data structure that combines events and corresponding changes in topology. This hierarchical Voronoi data structure has an implicit time ordering of events visible through changes in topology, and it is equivalent to an event structure that can support temporal data without precise temporal information. This formal model of spatio-temporal change representation is currently applied to retroactive map updates and visualization of map evolution. It offers new possibilities in the domains of temporal GIS, transaction processing, spatio-temporal queries, spatio-temporal analysis, map animation and map visualization.

Exploring the spatio-temporal neural basis of face learning

PubMed Central

Yang, Ying; Xu, Yang; Jew, Carol A.; Pyles, John A.; Kass, Robert E.; Tarr, Michael J.

2017-01-01

Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150–250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150–250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces. PMID:28570739

Exploring the spatio-temporal neural basis of face learning.

PubMed

Yang, Ying; Xu, Yang; Jew, Carol A; Pyles, John A; Kass, Robert E; Tarr, Michael J

2017-06-01

Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150-250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150-250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces.

Spatio-temporal cluster detection of chickenpox in Valencia, Spain in the period 2008-2012.

PubMed

Iftimi, Adina; Martínez-Ruiz, Francisco; Míguez Santiyán, Ana; Montes, Francisco

2015-05-18

Chickenpox is a highly contagious airborne disease caused by Varicella zoster, which affects nearly all non-immune children worldwide with an annual incidence estimated at 80-90 million cases. To analyze the spatiotemporal pattern of the chickenpox incidence in the city of Valencia, Spain two complementary statistical approaches were used. First, we evaluated the existence of clusters and spatio-temporal interaction; secondly, we used this information to find the locations of the spatio-temporal clusters via the space-time permutation model. The first method used detects any aggregation in our data but does not provide the spatial and temporal information. The second method gives the locations, areas and time-frame for the spatio-temporal clusters. An overall decreasing time trend, a pronounced 12-monthly periodicity and two complementary periods were observed. Several areas with high incidence, surrounding the center of the city were identified. The existence of aggregation in time and space was observed, and a number of spatio-temporal clusters were located.

Spatio-temporal interactions facilitate large carnivore sympatry across a resource gradient

PubMed Central

Karanth, K. Ullas; Srivathsa, Arjun; Puri, Mahi; Parameshwaran, Ravishankar; Kumar, N. Samba

2017-01-01

Species within a guild vary their use of time, space and resources, thereby enabling sympatry. As intra-guild competition intensifies, such behavioural adaptations may become prominent. We assessed mechanisms of facilitating sympatry among dhole (Cuon alpinus), leopard (Panthera pardus) and tiger (Panthera tigris) in tropical forests of India using camera-trap surveys. We examined population-level temporal, spatial and spatio-temporal segregation among them across four reserves representing a gradient of carnivore and prey densities. Temporal and spatial overlaps were higher at lower prey densities. Combined spatio-temporal overlap was minimal, possibly due to chance. We found fine-scale avoidance behaviours at one high-density reserve. Our results suggest that: (i) patterns of spatial, temporal and spatio-temporal segregation in sympatric carnivores do not necessarily mirror each other; (ii) carnivores are likely to adopt temporal, spatial, and spatio-temporal segregation as alternative mechanisms to facilitate sympatry; and (iii) carnivores show adaptability across a gradient of resource availability, a driver of inter-species competition. We discuss behavioural mechanisms that permit carnivores to co-occupy rather than dominate functional niches, and adaptations to varying intensities of competition that are likely to shape structure and dynamics of carnivore guilds. PMID:28179511

Spatio-temporal interactions facilitate large carnivore sympatry across a resource gradient.

PubMed

Karanth, K Ullas; Srivathsa, Arjun; Vasudev, Divya; Puri, Mahi; Parameshwaran, Ravishankar; Kumar, N Samba

2017-02-08

Species within a guild vary their use of time, space and resources, thereby enabling sympatry. As intra-guild competition intensifies, such behavioural adaptations may become prominent. We assessed mechanisms of facilitating sympatry among dhole ( Cuon alpinus ), leopard ( Panthera pardus ) and tiger ( Panthera tigris ) in tropical forests of India using camera-trap surveys. We examined population-level temporal, spatial and spatio-temporal segregation among them across four reserves representing a gradient of carnivore and prey densities. Temporal and spatial overlaps were higher at lower prey densities. Combined spatio-temporal overlap was minimal, possibly due to chance. We found fine-scale avoidance behaviours at one high-density reserve. Our results suggest that: (i) patterns of spatial, temporal and spatio-temporal segregation in sympatric carnivores do not necessarily mirror each other; (ii) carnivores are likely to adopt temporal, spatial, and spatio-temporal segregation as alternative mechanisms to facilitate sympatry; and (iii) carnivores show adaptability across a gradient of resource availability, a driver of inter-species competition. We discuss behavioural mechanisms that permit carnivores to co-occupy rather than dominate functional niches, and adaptations to varying intensities of competition that are likely to shape structure and dynamics of carnivore guilds. © 2017 The Author(s).

Formally grounding spatio-temporal thinking.

PubMed

Klippel, Alexander; Wallgrün, Jan Oliver; Yang, Jinlong; Li, Rui; Dylla, Frank

2012-08-01

To navigate through daily life, humans use their ability to conceptualize spatio-temporal information, which ultimately leads to a system of categories. Likewise, the spatial sciences rely heavily on conceptualization and categorization as means to create knowledge when they process spatio-temporal data. In the spatial sciences and in related branches of artificial intelligence, an approach has been developed for processing spatio-temporal data on the level of coarse categories: qualitative spatio-temporal representation and reasoning (QSTR). Calculi developed in QSTR allow for the meaningful processing of and reasoning with spatio-temporal information. While qualitative calculi are widely acknowledged in the cognitive sciences, there is little behavioral assessment whether these calculi are indeed cognitively adequate. This is an astonishing conundrum given that these calculi are ubiquitous, are often intended to improve processes at the human-machine interface, and are on several occasions claimed to be cognitively adequate. We have systematically evaluated several approaches to formally characterize spatial relations from a cognitive-behavioral perspective for both static and dynamically changing spatial relations. This contribution will detail our framework, which is addressing the question how formal characterization of space can help us understand how people think with, in, and about space.

Mining moving object trajectories in location-based services for spatio-temporal database update

NASA Astrophysics Data System (ADS)

Guo, Danhuai; Cui, Weihong

2008-10-01

Advances in wireless transmission and mobile technology applied to LBS (Location-based Services) flood us with amounts of moving objects data. Vast amounts of gathered data from position sensors of mobile phones, PDAs, or vehicles hide interesting and valuable knowledge and describe the behavior of moving objects. The correlation between temporal moving patterns of moving objects and geo-feature spatio-temporal attribute was ignored, and the value of spatio-temporal trajectory data was not fully exploited too. Urban expanding or frequent town plan change bring about a large amount of outdated or imprecise data in spatial database of LBS, and they cannot be updated timely and efficiently by manual processing. In this paper we introduce a data mining approach to movement pattern extraction of moving objects, build a model to describe the relationship between movement patterns of LBS mobile objects and their environment, and put up with a spatio-temporal database update strategy in LBS database based on trajectories spatiotemporal mining. Experimental evaluation reveals excellent performance of the proposed model and strategy. Our original contribution include formulation of model of interaction between trajectory and its environment, design of spatio-temporal database update strategy based on moving objects data mining, and the experimental application of spatio-temporal database update by mining moving objects trajectories.

Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach.

PubMed

Luan, Hui; Law, Jane; Quick, Matthew

2015-12-30

Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density. This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas. For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps. This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.

A bayesian hierarchical model for spatio-temporal prediction and uncertainty assessment using repeat LiDAR acquisitions for the Kenai Peninsula, AK, USA

Treesearch

Chad Babcock; Hans Andersen; Andrew O. Finley; Bruce D. Cook

2015-01-01

Models leveraging repeat LiDAR and field collection campaigns may be one possible mechanism to monitor carbon flux in remote forested regions. Here, we look to the spatio-temporally data-rich Kenai Peninsula in Alaska, USA to examine the potential for Bayesian spatio-temporal mapping of terrestrial forest carbon storage and uncertainty.

Natural world physical, brain operational, and mind phenomenal space-time

NASA Astrophysics Data System (ADS)

Fingelkurts, Andrew A.; Fingelkurts, Alexander A.; Neves, Carlos F. H.

2010-06-01

Concepts of space and time are widely developed in physics. However, there is a considerable lack of biologically plausible theoretical frameworks that can demonstrate how space and time dimensions are implemented in the activity of the most complex life-system - the brain with a mind. Brain activity is organized both temporally and spatially, thus representing space-time in the brain. Critical analysis of recent research on the space-time organization of the brain's activity pointed to the existence of so-called operational space-time in the brain. This space-time is limited to the execution of brain operations of differing complexity. During each such brain operation a particular short-term spatio-temporal pattern of integrated activity of different brain areas emerges within related operational space-time. At the same time, to have a fully functional human brain one needs to have a subjective mental experience. Current research on the subjective mental experience offers detailed analysis of space-time organization of the mind. According to this research, subjective mental experience (subjective virtual world) has definitive spatial and temporal properties similar to many physical phenomena. Based on systematic review of the propositions and tenets of brain and mind space-time descriptions, our aim in this review essay is to explore the relations between the two. To be precise, we would like to discuss the hypothesis that via the brain operational space-time the mind subjective space-time is connected to otherwise distant physical space-time reality.

In vivo optoacoustic monitoring of calcium activity in the brain (Conference Presentation)

NASA Astrophysics Data System (ADS)

Deán-Ben, Xose Luís.; Gottschalk, Sven; Sela, Gali; Lauri, Antonella; Kneipp, Moritz; Ntziachristos, Vasilis; Westmeyer, Gil G.; Shoham, Shy; Razansky, Daniel

2017-03-01

Non-invasive observation of spatio-temporal neural activity of large neural populations distributed over the entire brain of complex organisms is a longstanding goal of neuroscience [1,2]. Recently, genetically encoded calcium indicators (GECIs) have revolutionized neuroimaging by enabling mapping the activity of entire neuronal populations in vivo [3]. Visualization of these powerful sensors with fluorescence microscopy has however been limited to superficial regions while deep brain areas have so far remained unreachable [4]. We have developed a volumetric multispectral optoacoustic tomography platform for imaging neural activation deep in scattering brains [5]. The developed methodology can render 100 volumetric frames per second across scalable fields of view ranging between 50-1000 mm3 with respective spatial resolution of 35-150µm. Experiments performed in immobilized and freely swimming larvae and in adult zebrafish brains expressing the genetically-encoded calcium indicator GCaMP5G demonstrated, for the first time, the fundamental ability to directly track neural dynamics using optoacoustics while overcoming the depth barrier of optical imaging in scattering brains [6]. It was further possible to monitor calcium transients in a scattering brain of a living adult transgenic zebrafish expressing GCaMP5G calcium indicator [7]. Fast changes in optoacoustic traces associated to GCaMP5G activity were detectable in the presence of other strongly absorbing endogenous chromophores, such as hemoglobin. The results indicate that the optoacoustic signal traces generally follow the GCaMP5G fluorescence dynamics and further enable overcoming the longstanding optical-diffusion penetration barrier associated to scattering in biological tissues [6]. The new functional optoacoustic neuroimaging method can visualize neural activity at penetration depths and spatio-temporal resolution scales not covered with the existing neuroimaging techniques. Thus, in addition to the well-established capacity of optoacoustics to resolve vascular anatomy and multiple hemodynamic parameters deep in scattering tissues, the newly developed methodology offers unprecedented capabilities for functional whole brain observations of fast calcium dynamics.

Sustainable microbial water quality monitoring programme design using phage-lysis and multivariate techniques.

PubMed

Nnane, Daniel Ekane

2011-11-15

Contamination of surface waters is a pervasive threat to human health, hence, the need to better understand the sources and spatio-temporal variations of contaminants within river catchments. River catchment managers are required to sustainably monitor and manage the quality of surface waters. Catchment managers therefore need cost-effective low-cost long-term sustainable water quality monitoring and management designs to proactively protect public health and aquatic ecosystems. Multivariate and phage-lysis techniques were used to investigate spatio-temporal variations of water quality, main polluting chemophysical and microbial parameters, faecal micro-organisms sources, and to establish 'sentry' sampling sites in the Ouse River catchment, southeast England, UK. 350 river water samples were analysed for fourteen chemophysical and microbial water quality parameters in conjunction with the novel human-specific phages of Bacteroides GB-124 (Bacteroides GB-124). Annual, autumn, spring, summer, and winter principal components (PCs) explained approximately 54%, 75%, 62%, 48%, and 60%, respectively, of the total variance present in the datasets. Significant loadings of Escherichia coli, intestinal enterococci, turbidity, and human-specific Bacteroides GB-124 were observed in all datasets. Cluster analysis successfully grouped sampling sites into five clusters. Importantly, multivariate and phage-lysis techniques were useful in determining the sources and spatial extent of water contamination in the catchment. Though human faecal contamination was significant during dry periods, the main source of contamination was non-human. Bacteroides GB-124 could potentially be used for catchment routine microbial water quality monitoring. For a cost-effective low-cost long-term sustainable water quality monitoring design, E. coli or intestinal enterococci, turbidity, and Bacteroides GB-124 should be monitored all-year round in this river catchment. Copyright © 2011 Elsevier B.V. All rights reserved.

Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models

NASA Astrophysics Data System (ADS)

Allen, J. I.; Somerfield, P. J.; Gilbert, F. J.

2007-01-01

Marine ecosystem models are becoming increasingly complex and sophisticated, and are being used to estimate the effects of future changes in the earth system with a view to informing important policy decisions. Despite their potential importance, far too little attention has been, and is generally, paid to model errors and the extent to which model outputs actually relate to real-world processes. With the increasing complexity of the models themselves comes an increasing complexity among model results. If we are to develop useful modelling tools for the marine environment we need to be able to understand and quantify the uncertainties inherent in the simulations. Analysing errors within highly multivariate model outputs, and relating them to even more complex and multivariate observational data, are not trivial tasks. Here we describe the application of a series of techniques, including a 2-stage self-organising map (SOM), non-parametric multivariate analysis, and error statistics, to a complex spatio-temporal model run for the period 1988-1989 in the Southern North Sea, coinciding with the North Sea Project which collected a wealth of observational data. We use model output, large spatio-temporally resolved data sets and a combination of methodologies (SOM, MDS, uncertainty metrics) to simplify the problem and to provide tractable information on model performance. The use of a SOM as a clustering tool allows us to simplify the dimensions of the problem while the use of MDS on independent data grouped according to the SOM classification allows us to validate the SOM. The combination of classification and uncertainty metrics allows us to pinpoint the variables and associated processes which require attention in each region. We recommend the use of this combination of techniques for simplifying complex comparisons of model outputs with real data, and analysis of error distributions.

Spatio-temporal Eigenvector Filtering: Application on Bioenergy Crop Impacts

NASA Astrophysics Data System (ADS)

Wang, M.; Kamarianakis, Y.; Georgescu, M.

2017-12-01

A suite of 10-year ensemble-based simulations was conducted to investigate the hydroclimatic impacts due to large-scale deployment of perennial bioenergy crops across the continental United States. Given the large size of the simulated dataset (about 60Tb), traditional hierarchical spatio-temporal statistical modelling cannot be implemented for the evaluation of physics parameterizations and biofuel impacts. In this work, we propose a filtering algorithm that takes into account the spatio-temporal autocorrelation structure of the data while avoiding spatial confounding. This method is used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations and observational datasets. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.

Spatio-temporally resolved spectral measurements of laser-produced plasma and semiautomated spectral measurement-control and analysis software

NASA Astrophysics Data System (ADS)

Cao, S. Q.; Su, M. G.; Min, Q.; Sun, D. X.; O'Sullivan, G.; Dong, C. Z.

2018-02-01

A spatio-temporally resolved spectral measurement system of highly charged ions from laser-produced plasmas is presented. Corresponding semiautomated computer software for measurement control and spectral analysis has been written to achieve the best synchronicity possible among the instruments. This avoids the tedious comparative processes between experimental and theoretical results. To demonstrate the capabilities of this system, a series of spatio-temporally resolved experiments of laser-produced Al plasmas have been performed and applied to benchmark the software. The system is a useful tool for studying the spectral structures of highly charged ions and for evaluating the spatio-temporal evolution of laser-produced plasmas.

Mapping Epileptic Activity: Sources or Networks for the Clinicians?

PubMed Central

Pittau, Francesca; Mégevand, Pierre; Sheybani, Laurent; Abela, Eugenio; Grouiller, Frédéric; Spinelli, Laurent; Michel, Christoph M.; Seeck, Margitta; Vulliemoz, Serge

2014-01-01

Epileptic seizures of focal origin are classically considered to arise from a focal epileptogenic zone and then spread to other brain regions. This is a key concept for semiological electro-clinical correlations, localization of relevant structural lesions, and selection of patients for epilepsy surgery. Recent development in neuro-imaging and electro-physiology and combinations, thereof, have been validated as contributory tools for focus localization. In parallel, these techniques have revealed that widespread networks of brain regions, rather than a single epileptogenic region, are implicated in focal epileptic activity. Sophisticated multimodal imaging and analysis strategies of brain connectivity patterns have been developed to characterize the spatio-temporal relationships within these networks by combining the strength of both techniques to optimize spatial and temporal resolution with whole-brain coverage and directional connectivity. In this paper, we review the potential clinical contribution of these functional mapping techniques as well as invasive electrophysiology in human beings and animal models for characterizing network connectivity. PMID:25414692

Discriminability limits in spatio-temporal stereo block matching.

PubMed

Jain, Ankit K; Nguyen, Truong Q

2014-05-01

Disparity estimation is a fundamental task in stereo imaging and is a well-studied problem. Recently, methods have been adapted to the video domain where motion is used as a matching criterion to help disambiguate spatially similar candidates. In this paper, we analyze the validity of the underlying assumptions of spatio-temporal disparity estimation, and determine the extent to which motion aids the matching process. By analyzing the error signal for spatio-temporal block matching under the sum of squared differences criterion and treating motion as a stochastic process, we determine the probability of a false match as a function of image features, motion distribution, image noise, and number of frames in the spatio-temporal patch. This performance quantification provides insight into when spatio-temporal matching is most beneficial in terms of the scene and motion, and can be used as a guide to select parameters for stereo matching algorithms. We validate our results through simulation and experiments on stereo video.

Dynamical Properties of Transient Spatio-Temporal Patterns in Bacterial Colony of Proteus mirabilis

NASA Astrophysics Data System (ADS)

Watanabe, Kazuhiko; Wakita, Jun-ichi; Itoh, Hiroto; Shimada, Hirotoshi; Kurosu, Sayuri; Ikeda, Takemasa; Yamazaki, Yoshihiro; Matsuyama, Tohey; Matsushita, Mitsugu

2002-02-01

Spatio-temporal patterns emerged inside a colony of bacterial species Proteus mirabilis on the surface of nutrient-rich semisolid agar medium have been investigated. We observed various patterns composed of the following basic types: propagating stripe, propagating stripe with fixed dislocation, expanding and shrinking target, and rotating spiral. The remarkable point is that the pattern changes immediately when we alter the position for observation, but it returns to the original if we restore the observing position within a few minutes. We further investigated mesoscopic and microscopic properties of the spatio-temporal patterns. It turned out that whenever the spatio-temporal patterns are observed in a colony, the areas are composed of two superimposed monolayers of elongated bacterial cells. In each area they are aligned almost parallel with each other like a two-dimensional nematic liquid crystal, and move collectively and independently of another layer. It has been found that the observed spatio-temporal patterns are explained as the moiré effect.

Measurement of Local Partial Pressure of Oxygen in the Brain Tissue under Normoxia and Epilepsy with Phosphorescence Lifetime Microscopy.

PubMed

Zhang, Cong; Bélanger, Samuel; Pouliot, Philippe; Lesage, Frédéric

2015-01-01

In this work a method for measuring brain oxygen partial pressure with confocal phosphorescence lifetime microscopy system is reported. When used in conjunction with a dendritic phosphorescent probe, Oxyphor G4, this system enabled minimally invasive measurements of oxygen partial pressure (pO2) in cerebral tissue with high spatial and temporal resolution during 4-AP induced epileptic seizures. Investigating epileptic events, we characterized the spatio-temporal distribution of the "initial dip" in pO2 near the probe injection site and along nearby arterioles. Our results reveal a correlation between the percent change in the pO2 signal during the "initial dip" and the duration of seizure-like activity, which can help localize the epileptic focus and predict the length of seizure.

Spatio-temporal alignment of multiple sensors

NASA Astrophysics Data System (ADS)

Zhang, Tinghua; Ni, Guoqiang; Fan, Guihua; Sun, Huayan; Yang, Biao

2018-01-01

Aiming to achieve the spatio-temporal alignment of multi sensor on the same platform for space target observation, a joint spatio-temporal alignment method is proposed. To calibrate the parameters and measure the attitude of cameras, an astronomical calibration method is proposed based on star chart simulation and collinear invariant features of quadrilateral diagonal between the observed star chart. In order to satisfy a temporal correspondence and spatial alignment similarity simultaneously, the method based on the astronomical calibration and attitude measurement in this paper formulates the video alignment to fold the spatial and temporal alignment into a joint alignment framework. The advantage of this method is reinforced by exploiting the similarities and prior knowledge of velocity vector field between adjacent frames, which is calculated by the SIFT Flow algorithm. The proposed method provides the highest spatio-temporal alignment accuracy compared to the state-of-the-art methods on sequences recorded from multi sensor at different times.

Spatio-temporal hierarchical modeling of rates and variability of Holocene sea-level changes in the western North Atlantic and the Caribbean

NASA Astrophysics Data System (ADS)

Ashe, E.; Kopp, R. E.; Khan, N.; Horton, B.; Engelhart, S. E.

2016-12-01

Sea level varies over of both space and time. Prior to the instrumental period, the sea-level record depends upon geological reconstructions that contain vertical and temporal uncertainty. Spatio-temporal statistical models enable the interpretation of RSL and rates of change as well as the reconstruction of the entire sea-level field from such noisy data. Hierarchical models explicitly distinguish between a process level, which characterizes the spatio-temporal field, and a data level, by which sparse proxy data and its noise is recorded. A hyperparameter level depicts prior expectations about the structure of variability in the spatio-temporal field. Spatio-temporal hierarchical models are amenable to several analysis approaches, with tradeoffs regarding computational efficiency and comprehensiveness of uncertainty characterization. A fully-Bayesian hierarchical model (BHM), which places prior probability distributions upon the hyperparameters, is more computationally intensive than an empirical hierarchical model (EHM), which uses point estimates of hyperparameters, derived from the data [1]. Here, we assess the sensitivity of posterior estimates of relative sea level (RSL) and rates to different statistical approaches by varying prior assumptions about the spatial and temporal structure of sea-level variability and applying multiple analytical approaches to Holocene sea-level proxies along the Atlantic coast of North American and the Caribbean [2]. References: 1. N Cressie, Wikle CK (2011) Statistics for spatio-temporal data (John Wiley & Sons). 2. Kahn N et al. (2016). Quaternary Science Reviews (in revision).

Spatio-Temporal Fluctuations of Neural Dynamics in Mild Cognitive Impairment and Alzheimer's Disease.

PubMed

Poza, Jesús; Gómez, Carlos; García, María; Tola-Arribas, Miguel A; Carreres, Alicia; Cano, Mónica; Hornero, Roberto

2017-01-01

An accurate characterization of neural dynamics in mild cognitive impairment (MCI) is of paramount importance to gain further insights into the underlying neural mechanisms in Alzheimer's disease (AD). Nevertheless, there has been relatively little research on brain dynamics in prodromal AD. As a consequence, its neural substrates remain unclear. In the present research, electroencephalographic (EEG) recordings from patients with dementia due to AD, subjects with MCI due to AD and healthy controls (HC) were analyzed using relative power (RP) in conventional EEG frequency bands and a novel parameter useful to explore the spatio-temporal fluctuations of neural dynamics: the spectral flux (SF). Our results suggest that dementia due to AD is associated with a significant slowing of EEG activity and several significant alterations in spectral fluctuations at low (i.e. theta) and high (i.e. beta and gamma) frequency bands compared to HC (p < 0.05). Furthermore, subjects with MCI due to AD exhibited a specific frequency-dependent pattern of spatio-temporal abnormalities, which can help identify neural mechanisms involved in cognitive impairment preceding AD. Classification analyses using linear discriminant analysis with a leave-one-out cross-validation procedure showed that the combination of RP and within-electrode SF at the beta band was useful to obtain a 77.3 % of accuracy to discriminate between HC and AD patients. In the case of comparison between HC and MCI subjects, the classification accuracy reached a value of 79.2 %, combining within-electrode SF at beta and gamma bands. SF has proven to be a useful measure to obtain an original description of brain dynamics at different stages of AD. Consequently, SF may contribute to gain a more comprehensive understanding into neural substrates underlying MCI, as well as to develop potential early AD biomarkers. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Spatial Memory in the Morris Water Maze and Activation of Cyclic AMP Response Element-Binding (CREB) Protein within the Mouse Hippocampus

ERIC Educational Resources Information Center

Porte, Yves; Buhot, Marie Christine; Mons, Nicole E.

2008-01-01

We investigated the spatio-temporal dynamics of learning-induced cAMP response element-binding protein activation/phosphorylation (pCREB) in mice trained in a spatial reference memory task in the water maze. Using immunohistochemistry, we examined pCREB immunoreactivity (pCREB-ir) in hippocampal CA1 and CA3 and related brain structures. During the…

Spatio-temporal cerebral blood flow perfusion patterns in cortical spreading depression

NASA Astrophysics Data System (ADS)

Verisokin, Andrey Yu.; Verveyko, Darya V.; Postnov, Dmitry E.

2017-04-01

Cortical spreading depression (CSD) is an example of one of the most common abnormalities in biophysical brain functioning. Despite the fact that there are many mathematical models describing the cortical spreading depression (CSD), most of them do not take into consideration the role of redistribution of cerebral blood flow (CBF), that results in the formation of spatio-temporal patterns. The paper presents a mathematical model, which successfully explains the CBD role in the CSD process. Numerical study of this model has revealed the formation of stationary dissipative structures, visually analogous to Turing structures. However, the mechanism of their formation is not diffusion. We show these structures occur due to another type of spatial coupling, that is related to tissue perfusion rate. The proposed model predicts that at similar state of neurons the distribution of blood flow and oxygenation may by different. Currently, this effect is not taken into account when the Blood oxygen-level dependent (BOLD) contrast imaging used in functional magnetic resonance imaging (fMRI). Thus, the diagnosis on the BOLD signal can be ambiguous. We believe that our results can be used in the future for a more correct interpretation of the data obtained with fMRI, NIRS and other similar methods for research of the brain activity.

[Spatio-temporal problems of geographic information system in marine fishery].

PubMed

Su, Fenzhen; Zhou, Chenghu; Du, Yunyan; Zhang, Tianyu; Shao, Quanqin

2003-09-01

In marine fisheries, it is very important to understand and grasp the spatio-temporal nature. Geographical Information System (GIS) has been applied to describe or forecast the dynamic trend of resources or to set up evaluation model, which is one of high technologies in modern marine fisheries. Based on the review of the development of marine fishery GIS (MFGIS), four spatio-temporal problems it occurred were discussed, and the possible resolutions were prospected.

Towards the utilization of EEG as a brain imaging tool.

PubMed

Michel, Christoph M; Murray, Micah M

2012-06-01

Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications. Copyright © 2011 Elsevier Inc. All rights reserved.

Defined types of cortical interneurone structure space and spike timing in the hippocampus

PubMed Central

Somogyi, Peter; Klausberger, Thomas

2005-01-01

The cerebral cortex encodes, stores and combines information about the internal and external environment in rhythmic activity of multiple frequency ranges. Neurones of the cortex can be defined, recognized and compared on the comprehensive application of the following measures: (i) brain area- and cell domain-specific distribution of input and output synapses, (ii) expression of molecules involved in cell signalling, (iii) membrane and synaptic properties reflecting the expression of membrane proteins, (iv) temporal structure of firing in vivo, resulting from (i)–(iii). Spatial and temporal measures of neurones in the network reflect an indivisible unity of evolutionary design, i.e. neurones do not have separate structure or function. The blueprint of this design is most easily accessible in the CA1 area of the hippocampus, where a relatively uniform population of pyramidal cells and their inputs follow an instantly recognizable laminated pattern and act within stereotyped network activity patterns. Reviewing the cell types and their spatio-temporal interactions, we suggest that CA1 pyramidal cells are supported by at least 16 distinct types of GABAergic neurone. During a given behaviour-contingent network oscillation, interneurones of a given type exhibit similar firing patterns. During different network oscillations representing two distinct brain states, interneurones of the same class show different firing patterns modulating their postsynaptic target-domain in a brain-state-dependent manner. These results suggest roles for specific interneurone types in structuring the activity of pyramidal cells via their respective target domains, and accurately timing and synchronizing pyramidal cell discharge, rather than providing generalized inhibition. Finally, interneurones belonging to different classes may fire preferentially at distinct time points during a given oscillation. As different interneurones innervate distinct domains of the pyramidal cells, the different compartments will receive GABAergic input differentiated in time. Such a dynamic, spatio-temporal, GABAergic control, which evolves distinct patterns during different brain states, is ideally suited to regulating the input integration of individual pyramidal cells contributing to the formation of cell assemblies and representations in the hippocampus and, probably, throughout the cerebral cortex. PMID:15539390

Multivariate pattern dependence

PubMed Central

Saxe, Rebecca

2017-01-01

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. PMID:29155809

An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City.

PubMed

Zhou, Lianjie; Chen, Nengcheng; Yuan, Sai; Chen, Zeqiang

2016-10-29

The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as traffic scheduling, traffic planning and traffic behavior management in intelligent public transportation systems.

Cubic map algebra functions for spatio-temporal analysis

USGS Publications Warehouse

Mennis, J.; Viger, R.; Tomlin, C.D.

2005-01-01

We propose an extension of map algebra to three dimensions for spatio-temporal data handling. This approach yields a new class of map algebra functions that we call "cube functions." Whereas conventional map algebra functions operate on data layers representing two-dimensional space, cube functions operate on data cubes representing two-dimensional space over a third-dimensional period of time. We describe the prototype implementation of a spatio-temporal data structure and selected cube function versions of conventional local, focal, and zonal map algebra functions. The utility of cube functions is demonstrated through a case study analyzing the spatio-temporal variability of remotely sensed, southeastern U.S. vegetation character over various land covers and during different El Nin??o/Southern Oscillation (ENSO) phases. Like conventional map algebra, the application of cube functions may demand significant data preprocessing when integrating diverse data sets, and are subject to limitations related to data storage and algorithm performance. Solutions to these issues include extending data compression and computing strategies for calculations on very large data volumes to spatio-temporal data handling.

New robust algorithm for tracking cells in videos of Drosophila morphogenesis based on finding an ideal path in segmented spatio-temporal cellular structures.

PubMed

Bellaïche, Yohanns; Bosveld, Floris; Graner, François; Mikula, Karol; Remesíková, Mariana; Smísek, Michal

2011-01-01

In this paper, we present a novel algorithm for tracking cells in time lapse confocal microscopy movie of a Drosophila epithelial tissue during pupal morphogenesis. We consider a 2D + time video as a 3D static image, where frames are stacked atop each other, and using a spatio-temporal segmentation algorithm we obtain information about spatio-temporal 3D tubes representing evolutions of cells. The main idea for tracking is the usage of two distance functions--first one from the cells in the initial frame and second one from segmented boundaries. We track the cells backwards in time. The first distance function attracts the subsequently constructed cell trajectories to the cells in the initial frame and the second one forces them to be close to centerlines of the segmented tubular structures. This makes our tracking algorithm robust against noise and missing spatio-temporal boundaries. This approach can be generalized to a 3D + time video analysis, where spatio-temporal tubes are 4D objects.

Spatio-temporal diffusion of dynamic PET images

NASA Astrophysics Data System (ADS)

Tauber, C.; Stute, S.; Chau, M.; Spiteri, P.; Chalon, S.; Guilloteau, D.; Buvat, I.

2011-10-01

Positron emission tomography (PET) images are corrupted by noise. This is especially true in dynamic PET imaging where short frames are required to capture the peak of activity concentration after the radiotracer injection. High noise results in a possible bias in quantification, as the compartmental models used to estimate the kinetic parameters are sensitive to noise. This paper describes a new post-reconstruction filter to increase the signal-to-noise ratio in dynamic PET imaging. It consists in a spatio-temporal robust diffusion of the 4D image based on the time activity curve (TAC) in each voxel. It reduces the noise in homogeneous areas while preserving the distinct kinetics in regions of interest corresponding to different underlying physiological processes. Neither anatomical priors nor the kinetic model are required. We propose an automatic selection of the scale parameter involved in the diffusion process based on a robust statistical analysis of the distances between TACs. The method is evaluated using Monte Carlo simulations of brain activity distributions. We demonstrate the usefulness of the method and its superior performance over two other post-reconstruction spatial and temporal filters. Our simulations suggest that the proposed method can be used to significantly increase the signal-to-noise ratio in dynamic PET imaging.

Spatio-temporal modeling and optimization of a deformable-grating compressor for short high-energy laser pulses

DOE PAGES

Qiao, Jie; Papa, J.; Liu, X.

2015-09-24

Monolithic large-scale diffraction gratings are desired to improve the performance of high-energy laser systems and scale them to higher energy, but the surface deformation of these diffraction gratings induce spatio-temporal coupling that is detrimental to the focusability and compressibility of the output pulse. A new deformable-grating-based pulse compressor architecture with optimized actuator positions has been designed to correct the spatial and temporal aberrations induced by grating wavefront errors. An integrated optical model has been built to analyze the effect of grating wavefront errors on the spatio-temporal performance of a compressor based on four deformable gratings. Moreover, a 1.5-meter deformable gratingmore » has been optimized using an integrated finite-element-analysis and genetic-optimization model, leading to spatio-temporal performance similar to the baseline design with ideal gratings.« less

Different spatio-temporal electroencephalography features drive the successful decoding of binaural and monaural cues for sound localization.

PubMed

Bednar, Adam; Boland, Francis M; Lalor, Edmund C

2017-03-01

The human ability to localize sound is essential for monitoring our environment and helps us to analyse complex auditory scenes. Although the acoustic cues mediating sound localization have been established, it remains unknown how these cues are represented in human cortex. In particular, it is still a point of contention whether binaural and monaural cues are processed by the same or distinct cortical networks. In this study, participants listened to a sequence of auditory stimuli from different spatial locations while we recorded their neural activity using electroencephalography (EEG). The stimuli were presented over a loudspeaker array, which allowed us to deliver realistic, free-field stimuli in both the horizontal and vertical planes. Using a multivariate classification approach, we showed that it is possible to decode sound source location from scalp-recorded EEG. Robust and consistent decoding was shown for stimuli that provide binaural cues (i.e. Left vs. Right stimuli). Decoding location when only monaural cues were available (i.e. Front vs. Rear and elevational stimuli) was successful for a subset of subjects and showed less consistency. Notably, the spatio-temporal pattern of EEG features that facilitated decoding differed based on the availability of binaural and monaural cues. In particular, we identified neural processing of binaural cues at around 120 ms post-stimulus and found that monaural cues are processed later between 150 and 200 ms. Furthermore, different spatial activation patterns emerged for binaural and monaural cue processing. These spatio-temporal dissimilarities suggest the involvement of separate cortical mechanisms in monaural and binaural acoustic cue processing. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Neuronal cell fate specification by the molecular convergence of different spatio-temporal cues on a common initiator terminal selector gene

PubMed Central

Stratmann, Johannes

2017-01-01

The extensive genetic regulatory flows underlying specification of different neuronal subtypes are not well understood at the molecular level. The Nplp1 neuropeptide neurons in the developing Drosophila nerve cord belong to two sub-classes; Tv1 and dAp neurons, generated by two distinct progenitors. Nplp1 neurons are specified by spatial cues; the Hox homeotic network and GATA factor grn, and temporal cues; the hb -> Kr -> Pdm -> cas -> grh temporal cascade. These spatio-temporal cues combine into two distinct codes; one for Tv1 and one for dAp neurons that activate a common terminal selector feedforward cascade of col -> ap/eya -> dimm -> Nplp1. Here, we molecularly decode the specification of Nplp1 neurons, and find that the cis-regulatory organization of col functions as an integratory node for the different spatio-temporal combinatorial codes. These findings may provide a logical framework for addressing spatio-temporal control of neuronal sub-type specification in other systems. PMID:28414802

Measurement of Local Partial Pressure of Oxygen in the Brain Tissue under Normoxia and Epilepsy with Phosphorescence Lifetime Microscopy

PubMed Central

Zhang, Cong; Bélanger, Samuel; Pouliot, Philippe; Lesage, Frédéric

2015-01-01

In this work a method for measuring brain oxygen partial pressure with confocal phosphorescence lifetime microscopy system is reported. When used in conjunction with a dendritic phosphorescent probe, Oxyphor G4, this system enabled minimally invasive measurements of oxygen partial pressure (pO2) in cerebral tissue with high spatial and temporal resolution during 4-AP induced epileptic seizures. Investigating epileptic events, we characterized the spatio-temporal distribution of the "initial dip" in pO2 near the probe injection site and along nearby arterioles. Our results reveal a correlation between the percent change in the pO2 signal during the "initial dip" and the duration of seizure-like activity, which can help localize the epileptic focus and predict the length of seizure. PMID:26305777

Presence of nonlinearity in intracranial EEG recordings: detected by Lyapunov exponents

NASA Astrophysics Data System (ADS)

Liu, Chang-Chia; Shiau, Deng-Shan; Chaovalitwongse, W. Art; Pardalos, Panos M.; Sackellares, J. C.

2007-11-01

In this communication, we performed nonlinearity analysis in the EEG signals recorded from patients with temporal lobe epilepsy (TLE). The largest Lyapunov exponent (Lmax) and phase randomization surrogate data technique were employed to form the statistical test. EEG recordings were acquired invasively from three patients in six brain regions (left and right temporal depth, sub-temporal and orbitofrontal) with 28-32 depth electrodes placed in depth and subdural of the brain. All three patients in this study have unilateral epileptic focus region on the right hippocampus(RH). Nonlinearity was detected by comparing the Lmax profiles of the EEG recordings to its surrogates. The nonlinearity was seen in all different states of the patient with the highest found in post-ictal state. Further our results for all patients exhibited higher degree of differences, quantified by paired t-test, in Lmax values between original and its surrogate from EEG signals recorded from epileptic focus regions. The results of this study demonstrated the Lmax is capable to capture spatio-temporal dynamics that may not be able to detect by linear measurements in the intracranial EEG recordings.

Characterizing nonlinearity in invasive EEG recordings from temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Casdagli, M. C.; Iasemidis, L. D.; Sackellares, J. C.; Roper, S. N.; Gilmore, R. L.; Savit, R. S.

Invasive electroencephalographic (EEG) recordings from depth and subdural electrodes, performed in eight patients with temporal lobe epilepsy, are analyzed using a variety of nonlinear techniques. A surrogate data technique is used to find strong evidence for nonlinearities in epileptogenic regions of the brain. Most of these nonlinearities are characterized as “spiking” by a wavelet analysis. A small fraction of the nonlinearities are characterized as “recurrent” by a nonlinear prediction algorithm. Recurrent activity is found to occur in spatio-temporal patterns related to the location of the epileptogenic focus. Residual delay maps, used to characterize “lag-one nonlinearity”, are remarkably stationary for a given electrode, and exhibit striking variations among electrodes. The clinical and theoretical implications of these results are discussed.

Mining and Integration of Environmental Data

NASA Astrophysics Data System (ADS)

Tran, V.; Hluchy, L.; Habala, O.; Ciglan, M.

2009-04-01

The project ADMIRE (Advanced Data Mining and Integration Research for Europe) is a 7th FP EU ICT project aims to deliver a consistent and easy-to-use technology for extracting information and knowledge. The project is motivated by the difficulty of extracting meaningful information by data mining combinations of data from multiple heterogeneous and distributed resources. It will also provide an abstract view of data mining and integration, which will give users and developers the power to cope with complexity and heterogeneity of services, data and processes. The data sets describing phenomena from domains like business, society, and environment often contain spatial and temporal dimensions. Integration of spatio-temporal data from different sources is a challenging task due to those dimensions. Different spatio-temporal data sets contain data at different resolutions (e.g. size of the spatial grid) and frequencies. This heterogeneity is the principal challenge of geo-spatial and temporal data sets integration - the integrated data set should hold homogeneous data of the same resolution and frequency. Thus, to integrate heterogeneous spatio-temporal data from distinct source, transformation of one or more data sets is necessary. Following transformation operation are required: • transformation to common spatial and temporal representation - (e.g. transformation to common coordinate system), • spatial and/or temporal aggregation - data from detailed data source are aggregated to match the resolution of other resources involved in the integration process, • spatial and/or temporal record decomposition - records from source with lower resolution data are decomposed to match the granularity of the other data source. This operation decreases data quality (e.g. transformation of data from 50km grid to 10 km grid) - data from lower resolution data set in the integrated schema are imprecise, but it allows us to preserve higher resolution data. We can decompose the spatio-temporal data integration to following phases: • pre-integration data processing - different data set can be physically stored in different formats (e.g. relational databases, text files); it might be necessary to pre-process the data sets to be integrated, • identification of transformation operations necessary to integrate data in spatio-temporal dimensions, • identification of transformation operations to be performed on non-spatio-temporal attributes and • output data schema and set generation - given prepared data and the set of transformation, operations, the final integrated schema is produces. Spatio-temporal dimension brings its specifics also to the problem of mining spatio-temporal data sets. Spatio-temporal relationships exist among records in (s-t) data sets and those relationships should be considered in mining operation. This means that when analyzing a record in spatio-temporal data set, the records in its spatial and/or temporal proximity should be taken into account. In addition, the relationships discovered in spatio-temporal data can be different when mining the same data on different scales (e.g. mining the same data sets on 50 km grid with daily data vs. 10 km grid with hourly data). To be able to do effective data mining, we first needed to gather a sufficient amount of environmental data covering similar area and time span. For this purpose we have engaged in cooperation with several organizations working in the environmental domain in Slovakia, some of which are also our partners from previous research efforts. The organizations which volunteered some of their data are the Slovak Hydro-meteorological Institute (SHMU), the Slovak Water Enterprise (SVP), the Soil Science and Conservation Institute (VUPOP), and the Institute of Hydrology of the Slovak Academy of Sciences (UHSAV). We have prepared scenarios from general meteorology, as well as specialized in hydrology and soil protection.

Research on target tracking algorithm based on spatio-temporal context

NASA Astrophysics Data System (ADS)

Li, Baiping; Xu, Sanmei; Kang, Hongjuan

2017-07-01

In this paper, a novel target tracking algorithm based on spatio-temporal context is proposed. During the tracking process, the camera shaking or occlusion may lead to the failure of tracking. The proposed algorithm can solve this problem effectively. The method use the spatio-temporal context algorithm as the main research object. We get the first frame's target region via mouse. Then the spatio-temporal context algorithm is used to get the tracking targets of the sequence of frames. During this process a similarity measure function based on perceptual hash algorithm is used to judge the tracking results. If tracking failed, reset the initial value of Mean Shift algorithm for the subsequent target tracking. Experiment results show that the proposed algorithm can achieve real-time and stable tracking when camera shaking or target occlusion.

Spatio-Temporal Dynamics of Exploited Groundfish Species Assemblages Faced to Environmental and Fishing Forcings: Insights from the Mauritanian Exclusive Economic Zone

PubMed Central

Kidé, Saïkou Oumar; Manté, Claude; Dubroca, Laurent; Demarcq, Hervé; Mérigot, Bastien

2015-01-01

Environmental changes and human activities can have strong impacts on biodiversity and ecosystem functioning. This study investigates how, from a quantitative point of view, simultaneously both environmental and anthropogenic factors affect species composition and abundance of exploited groundfish assemblages (i.e. target and non-target species) at large spatio-temporal scales. We aim to investigate (1) the spatial and annual stability of groundfish assemblages, (2) relationships between these assemblages and structuring factors in order to better explain the dynamic of the assemblages’ structure. The Mauritanian Exclusive Economic Zone (MEEZ) is of particular interest as it embeds a productive ecosystem due to upwelling, producing abundant and diverse resources which constitute an attractive socio-economic development. We applied the multi-variate and multi-table STATICO method on a data set consisting of 854 hauls collected during 14-years (1997–2010) from scientific trawl surveys (species abundance), logbooks of industrial fishery (fishing effort), sea surface temperature and chlorophyll a concentration as environmental variables. Our results showed that abiotic factors drove four main persistent fish assemblages. Overall, chlorophyll a concentration and sea surface temperature mainly influenced the structure of assemblages of coastal soft bottoms and those of the offshore near rocky bottoms where upwellings held. While highest levels of fishing effort were located in the northern permanent upwelling zone, effects of this variable on species composition and abundances of assemblages were relatively low, even if not negligible in some years and areas. The temporal trajectories between environmental and fishing conditions and assemblages did not match for all the entire time series analyzed in the MEEZ, but interestingly for some specific years and areas. The quantitative approach used in this work may provide to stakeholders, scientists and fishers a useful assessment for the spatio-temporal dynamics of exploited assemblages under stable or changing conditions in fishing and environment. PMID:26505198

Factors Related to Rape Reporting Behavior in Brazil: Examining the Role of Spatio-Temporal Factors.

PubMed

Melo, Silas Nogueira de; Beauregard, Eric; Andresen, Martin A

2016-07-01

The reporting of rape to police is an important component of this crime to have the criminal justice system involved and, potentially, punish offenders. However, for a number of reasons (fear of retribution, self-blame, etc.), most rapes are not reported to police. Most often, the research investigating this phenomenon considers incident and victim factors with little attention to the spatio-temporal factors of the rape. In this study, we consider incident, victim, and spatio-temporal factors relating to rape reporting in Campinas, Brazil. Our primary research question is whether or not the spatio-temporal factors play a significant role in the reporting of rape, over and above incident and victim factors. The subjects under study are women who were admitted to the Women's Integrated Healthcare Center at the State University of Campinas, Brazil, and surveyed by a psychologist or a social worker. Rape reporting to police was measured using a dichotomous variable. Logistic regression was used to predict the probability of rape reporting based on incident, victim, and spatio-temporal factors. Although we find that incident and victim factors matter for rape reporting, spatio-temporal factors (rape/home location and whether the rape was in a private or public place) play an important role in rape reporting, similar to the literature that considers these factors. This result has significant implications for sexual violence education. Only when we know why women decide not to report a rape may we begin to work on strategies to overcome these hurdles.

Enzyme-linked DNA dendrimer nanosensors for acetylcholine

PubMed Central

Walsh, Ryan; Morales, Jennifer M.; Skipwith, Christopher G.; Ruckh, Timothy T.; Clark, Heather A.

2015-01-01

It is currently difficult to measure small dynamics of molecules in the brain with high spatial and temporal resolution while connecting them to the bigger picture of brain function. A step towards understanding the underlying neural networks of the brain is the ability to sense discrete changes of acetylcholine within a synapse. Here we show an efficient method for generating acetylcholine-detecting nanosensors based on DNA dendrimer scaffolds that incorporate butyrylcholinesterase and fluorescein in a nanoscale arrangement. These nanosensors are selective for acetylcholine and reversibly respond to levels of acetylcholine in the neurophysiological range. This DNA dendrimer architecture has the potential to overcome current obstacles to sensing in the synaptic environment, including the nanoscale size constraints of the synapse and the ability to quantify the spatio-temporal fluctuations of neurotransmitter release. By combining the control of nanosensor architecture with the strategic placement of fluorescent reporters and enzymes, this novel nanosensor platform can facilitate the development of new selective imaging tools for neuroscience. PMID:26442999

Enzyme-linked DNA dendrimer nanosensors for acetylcholine.

PubMed

Walsh, Ryan; Morales, Jennifer M; Skipwith, Christopher G; Ruckh, Timothy T; Clark, Heather A

2015-10-07

It is currently difficult to measure small dynamics of molecules in the brain with high spatial and temporal resolution while connecting them to the bigger picture of brain function. A step towards understanding the underlying neural networks of the brain is the ability to sense discrete changes of acetylcholine within a synapse. Here we show an efficient method for generating acetylcholine-detecting nanosensors based on DNA dendrimer scaffolds that incorporate butyrylcholinesterase and fluorescein in a nanoscale arrangement. These nanosensors are selective for acetylcholine and reversibly respond to levels of acetylcholine in the neurophysiological range. This DNA dendrimer architecture has the potential to overcome current obstacles to sensing in the synaptic environment, including the nanoscale size constraints of the synapse and the ability to quantify the spatio-temporal fluctuations of neurotransmitter release. By combining the control of nanosensor architecture with the strategic placement of fluorescent reporters and enzymes, this novel nanosensor platform can facilitate the development of new selective imaging tools for neuroscience.

Enzyme-linked DNA dendrimer nanosensors for acetylcholine

NASA Astrophysics Data System (ADS)

Walsh, Ryan; Morales, Jennifer M.; Skipwith, Christopher G.; Ruckh, Timothy T.; Clark, Heather A.

2015-10-01

It is currently difficult to measure small dynamics of molecules in the brain with high spatial and temporal resolution while connecting them to the bigger picture of brain function. A step towards understanding the underlying neural networks of the brain is the ability to sense discrete changes of acetylcholine within a synapse. Here we show an efficient method for generating acetylcholine-detecting nanosensors based on DNA dendrimer scaffolds that incorporate butyrylcholinesterase and fluorescein in a nanoscale arrangement. These nanosensors are selective for acetylcholine and reversibly respond to levels of acetylcholine in the neurophysiological range. This DNA dendrimer architecture has the potential to overcome current obstacles to sensing in the synaptic environment, including the nanoscale size constraints of the synapse and the ability to quantify the spatio-temporal fluctuations of neurotransmitter release. By combining the control of nanosensor architecture with the strategic placement of fluorescent reporters and enzymes, this novel nanosensor platform can facilitate the development of new selective imaging tools for neuroscience.

Finding Spatio-Temporal Patterns in Large Sensor Datasets

ERIC Educational Resources Information Center

McGuire, Michael Patrick

2010-01-01

Spatial or temporal data mining tasks are performed in the context of the relevant space, defined by a spatial neighborhood, and the relevant time period, defined by a specific time interval. Furthermore, when mining large spatio-temporal datasets, interesting patterns typically emerge where the dataset is most dynamic. This dissertation is…

Determining Spatio-Temporal Cadastral Data Requirement for Infrastructure of Ladm for Turkey

NASA Astrophysics Data System (ADS)

Alkan, M.; Polat, Z. A.

2016-06-01

Nowadays, the nature of land title and cadastral (LTC) data in the Turkey is dynamic from a temporal perspective which depends on the LTC operations. Functional requirements with respect to the characteristics are investigated based upon interviews of professionals in public and private sectors. These are; Legal authorities, Land Registry and Cadastre offices, Highway departments, Foundations, Ministries of Budget, Transportation, Justice, Public Works and Settlement, Environment and Forestry, Agriculture and Rural Affairs, Culture and Internal Affairs, State Institute of Statistics (SIS), execution offices, tax offices, real estate offices, private sector, local governments and banks. On the other hand, spatio-temporal LTC data very important component for creating infrastructure of Land Administration Model (LADM). For this reason, spatio-temporal LTC data needs for LADM not only updated but also temporal. The investigations ended up with determine temporal analyses of LTC data, traditional LTC system and tracing temporal analyses in traditional LTC system. In the traditional system, the temporal analyses needed by all these users could not be performed in a rapid and reliable way. The reason for this is that the traditional LTC system is a manual archiving system. The aims and general contents of this paper: (1) define traditional LTC system of Turkey; (2) determining the need for spatio-temporal LTC data and analyses for core domain model for LADM. As a results of temporal and spatio-temporal analysis LTC data needs, new system design is important for the Turkish LADM model. Designing and realizing an efficient and functional Temporal Geographic Information Systems (TGIS) is inevitable for the Turkish LADM core infrastructure. Finally this paper outcome is creating infrastructure for design and develop LADM for Turkey.

How a (sub)Cellular Coincidence Detection Mechanism Featuring Layer-5 Pyramidal Cells May Help Produce Various Visual Phenomena.

PubMed

Bachmann, Talis

2015-01-01

Perceptual phenomena such as spatio-temporal illusions and masking are typically explained by psychological (cognitive) processing theories or large-scale neural theories involving inter-areal connectivity and neural circuits comprising of hundreds or more interconnected single cells. Subcellular mechanisms are hardly used for such purpose. Here, a mechanistic theoretical view is presented on how a subcellular brain mechanism of integration of presynaptic signals that arrive at different compartments of layer-5 pyramidal neurons could explain a couple of spatiotemporal visual-phenomenal effects unfolding along very brief time intervals within the range of the sub-second temporal scale.

Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000-2007

NASA Astrophysics Data System (ADS)

Yin, Ping; Mu, Lan; Madden, Marguerite; Vena, John E.

2014-10-01

Lung cancer is the second most commonly diagnosed cancer in both men and women in Georgia, USA. However, the spatio-temporal patterns of lung cancer risk in Georgia have not been fully studied. Hierarchical Bayesian models are used here to explore the spatio-temporal patterns of lung cancer incidence risk by race and gender in Georgia for the period of 2000-2007. With the census tract level as the spatial scale and the 2-year period aggregation as the temporal scale, we compare a total of seven Bayesian spatio-temporal models including two under a separate modeling framework and five under a joint modeling framework. One joint model outperforms others based on the deviance information criterion. Results show that the northwest region of Georgia has consistently high lung cancer incidence risk for all population groups during the study period. In addition, there are inverse relationships between the socioeconomic status and the lung cancer incidence risk among all Georgian population groups, and the relationships in males are stronger than those in females. By mapping more reliable variations in lung cancer incidence risk at a relatively fine spatio-temporal scale for different Georgian population groups, our study aims to better support healthcare performance assessment, etiological hypothesis generation, and health policy making.

Outlier Detection in Urban Air Quality Sensor Networks.

PubMed

van Zoest, V M; Stein, A; Hoek, G

2018-01-01

Low-cost urban air quality sensor networks are increasingly used to study the spatio-temporal variability in air pollutant concentrations. Recently installed low-cost urban sensors, however, are more prone to result in erroneous data than conventional monitors, e.g., leading to outliers. Commonly applied outlier detection methods are unsuitable for air pollutant measurements that have large spatial and temporal variations as occur in urban areas. We present a novel outlier detection method based upon a spatio-temporal classification, focusing on hourly NO 2 concentrations. We divide a full year's observations into 16 spatio-temporal classes, reflecting urban background vs. urban traffic stations, weekdays vs. weekends, and four periods per day. For each spatio-temporal class, we detect outliers using the mean and standard deviation of the normal distribution underlying the truncated normal distribution of the NO 2 observations. Applying this method to a low-cost air quality sensor network in the city of Eindhoven, the Netherlands, we found 0.1-0.5% of outliers. Outliers could reflect measurement errors or unusual high air pollution events. Additional evaluation using expert knowledge is needed to decide on treatment of the identified outliers. We conclude that our method is able to detect outliers while maintaining the spatio-temporal variability of air pollutant concentrations in urban areas.

Application of 3D Spatio-Temporal Data Modeling, Management, and Analysis in DB4GEO

NASA Astrophysics Data System (ADS)

Kuper, P. V.; Breunig, M.; Al-Doori, M.; Thomsen, A.

2016-10-01

Many of todaýs world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given.

Topologically Consistent Models for Efficient Big Geo-Spatio Data Distribution

NASA Astrophysics Data System (ADS)

Jahn, M. W.; Bradley, P. E.; Doori, M. Al; Breunig, M.

2017-10-01

Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment.

Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications.

PubMed

Kasabov, Nikola; Scott, Nathan Matthew; Tu, Enmei; Marks, Stefan; Sengupta, Neelava; Capecci, Elisa; Othman, Muhaini; Doborjeh, Maryam Gholami; Murli, Norhanifah; Hartono, Reggio; Espinosa-Ramos, Josafath Israel; Zhou, Lei; Alvi, Fahad Bashir; Wang, Grace; Taylor, Denise; Feigin, Valery; Gulyaev, Sergei; Mahmoud, Mahmoud; Hou, Zeng-Guang; Yang, Jie

2016-06-01

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include 'on the fly' new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this is presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatio-Temporal Process Simulation of Dam-Break Flood Based on SPH

NASA Astrophysics Data System (ADS)

Wang, H.; Ye, F.; Ouyang, S.; Li, Z.

2018-04-01

On the basis of introducing the SPH (Smooth Particle Hydrodynamics) simulation method, the key research problems were given solutions in this paper, which ere the spatial scale and temporal scale adapting to the GIS(Geographical Information System) application, the boundary condition equations combined with the underlying surface, and the kernel function and parameters applicable to dam-break flood simulation. In this regards, a calculation method of spatio-temporal process emulation with elaborate particles for dam-break flood was proposed. Moreover the spatio-temporal process was dynamic simulated by using GIS modelling and visualization. The results show that the method gets more information, objectiveness and real situations.

Spatio-temporal neural stem cell behavior that leads to both perfect and imperfect structural brain regeneration in adult newts.

PubMed

Urata, Yuko; Yamashita, Wataru; Inoue, Takeshi; Agata, Kiyokazu

2018-06-14

Adult newts can regenerate large parts of their brain from adult neural stem cells (NSCs), but how adult NSCs reorganize brain structures during regeneration remains unclear. In development, elaborate brain structures are produced under broadly coordinated regulations of embryonic NSCs in the neural tube, whereas brain regeneration entails exquisite control of the reestablishment of certain brain parts, suggesting a yet-unknown mechanism directs NSCs upon partial brain excision. Here we report that upon one-quarter excision of the adult newt ( Pleurodeles waltl ) mesencephalon, active participation of local NSCs around specific brain subregions' boundaries leads to some imperfect and some perfect brain regeneration along an individual's rostrocaudal axis. Regeneration phenotypes depend on how the wound closing occurs using local NSCs, and perfect regeneration replicates development-like processes but takes more than one year. Our findings indicate that newt brain regeneration is supported by modularity of boundary-domain NSCs with self-organizing ability in neighboring fields. © 2018. Published by The Company of Biologists Ltd.

Spatio-temporal characterisation of a 100 kHz 24 W sub-3-cycle NOPCPA laser system

NASA Astrophysics Data System (ADS)

Witting, Tobias; Furch, Federico J.; Vrakking, Marc J. J.

2018-04-01

In recent years, OPCPA and NOPCPA laser systems have shown the potential to supersede Ti:sapphire plus post-compression based laser systems to drive next generation attosecond light sources via direct amplification of few-cycle pulses to high pulse energies at high repetition rates. In this paper, we present a sub 3-cycle, 100 kHz, 24 W NOPA laser system and characterise its spatio-temporal properties using the SEA-F-SPIDER technique. Our results underline the importance of spatio-temporal diagnostics for these emerging laser systems.

A pipeline of spatio-temporal filtering for predicting the laterality of self-initiated fine movements from single trial readiness potentials.

PubMed

Zeid, Elias Abou; Sereshkeh, Alborz Rezazadeh; Chau, Tom

2016-12-01

In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have attempted single trial classification of RP via spatial and temporal filtering methods, or by combining the RP with event-related desynchronization. However, RP feature extraction remains challenging due to the slow non-oscillatory nature of the potential, its variability among participants and the inherent noise in EEG signals. Here, we propose a participant-specific, individually optimized pipeline of spatio-temporal filtering (PSTF) to improve RP feature extraction for laterality prediction. PSTF applies band-pass filtering on RP signals, followed by Fisher criterion spatial filtering to maximize class separation, and finally temporal window averaging for feature dimension reduction. Optimal parameters are simultaneously found by cross-validation for each participant. Using EEG data from 14 participants performing self-initiated left or right key presses as well as two benchmark BCI datasets, we compared the performance of PSTF to two popular methods: common spatial subspace decomposition, and adaptive spatio-temporal filtering. On the BCI benchmark data sets, PSTF performed comparably to both existing methods. With the key press EEG data, PSTF extracted more discriminative features, thereby leading to more accurate (74.99% average accuracy) predictions of RP laterality than that achievable with existing methods. Naturalistic and volitional interaction with the world is an important capacity that is lost with traditional system-paced BCIs. We demonstrated a significant improvement in fine movement laterality prediction from RP features alone. Our work supports further study of RP-based BCI for intuitive asynchronous control of the environment, such as augmentative communication or wheelchair navigation.

A pipeline of spatio-temporal filtering for predicting the laterality of self-initiated fine movements from single trial readiness potentials

NASA Astrophysics Data System (ADS)

Abou Zeid, Elias; Rezazadeh Sereshkeh, Alborz; Chau, Tom

2016-12-01

Objective. In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have attempted single trial classification of RP via spatial and temporal filtering methods, or by combining the RP with event-related desynchronization. However, RP feature extraction remains challenging due to the slow non-oscillatory nature of the potential, its variability among participants and the inherent noise in EEG signals. Here, we propose a participant-specific, individually optimized pipeline of spatio-temporal filtering (PSTF) to improve RP feature extraction for laterality prediction. Approach. PSTF applies band-pass filtering on RP signals, followed by Fisher criterion spatial filtering to maximize class separation, and finally temporal window averaging for feature dimension reduction. Optimal parameters are simultaneously found by cross-validation for each participant. Using EEG data from 14 participants performing self-initiated left or right key presses as well as two benchmark BCI datasets, we compared the performance of PSTF to two popular methods: common spatial subspace decomposition, and adaptive spatio-temporal filtering. Main results. On the BCI benchmark data sets, PSTF performed comparably to both existing methods. With the key press EEG data, PSTF extracted more discriminative features, thereby leading to more accurate (74.99% average accuracy) predictions of RP laterality than that achievable with existing methods. Significance. Naturalistic and volitional interaction with the world is an important capacity that is lost with traditional system-paced BCIs. We demonstrated a significant improvement in fine movement laterality prediction from RP features alone. Our work supports further study of RP-based BCI for intuitive asynchronous control of the environment, such as augmentative communication or wheelchair navigation.

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sreepathi, Sarat; Kumar, Jitendra; Mills, Richard T.

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like themore » Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.« less

User requirements for geo-collaborative work with spatio-temporal data in a web-based virtual globe environment.

PubMed

Yovcheva, Zornitza; van Elzakker, Corné P J M; Köbben, Barend

2013-11-01

Web-based tools developed in the last couple of years offer unique opportunities to effectively support scientists in their effort to collaborate. Communication among environmental researchers often involves not only work with geographical (spatial), but also with temporal data and information. Literature still provides limited documentation when it comes to user requirements for effective geo-collaborative work with spatio-temporal data. To start filling this gap, our study adopted a User-Centered Design approach and first explored the user requirements of environmental researchers working on distributed research projects for collaborative dissemination, exchange and work with spatio-temporal data. Our results show that system design will be mainly influenced by the nature and type of data users work with. From the end-users' perspective, optimal conversion of huge files of spatio-temporal data for further dissemination, accuracy of conversion, organization of content and security have a key role for effective geo-collaboration. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Image Motion Detection And Estimation: The Modified Spatio-Temporal Gradient Scheme

NASA Astrophysics Data System (ADS)

Hsin, Cheng-Ho; Inigo, Rafael M.

1990-03-01

The detection and estimation of motion are generally involved in computing a velocity field of time-varying images. A completely new modified spatio-temporal gradient scheme to determine motion is proposed. This is derived by using gradient methods and properties of biological vision. A set of general constraints is proposed to derive motion constraint equations. The constraints are that the second directional derivatives of image intensity at an edge point in the smoothed image will be constant at times t and t+L . This scheme basically has two stages: spatio-temporal filtering, and velocity estimation. Initially, image sequences are processed by a set of oriented spatio-temporal filters which are designed using a Gaussian derivative model. The velocity is then estimated for these filtered image sequences based on the gradient approach. From a computational stand point, this scheme offers at least three advantages over current methods. The greatest advantage of the modified spatio-temporal gradient scheme over the traditional ones is that an infinite number of motion constraint equations are derived instead of only one. Therefore, it solves the aperture problem without requiring any additional assumptions and is simply a local process. The second advantage is that because of the spatio-temporal filtering, the direct computation of image gradients (discrete derivatives) is avoided. Therefore the error in gradients measurement is reduced significantly. The third advantage is that during the processing of motion detection and estimation algorithm, image features (edges) are produced concurrently with motion information. The reliable range of detected velocity is determined by parameters of the oriented spatio-temporal filters. Knowing the velocity sensitivity of a single motion detection channel, a multiple-channel mechanism for estimating image velocity, seldom addressed by other motion schemes in machine vision, can be constructed by appropriately choosing and combining different sets of parameters. By applying this mechanism, a great range of velocity can be detected. The scheme has been tested for both synthetic and real images. The results of simulations are very satisfactory.

An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City

PubMed Central

Zhou, Lianjie; Chen, Nengcheng; Yuan, Sai; Chen, Zeqiang

2016-01-01

The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as traffic scheduling, traffic planning and traffic behavior management in intelligent public transportation systems. PMID:27801869

Travelling waves and spatial hierarchies in measles epidemics

NASA Astrophysics Data System (ADS)

Grenfell, B. T.; Bjørnstad, O. N.; Kappey, J.

2001-12-01

Spatio-temporal travelling waves are striking manifestations of predator-prey and host-parasite dynamics. However, few systems are well enough documented both to detect repeated waves and to explain their interaction with spatio-temporal variations in population structure and demography. Here, we demonstrate recurrent epidemic travelling waves in an exhaustive spatio-temporal data set for measles in England and Wales. We use wavelet phase analysis, which allows for dynamical non-stationarity-a complication in interpreting spatio-temporal patterns in these and many other ecological time series. In the pre-vaccination era, conspicuous hierarchical waves of infection moved regionally from large cities to small towns; the introduction of measles vaccination restricted but did not eliminate this hierarchical contagion. A mechanistic stochastic model suggests a dynamical explanation for the waves-spread via infective `sparks' from large `core' cities to smaller `satellite' towns. Thus, the spatial hierarchy of host population structure is a prerequisite for these infection waves.

Hierarchic spatio-temporal dynamics in glycolysis

NASA Astrophysics Data System (ADS)

Shinjyo, Takahiro; Nakagawa, Yoshiyuki; Ueda, Tetsuo

Yeast extracts exhibit oscillations when the glycolytic system is far away from equilibrium. Spatio-temporal dynamics in this system was studied in the newly developed gel as well as in the solution. Small regions (about 10 um) with very complex shape with high or low concentrations of NADH appeared, and upon these small structures large-scale dynamics were superimposed. Concentration waves propagated, and the source of wave was induced by contact with high ADP. Sink of waves was generated by contacting the reaction gel to two small gels rich in ADP. Upon these spatio-temporal dynamics were superimposed much slower global oscillations throughout the system with a period of about 40 min. Similar dynamics was seen in a solution of yeast extract, but the size of domains was about ten times larger than that in the gel. In this way, the multi-enzyme system of glycolysis exhibits self-organization of hierarchy in spatio-temporal dynamics.

A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates.

PubMed

Lindström, Johan; Szpiro, Adam A; Sampson, Paul D; Oron, Assaf P; Richards, Mark; Larson, Tim V; Sheppard, Lianne

2014-09-01

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of NO x in the Los Angeles area during a ten year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated R 2 of approximately 0.7 at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.

Optogenetic Modulation and Multi-Electrode Analysis of Cerebellar Networks In Vivo

PubMed Central

Kruse, Wolfgang; Krause, Martin; Aarse, Janna; Mark, Melanie D.; Manahan-Vaughan, Denise; Herlitze, Stefan

2014-01-01

The firing patterns of cerebellar Purkinje cells (PCs), as the sole output of the cerebellar cortex, determine and tune motor behavior. PC firing is modulated by various inputs from different brain regions and by cell-types including granule cells (GCs), climbing fibers and inhibitory interneurons. To understand how signal integration in PCs occurs and how subtle changes in the modulation of PC firing lead to adjustment of motor behaviors, it is important to precisely record PC firing in vivo and to control modulatory pathways in a spatio-temporal manner. Combining optogenetic and multi-electrode approaches, we established a new method to integrate light-guides into a multi-electrode system. With this method we are able to variably position the light-guide in defined regions relative to the recording electrode with micrometer precision. We show that PC firing can be precisely monitored and modulated by light-activation of channelrhodopsin-2 (ChR2) expressed in PCs, GCs and interneurons. Thus, this method is ideally suited to investigate the spatio/temporal modulation of PCs in anesthetized and in behaving mice. PMID:25144735

Neural dynamics of reward probability coding: a Magnetoencephalographic study in humans

PubMed Central

Thomas, Julie; Vanni-Mercier, Giovanna; Dreher, Jean-Claude

2013-01-01

Prediction of future rewards and discrepancy between actual and expected outcomes (prediction error) are crucial signals for adaptive behavior. In humans, a number of fMRI studies demonstrated that reward probability modulates these two signals in a large brain network. Yet, the spatio-temporal dynamics underlying the neural coding of reward probability remains unknown. Here, using magnetoencephalography, we investigated the neural dynamics of prediction and reward prediction error computations while subjects learned to associate cues of slot machines with monetary rewards with different probabilities. We showed that event-related magnetic fields (ERFs) arising from the visual cortex coded the expected reward value 155 ms after the cue, demonstrating that reward value signals emerge early in the visual stream. Moreover, a prediction error was reflected in ERF peaking 300 ms after the rewarded outcome and showing decreasing amplitude with higher reward probability. This prediction error signal was generated in a network including the anterior and posterior cingulate cortex. These findings pinpoint the spatio-temporal characteristics underlying reward probability coding. Together, our results provide insights into the neural dynamics underlying the ability to learn probabilistic stimuli-reward contingencies. PMID:24302894

Transparent, Flexible, Low Noise Graphene Electrodes for Simultaneous Electrophysiology and Neuroimaging

PubMed Central

Kuzum, Duygu; Takano, Hajime; Shim, Euijae; Reed, Jason C; Juul, Halvor; Richardson, Andrew G.; de Vries, Julius; Bink, Hank; Dichter, Marc A.; Lucas, Timothy H.; Coulter, Douglas A.; Cubukcu, Ertugrul; Litt, Brian

2014-01-01

Calcium imaging is a versatile experimental approach capable of resolving single neurons with single-cell spatial resolution in the brain. Electrophysiological recordings provide high temporal, but limited spatial resolution, due to the geometrical inaccessibility of the brain. An approach that integrates the advantages of both techniques could provide new insights into functions of neural circuits. Here, we report a transparent, flexible neural electrode technology based on graphene, which enables simultaneous optical imaging and electrophysiological recording. We demonstrate that hippocampal slices can be imaged through transparent graphene electrodes by both confocal and two-photon microscopy without causing any light-induced artifacts in the electrical recordings. Graphene electrodes record high frequency bursting activity and slow synaptic potentials that are hard to resolve by multi-cellular calcium imaging. This transparent electrode technology may pave the way for high spatio-temporal resolution electrooptic mapping of the dynamic neuronal activity. PMID:25327632

Temporal and spatio-temporal vibrotactile displays for voice fundamental frequency: an initial evaluation of a new vibrotactile speech perception aid with normal-hearing and hearing-impaired individuals.

PubMed

Auer, E T; Bernstein, L E; Coulter, D C

1998-10-01

Four experiments were performed to evaluate a new wearable vibrotactile speech perception aid that extracts fundamental frequency (F0) and displays the extracted F0 as a single-channel temporal or an eight-channel spatio-temporal stimulus. Specifically, we investigated the perception of intonation (i.e., question versus statement) and emphatic stress (i.e., stress on the first, second, or third word) under Visual-Alone (VA), Visual-Tactile (VT), and Tactile-Alone (TA) conditions and compared performance using the temporal and spatio-temporal vibrotactile display. Subjects were adults with normal hearing in experiments I-III and adults with severe to profound hearing impairments in experiment IV. Both versions of the vibrotactile speech perception aid successfully conveyed intonation. Vibrotactile stress information was successfully conveyed, but vibrotactile stress information did not enhance performance in VT conditions beyond performance in VA conditions. In experiment III, which involved only intonation identification, a reliable advantage for the spatio-temporal display was obtained. Differences between subject groups were obtained for intonation identification, with more accurate VT performance by those with normal hearing. Possible effects of long-term hearing status are discussed.

A geostatistical state-space model of animal densities for stream networks.

PubMed

Hocking, Daniel J; Thorson, James T; O'Neil, Kyle; Letcher, Benjamin H

2018-06-21

Population dynamics are often correlated in space and time due to correlations in environmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses of populations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty under-estimated. We developed a novel statistical method to account for spatio-temporal correlations within dendritic stream networks, while accounting for imperfect detection in the surveys. Through simulations, we found this model decreased predictive error relative to standard statistical methods when data were spatially correlated based on stream distance and performed similarly when data were not correlated. We found that increasing the number of years surveyed substantially improved the model accuracy when estimating spatial and temporal correlation coefficients, especially from 10 to 15 years. Increasing the number of survey sites within the network improved the performance of the non-spatial model but only marginally improved the density estimates in the spatio-temporal model. We applied this model to Brook Trout data from the West Susquehanna Watershed in Pennsylvania collected over 34 years from 1981 - 2014. We found the model including temporal and spatio-temporal autocorrelation best described young-of-the-year (YOY) and adult density patterns. YOY densities were positively related to forest cover and negatively related to spring temperatures with low temporal autocorrelation and moderately-high spatio-temporal correlation. Adult densities were less strongly affected by climatic conditions and less temporally variable than YOY but with similar spatio-temporal correlation and higher temporal autocorrelation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Brain organization underlying superior mathematical abilities in children with autism.

PubMed

Iuculano, Teresa; Rosenberg-Lee, Miriam; Supekar, Kaustubh; Lynch, Charles J; Khouzam, Amirah; Phillips, Jennifer; Uddin, Lucina Q; Menon, Vinod

2014-02-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication deficits. While such deficits have been the focus of most research, recent evidence suggests that individuals with ASD may exhibit cognitive strengths in domains such as mathematics. Cognitive assessments and functional brain imaging were used to investigate mathematical abilities in 18 children with ASD and 18 age-, gender-, and IQ-matched typically developing (TD) children. Multivariate classification and regression analyses were used to investigate whether brain activity patterns during numerical problem solving were significantly different between the groups and predictive of individual mathematical abilities. Children with ASD showed better numerical problem solving abilities and relied on sophisticated decomposition strategies for single-digit addition problems more frequently than TD peers. Although children with ASD engaged similar brain areas as TD children, they showed different multivariate activation patterns related to arithmetic problem complexity in ventral temporal-occipital cortex, posterior parietal cortex, and medial temporal lobe. Furthermore, multivariate activation patterns in ventral temporal-occipital cortical areas typically associated with face processing predicted individual numerical problem solving abilities in children with ASD but not in TD children. Our study suggests that superior mathematical information processing in children with ASD is characterized by a unique pattern of brain organization and that cortical regions typically involved in perceptual expertise may be utilized in novel ways in ASD. Our findings of enhanced cognitive and neural resources for mathematics have critical implications for educational, professional, and social outcomes for individuals with this lifelong disorder. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

A LANGUAGE FOR MODULAR SPATIO-TEMPORAL SIMULATION (R824766)

EPA Science Inventory

Creating an effective environment for collaborative spatio-temporal model development will require computational systems that provide support for the user in three key areas: (1) Support for modular, hierarchical model construction and archiving/linking of simulation modules; (2)...

Bayesian hierarchical models for regional climate reconstructions of the last glacial maximum

NASA Astrophysics Data System (ADS)

Weitzel, Nils; Hense, Andreas; Ohlwein, Christian

2017-04-01

Spatio-temporal reconstructions of past climate are important for the understanding of the long term behavior of the climate system and the sensitivity to forcing changes. Unfortunately, they are subject to large uncertainties, have to deal with a complex proxy-climate structure, and a physically reasonable interpolation between the sparse proxy observations is difficult. Bayesian Hierarchical Models (BHMs) are a class of statistical models that is well suited for spatio-temporal reconstructions of past climate because they permit the inclusion of multiple sources of information (e.g. records from different proxy types, uncertain age information, output from climate simulations) and quantify uncertainties in a statistically rigorous way. BHMs in paleoclimatology typically consist of three stages which are modeled individually and are combined using Bayesian inference techniques. The data stage models the proxy-climate relation (often named transfer function), the process stage models the spatio-temporal distribution of the climate variables of interest, and the prior stage consists of prior distributions of the model parameters. For our BHMs, we translate well-known proxy-climate transfer functions for pollen to a Bayesian framework. In addition, we can include Gaussian distributed local climate information from preprocessed proxy records. The process stage combines physically reasonable spatial structures from prior distributions with proxy records which leads to a multivariate posterior probability distribution for the reconstructed climate variables. The prior distributions that constrain the possible spatial structure of the climate variables are calculated from climate simulation output. We present results from pseudoproxy tests as well as new regional reconstructions of temperatures for the last glacial maximum (LGM, ˜ 21,000 years BP). These reconstructions combine proxy data syntheses with information from climate simulations for the LGM that were performed in the PMIP3 project. The proxy data syntheses consist either of raw pollen data or of normally distributed climate data from preprocessed proxy records. Future extensions of our method contain the inclusion of other proxy types (transfer functions), the implementation of other spatial interpolation techniques, the use of age uncertainties, and the extension to spatio-temporal reconstructions of the last deglaciation. Our work is part of the PalMod project funded by the German Federal Ministry of Education and Science (BMBF).

Effective and efficient analysis of spatio-temporal data

NASA Astrophysics Data System (ADS)

Zhang, Zhongnan

Spatio-temporal data mining, i.e., mining knowledge from large amount of spatio-temporal data, is a highly demanding field because huge amounts of spatio-temporal data have been collected in various applications, ranging from remote sensing, to geographical information systems (GIS), computer cartography, environmental assessment and planning, etc. The collection data far exceeded human's ability to analyze which make it crucial to develop analysis tools. Recent studies on data mining have extended to the scope of data mining from relational and transactional datasets to spatial and temporal datasets. Among the various forms of spatio-temporal data, remote sensing images play an important role, due to the growing wide-spreading of outer space satellites. In this dissertation, we proposed two approaches to analyze the remote sensing data. The first one is about applying association rules mining onto images processing. Each image was divided into a number of image blocks. We built a spatial relationship for these blocks during the dividing process. This made a large number of images into a spatio-temporal dataset since each image was shot in time-series. The second one implemented co-occurrence patterns discovery from these images. The generated patterns represent subsets of spatial features that are located together in space and time. A weather analysis is composed of individual analysis of several meteorological variables. These variables include temperature, pressure, dew point, wind, clouds, visibility and so on. Local-scale models provide detailed analysis and forecasts of meteorological phenomena ranging from a few kilometers to about 100 kilometers in size. When some of above meteorological variables have some special change tendency, some kind of severe weather will happen in most cases. Using the discovery of association rules, we found that some special meteorological variables' changing has tight relation with some severe weather situation that will happen very soon. This dissertation is composed of three parts: an introduction, some basic knowledges and relative works, and my own three contributions to the development of approaches for spatio-temporal data mining: DYSTAL algorithm, STARSI algorithm, and COSTCOP+ algorithm.

Integrating real-time and manual monitored data to predict hillslope soil moisture dynamics with high spatio-temporal resolution using linear and non-linear models

USDA-ARS?s Scientific Manuscript database

Spatio-temporal variability of soil moisture (') is a challenge that remains to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time ' monitoring methods. This restricted the comprehensive and intensive examination of ' dynamic...

Quantification of amyloid deposits and oxygen extraction fraction in the brain with multispectral optoacoustic imaging in arcAβ mouse model of Alzheimer's disease

NASA Astrophysics Data System (ADS)

Ni, Ruiqing; Vaas, Markus; Rudin, Markus; Klohs, Jan

2018-02-01

Beta-amyloid (Aβ) deposition and vascular dysfunction are important contributors to the pathogenesis in Alzheimer's disease (AD). However, the spatio-temporal relationship between an altered oxygen metabolism and Aβ deposition in the brain remains elusive. Here we provide novel in-vivo estimates of brain Aβ load with Aβ-binding probe CRANAD-2 and measures of brain oxygen saturation by using multi-spectral optoacoustic imaging (MSOT) and perfusion imaging with magnetic resonance imaging (MRI) in arcAβ mouse models of AD. We demonstrated a decreased cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2) in the cortical region of the arcAβ mice compared to wildtype littermates at 24 months. In addition, we showed proof-of-concept for the detection of cerebral Aβ deposits in brain from arcAβ mice compared to wild-type littermates.

A dynamic spatio-temporal model for spatial data

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin; Walsh, Daniel P.

2017-01-01

Analyzing spatial data often requires modeling dependencies created by a dynamic spatio-temporal data generating process. In many applications, a generalized linear mixed model (GLMM) is used with a random effect to account for spatial dependence and to provide optimal spatial predictions. Location-specific covariates are often included as fixed effects in a GLMM and may be collinear with the spatial random effect, which can negatively affect inference. We propose a dynamic approach to account for spatial dependence that incorporates scientific knowledge of the spatio-temporal data generating process. Our approach relies on a dynamic spatio-temporal model that explicitly incorporates location-specific covariates. We illustrate our approach with a spatially varying ecological diffusion model implemented using a computationally efficient homogenization technique. We apply our model to understand individual-level and location-specific risk factors associated with chronic wasting disease in white-tailed deer from Wisconsin, USA and estimate the location the disease was first introduced. We compare our approach to several existing methods that are commonly used in spatial statistics. Our spatio-temporal approach resulted in a higher predictive accuracy when compared to methods based on optimal spatial prediction, obviated confounding among the spatially indexed covariates and the spatial random effect, and provided additional information that will be important for containing disease outbreaks.

Spatial distribution of tree species governs the spatio-temporal interaction of leaf area index and soil moisture across a forested landscape.

PubMed

Naithani, Kusum J; Baldwin, Doug C; Gaines, Katie P; Lin, Henry; Eissenstat, David M

2013-01-01

Quantifying coupled spatio-temporal dynamics of phenology and hydrology and understanding underlying processes is a fundamental challenge in ecohydrology. While variation in phenology and factors influencing it have attracted the attention of ecologists for a long time, the influence of biodiversity on coupled dynamics of phenology and hydrology across a landscape is largely untested. We measured leaf area index (L) and volumetric soil water content (θ) on a co-located spatial grid to characterize forest phenology and hydrology across a forested catchment in central Pennsylvania during 2010. We used hierarchical Bayesian modeling to quantify spatio-temporal patterns of L and θ. Our results suggest that the spatial distribution of tree species across the landscape created unique spatio-temporal patterns of L, which created patterns of water demand reflected in variable soil moisture across space and time. We found a lag of about 11 days between increase in L and decline in θ. Vegetation and soil moisture become increasingly homogenized and coupled from leaf-onset to maturity but heterogeneous and uncoupled from leaf maturity to senescence. Our results provide insight into spatio-temporal coupling between biodiversity and soil hydrology that is useful to enhance ecohydrological modeling in humid temperate forests.

Spatio-temporal scaling effects on longshore sediment transport pattern along the nearshore zone

NASA Astrophysics Data System (ADS)

Khorram, Saeed; Ergil, Mustafa

2018-03-01

A measure of uncertainties, entropy has been employed in such different applications as coastal engineering probability inferences. Entropy sediment transport integration theories present novel visions in coastal analyses/modeling the application and development of which are still far-reaching. Effort has been made in the present paper to propose a method that needs an entropy-power index for spatio-temporal patterns analyses. Results have shown that the index is suitable for marine/hydrological ecosystem components analyses based on a beach area case study. The method makes use of six Makran Coastal monthly data (1970-2015) and studies variables such as spatio-temporal patterns, LSTR (long-shore sediment transport rate), wind speed, and wave height all of which are time-dependent and play considerable roles in terrestrial coastal investigations; the mentioned variables show meaningful spatio-temporal variability most of the time, but explanation of their combined performance is not easy. Accordingly, the use of an entropy-power index can show considerable signals that facilitate the evaluation of water resources and will provide an insight regarding hydrological parameters' interactions at scales as large as beach areas. Results have revealed that an STDDPI (entropy based spatio-temporal disorder dynamics power index) can simulate wave, long-shore sediment transport rate, and wind when granulometry, concentration, and flow conditions vary.

Dynamical Structure of Madden-Julian Oscillation over Malay Peninsula

NASA Astrophysics Data System (ADS)

Djamil, Y. S.; Koh, T. Y.; Chandimala, J.; Teo, C. K.

2014-12-01

Madden-Julian Oscillation (MJO) is the dominant weather event in the intraseasonal time scale over Malay Peninsula region. The MJO signals are represented by the first two modes of radiosonde records extracted using Extended Empirical Orthogonal Function (EEOF) analyses which we label as Local Multivariate MJO (LMM). LMM is able to capture the spatio-temporal profile of MJO along the global tropics in all seasons. With the help of LMM, we clarify the dynamical and thermodynamical structure of the MJO over Malay Peninsula, including the unique "boomerang-shaped" feature in the time-height temperature profile identified in previous literature.

Spatio-temporal distribution of brain activity associated with audio-visually congruent and incongruent speech and the McGurk Effect.

PubMed

Pratt, Hillel; Bleich, Naomi; Mittelman, Nomi

2015-11-01

Spatio-temporal distributions of cortical activity to audio-visual presentations of meaningless vowel-consonant-vowels and the effects of audio-visual congruence/incongruence, with emphasis on the McGurk effect, were studied. The McGurk effect occurs when a clearly audible syllable with one consonant, is presented simultaneously with a visual presentation of a face articulating a syllable with a different consonant and the resulting percept is a syllable with a consonant other than the auditorily presented one. Twenty subjects listened to pairs of audio-visually congruent or incongruent utterances and indicated whether pair members were the same or not. Source current densities of event-related potentials to the first utterance in the pair were estimated and effects of stimulus-response combinations, brain area, hemisphere, and clarity of visual articulation were assessed. Auditory cortex, superior parietal cortex, and middle temporal cortex were the most consistently involved areas across experimental conditions. Early (<200 msec) processing of the consonant was overall prominent in the left hemisphere, except right hemisphere prominence in superior parietal cortex and secondary visual cortex. Clarity of visual articulation impacted activity in secondary visual cortex and Wernicke's area. McGurk perception was associated with decreased activity in primary and secondary auditory cortices and Wernicke's area before 100 msec, increased activity around 100 msec which decreased again around 180 msec. Activity in Broca's area was unaffected by McGurk perception and was only increased to congruent audio-visual stimuli 30-70 msec following consonant onset. The results suggest left hemisphere prominence in the effects of stimulus and response conditions on eight brain areas involved in dynamically distributed parallel processing of audio-visual integration. Initially (30-70 msec) subcortical contributions to auditory cortex, superior parietal cortex, and middle temporal cortex occur. During 100-140 msec, peristriate visual influences and Wernicke's area join in the processing. Resolution of incongruent audio-visual inputs is then attempted, and if successful, McGurk perception occurs and cortical activity in left hemisphere further increases between 170 and 260 msec.

Discrete simulations of spatio-temporal dynamics of small water bodies under varied stream flow discharges

NASA Astrophysics Data System (ADS)

Daya Sagar, B. S.

2005-01-01

Spatio-temporal patterns of small water bodies (SWBs) under the influence of temporally varied stream flow discharge are simulated in discrete space by employing geomorphologically realistic expansion and contraction transformations. Cascades of expansion-contraction are systematically performed by synchronizing them with stream flow discharge simulated via the logistic map. Templates with definite characteristic information are defined from stream flow discharge pattern as the basis to model the spatio-temporal organization of randomly situated surface water bodies of various sizes and shapes. These spatio-temporal patterns under varied parameters (λs) controlling stream flow discharge patterns are characterized by estimating their fractal dimensions. At various λs, nonlinear control parameters, we show the union of boundaries of water bodies that traverse the water body and non-water body spaces as geomorphic attractors. The computed fractal dimensions of these attractors are 1.58, 1.53, 1.78, 1.76, 1.84, and 1.90, respectively, at λs of 1, 2, 3, 3.46, 3.57, and 3.99. These values are in line with general visual observations.

Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China

PubMed Central

Fan, Yaxin; Zhu, Xinyan; Guo, Wei; Guo, Tao

2018-01-01

The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran’s I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions. PMID:29672551

Rapid and efficient gene delivery into the adult mouse brain via focal electroporation

PubMed Central

Nomura, Tadashi; Nishimura, Yusuke; Gotoh, Hitoshi; Ono, Katsuhiko

2016-01-01

In vivo gene delivery is required for studying the cellular and molecular mechanisms of various biological events. Virus-mediated gene transfer or generation of transgenic animals is widely used; however, these methods are time-consuming and expensive. Here we show an improved electroporation technique for acute gene delivery into the adult mouse brain. Using a syringe-based microelectrode, local DNA injection and the application of electric current can be performed simultaneously; this allows rapid and efficient gene transduction of adult non-neuronal cells. Combining this technique with various expression vectors that carry specific promoters resulted in targeted gene expression in astrocytic cells. Our results constitute a powerful strategy for the genetic manipulation of adult brains in a spatio-temporally controlled manner. PMID:27430903

Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition.

PubMed

Yuan, Chunfeng; Li, Xi; Hu, Weiming; Ling, Haibin; Maybank, Stephen J

2014-02-01

In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved.

Evidence-based Controls for Epidemics Using Spatio-temporal Stochastic Model as a Bayesian Framwork

USDA-ARS?s Scientific Manuscript database

The control of highly infectious diseases of agricultural and plantation crops and livestock represents a key challenge in epidemiological and ecological modelling, with implemented control strategies often being controversial. Mathematical models, including the spatio-temporal stochastic models con...

Spatio-temporal Outlier Detection in Precipitation Data

NASA Astrophysics Data System (ADS)

Wu, Elizabeth; Liu, Wei; Chawla, Sanjay

The detection of outliers from spatio-temporal data is an important task due to the increasing amount of spatio-temporal data available and the need to understand and interpret it. Due to the limitations of current data mining techniques, new techniques to handle this data need to be developed. We propose a spatio-temporal outlier detection algorithm called Outstretch, which discovers the outlier movement patterns of the top-k spatial outliers over several time periods. The top-k spatial outliers are found using the Exact-Grid Top- k and Approx-Grid Top- k algorithms, which are an extension of algorithms developed by Agarwal et al. [1]. Since they use the Kulldorff spatial scan statistic, they are capable of discovering all outliers, unaffected by neighbouring regions that may contain missing values. After generating the outlier sequences, we show one way they can be interpreted, by comparing them to the phases of the El Niño Southern Oscilliation (ENSO) weather phenomenon to provide a meaningful analysis of the results.

Emergent dynamics of spatio-temporal chaos in a heterogeneous excitable medium.

PubMed

Bittihn, Philip; Berg, Sebastian; Parlitz, Ulrich; Luther, Stefan

2017-09-01

Self-organized activation patterns in excitable media such as spiral waves and spatio-temporal chaos underlie dangerous cardiac arrhythmias. While the interaction of single spiral waves with different types of heterogeneity has been studied extensively, the effect of heterogeneity on fully developed spatio-temporal chaos remains poorly understood. We investigate how the complexity and stability properties of spatio-temporal chaos in the Bär-Eiswirth model of excitable media depend on the heterogeneity of the underlying medium. We employ different measures characterizing the chaoticity of the system and find that the spatial arrangement of multiple discrete lower excitability regions has a strong impact on the complexity of the dynamics. Varying the number, shape, and spatial arrangement of the heterogeneities, we observe strong emergent effects ranging from increases in chaoticity to the complete cessation of chaos, contrasting the expectation from the homogeneous behavior. The implications of our findings for the development and treatment of arrhythmias in the heterogeneous cardiac muscle are discussed.

Spatio-temporal mapping of variation potentials in leaves of Helianthus annuus L. seedlings in situ using multi-electrode array

PubMed Central

Zhao, Dong-Jie; Wang, Zhong-Yi; Huang, Lan; Jia, Yong-Peng; Leng, John Q.

2014-01-01

Damaging thermal stimuli trigger long-lasting variation potentials (VPs) in higher plants. Owing to limitations in conventional plant electrophysiological recording techniques, recorded signals are composed of signals originating from all of the cells that are connected to an electrode. This limitation does not enable detailed spatio-temporal distributions of transmission and electrical activities in plants to be visualised. Multi-electrode array (MEA) enables the recording and imaging of dynamic spatio-temporal electrical activities in higher plants. Here, we used an 8 × 8 MEA with a polar distance of 450 μm to measure electrical activities from numerous cells simultaneously. The mapping of the data that were recorded from the MEA revealed the transfer mode of the thermally induced VPs in the leaves of Helianthus annuus L. seedlings in situ. These results suggest that MEA can enable recordings with high spatio-temporal resolution that facilitate the determination of the bioelectrical response mode of higher plants under stress. PMID:24961469

Spatio-temporal mapping of variation potentials in leaves of Helianthus annuus L. seedlings in situ using multi-electrode array.

PubMed

Zhao, Dong-Jie; Wang, Zhong-Yi; Huang, Lan; Jia, Yong-Peng; Leng, John Q

2014-06-25

Damaging thermal stimuli trigger long-lasting variation potentials (VPs) in higher plants. Owing to limitations in conventional plant electrophysiological recording techniques, recorded signals are composed of signals originating from all of the cells that are connected to an electrode. This limitation does not enable detailed spatio-temporal distributions of transmission and electrical activities in plants to be visualised. Multi-electrode array (MEA) enables the recording and imaging of dynamic spatio-temporal electrical activities in higher plants. Here, we used an 8 × 8 MEA with a polar distance of 450 μm to measure electrical activities from numerous cells simultaneously. The mapping of the data that were recorded from the MEA revealed the transfer mode of the thermally induced VPs in the leaves of Helianthus annuus L. seedlings in situ. These results suggest that MEA can enable recordings with high spatio-temporal resolution that facilitate the determination of the bioelectrical response mode of higher plants under stress.

Emergent dynamics of spatio-temporal chaos in a heterogeneous excitable medium

NASA Astrophysics Data System (ADS)

Bittihn, Philip; Berg, Sebastian; Parlitz, Ulrich; Luther, Stefan

2017-09-01

Self-organized activation patterns in excitable media such as spiral waves and spatio-temporal chaos underlie dangerous cardiac arrhythmias. While the interaction of single spiral waves with different types of heterogeneity has been studied extensively, the effect of heterogeneity on fully developed spatio-temporal chaos remains poorly understood. We investigate how the complexity and stability properties of spatio-temporal chaos in the Bär-Eiswirth model of excitable media depend on the heterogeneity of the underlying medium. We employ different measures characterizing the chaoticity of the system and find that the spatial arrangement of multiple discrete lower excitability regions has a strong impact on the complexity of the dynamics. Varying the number, shape, and spatial arrangement of the heterogeneities, we observe strong emergent effects ranging from increases in chaoticity to the complete cessation of chaos, contrasting the expectation from the homogeneous behavior. The implications of our findings for the development and treatment of arrhythmias in the heterogeneous cardiac muscle are discussed.

Real-Time Spatio-Temporal Twice Whitening for MIMO Energy Detector

DOE Office of Scientific and Technical Information (OSTI.GOV)

Humble, Travis S; Mitra, Pramita; Barhen, Jacob

2010-01-01

While many techniques exist for local spectrum sensing of a primary user, each represents a computationally demanding task to secondary user receivers. In software-defined radio, computational complexity lengthens the time for a cognitive radio to recognize changes in the transmission environment. This complexity is even more significant for spatially multiplexed receivers, e.g., in SIMO and MIMO, where the spatio-temporal data sets grow in size with the number of antennae. Limits on power and space for the processor hardware further constrain SDR performance. In this report, we discuss improvements in spatio-temporal twice whitening (STTW) for real-time local spectrum sensing by demonstratingmore » a form of STTW well suited for MIMO environments. We implement STTW on the Coherent Logix hx3100 processor, a multicore processor intended for low-power, high-throughput software-defined signal processing. These results demonstrate how coupling the novel capabilities of emerging multicore processors with algorithmic advances can enable real-time, software-defined processing of large spatio-temporal data sets.« less

Adaptive spatio-temporal filtering of disturbed ECGs: a multi-channel approach to heartbeat detection in smart clothing.

PubMed

Wiklund, Urban; Karlsson, Marcus; Ostlund, Nils; Berglin, Lena; Lindecrantz, Kaj; Karlsson, Stefan; Sandsjö, Leif

2007-06-01

Intermittent disturbances are common in ECG signals recorded with smart clothing: this is mainly because of displacement of the electrodes over the skin. We evaluated a novel adaptive method for spatio-temporal filtering for heartbeat detection in noisy multi-channel ECGs including short signal interruptions in single channels. Using multi-channel database recordings (12-channel ECGs from 10 healthy subjects), the results showed that multi-channel spatio-temporal filtering outperformed regular independent component analysis. We also recorded seven channels of ECG using a T-shirt with textile electrodes. Ten healthy subjects performed different sequences during a 10-min recording: resting, standing, flexing breast muscles, walking and pushups. Using adaptive multi-channel filtering, the sensitivity and precision was above 97% in nine subjects. Adaptive multi-channel spatio-temporal filtering can be used to detect heartbeats in ECGs with high noise levels. One application is heartbeat detection in noisy ECG recordings obtained by integrated textile electrodes in smart clothing.

Spatio-temporal variability of ichthyophagous bird assemblage around western Mediterranean open-sea cage fish farms.

PubMed

Aguado-Giménez, Felipe; Eguía-Martínez, Sergio; Cerezo-Valverde, Jesús; García-García, Benjamín

2018-06-14

Ichthyophagous birds aggregate at cage fish farms attracted by caged and associated wild fish. Spatio-temporal variability of such birds was studied for a year through seasonal visual counts at eight farms in the western Mediterranean. Correlation with farm and location descriptors was assessed. Considerable spatio-temporal variability in fish-eating bird density and assemblage structure was observed among farms and seasons. Bird density increased from autumn to winter, with the great cormorant being the most abundant species, also accounting largely for differences among farms. Grey heron and little egret were also numerous at certain farms during the coldest seasons. Cattle egret was only observed at one farm. No shags were observed during winter. During spring and summer, bird density decreased markedly and only shags and little egrets were observed at only a few farms. Season and distance from farms to bird breeding/wintering grounds helped to explain some of the spatio-temporal variability. Copyright © 2018 Elsevier Ltd. All rights reserved.

A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health.

PubMed

Lee, Duncan; Mukhopadhyay, Sabyasachi; Rushworth, Alastair; Sahu, Sujit K

2017-04-01

In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Temporal Lobe and “Default” Hemodynamic Brain Modes Discriminate Between Schizophrenia and Bipolar Disorder

PubMed Central

Calhoun, Vince D.; Maciejewski, Paul K.; Pearlson, Godfrey D.; Kiehl, Kent A.

2009-01-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or “spatial modes” exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder. PMID:17894392

Temporal lobe and "default" hemodynamic brain modes discriminate between schizophrenia and bipolar disorder.

PubMed

Calhoun, Vince D; Maciejewski, Paul K; Pearlson, Godfrey D; Kiehl, Kent A

2008-11-01

Schizophrenia and bipolar disorder are currently diagnosed on the basis of psychiatric symptoms and longitudinal course. The determination of a reliable, biologically-based diagnostic indicator of these diseases (a biomarker) could provide the groundwork for developing more rigorous tools for differential diagnosis and treatment assignment. Recently, methods have been used to identify distinct sets of brain regions or "spatial modes" exhibiting temporally coherent brain activity. Using functional magnetic resonance imaging (fMRI) data and a multivariate analysis method, independent component analysis, we combined the temporal lobe and the default modes to discriminate subjects with bipolar disorder, chronic schizophrenia, and healthy controls. Temporal lobe and default mode networks were reliably identified in all participants. Classification results on an independent set of individuals revealed an average sensitivity and specificity of 90 and 95%, respectively. The use of coherent brain networks such as the temporal lobe and default mode networks may provide a more reliable measure of disease state than task-correlated fMRI activity. A combination of two such hemodynamic brain networks shows promise as a biomarker for schizophrenia and bipolar disorder.

[Mortality from Suicide in the Municipalities of Mainland Portugal: Spatio-Temporal Evolution between 1980 and 2015].

PubMed

Loureiro, Adriana; Almendra, Ricardo; Costa, Cláudia; Santana, Paula

2018-01-31

Suicide is considered a public health priority. It is a complex phenomenon resulting from the interaction of several factors, which do not depend solely on individual conditions. This study analyzes the spatio-temporal evolution of suicide mortality between 1980 and 2015, identifying areas of high risk, and their variation, in the 278 municipalities of Continental Portugal. Based on the number of self-inflicted injuries and deaths from suicide and the resident population, the spatio-temporal evolution of the suicide mortality rate was assessed via: i) a Poisson joinpoint regression model, and ii) spatio-temporal clustering methods. The suicide mortality rate evolution showed statistically significant increases over three periods (1980 - 1984; 1999 - 2002 and 2006 - 2015) and two statistically significant periods of decrease (1984 - 1995 and 1995 - 1999). The spatio-temporal analysis identified five clusters of high suicide risk (relative risk >1) and four clusters of low suicide risk (relative risk < 1). The periods when suicide mortality increases seem to overlap with times of economic and financial instability. The geographical pattern of suicide risk has changed: presently, the suicide rates from the municipalities in the Center and North are showing more similarity with those seen in the South, thus increasing the ruralization of the phenomenon of suicide. Between 1980 and 2015 the spacio-temporal pattern of mortality from suicide has been changing and is a phenomenon that is currently experiencing a growing trend (since 2006) and is of higher risk in rural areas.

Spatio-temporal variation in Helicoverpa egg parasitism by Trichogramma in a tropical Bt-transgenic cotton landscape

USDA-ARS?s Scientific Manuscript database

Understanding the spatio-temporal dynamics of insects in agroecosystems is crucial when developing effective management strategies that emphasise biological control of pests. Wild populations of Trichogramma Westwood egg parasitoids are utilised for biological suppression of the potentially resistan...

Near-instant automatic access to visually presented words in the human neocortex: neuromagnetic evidence.

PubMed

Shtyrov, Yury; MacGregor, Lucy J

2016-05-24

Rapid and efficient processing of external information by the brain is vital to survival in a highly dynamic environment. The key channel humans use to exchange information is language, but the neural underpinnings of its processing are still not fully understood. We investigated the spatio-temporal dynamics of neural access to word representations in the brain by scrutinising the brain's activity elicited in response to psycholinguistically, visually and phonologically matched groups of familiar words and meaningless pseudowords. Stimuli were briefly presented on the visual-field periphery to experimental participants whose attention was occupied with a non-linguistic visual feature-detection task. The neural activation elicited by these unattended orthographic stimuli was recorded using multi-channel whole-head magnetoencephalography, and the timecourse of lexically-specific neuromagnetic responses was assessed in sensor space as well as at the level of cortical sources, estimated using individual MR-based distributed source reconstruction. Our results demonstrate a neocortical signature of automatic near-instant access to word representations in the brain: activity in the perisylvian language network characterised by specific activation enhancement for familiar words, starting as early as ~70 ms after the onset of unattended word stimuli and underpinned by temporal and inferior-frontal cortices.

Individuation of objects and events: a developmental study.

PubMed

Wagner, Laura; Carey, Susan

2003-12-01

This study investigates children's ability to use language to guide their choice of individuation criterion in the domains of objects and events. Previous work (Shipley, E. F., & Shepperson, B. (1990). Countable entities: developmental changes. Cognition, 34, 109-136.) has shown that children have a strong bias to use a spatio-temporal individuation strategy when counting objects and that children will ignore a conflicting linguistic description in favor of this spatio-temporal bias. Experiment 1 asked children (3-, 4-, and 5-year-olds) and adults to count objects and events under different linguistic descriptions. In the object task, subjects counted pictures of familiar objects split into multiple pieces (as in Shipley, E. F., & Shepperson, B. (1990). Countable entities: developmental changes. Cognition, 34, 109-136.) and described either using an appropriate kind label (e.g. "car") or the general term "thing". In the event task, subjects watched short animated movies consisting of a goal-oriented event achieved via multiple, temporally separated steps. The events were described either with an appropriate telic predicate targeting the goal (e.g. "paint a flower") or with an atelic predicate targeting the steps in the process (e.g. "paint") and the subjects' task was to count the events. Relative to adults, children preferred a spatio-temporal counting strategy in both tasks; there was no difference among the three groups of children. However, children were able to significantly change their counting strategy to follow the linguistic description in the event but not the object task. Experiment 2 extended the object task to include counting of other types of non-spatio-temporal units such as sub-parts of objects and collections. Results showed that children could use the linguistic descriptions to guide their counting strategy for these new items, though they continued to show a bias for a spatio-temporal individuation strategy with the collections. We suggest potential cognitive origins for the spatio-temporal individuation bias and how it interacts with children's developing linguistic knowledge.

Mapping child maltreatment risk: a 12-year spatio-temporal analysis of neighborhood influences.

PubMed

Gracia, Enrique; López-Quílez, Antonio; Marco, Miriam; Lila, Marisol

2017-10-18

'Place' matters in understanding prevalence variations and inequalities in child maltreatment risk. However, most studies examining ecological variations in child maltreatment risk fail to take into account the implications of the spatial and temporal dimensions of neighborhoods. In this study, we conduct a high-resolution small-area study to analyze the influence of neighborhood characteristics on the spatio-temporal epidemiology of child maltreatment risk. We conducted a 12-year (2004-2015) small-area Bayesian spatio-temporal epidemiological study with all families with child maltreatment protection measures in the city of Valencia, Spain. As neighborhood units, we used 552 census block groups. Cases were geocoded using the family address. Neighborhood-level characteristics analyzed included three indicators of neighborhood disadvantage-neighborhood economic status, neighborhood education level, and levels of policing activity-, immigrant concentration, and residential instability. Bayesian spatio-temporal modelling and disease mapping methods were used to provide area-specific risk estimations. Results from a spatio-temporal autoregressive model showed that neighborhoods with low levels of economic and educational status, with high levels of policing activity, and high immigrant concentration had higher levels of substantiated child maltreatment risk. Disease mapping methods were used to analyze areas of excess risk. Results showed chronic spatial patterns of high child maltreatment risk during the years analyzed, as well as stability over time in areas of low risk. Areas with increased or decreased child maltreatment risk over the years were also observed. A spatio-temporal epidemiological approach to study the geographical patterns, trends over time, and the contextual determinants of child maltreatment risk can provide a useful method to inform policy and action. This method can offer a more accurate description of the problem, and help to inform more localized prevention and intervention strategies. This new approach can also contribute to an improved epidemiological surveillance system to detect ecological variations in risk, and to assess the effectiveness of the initiatives to reduce this risk.

Effects of climate change adaptation scenarios on perceived spatio-temporal characteristics of drought events

NASA Astrophysics Data System (ADS)

Vidal, J.-P.; Martin, E.; Kitova, N.; Najac, J.; Soubeyroux, J.-M.

2012-04-01

Drought events develop in both space and time and they are therefore best described through summary joint spatio-temporal characteristics, like mean duration, mean affected area and total magnitude. This study addresses the issue of future projections of such characteristics of drought events over France through three main research questions: (1) Are downscaled climate projections able to reproduce spatio-temporal characteristics of meteorological and agricultural droughts in France over a present-day period? (2) How such characteristics will evolve over the 21st century under different emissions scenarios? (3) How would perceived drought characteristics evolve under theoretical adaptation scenarios? These questions are addressed using the Isba land surface model, downscaled climate projections from the ARPEGE General Circulation Model under three emissions scenarios, as well as results from a previously performed 50-year multilevel and multiscale drought reanalysis over France (Vidal et al., 2010). Spatio-temporal characteristics of meteorological and agricultural drought events are computed using the Standardized Precipitation Index (SPI) and the Standardized Soil Wetness Index (SSWI), respectively, and for time scales of 3 and 12 months. Results first show that the distributions of joint spatio-temporal characteristics of observed events are well reproduced by the downscaled hydroclimate projections over a present-day period. All spatio-temporal characteristics of drought events are then found to dramatically increase over the 21st century under all considered emissions scenarios, with stronger changes for agricultural droughts. Two theoretical adaptation scenarios are eventually built based on hypotheses of adaptation to evolving climate and hydrological normals. The two scenarios differ by the way the transient adaptation is performed for a given date in the future, with reference to the normals over either the previous 30-year window ("retrospective" adaptation) or over a 30-year period centred around the date considered ("prospective" adaptation). These adaptation scenarios are translated into local-scale transient drought thresholds, as opposed to a non-adaptation scenario where the drought threshold remains constant. The perceived spatio-temporal characteristics derived from the theoretical adaptation scenarios show much reduced changes, but they call for more realistic scenarios at both the catchment and national scale in order to accurately assess the combined effect of local-scale adaptation and global-scale mitigation. This study thus proposes a proof of concept for using standardized drought indices for (1) assessing projections of spatio-temporal drought characteristics and (2) building theoretical adaptation scenarios and associated perceived changes in hydrological impact studies (Vidal et al., submitted). Vidal J.-P., Martin E., Franchistéguy L., Habets F., Soubeyroux J.-M., Blanchard M. & Baillon M. (2010) Multilevel and multiscale drought reanalysis over France with the Safran-Isba-Modcou hydrometeorological suite. Hydrology and Earth System Sciences, 14, 459-478.doi: 10.5194/hess-14-459-2010 Vidal J.-P., Martin E., Kitova N., Najac J. & Soubeyroux, J. M. (submitted) Evolution of spatio-temporal drought characteristics: validation, projections and effect of adaptation scenarios. Submitted to Hydrology and earth System Sciences

Effects of Spatio-Temporal Aliasing on Pilot Performance in Active Control Tasks

NASA Technical Reports Server (NTRS)

Zaal, Peter; Sweet, Barbara

2010-01-01

Spatio-temporal aliasing affects pilot performance and control behavior. For increasing refresh rates: 1) Significant change in control behavior: a) Increase in visual gain and neuromuscular frequency. b) Decrease in visual time delay. 2) Increase in tracking performance: a) Decrease in RMSe. b) Increase in crossover frequency.

Fast Spatio-Temporal Data Mining from Large Geophysical Datasets

NASA Technical Reports Server (NTRS)

Stolorz, P.; Mesrobian, E.; Muntz, R.; Santos, J. R.; Shek, E.; Yi, J.; Mechoso, C.; Farrara, J.

1995-01-01

Use of the UCLA CONQUEST (CONtent-based Querying in Space and Time) is reviewed for performance of automatic cyclone extraction and detection of spatio-temporal blocking conditions on MPP. CONQUEST is a data analysis environment for knowledge and data mining to aid in high-resolution modeling of climate modeling.

Modeling spatio-temporal wildfire ignition point patterns

Treesearch

Amanda S. Hering; Cynthia L. Bell; Marc G. Genton

2009-01-01

We analyze and model the structure of spatio-temporal wildfire ignitions in the St. Johns River Water Management District in northeastern Florida. Previous studies, based on the K-function and an assumption of homogeneity, have shown that wildfire events occur in clusters. We revisit this analysis based on an inhomogeneous K-...

Fire, native species, and soil resource interactions influence the spatio-temporal invasion pattern of Bromus tectorum

Treesearch

Michael J. Gundale; Steve Sutherland; Thomas H. DeLuca; others

2008-01-01

Bromus tectorum (cheatgrass) is an invasive annual that occupies perennial grass and shrub communities throughout the western United States. Bromus tectorum exhibits an intriguing spatio-temporal pattern of invasion in low elevation ponderosa pine Pinus ponderosa/bunchgrass communities in western Montana where it...

Cortical Spatio-Temporal Dynamics Underlying Phonological Target Detection in Humans

ERIC Educational Resources Information Center

Chang, Edward F.; Edwards, Erik; Nagarajan, Srikantan S.; Fogelson, Noa; Dalal, Sarang S.; Canolty, Ryan T.; Kirsch, Heidi E.; Barbaro, Nicholas M.; Knight, Robert T.

2011-01-01

Selective processing of task-relevant stimuli is critical for goal-directed behavior. We used electrocorticography to assess the spatio-temporal dynamics of cortical activation during a simple phonological target detection task, in which subjects press a button when a prespecified target syllable sound is heard. Simultaneous surface potential…

What Is Spatio-Temporal Data Warehousing?

NASA Astrophysics Data System (ADS)

Vaisman, Alejandro; Zimányi, Esteban

In the last years, extending OLAP (On-Line Analytical Processing) systems with spatial and temporal features has attracted the attention of the GIS (Geographic Information Systems) and database communities. However, there is no a commonly agreed definition of what is a spatio-temporal data warehouse and what functionality such a data warehouse should support. Further, the solutions proposed in the literature vary considerably in the kind of data that can be represented as well as the kind of queries that can be expressed. In this paper we present a conceptual framework for defining spatio-temporal data warehouses using an extensible data type system. We also define a taxonomy of different classes of queries of increasing expressive power, and show how to express such queries using an extension of the tuple relational calculus with aggregated functions.

The use of spatio-temporal correlation to forecast critical transitions

NASA Astrophysics Data System (ADS)

Karssenberg, Derek; Bierkens, Marc F. P.

2010-05-01

Complex dynamical systems may have critical thresholds at which the system shifts abruptly from one state to another. Such critical transitions have been observed in systems ranging from the human body system to financial markets and the Earth system. Forecasting the timing of critical transitions before they are reached is of paramount importance because critical transitions are associated with a large shift in dynamical regime of the system under consideration. However, it is hard to forecast critical transitions, because the state of the system shows relatively little change before the threshold is reached. Recently, it was shown that increased spatio-temporal autocorrelation and variance can serve as alternative early warning signal for critical transitions. However, thus far these second order statistics have not been used for forecasting in a data assimilation framework. Here we show that the use of spatio-temporal autocorrelation and variance in the state of the system reduces the uncertainty in the predicted timing of critical transitions compared to classical approaches that use the value of the system state only. This is shown by assimilating observed spatio-temporal autocorrelation and variance into a dynamical system model using a Particle Filter. We adapt a well-studied distributed model of a logistically growing resource with a fixed grazing rate. The model describes the transition from an underexploited system with high resource biomass to overexploitation as grazing pressure crosses the critical threshold, which is a fold bifurcation. To represent limited prior information, we use a large variance in the prior probability distributions of model parameters and the system driver (grazing rate). First, we show that the rate of increase in spatio-temporal autocorrelation and variance prior to reaching the critical threshold is relatively consistent across the uncertainty range of the driver and parameter values used. This indicates that an increase in spatio-temporal autocorrelation and variance are consistent predictors of a critical transition, even under the condition of a poorly defined system. Second, we perform data assimilation experiments using an artificial exhaustive data set generated by one realization of the model. To mimic real-world sampling, an observational data set is created from this exhaustive data set. This is done by sampling on a regular spatio-temporal grid, supplemented by sampling locations at a short distance. Spatial and temporal autocorrelation in this observational data set is calculated for different spatial and temporal separation (lag) distances. To assign appropriate weights to observations (here, autocorrelation values and variance) in the Particle Filter, the covariance matrix of the error in these observations is required. This covariance matrix is estimated using Monte Carlo sampling, selecting a different random position of the sampling network relative to the exhaustive data set for each realization. At each update moment in the Particle Filter, observed autocorrelation values are assimilated into the model and the state of the model is updated. Using this approach, it is shown that the use of autocorrelation reduces the uncertainty in the forecasted timing of a critical transition compared to runs without data assimilation. The performance of the use of spatial autocorrelation versus temporal autocorrelation depends on the timing and number of observational data. This study is restricted to a single model only. However, it is becoming increasingly clear that spatio-temporal autocorrelation and variance can be used as early warning signals for a large number of systems. Thus, it is expected that spatio-temporal autocorrelation and variance are valuable in data assimilation frameworks in a large number of dynamical systems.

Spatio-temporal factors associated with meningococcal meningitis annual incidence at the health centre level in Niger, 2004-2010.

PubMed

Paireau, Juliette; Maïnassara, Halima B; Jusot, Jean-François; Collard, Jean-Marc; Idi, Issa; Moulia-Pelat, Jean-Paul; Mueller, Judith E; Fontanet, Arnaud

2014-05-01

Epidemics of meningococcal meningitis (MM) recurrently strike the African Meningitis Belt. This study aimed at investigating factors, still poorly understood, that influence annual incidence of MM serogroup A, the main etiologic agent over 2004-2010, at a fine spatial scale in Niger. To take into account data dependencies over space and time and control for unobserved confounding factors, we developed an explanatory Bayesian hierarchical model over 2004-2010 at the health centre catchment area (HCCA) level. The multivariate model revealed that both climatic and non-climatic factors were important for explaining spatio-temporal variations in incidence: mean relative humidity during November-June over the study region (posterior mean Incidence Rate Ratio (IRR) = 0.656, 95% Credible Interval (CI) 0.405-0.949) and occurrence of early rains in March in a HCCA (IRR = 0.353, 95% CI 0.239-0.502) were protective factors; a higher risk was associated with the percentage of neighbouring HCCAs having at least one MM A case during the same year (IRR = 2.365, 95% CI 2.078-2.695), the presence of a road crossing the HCCA (IRR = 1.743, 95% CI 1.173-2.474) and the occurrence of cases before 31 December in a HCCA (IRR = 6.801, 95% CI 4.004-10.910). At the study region level, higher annual incidence correlated with greater geographic spread and, to a lesser extent, with higher intensity of localized outbreaks. Based on these findings, we hypothesize that spatio-temporal variability of MM A incidence between years and HCCAs result from variations in the intensity or duration of the dry season climatic effects on disease risk, and is further impacted by factors of spatial contacts, representing facilitated pathogen transmission. Additional unexplained factors may contribute to the observed incidence patterns and should be further investigated.

On the potential for the Partial Triadic Analysis to grasp the spatio-temporal variability of groundwater hydrochemistry

NASA Astrophysics Data System (ADS)

Gourdol, L.; Hissler, C.; Pfister, L.

2012-04-01

The Luxembourg sandstone aquifer is of major relevance for the national supply of drinking water in Luxembourg. The city of Luxembourg (20% of the country's population) gets almost 2/3 of its drinking water from this aquifer. As a consequence, the study of both the groundwater hydrochemistry, as well as its spatial and temporal variations, are considered as of highest priority. Since 2005, a monitoring network has been implemented by the Water Department of Luxembourg City, with a view to a more sustainable management of this strategic water resource. The data collected to date forms a large and complex dataset, describing spatial and temporal variations of many hydrochemical parameters. The data treatment issue is tightly connected to this kind of water monitoring programs and complex databases. Standard multivariate statistical techniques, such as principal components analysis and hierarchical cluster analysis, have been widely used as unbiased methods for extracting meaningful information from groundwater quality data and are now classically used in many hydrogeological studies, in particular to characterize temporal or spatial hydrochemical variations induced by natural and anthropogenic factors. But these classical multivariate methods deal with two-way matrices, usually parameters/sites or parameters/time, while often the dataset resulting from qualitative water monitoring programs should be seen as a datacube parameters/sites/time. Three-way matrices, such as the one we propose here, are difficult to handle and to analyse by classical multivariate statistical tools and thus should be treated with approaches dealing with three-way data structures. One possible analysis approach consists in the use of partial triadic analysis (PTA). The PTA was previously used with success in many ecological studies but never to date in the domain of hydrogeology. Applied to the dataset of the Luxembourg Sandstone aquifer, the PTA appears as a new promising statistical instrument for hydrogeologists, in particular to characterize temporal and spatial hydrochemical variations induced by natural and anthropogenic factors. This new approach for groundwater management offers potential for 1) identifying a common multivariate spatial structure, 2) untapping the different hydrochemical patterns and explaining their controlling factors and 3) analysing the temporal variability of this structure and grasping hydrochemical changes.

Complexity Measures in Magnetoencephalography: Measuring "Disorder" in Schizophrenia

PubMed Central

Brookes, Matthew J.; Hall, Emma L.; Robson, Siân E.; Price, Darren; Palaniyappan, Lena; Liddle, Elizabeth B.; Liddle, Peter F.; Robinson, Stephen E.; Morris, Peter G.

2015-01-01

This paper details a methodology which, when applied to magnetoencephalography (MEG) data, is capable of measuring the spatio-temporal dynamics of ‘disorder’ in the human brain. Our method, which is based upon signal entropy, shows that spatially separate brain regions (or networks) generate temporally independent entropy time-courses. These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal. We explore the relationship between entropy and the more established time-frequency decomposition methods, which elucidate the temporal evolution of neural oscillations. We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary. Finally, we provide a demonstration of the clinical utility of our method, using it to shed light on aberrant neurophysiological processing in schizophrenia. We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network. These findings demonstrate potential clinical utility for our method and support a recent hypothesis that schizophrenia can be characterised by abnormalities in the salience network (a well characterised distributed network comprising bilateral insula and cingulate cortices). PMID:25886553

Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system

PubMed Central

Sunkin, Susan M.; Ng, Lydia; Lau, Chris; Dolbeare, Tim; Gilbert, Terri L.; Thompson, Carol L.; Hawrylycz, Michael; Dang, Chinh

2013-01-01

The Allen Brain Atlas (http://www.brain-map.org) provides a unique online public resource integrating extensive gene expression data, connectivity data and neuroanatomical information with powerful search and viewing tools for the adult and developing brain in mouse, human and non-human primate. Here, we review the resources available at the Allen Brain Atlas, describing each product and data type [such as in situ hybridization (ISH) and supporting histology, microarray, RNA sequencing, reference atlases, projection mapping and magnetic resonance imaging]. In addition, standardized and unique features in the web applications are described that enable users to search and mine the various data sets. Features include both simple and sophisticated methods for gene searches, colorimetric and fluorescent ISH image viewers, graphical displays of ISH, microarray and RNA sequencing data, Brain Explorer software for 3D navigation of anatomy and gene expression, and an interactive reference atlas viewer. In addition, cross data set searches enable users to query multiple Allen Brain Atlas data sets simultaneously. All of the Allen Brain Atlas resources can be accessed through the Allen Brain Atlas data portal. PMID:23193282

Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Huang, Bo; Lin, Chuan-Yao

2015-02-01

This paper explores the spatio-temporal patterns of particulate matter (PM) in Taiwan based on a series of methods. Using fuzzy c-means clustering first, the spatial heterogeneity (six clusters) in the PM data collected between 2005 and 2009 in Taiwan are identified and the industrial and urban areas of Taiwan (southwestern, west central, northwestern, and northern Taiwan) are found to have high PM concentrations. The PM10-PM2.5 relationship is then modeled with global ordinary least squares regression, geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR). The GTWR and GWR produce consistent results; however, GTWR provides more detailed information of spatio-temporal variations of the PM10-PM2.5 relationship. The results also show that GTWR provides a relatively high goodness of fit and sufficient space-time explanatory power. In particular, the PM2.5 or PM10 varies with time and space, depending on weather conditions and the spatial distribution of land use and emission patterns in local areas. Such information can be used to determine patterns of spatio-temporal heterogeneity in PM that will allow the control of pollutants and the reduction of public exposure.

Zero-inflated spatio-temporal models for disease mapping.

PubMed

Torabi, Mahmoud

2017-05-01

In this paper, our aim is to analyze geographical and temporal variability of disease incidence when spatio-temporal count data have excess zeros. To that end, we consider random effects in zero-inflated Poisson models to investigate geographical and temporal patterns of disease incidence. Spatio-temporal models that employ conditionally autoregressive smoothing across the spatial dimension and B-spline smoothing over the temporal dimension are proposed. The analysis of these complex models is computationally difficult from the frequentist perspective. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of complex models computationally convenient. Recently developed data cloning method provides a frequentist approach to mixed models that is also computationally convenient. We propose to use data cloning, which yields to maximum likelihood estimation, to conduct frequentist analysis of zero-inflated spatio-temporal modeling of disease incidence. One of the advantages of the data cloning approach is that the prediction and corresponding standard errors (or prediction intervals) of smoothing disease incidence over space and time is easily obtained. We illustrate our approach using a real dataset of monthly children asthma visits to hospital in the province of Manitoba, Canada, during the period April 2006 to March 2010. Performance of our approach is also evaluated through a simulation study. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spatio-temporal regulations and functions of neuronal alternative RNA splicing in developing and adult brains.

PubMed

Iijima, Takatoshi; Hidaka, Chiharu; Iijima, Yoko

2016-08-01

Alternative pre-mRNA splicing is a fundamental mechanism that generates molecular diversity from a single gene. In the central nervous system (CNS), key neural developmental steps are thought to be controlled by alternative splicing decisions, including the molecular diversity underlying synaptic wiring, plasticity, and remodeling. Significant progress has been made in understanding the molecular mechanisms and functions of alternative pre-mRNA splicing in neurons through studies in invertebrate systems; however, recent studies have begun to uncover the potential role of neuronal alternative splicing in the mammalian CNS. This article provides an overview of recent findings regarding the regulation and function of neuronal alternative splicing. In particular, we focus on the spatio-temporal regulation of neurexin, a synaptic adhesion molecule, by neuronal cell type-specific factors and neuronal activity, which are thought to be especially important for characterizing neural development and function within the mammalian CNS. Notably, there is increasing evidence that implicates the dysregulation of neuronal splicing events in several neurological disorders. Therefore, understanding the detailed mechanisms of neuronal alternative splicing in the mammalian CNS may provide plausible treatment strategies for these diseases. Copyright © 2016 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

Algorithm for real-time detection of signal patterns using phase synchrony: an application to an electrode array

NASA Astrophysics Data System (ADS)

Sadeghi, Saman; MacKay, William A.; van Dam, R. Michael; Thompson, Michael

2011-02-01

Real-time analysis of multi-channel spatio-temporal sensor data presents a considerable technical challenge for a number of applications. For example, in brain-computer interfaces, signal patterns originating on a time-dependent basis from an array of electrodes on the scalp (i.e. electroencephalography) must be analyzed in real time to recognize mental states and translate these to commands which control operations in a machine. In this paper we describe a new technique for recognition of spatio-temporal patterns based on performing online discrimination of time-resolved events through the use of correlation of phase dynamics between various channels in a multi-channel system. The algorithm extracts unique sensor signature patterns associated with each event during a training period and ranks importance of sensor pairs in order to distinguish between time-resolved stimuli to which the system may be exposed during real-time operation. We apply the algorithm to electroencephalographic signals obtained from subjects tested in the neurophysiology laboratories at the University of Toronto. The extension of this algorithm for rapid detection of patterns in other sensing applications, including chemical identification via chemical or bio-chemical sensor arrays, is also discussed.

Spatiotemporal database of US congressional elections, 1896–2014

PubMed Central

Wolf, Levi John

2017-01-01

High-quality historical data about US Congressional elections has long provided common ground for electoral studies. However, advances in geographic information science have recently made it efficient to compile, distribute, and analyze large spatio-temporal data sets on the structure of US Congressional districts. A single spatio-temporal data set that relates US Congressional election results to the spatial extent of the constituencies has not yet been developed. To address this, existing high-quality data sets of elections returns were combined with a spatiotemporal data set on Congressional district boundaries to generate a new spatio-temporal database of US Congressional election results that are explicitly linked to the geospatial data about the districts themselves. PMID:28809849

Spatio-temporal dependencies between hospital beds, physicians and health expenditure using visual variables and data classification in statistical table

NASA Astrophysics Data System (ADS)

Medyńska-Gulij, Beata; Cybulski, Paweł

2016-06-01

This paper analyses the use of table visual variables of statistical data of hospital beds as an important tool for revealing spatio-temporal dependencies. It is argued that some of conclusions from the data about public health and public expenditure on health have a spatio-temporal reference. Different from previous studies, this article adopts combination of cartographic pragmatics and spatial visualization with previous conclusions made in public health literature. While the significant conclusions about health care and economic factors has been highlighted in research papers, this article is the first to apply visual analysis to statistical table together with maps which is called previsualisation.

A BAYESIAN SPATIAL AND TEMPORAL MODELING APPROACH TO MAPPING GEOGRAPHIC VARIATION IN MORTALITY RATES FOR SUBNATIONAL AREAS WITH R-INLA.

PubMed

Khana, Diba; Rossen, Lauren M; Hedegaard, Holly; Warner, Margaret

2018-01-01

Hierarchical Bayes models have been used in disease mapping to examine small scale geographic variation. State level geographic variation for less common causes of mortality outcomes have been reported however county level variation is rarely examined. Due to concerns about statistical reliability and confidentiality, county-level mortality rates based on fewer than 20 deaths are suppressed based on Division of Vital Statistics, National Center for Health Statistics (NCHS) statistical reliability criteria, precluding an examination of spatio-temporal variation in less common causes of mortality outcomes such as suicide rates (SRs) at the county level using direct estimates. Existing Bayesian spatio-temporal modeling strategies can be applied via Integrated Nested Laplace Approximation (INLA) in R to a large number of rare causes of mortality outcomes to enable examination of spatio-temporal variations on smaller geographic scales such as counties. This method allows examination of spatiotemporal variation across the entire U.S., even where the data are sparse. We used mortality data from 2005-2015 to explore spatiotemporal variation in SRs, as one particular application of the Bayesian spatio-temporal modeling strategy in R-INLA to predict year and county-specific SRs. Specifically, hierarchical Bayesian spatio-temporal models were implemented with spatially structured and unstructured random effects, correlated time effects, time varying confounders and space-time interaction terms in the software R-INLA, borrowing strength across both counties and years to produce smoothed county level SRs. Model-based estimates of SRs were mapped to explore geographic variation.

Spatio-temporal Reconstruction of Neural Sources Using Indirect Dominant Mode Rejection.

PubMed

Jafadideh, Alireza Talesh; Asl, Babak Mohammadzadeh

2018-04-27

Adaptive minimum variance based beamformers (MVB) have been successfully applied to magnetoencephalogram (MEG) and electroencephalogram (EEG) data to localize brain activities. However, the performance of these beamformers falls down in situations where correlated or interference sources exist. To overcome this problem, we propose indirect dominant mode rejection (iDMR) beamformer application in brain source localization. This method by modifying measurement covariance matrix makes MVB applicable in source localization in the presence of correlated and interference sources. Numerical results on both EEG and MEG data demonstrate that presented approach accurately reconstructs time courses of active sources and localizes those sources with high spatial resolution. In addition, the results of real AEF data show the good performance of iDMR in empirical situations. Hence, iDMR can be reliably used for brain source localization especially when there are correlated and interference sources.

A K-means multivariate approach for clustering independent components from magnetoencephalographic data.

PubMed

Spadone, Sara; de Pasquale, Francesco; Mantini, Dante; Della Penna, Stefania

2012-09-01

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering. Copyright © 2012 Elsevier Inc. All rights reserved.

Learning large-scale dynamic discrete choice models of spatio-temporal preferences with application to migratory pastoralism in East Africa

USDA-ARS?s Scientific Manuscript database

Understanding spatio-temporal resource preferences is paramount in the design of policies for sustainable development. Unfortunately, resource preferences are often unknown to policy-makers and have to be inferred from data. In this paper we consider the problem of inferring agents’ preferences fro...

A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London.

PubMed

Rushworth, Alastair; Lee, Duncan; Mitchell, Richard

2014-07-01

It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Toward an understanding of the cerebral substrates of woman's orgasm.

PubMed

Bianchi-Demicheli, Francesco; Ortigue, Stephanie

2007-09-20

The way women experience orgasm is of interest to scientists, clinicians, and laypeople. Whereas the origin and the function of a woman's orgasm remains controversial, the current models of sexual function acknowledge a combined role of central (spinal and cerebral) and peripheral processes during orgasm experience. At the central level, although it is accepted that the spinal cord drives orgasm, the cerebral involvement and cognitive representation of a woman's orgasm has not been extensively investigated. Important gaps in our knowledge remain. Recently, the astonishing advances of neuroimaging techniques applied in parallel with a neuropsychological approach allowed the unravelling of specific functional neuroanatomy of a woman's orgasm. Here, clinical and experimental findings on the cortico-subcortical pathway of a woman's orgasm are reviewed and compared with the neural basis of a man's orgasm. By defining the specific brain areas that sustain the assumed higher-order representation of a woman's orgasm, this review provides a foundation for future studies. The next challenge of functional imaging and neuropsychological studies is to understand the hierarchical interactions between these multiple cortical areas, not only with a correlation analysis but also with high spatio-temporal resolution techniques demonstrating the causal necessity, the temporal time course and the direction of the causality. Further studies using a multi-disciplinary approach are needed to identify the spatio-temporal dynamic of a woman's orgasm, its dysfunctions and possible new treatments.

Spatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery.

PubMed

Saglam, Bülent; Bilgili, Ertugrul; Dincdurmaz, Bahar; Kadiogulari, Ali Ihsan; Kücük, Ömer

2008-06-20

Computing fire danger and fire risk on a spatio-temporal scale is of crucial importance in fire management planning, and in the simulation of fire growth and development across a landscape. However, due to the complex nature of forests, fire risk and danger potential maps are considered one of the most difficult thematic layers to build up. Remote sensing and digital terrain data have been introduced for efficient discrete classification of fire risk and fire danger potential. In this study, two time-series data of Landsat imagery were used for determining spatio-temporal change of fire risk and danger potential in Korudag forest planning unit in northwestern Turkey. The method comprised the following two steps: (1) creation of indices of the factors influencing fire risk and danger; (2) evaluation of spatio-temporal changes in fire risk and danger of given areas using remote sensing as a quick and inexpensive means and determining the pace of forest cover change. Fire risk and danger potential indices were based on species composition, stand crown closure, stand development stage, insolation, slope and, proximity of agricultural lands to forest and distance from settlement areas. Using the indices generated, fire risk and danger maps were produced for the years 1987 and 2000. Spatio-temporal analyses were then realized based on the maps produced. Results obtained from the study showed that the use of Landsat imagery provided a valuable characterization and mapping of vegetation structure and type with overall classification accuracy higher than 83%.

Spatio-temporal analysis of irregular vocal fold oscillations: Biphonation due to desynchronization of spatial modes

NASA Astrophysics Data System (ADS)

Neubauer, Jürgen; Mergell, Patrick; Eysholdt, Ulrich; Herzel, Hanspeter

2001-12-01

This report is on direct observation and modal analysis of irregular spatio-temporal vibration patterns of vocal fold pathologies in vivo. The observed oscillation patterns are described quantitatively with multiline kymograms, spectral analysis, and spatio-temporal plots. The complex spatio-temporal vibration patterns are decomposed by empirical orthogonal functions into independent vibratory modes. It is shown quantitatively that biphonation can be induced either by left-right asymmetry or by desynchronized anterior-posterior vibratory modes, and the term ``AP (anterior-posterior) biphonation'' is introduced. The presented phonation examples show that for normal phonation the first two modes sufficiently explain the glottal dynamics. The spatio-temporal oscillation pattern associated with biphonation due to left-right asymmetry can be explained by the first three modes. Higher-order modes are required to describe the pattern for biphonation induced by anterior-posterior vibrations. Spatial irregularity is quantified by an entropy measure, which is significantly higher for irregular phonation than for normal phonation. Two asymmetry measures are introduced: the left-right asymmetry and the anterior-posterior asymmetry, as the ratios of the fundamental frequencies of left and right vocal fold and of anterior-posterior modes, respectively. These quantities clearly differentiate between left-right biphonation and anterior-posterior biphonation. This paper proposes methods to analyze quantitatively irregular vocal fold contour patterns in vivo and complements previous findings of desynchronization of vibration modes in computer modes and in in vitro experiments.

A semiparametric spatio-temporal model for solar irradiance data

DOE PAGES

Patrick, Joshua D.; Harvill, Jane L.; Hansen, Clifford W.

2016-03-01

Here, we evaluate semiparametric spatio-temporal models for global horizontal irradiance at high spatial and temporal resolution. These models represent the spatial domain as a lattice and are capable of predicting irradiance at lattice points, given data measured at other lattice points. Using data from a 1.2 MW PV plant located in Lanai, Hawaii, we show that a semiparametric model can be more accurate than simple interpolation between sensor locations. We investigate spatio-temporal models with separable and nonseparable covariance structures and find no evidence to support assuming a separable covariance structure. These results indicate a promising approach for modeling irradiance atmore » high spatial resolution consistent with available ground-based measurements. Moreover, this kind of modeling may find application in design, valuation, and operation of fleets of utility-scale photovoltaic power systems.« less

Spatio-Temporal Patterning in Primary Motor Cortex at Movement Onset.

PubMed

Best, Matthew D; Suminski, Aaron J; Takahashi, Kazutaka; Brown, Kevin A; Hatsopoulos, Nicholas G

2017-02-01

Voluntary movement initiation involves the engagement of large populations of motor cortical neurons around movement onset. Despite knowledge of the temporal dynamics that lead to movement, the spatial structure of these dynamics across the cortical surface remains unknown. In data from 4 rhesus macaques, we show that the timing of attenuation of beta frequency local field potential oscillations, a correlate of locally activated cortex, forms a spatial gradient across primary motor cortex (MI). We show that these spatio-temporal dynamics are recapitulated in the engagement order of ensembles of MI neurons. We demonstrate that these patterns are unique to movement onset and suggest that movement initiation requires a precise spatio-temporal sequential activation of neurons in MI. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Annotating spatio-temporal datasets for meaningful analysis in the Web

NASA Astrophysics Data System (ADS)

Stasch, Christoph; Pebesma, Edzer; Scheider, Simon

2014-05-01

More and more environmental datasets that vary in space and time are available in the Web. This comes along with an advantage of using the data for other purposes than originally foreseen, but also with the danger that users may apply inappropriate analysis procedures due to lack of important assumptions made during the data collection process. In order to guide towards a meaningful (statistical) analysis of spatio-temporal datasets available in the Web, we have developed a Higher-Order-Logic formalism that captures some relevant assumptions in our previous work [1]. It allows to proof on meaningful spatial prediction and aggregation in a semi-automated fashion. In this poster presentation, we will present a concept for annotating spatio-temporal datasets available in the Web with concepts defined in our formalism. Therefore, we have defined a subset of the formalism as a Web Ontology Language (OWL) pattern. It allows capturing the distinction between the different spatio-temporal variable types, i.e. point patterns, fields, lattices and trajectories, that in turn determine whether a particular dataset can be interpolated or aggregated in a meaningful way using a certain procedure. The actual annotations that link spatio-temporal datasets with the concepts in the ontology pattern are provided as Linked Data. In order to allow data producers to add the annotations to their datasets, we have implemented a Web portal that uses a triple store at the backend to store the annotations and to make them available in the Linked Data cloud. Furthermore, we have implemented functions in the statistical environment R to retrieve the RDF annotations and, based on these annotations, to support a stronger typing of spatio-temporal datatypes guiding towards a meaningful analysis in R. [1] Stasch, C., Scheider, S., Pebesma, E., Kuhn, W. (2014): "Meaningful spatial prediction and aggregation", Environmental Modelling & Software, 51, 149-165.

Somatic growth dynamics of West Atlantic hawksbill sea turtles: a spatio-temporal perspective

USGS Publications Warehouse

Bjorndal, Karen A.; Chaloupka, Milani; Saba, Vincent S.; Diez, Carlos E.; van Dam, Robert P.; Krueger, Barry H.; Horrocks, Julia A.; Santos, Armando J.B.; Bellini, Cláudio; Marcovaldi, Maria A.G.; Nava, Mabel; Willis, Sue; Godley, Brendan J.; Gore, Shannon; Hawkes, Lucy A.; McGowan, Andrew; Witt, Matthew J.; Stringell, Thomas B.; Sanghera, Amdeep; Richardson, Peter B.; Broderick, Annette C.; Phillips, Quinton; Calosso, Marta C.; Claydon, John A.B.; Blumenthal, Janice; Moncada, Felix; Nodarse, Gonzalo; Medina, Yosvani; Dunbar, Stephen G.; Wood, Lawrence D.; Lagueux, Cynthia J.; Campbell, Cathi L.; Meylan, Anne B.; Meylan, Peter A.; Burns Perez, Virginia R.; Coleman, Robin A.; Strindberg, Samantha; Guzmán-H, Vicente; Hart, Kristen M.; Cherkiss, Michael S.; Hillis-Starr, Zandy; Lundgren, Ian; Boulon, Ralf H.; Connett, Stephen; Outerbridge, Mark E.; Bolten, Alan B.

2016-01-01

Somatic growth dynamics are an integrated response to environmental conditions. Hawksbill sea turtles (Eretmochelys imbricata) are long-lived, major consumers in coral reef habitats that move over broad geographic areas (hundreds to thousands of kilometers). We evaluated spatio-temporal effects on hawksbill growth dynamics over a 33-yr period and 24 study sites throughout the West Atlantic and explored relationships between growth dynamics and climate indices. We compiled the largest ever data set on somatic growth rates for hawksbills – 3541 growth increments from 1980 to 2013. Using generalized additive mixed model analyses, we evaluated 10 covariates, including spatial and temporal variation, that could affect growth rates. Growth rates throughout the region responded similarly over space and time. The lack of a spatial effect or spatio-temporal interaction and the very strong temporal effect reveal that growth rates in West Atlantic hawksbills are likely driven by region-wide forces. Between 1997 and 2013, mean growth rates declined significantly and steadily by 18%. Regional climate indices have significant relationships with annual growth rates with 0- or 1-yr lags: positive with the Multivariate El Niño Southern Oscillation Index (correlation = 0.99) and negative with Caribbean sea surface temperature (correlation = −0.85). Declines in growth rates between 1997 and 2013 throughout the West Atlantic most likely resulted from warming waters through indirect negative effects on foraging resources of hawksbills. These climatic influences are complex. With increasing temperatures, trajectories of decline of coral cover and availability in reef habitats of major prey species of hawksbills are not parallel. Knowledge of how choice of foraging habitats, prey selection, and prey abundance are affected by warming water temperatures is needed to understand how climate change will affect productivity of consumers that live in association with coral reefs. Main conclusions The decadal declines in growth rates between 1997 and 2013 throughout the West Atlantic most likely resulted from warming waters through indirect negative effects on the foraging resources of hawksbills. These climatic influences are complex. With increasing temperatures, the trajectories of decline of coral cover and availability in reef habitats of major prey species of hawksbills are not parallel. Knowledge of how choice of foraging habitats, prey selection, and prey abundance are affected by warming water temperatures is needed to understand how climate change will affect productivity of consumers that live in association with coral reefs.

Extending Geographic Weights of Evidence Models for Use in Location Based Services

ERIC Educational Resources Information Center

Sonwalkar, Mukul Dinkar

2012-01-01

This dissertation addresses the use and modeling of spatio-temporal data for the purposes of providing applications for location based services. One of the major issues in dealing with spatio-temporal data for location based services is the availability and sparseness of such data. Other than the hardware costs associated with collecting movement…

Spatio-temporal variability of hyporheic exchange through a pool-riffle-pool sequence

Treesearch

Frank P. Gariglio; Daniele Tonina; Charles H. Luce

2013-01-01

Stream water enters and exits the streambed sediment due to hyporheic fluxes, which stem primarily from the interaction between surface water hydraulics and streambed morphology. These fluxes sustain a rich ecotone, whose habitat quality depends on their direction and magnitude. The spatio-temporal variability of hyporheic fluxes is not well understood over several...

Spatio-temporal dynamics of pond use and recruitment in Florida gopher frogs (Rana capito aesopus)

Treesearch

Cathryn H. Greenberg

2001-01-01

This study examines spatio-temporal dynamics of Florida gopher frog (Rang capito aesopus) breeding and juvenile recruitment. Ponds were situated within a hardwood-invaded or a savanna-like longleaf pine-wiregrass upland matrix. Movement (N = 1444) was monitored using intermittent drift fences with pitfall and funnel traps at eight...

Meteor tracking via local pattern clustering in spatio-temporal domain

NASA Astrophysics Data System (ADS)

Kukal, Jaromír.; Klimt, Martin; Švihlík, Jan; Fliegel, Karel

2016-09-01

Reliable meteor detection is one of the crucial disciplines in astronomy. A variety of imaging systems is used for meteor path reconstruction. The traditional approach is based on analysis of 2D image sequences obtained from a double station video observation system. Precise localization of meteor path is difficult due to atmospheric turbulence and other factors causing spatio-temporal fluctuations of the image background. The proposed technique performs non-linear preprocessing of image intensity using Box-Cox transform as recommended in our previous work. Both symmetric and asymmetric spatio-temporal differences are designed to be robust in the statistical sense. Resulting local patterns are processed by data whitening technique and obtained vectors are classified via cluster analysis and Self-Organized Map (SOM).

Spatio-temporal pattern of viral meningitis in Michigan, 1993-2001

NASA Astrophysics Data System (ADS)

Greene, Sharon K.; Schmidt, Mark A.; Stobierski, Mary Grace; Wilson, Mark L.

2005-05-01

To characterize Michigan's high viral meningitis incidence rates, 8,803 cases from 1993-2001 were analyzed for standard epidemiological indices, geographic distribution, and spatio-temporal clusters. Blacks and infants were found to be high-risk groups. Annual seasonality and interannual variability in epidemic magnitude were apparent. Cases were concentrated in southern Michigan, and cumulative incidence was correlated with population density at the county level (r=0.45, p<0.001). Kulldorff's Scan test identified the occurrence of spatio-temporal clusters in Lower Michigan during July-October 1998 and 2001 (p=0.01). More extensive data on cases, laboratory isolates, sociodemographics, and environmental exposures should improve detection and enhance the effectiveness of a Space-Time Information System aimed at prevention.

Spatio-Temporal Neural Networks for Vision, Reasoning and Rapid Decision Making

DTIC Science & Technology

1994-08-31

something that is obviously not pattern for long-term knowledge base (LTKB) facts. As a matter possiblc in common neural networks (as units in a...Conferences on Neural Davis, P. (19W0) Application of op~tical chaos to temporal pattern search in a Networks . Piscataway, NJ. [SC] nonlinear optical...Science Institute PROJECT TITLE: Spatio-temporal Neural Networks for Vision, Reasoning and Rapid Decision Making (N00014-93-1-1149) Number of ONR

Spatio-Temporal Data Comparisons for Global Highly Pathogenic Avian Influenza (HPAI) H5N1 Outbreaks

PubMed Central

Chen, Dongmei; Chen, Yue; Wang, Lei; Zhao, Fei; Yao, Baodong

2010-01-01

Highly pathogenic avian influenza subtype H5N1 is a zoonotic disease and control of the disease is one of the highest priority in global health. Disease surveillance systems are valuable data sources for various researches and management projects, but the data quality has not been paid much attention in previous studies. Based on data from two commonly used databases (Office International des Epizooties (OIE) and Food and Agriculture Organization of the United Nations (FAO)) of global HPAI H5N1 outbreaks during the period of 2003–2009, we examined and compared their patterns of temporal, spatial and spatio-temporal distributions for the first time. OIE and FAO data showed similar trends in temporal and spatial distributions if they were considered separately. However, more advanced approaches detected a significant difference in joint spatio-temporal distribution. Because of incompleteness for both OIE and FAO data, an integrated dataset would provide a more complete picture of global HPAI H5N1 outbreaks. We also displayed a mismatching profile of global HPAI H5N1 outbreaks and found that the degree of mismatching was related to the epidemic severity. The ideas and approaches used here to assess spatio-temporal data on the same disease from different sources are useful for other similar studies. PMID:21187964

An integrated GIS-based data model for multimodal urban public transportation analysis and management

NASA Astrophysics Data System (ADS)

Chen, Shaopei; Tan, Jianjun; Ray, C.; Claramunt, C.; Sun, Qinqin

2008-10-01

Diversity is one of the main characteristics of transportation data collected from multiple sources or formats, which can be extremely complex and disparate. Moreover, these multimodal transportation data are usually characterised by spatial and temporal properties. Multimodal transportation network data modelling involves both an engineering and research domain that has attracted the design of a number of spatio-temporal data models in the geographic information system (GIS). However, the application of these specific models to multimodal transportation network is still a challenging task. This research addresses this challenge from both integrated multimodal data organization and object-oriented modelling perspectives, that is, how a complex urban transportation network should be organized, represented and modeled appropriately when considering a multimodal point of view, and using object-oriented modelling method. We proposed an integrated GIS-based data model for multimodal urban transportation network that lays a foundation to enhance the multimodal transportation network analysis and management. This modelling method organizes and integrates multimodal transit network data, and supports multiple representations for spatio-temporal objects and relationship as both visual and graphic views. The data model is expressed by using a spatio-temporal object-oriented modelling method, i.e., the unified modelling language (UML) extended to spatial and temporal plug-in for visual languages (PVLs), which provides an essential support to the spatio-temporal data modelling for transportation GIS.

Interhemispheric coupling between the posterior sylvian regions impacts successful auditory temporal order judgment.

PubMed

Bernasconi, Fosco; Grivel, Jeremy; Murray, Micah M; Spierer, Lucas

2010-07-01

Accurate perception of the temporal order of sensory events is a prerequisite in numerous functions ranging from language comprehension to motor coordination. We investigated the spatio-temporal brain dynamics of auditory temporal order judgment (aTOJ) using electrical neuroimaging analyses of auditory evoked potentials (AEPs) recorded while participants completed a near-threshold task requiring spatial discrimination of left-right and right-left sound sequences. AEPs to sound pairs modulated topographically as a function of aTOJ accuracy over the 39-77ms post-stimulus period, indicating the engagement of distinct configurations of brain networks during early auditory processing stages. Source estimations revealed that accurate and inaccurate performance were linked to bilateral posterior sylvian regions activity (PSR). However, activity within left, but not right, PSR predicted behavioral performance suggesting that left PSR activity during early encoding phases of pairs of auditory spatial stimuli appears critical for the perception of their order of occurrence. Correlation analyses of source estimations further revealed that activity between left and right PSR was significantly correlated in the inaccurate but not accurate condition, indicating that aTOJ accuracy depends on the functional decoupling between homotopic PSR areas. These results support a model of temporal order processing wherein behaviorally relevant temporal information--i.e. a temporal 'stamp'--is extracted within the early stages of cortical processes within left PSR but critically modulated by inputs from right PSR. We discuss our results with regard to current models of temporal of temporal order processing, namely gating and latency mechanisms. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Applying spatio-temporal models to assess variations across health care areas and regions: Lessons from the decentralized Spanish National Health System.

PubMed

Librero, Julián; Ibañez, Berta; Martínez-Lizaga, Natalia; Peiró, Salvador; Bernal-Delgado, Enrique

2017-01-01

To illustrate the ability of hierarchical Bayesian spatio-temporal models in capturing different geo-temporal structures in order to explain hospital risk variations using three different conditions: Percutaneous Coronary Intervention (PCI), Colectomy in Colorectal Cancer (CCC) and Chronic Obstructive Pulmonary Disease (COPD). This is an observational population-based spatio-temporal study, from 2002 to 2013, with a two-level geographical structure, Autonomous Communities (AC) and Health Care Areas (HA). The Spanish National Health System, a quasi-federal structure with 17 regional governments (AC) with full responsibility in planning and financing, and 203 HA providing hospital and primary care to a defined population. A poisson-log normal mixed model in the Bayesian framework was fitted using the INLA efficient estimation procedure. The spatio-temporal hospitalization relative risks, the evolution of their variation, and the relative contribution (fraction of variation) of each of the model components (AC, HA, year and interaction AC-year). Following PCI-CCC-CODP order, the three conditions show differences in the initial hospitalization rates (from 4 to 21 per 10,000 person-years) and in their trends (upward, inverted V shape, downward). Most of the risk variation is captured by phenomena occurring at the HA level (fraction variance: 51.6, 54.7 and 56.9%). At AC level, the risk of PCI hospitalization follow a heterogeneous ascending dynamic (interaction AC-year: 17.7%), whereas in COPD the AC role is more homogenous and important (37%). In a system where the decisions loci are differentiated, the spatio-temporal modeling allows to assess the dynamic relative role of different levels of decision and their influence on health outcomes.

Discovery of spatio-temporal patterns from location-based social networks

NASA Astrophysics Data System (ADS)

Béjar, J.; Álvarez, S.; García, D.; Gómez, I.; Oliva, L.; Tejeda, A.; Vázquez-Salceda, J.

2016-03-01

Location-based social networks (LBSNs) such as Twitter or Instagram are a good source for user spatio-temporal behaviour. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user's location information can be obtained during large intervals of time that can be used to discover complex patterns, including mobility profiles, points of interest or unusual events. These patterns can be used as the elements of a knowledge base for different applications in different domains such as mobility route planning, touristic recommendation systems or city planning. The aim of this paper is twofold, first to analyse the frequent spatio-temporal patterns that users share when living and visiting a city. This behaviour is studied by means of frequent itemsets algorithms in order to establish some associations among visits that can be interpreted as interesting routes or spatio-temporal connections. Second, to analyse how the spatio-temporal behaviour of a large number of users can be segmented in different profiles. These behavioural profiles are obtained by means of clustering algorithms that show the different patterns of behaviour of visitors and citizens. The data analysed were obtained from the public data feeds of Twitter and Instagram within an area surrounding the cities of Barcelona and Milan for a period of several months. The analysis of these data shows that these kinds of algorithms can be successfully applied to data from any city (or general area) to discover useful patterns that can be interpreted on terms of singular places and areas and their temporal relationships.

Spatio-temporal precipitation climatology over complex terrain using a censored additive regression model.

PubMed

Stauffer, Reto; Mayr, Georg J; Messner, Jakob W; Umlauf, Nikolaus; Zeileis, Achim

2017-06-15

Flexible spatio-temporal models are widely used to create reliable and accurate estimates for precipitation climatologies. Most models are based on square root transformed monthly or annual means, where a normal distribution seems to be appropriate. This assumption becomes invalid on a daily time scale as the observations involve large fractions of zero observations and are limited to non-negative values. We develop a novel spatio-temporal model to estimate the full climatological distribution of precipitation on a daily time scale over complex terrain using a left-censored normal distribution. The results demonstrate that the new method is able to account for the non-normal distribution and the large fraction of zero observations. The new climatology provides the full climatological distribution on a very high spatial and temporal resolution, and is competitive with, or even outperforms existing methods, even for arbitrary locations.

A model for optimizing file access patterns using spatio-temporal parallelism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boonthanome, Nouanesengsy; Patchett, John; Geveci, Berk

2013-01-01

For many years now, I/O read time has been recognized as the primary bottleneck for parallel visualization and analysis of large-scale data. In this paper, we introduce a model that can estimate the read time for a file stored in a parallel filesystem when given the file access pattern. Read times ultimately depend on how the file is stored and the access pattern used to read the file. The file access pattern will be dictated by the type of parallel decomposition used. We employ spatio-temporal parallelism, which combines both spatial and temporal parallelism, to provide greater flexibility to possible filemore » access patterns. Using our model, we were able to configure the spatio-temporal parallelism to design optimized read access patterns that resulted in a speedup factor of approximately 400 over traditional file access patterns.« less

Visual search of cyclic spatio-temporal events

NASA Astrophysics Data System (ADS)

Gautier, Jacques; Davoine, Paule-Annick; Cunty, Claire

2018-05-01

The analysis of spatio-temporal events, and especially of relationships between their different dimensions (space-time-thematic attributes), can be done with geovisualization interfaces. But few geovisualization tools integrate the cyclic dimension of spatio-temporal event series (natural events or social events). Time Coil and Time Wave diagrams represent both the linear time and the cyclic time. By introducing a cyclic temporal scale, these diagrams may highlight the cyclic characteristics of spatio-temporal events. However, the settable cyclic temporal scales are limited to usual durations like days or months. Because of that, these diagrams cannot be used to visualize cyclic events, which reappear with an unusual period, and don't allow to make a visual search of cyclic events. Also, they don't give the possibility to identify the relationships between the cyclic behavior of the events and their spatial features, and more especially to identify localised cyclic events. The lack of possibilities to represent the cyclic time, outside of the temporal diagram of multi-view geovisualization interfaces, limits the analysis of relationships between the cyclic reappearance of events and their other dimensions. In this paper, we propose a method and a geovisualization tool, based on the extension of Time Coil and Time Wave, to provide a visual search of cyclic events, by allowing to set any possible duration to the diagram's cyclic temporal scale. We also propose a symbology approach to push the representation of the cyclic time into the map, in order to improve the analysis of relationships between space and the cyclic behavior of events.

Spatio-temporal dynamics and laterality effects of face inversion, feature presence and configuration, and face outline

PubMed Central

Marinkovic, Ksenija; Courtney, Maureen G.; Witzel, Thomas; Dale, Anders M.; Halgren, Eric

2014-01-01

Although a crucial role of the fusiform gyrus (FG) in face processing has been demonstrated with a variety of methods, converging evidence suggests that face processing involves an interactive and overlapping processing cascade in distributed brain areas. Here we examine the spatio-temporal stages and their functional tuning to face inversion, presence and configuration of inner features, and face contour in healthy subjects during passive viewing. Anatomically-constrained magnetoencephalography (aMEG) combines high-density whole-head MEG recordings and distributed source modeling with high-resolution structural MRI. Each person's reconstructed cortical surface served to constrain noise-normalized minimum norm inverse source estimates. The earliest activity was estimated to the occipital cortex at ~100 ms after stimulus onset and was sensitive to an initial coarse level visual analysis. Activity in the right-lateralized ventral temporal area (inclusive of the FG) peaked at ~160 ms and was largest to inverted faces. Images containing facial features in the veridical and rearranged configuration irrespective of the facial outline elicited intermediate level activity. The M160 stage may provide structural representations necessary for downstream distributed areas to process identity and emotional expression. However, inverted faces additionally engaged the left ventral temporal area at ~180 ms and were uniquely subserved by bilateral processing. This observation is consistent with the dual route model and spared processing of inverted faces in prosopagnosia. The subsequent deflection, peaking at ~240 ms in the anterior temporal areas bilaterally, was largest to normal, upright faces. It may reflect initial engagement of the distributed network subserving individuation and familiarity. These results support dynamic models suggesting that processing of unfamiliar faces in the absence of a cognitive task is subserved by a distributed and interactive neural circuit. PMID:25426044

Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI.

PubMed

Asif, M Salman; Hamilton, Lei; Brummer, Marijn; Romberg, Justin

2013-09-01

Accelerated magnetic resonance imaging techniques reduce signal acquisition time by undersampling k-space. A fundamental problem in accelerated magnetic resonance imaging is the recovery of quality images from undersampled k-space data. Current state-of-the-art recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this article, a new recovery algorithm, motion-adaptive spatio-temporal regularization, is presented that uses spatial and temporal structured sparsity of MR images in the compressed sensing framework to recover dynamic MR images from highly undersampled k-space data. In contrast to existing algorithms, our proposed algorithm models temporal sparsity using motion-adaptive linear transformations between neighboring images. The efficiency of motion-adaptive spatio-temporal regularization is demonstrated with experiments on cardiac magnetic resonance imaging for a range of reduction factors. Results are also compared with k-t FOCUSS with motion estimation and compensation-another recently proposed recovery algorithm for dynamic magnetic resonance imaging. . Copyright © 2012 Wiley Periodicals, Inc.

Neuroelectrical signs of selective attention to color in boys with attention-deficit hyperactivity disorder.

PubMed

van der Stelt, O; van der Molen, M; Boudewijn Gunning, W; Kok, A

2001-10-01

In order to gain insight into the functional and macroanatomical loci of visual selective processing deficits that may be basic to attention-deficit hyperactivity disorder (ADHD), the present study examined multi-channel event-related potentials (ERPs) recorded from 7- to 11-year-old boys clinically diagnosed as having ADHD (n=24) and age-matched healthy control boys (n=24) while they performed a visual (color) selective attention task. The spatio-temporal dynamics of several ERP components related to attention to color were characterized using topographic profile analysis, topographic mapping of the ERP and associated scalp current density distributions, and spatio-temporal source potential modeling. Boys with ADHD showed a lower target hit rate, a higher false-alarm rate, and a lower perceptual sensitivity than controls. Also, whereas color attention induced in the ERPs from controls a characteristic early frontally maximal selection positivity (FSP), ADHD boys displayed little or no FSP. Similarly, ADHD boys manifested P3b amplitude decrements that were partially lateralized (i.e., maximal at left temporal scalp locations) as well as affected by maturation. These results indicate that ADHD boys suffer from deficits at both relatively early (sensory) and late (semantic) levels of visual selective information processing. The data also support the hypothesis that the visual selective processing deficits observed in the ADHD boys originate from deficits in the strength of activation of a neural network comprising prefrontal and occipito-temporal brain regions. This network seems to be actively engaged during attention to color and may contain the major intracerebral generating sources of the associated scalp-recorded ERP components.

Spatio-temporal analysis of wildfire ignitions in the St. Johns River Water Management District, Florida

Treesearch

Marc G. Genton; David T. Butry; Marcia L. Gumpertz; Jeffrey P. Prestemon

2006-01-01

We analyse the spatio-temporal structure of wildfire ignitions in the St. Johns River Water Management District in north-eastern Florida. We show, using tools to analyse point patterns (e.g. the L-function), that wildfire events occur in clusters. Clustering of these events correlates with irregular distribution of fire ignitions, including lightning...

HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition.

PubMed

Lagorce, Xavier; Orchard, Garrick; Galluppi, Francesco; Shi, Bertram E; Benosman, Ryad B

2017-07-01

This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.

Economic agglomerations and spatio-temporal cycles in a spatial growth model with capital transport cost

NASA Astrophysics Data System (ADS)

Juchem Neto, J. P.; Claeyssen, J. C. R.; Pôrto Júnior, S. S.

2018-03-01

In this paper we introduce capital transport cost in a unidimensional spatial Solow-Swan model of economic growth with capital-induced labor migration, considered in an unbounded domain. Proceeding with a stability analysis, we show that there is a critical value for the capital transport cost where the dynamic behavior of the economy changes, provided that the intensity of capital-induced labor migration is strong enough. On the one hand, if the capital transport cost is higher than this critical value, the spatially homogeneous equilibrium of coexistence of the model is stable, and the economy converges to this spatially homogeneous state in the long run; on the other hand, if transport cost is lower than this critical value, the equilibrium is unstable, and the economy may develop different spatio-temporal dynamics, including the formation of stable economic agglomerations and spatio-temporal economic cycles, depending on the other parameters in the model. Finally, numerical simulations support the results of the stability analysis, and illustrate the spatio-temporal dynamics generated by the model, suggesting that the economy as a whole benefits from the formation of economic agglomerations and cycles, with a higher capital transport cost reducing this gain.

An agent-based model driven by tropical rainfall to understand the spatio-temporal heterogeneity of a chikungunya outbreak.

PubMed

Dommar, Carlos J; Lowe, Rachel; Robinson, Marguerite; Rodó, Xavier

2014-01-01

Vector-borne diseases, such as dengue, malaria and chikungunya, are increasing across their traditional ranges and continuing to infiltrate new, previously unaffected, regions. The spatio-temporal evolution of these diseases is determined by the interaction of the host and vector, which is strongly dependent on social structures and mobility patterns. We develop an agent-based model (ABM), in which each individual is explicitly represented and vector populations are linked to precipitation estimates in a tropical setting. The model is implemented on both scale-free and regular networks. The spatio-temporal transmission of chikungunya is analysed and the presence of asymptomatic silent spreaders within the population is investigated in the context of implementing travel restrictions during an outbreak. Preventing the movement of symptomatic individuals is found to be an insufficient mechanism to halt the spread of the disease, which can be readily carried to neighbouring nodes via sub-clinical individuals. Furthermore, the impact of topology structure vs. precipitation levels is assessed and precipitation is found to be the dominant factor driving spatio-temporal transmission. Copyright © 2013 Elsevier B.V. All rights reserved.

An empirical investigation of taxi driver response behavior to ride-hailing requests: A spatio-temporal perspective

PubMed Central

Xu, Ke; Sun, Luping; Wang, Hansheng

2018-01-01

Using data provided by a ride-hailing platform, this paper examines the factors that affect taxi driver response behavior to ride-hailing requests. The empirical investigation from a driver’s perspective is of great importance for ride-hailing service providers, given that approximately 40% of the hailing requests receive no response from any driver. To comprehensively understand taxi driver response behavior, we use a rich dataset to generate variables related to the spatio-temporal supply-demand intensities, the economic incentives, the requests’ and the drivers’ characteristics. The results show that drivers are more likely to respond to requests with economic incentives (especially a firm subsidy), and those with a lower spatio-temporal demand intensity or a higher spatio-temporal supply intensity. In addition, drivers are more likely to respond to requests involving rides covering a greater geographical distance and to those with a smaller number of repeated submissions. The drivers’ characteristics, namely, the number of requests received and the number of requests responded, however, have relatively little impacts on their response probability to the current request. Our findings contribute to the related literature and provide managerial implications for ride-hailing service providers. PMID:29883478

Spatio-Temporal Information Analysis of Event-Related BOLD Responses

PubMed Central

Alpert, Galit Fuhrmann; Handwerker, Dan; Sun, Felice T.; D’Esposito, Mark; Knight, Robert T.

2009-01-01

A new approach for analysis of event related fMRI (BOLD) signals is proposed. The technique is based on measures from information theory and is used both for spatial localization of task related activity, as well as for extracting temporal information regarding the task dependent propagation of activation across different brain regions. This approach enables whole brain visualization of voxels (areas) most involved in coding of a specific task condition, the time at which they are most informative about the condition, as well as their average amplitude at that preferred time. The approach does not require prior assumptions about the shape of the hemodynamic response function (HRF), nor about linear relations between BOLD response and presented stimuli (or task conditions). We show that relative delays between different brain regions can also be computed without prior knowledge of the experimental design, suggesting a general method that could be applied for analysis of differential time delays that occur during natural, uncontrolled conditions. Here we analyze BOLD signals recorded during performance of a motor learning task. We show that during motor learning, the BOLD response of unimodal motor cortical areas precedes the response in higher-order multimodal association areas, including posterior parietal cortex. Brain areas found to be associated with reduced activity during motor learning, predominantly in prefrontal brain regions, are informative about the task typically at significantly later times. PMID:17188515

Sensing Solutions for Collecting Spatio-Temporal Data for Wildlife Monitoring Applications: A Review

PubMed Central

Baratchi, Mitra; Meratnia, Nirvana; Havinga, Paul J. M.; Skidmore, Andrew K.; Toxopeus, Bert A. G.

2013-01-01

Movement ecology is a field which places movement as a basis for understanding animal behavior. To realize this concept, ecologists rely on data collection technologies providing spatio-temporal data in order to analyze movement. Recently, wireless sensor networks have offered new opportunities for data collection from remote places through multi-hop communication and collaborative capability of the nodes. Several technologies can be used in such networks for sensing purposes and for collecting spatio-temporal data from animals. In this paper, we investigate and review technological solutions which can be used for collecting data for wildlife monitoring. Our aim is to provide an overview of different sensing technologies used for wildlife monitoring and to review their capabilities in terms of data they provide for modeling movement behavior of animals. PMID:23666132

Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons

PubMed Central

Müller, Jan; Bakkum, Douglas J.; Hierlemann, Andreas

2012-01-01

We present a system to artificially correlate the spike timing between sets of arbitrary neurons that were interfaced to a complementary metal–oxide–semiconductor (CMOS) high-density microelectrode array (MEA). The system features a novel reprogrammable and flexible event engine unit to detect arbitrary spatio-temporal patterns of recorded action potentials and is capable of delivering sub-millisecond closed-loop feedback of electrical stimulation upon trigger events in real-time. The relative timing between action potentials of individual neurons as well as the temporal pattern among multiple neurons, or neuronal assemblies, is considered an important factor governing memory and learning in the brain. Artificially changing timings between arbitrary sets of spiking neurons with our system could provide a “knob” to tune information processing in the network. PMID:23335887

Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

PubMed Central

Fan, Yong; Batmanghelich, Nematollah; Clark, Chris M.; Davatzikos, Christos

2010-01-01

Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses. PMID:18053747

The Pathways for Intelligible Speech: Multivariate and Univariate Perspectives

PubMed Central

Evans, S.; Kyong, J.S.; Rosen, S.; Golestani, N.; Warren, J.E.; McGettigan, C.; Mourão-Miranda, J.; Wise, R.J.S.; Scott, S.K.

2014-01-01

An anterior pathway, concerned with extracting meaning from sound, has been identified in nonhuman primates. An analogous pathway has been suggested in humans, but controversy exists concerning the degree of lateralization and the precise location where responses to intelligible speech emerge. We have demonstrated that the left anterior superior temporal sulcus (STS) responds preferentially to intelligible speech (Scott SK, Blank CC, Rosen S, Wise RJS. 2000. Identification of a pathway for intelligible speech in the left temporal lobe. Brain. 123:2400–2406.). A functional magnetic resonance imaging study in Cerebral Cortex used equivalent stimuli and univariate and multivariate analyses to argue for the greater importance of bilateral posterior when compared with the left anterior STS in responding to intelligible speech (Okada K, Rong F, Venezia J, Matchin W, Hsieh IH, Saberi K, Serences JT,Hickok G. 2010. Hierarchical organization of human auditory cortex: evidence from acoustic invariance in the response to intelligible speech. 20: 2486–2495.). Here, we also replicate our original study, demonstrating that the left anterior STS exhibits the strongest univariate response and, in decoding using the bilateral temporal cortex, contains the most informative voxels showing an increased response to intelligible speech. In contrast, in classifications using local “searchlights” and a whole brain analysis, we find greater classification accuracy in posterior rather than anterior temporal regions. Thus, we show that the precise nature of the multivariate analysis used will emphasize different response profiles associated with complex sound to speech processing. PMID:23585519

Subliminal semantic priming changes the dynamic causal influence between the left frontal and temporal cortex.

PubMed

Matsumoto, Atsushi; Kakigi, Ryusuke

2014-01-01

Recent neuroimaging experiments have revealed that subliminal priming of a target stimulus leads to the reduction of neural activity in specific regions concerned with processing the target. Such findings lead to questions about the degree to which the subliminal priming effect is based only on decreased activity in specific local brain regions, as opposed to the influence of neural mechanisms that regulate communication between brain regions. To address this question, this study recorded EEG during performance of a subliminal semantic priming task. We adopted an information-based approach that used independent component analysis and multivariate autoregressive modeling. Results indicated that subliminal semantic priming caused significant modulation of alpha band activity in the left inferior frontal cortex and modulation of gamma band activity in the left inferior temporal regions. The multivariate autoregressive approach confirmed significant increases in information flow from the inferior frontal cortex to inferior temporal regions in the early time window that was induced by subliminal priming. In the later time window, significant enhancement of bidirectional causal flow between these two regions underlying subliminal priming was observed. Results suggest that unconscious processing of words influences not only local activity of individual brain regions but also the dynamics of neural communication between those regions.

Exploring the brain on multiple scales with correlative two-photon and light sheet microscopy

NASA Astrophysics Data System (ADS)

Silvestri, Ludovico; Allegra Mascaro, Anna Letizia; Costantini, Irene; Sacconi, Leonardo; Pavone, Francesco S.

2014-02-01

One of the unique features of the brain is that its activity cannot be framed in a single spatio-temporal scale, but rather spans many orders of magnitude both in space and time. A single imaging technique can reveal only a small part of this complex machinery. To obtain a more comprehensive view of brain functionality, complementary approaches should be combined into a correlative framework. Here, we describe a method to integrate data from in vivo two-photon fluorescence imaging and ex vivo light sheet microscopy, taking advantage of blood vessels as reference chart. We show how the apical dendritic arbor of a single cortical pyramidal neuron imaged in living thy1-GFP-M mice can be found in the large-scale brain reconstruction obtained with light sheet microscopy. Starting from the apical portion, the whole pyramidal neuron can then be segmented. The correlative approach presented here allows contextualizing within a three-dimensional anatomic framework the neurons whose dynamics have been observed with high detail in vivo.

Spatio-temporal Dynamics of Audiovisual Speech Processing

PubMed Central

Bernstein, Lynne E.; Auer, Edward T.; Wagner, Michael; Ponton, Curtis W.

2007-01-01

The cortical processing of auditory-alone, visual-alone, and audiovisual speech information is temporally and spatially distributed, and functional magnetic resonance imaging (fMRI) cannot adequately resolve its temporal dynamics. In order to investigate a hypothesized spatio-temporal organization for audiovisual speech processing circuits, event-related potentials (ERPs) were recorded using electroencephalography (EEG). Stimuli were congruent audiovisual /bα/, incongruent auditory /bα/ synchronized with visual /gα/, auditory-only /bα/, and visual-only /bα/ and /gα/. Current density reconstructions (CDRs) of the ERP data were computed across the latency interval of 50-250 milliseconds. The CDRs demonstrated complex spatio-temporal activation patterns that differed across stimulus conditions. The hypothesized circuit that was investigated here comprised initial integration of audiovisual speech by the middle superior temporal sulcus (STS), followed by recruitment of the intraparietal sulcus (IPS), followed by activation of Broca's area (Miller and d'Esposito, 2005). The importance of spatio-temporally sensitive measures in evaluating processing pathways was demonstrated. Results showed, strikingly, early (< 100 msec) and simultaneous activations in areas of the supramarginal and angular gyrus (SMG/AG), the IPS, the inferior frontal gyrus, and the dorsolateral prefrontal cortex. Also, emergent left hemisphere SMG/AG activation, not predicted based on the unisensory stimulus conditions was observed at approximately 160 to 220 msec. The STS was neither the earliest nor most prominent activation site, although it is frequently considered the sine qua non of audiovisual speech integration. As discussed here, the relatively late activity of the SMG/AG solely under audiovisual conditions is a possible candidate audiovisual speech integration response. PMID:17920933

Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry.

PubMed

Korczowski, L; Congedo, M; Jutten, C

2015-08-01

The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.

Spatio-temporal patterns of tree establishment are indicative of biotic interactions during early invasion of a montane meadow

Treesearch

J.M. Rice; C.B. Halpern; J.A. Antos; J.A. Jones

2012-01-01

Tree invasions of grasslands are occurring globally, with profound consequences for ecosystem structure and function. We explore the spatio-temporal dynamics of tree invasion of a montane meadow in the Cascade Mountains of Oregon, where meadow loss is a conservation concern. We examine the early stages of invasion, where extrinsic and intrinsic processes can be clearly...

Atomistic observation and simulation analysis of spatio-temporal fluctuations during radiation-induced amorphization.

PubMed

Watanabe, Seiichi; Hoshino, Misaki; Koike, Takuto; Suda, Takanori; Ohnuki, Soumei; Takahashi, Heishichirou; Lam, Nighi Q

2003-01-01

We performed a dynamical-atomistic study of radiation-induced amorphization in the NiTi intermetallic compound using in situ high-resolution high-voltage electron microscopy and molecular dynamics simulations in connection with image simulation. Spatio-temporal fluctuations as non-equilibrium fluctuations in an energy-dissipative system, due to transient atom-cluster formation during amorphization, were revealed by the present spatial autocorrelation analysis.

[Spatio-temporal variations of origin, distribution and diffusion of Oncomelania hupensis in Yangtze River Basin].

PubMed

Deng, Chen; Li-Yong, Wen

2017-10-24

As the only intermediate host of Schistosoma japonicum, Oncomelania hupensis in China is mainly distributed in the Yangtze River Basin. The origin of the O. hupensis and the spatio-temporal variations of its distribution and diffusion in the Yangtze River Basin and the influencing factors, as well as significances in schistosomiasis elimination in China are reviewed in this paper.

Using geovisual analytics in Google Earth to understand disease distribution: a case study of campylobacteriosis in the Czech Republic (2008-2012).

PubMed

Marek, Lukáš; Tuček, Pavel; Pászto, Vít

2015-01-28

Visual analytics aims to connect the processing power of information technologies and the user's ability of logical thinking and reasoning through the complex visual interaction. Moreover, the most of the data contain the spatial component. Therefore, the need for geovisual tools and methods arises. Either one can develop own system but the dissemination of findings and its usability might be problematic or the widespread and well-known platform can be utilized. The aim of this paper is to prove the applicability of Google Earth™ software as a tool for geovisual analytics that helps to understand the spatio-temporal patterns of the disease distribution. We combined the complex joint spatio-temporal analysis with comprehensive visualisation. We analysed the spatio-temporal distribution of the campylobacteriosis in the Czech Republic between 2008 and 2012. We applied three main approaches in the study: (1) the geovisual analytics of the surveillance data that were visualised in the form of bubble chart; (2) the geovisual analytics of the disease's weekly incidence surfaces computed by spatio-temporal kriging and (3) the spatio-temporal scan statistics that was employed in order to identify high or low rates clusters of affected municipalities. The final data are stored in Keyhole Markup Language files and visualised in Google Earth™ in order to apply geovisual analytics. Using geovisual analytics we were able to display and retrieve information from complex dataset efficiently. Instead of searching for patterns in a series of static maps or using numerical statistics, we created the set of interactive visualisations in order to explore and communicate results of analyses to the wider audience. The results of the geovisual analytics identified periodical patterns in the behaviour of the disease as well as fourteen spatio-temporal clusters of increased relative risk. We prove that Google Earth™ software is a usable tool for the geovisual analysis of the disease distribution. Google Earth™ has many indisputable advantages (widespread, freely available, intuitive interface, space-time visualisation capabilities and animations, communication of results), nevertheless it is still needed to combine it with pre-processing tools that prepare the data into a form suitable for the geovisual analytics itself.

GRASS GIS: The first Open Source Temporal GIS

NASA Astrophysics Data System (ADS)

Gebbert, Sören; Leppelt, Thomas

2015-04-01

GRASS GIS is a full featured, general purpose Open Source geographic information system (GIS) with raster, 3D raster and vector processing support[1]. Recently, time was introduced as a new dimension that transformed GRASS GIS into the first Open Source temporal GIS with comprehensive spatio-temporal analysis, processing and visualization capabilities[2]. New spatio-temporal data types were introduced in GRASS GIS version 7, to manage raster, 3D raster and vector time series. These new data types are called space time datasets. They are designed to efficiently handle hundreds of thousands of time stamped raster, 3D raster and vector map layers of any size. Time stamps can be defined as time intervals or time instances in Gregorian calendar time or relative time. Space time datasets are simplifying the processing and analysis of large time series in GRASS GIS, since these new data types are used as input and output parameter in temporal modules. The handling of space time datasets is therefore equal to the handling of raster, 3D raster and vector map layers in GRASS GIS. A new dedicated Python library, the GRASS GIS Temporal Framework, was designed to implement the spatio-temporal data types and their management. The framework provides the functionality to efficiently handle hundreds of thousands of time stamped map layers and their spatio-temporal topological relations. The framework supports reasoning based on the temporal granularity of space time datasets as well as their temporal topology. It was designed in conjunction with the PyGRASS [3] library to support parallel processing of large datasets, that has a long tradition in GRASS GIS [4,5]. We will present a subset of more than 40 temporal modules that were implemented based on the GRASS GIS Temporal Framework, PyGRASS and the GRASS GIS Python scripting library. These modules provide a comprehensive temporal GIS tool set. The functionality range from space time dataset and time stamped map layer management over temporal aggregation, temporal accumulation, spatio-temporal statistics, spatio-temporal sampling, temporal algebra, temporal topology analysis, time series animation and temporal topology visualization to time series import and export capabilities with support for NetCDF and VTK data formats. We will present several temporal modules that support parallel processing of raster and 3D raster time series. [1] GRASS GIS Open Source Approaches in Spatial Data Handling In Open Source Approaches in Spatial Data Handling, Vol. 2 (2008), pp. 171-199, doi:10.1007/978-3-540-74831-19 by M. Neteler, D. Beaudette, P. Cavallini, L. Lami, J. Cepicky edited by G. Brent Hall, Michael G. Leahy [2] Gebbert, S., Pebesma, E., 2014. A temporal GIS for field based environmental modeling. Environ. Model. Softw. 53, 1-12. [3] Zambelli, P., Gebbert, S., Ciolli, M., 2013. Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). ISPRS Intl Journal of Geo-Information 2, 201-219. [4] Löwe, P., Klump, J., Thaler, J. (2012): The FOSS GIS Workbench on the GFZ Load Sharing Facility compute cluster, (Geophysical Research Abstracts Vol. 14, EGU2012-4491, 2012), General Assembly European Geosciences Union (Vienna, Austria 2012). [5] Akhter, S., Aida, K., Chemin, Y., 2010. "GRASS GIS on High Performance Computing with MPI, OpenMP and Ninf-G Programming Framework". ISPRS Conference, Kyoto, 9-12 August 2010

Retina-Inspired Filter.

PubMed

Doutsi, Effrosyni; Fillatre, Lionel; Antonini, Marc; Gaulmin, Julien

2018-07-01

This paper introduces a novel filter, which is inspired by the human retina. The human retina consists of three different layers: the Outer Plexiform Layer (OPL), the inner plexiform layer, and the ganglionic layer. Our inspiration is the linear transform which takes place in the OPL and has been mathematically described by the neuroscientific model "virtual retina." This model is the cornerstone to derive the non-separable spatio-temporal OPL retina-inspired filter, briefly renamed retina-inspired filter, studied in this paper. This filter is connected to the dynamic behavior of the retina, which enables the retina to increase the sharpness of the visual stimulus during filtering before its transmission to the brain. We establish that this retina-inspired transform forms a group of spatio-temporal Weighted Difference of Gaussian (WDoG) filters when it is applied to a still image visible for a given time. We analyze the spatial frequency bandwidth of the retina-inspired filter with respect to time. It is shown that the WDoG spectrum varies from a lowpass filter to a bandpass filter. Therefore, while time increases, the retina-inspired filter enables to extract different kinds of information from the input image. Finally, we discuss the benefits of using the retina-inspired filter in image processing applications such as edge detection and compression.

Modeling of spatio-temporal variation in plague incidence in Madagascar from 1980 to 2007.

PubMed

Giorgi, Emanuele; Kreppel, Katharina; Diggle, Peter J; Caminade, Cyril; Ratsitorahina, Maherisoa; Rajerison, Minoarisoa; Baylis, Matthew

2016-11-01

Plague is an infectious disease caused by the bacterium Yersinia pestis, which, during the fourteenth century, caused the deaths of an estimated 75-200 million people in Europe. Plague epidemics still occur in Africa, Asia and South America. Madagascar is today one of the most endemic countries, reporting nearly one third of the human cases worldwide from 2004 to 2009. The persistence of plague in Madagascar is associated with environmental and climatic conditions. In this paper we present a case study of the spatio-temporal analysis of plague incidence in Madagascar from 1980 to 2007. We study the relationship of plague with temperature and precipitation anomalies, and with elevation. A joint spatio-temporal analysis of the data proves to be computationally intractable. We therefore develop a spatio-temporal log-Gaussian Cox process model, but then carry out marginal temporal and spatial analyses. We also introduce a spatially discrete approximation for Gaussian processes, whose parameters retain a spatially continuous interpretation. We find evidence of a cumulative effect, over time, of temperature anomalies on plague incidence, and of a very high relative risk of plague occurrence for locations above 800 m in elevation. Our approach provides a useful modeling framework to assess the relationship between exposures and plague risk, irrespective of the spatial resolution at which the latter has been recorded. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bayesian inference for the spatio-temporal invasion of alien species.

PubMed

Cook, Alex; Marion, Glenn; Butler, Adam; Gibson, Gavin

2007-08-01

In this paper we develop a Bayesian approach to parameter estimation in a stochastic spatio-temporal model of the spread of invasive species across a landscape. To date, statistical techniques, such as logistic and autologistic regression, have outstripped stochastic spatio-temporal models in their ability to handle large numbers of covariates. Here we seek to address this problem by making use of a range of covariates describing the bio-geographical features of the landscape. Relative to regression techniques, stochastic spatio-temporal models are more transparent in their representation of biological processes. They also explicitly model temporal change, and therefore do not require the assumption that the species' distribution (or other spatial pattern) has already reached equilibrium as is often the case with standard statistical approaches. In order to illustrate the use of such techniques we apply them to the analysis of data detailing the spread of an invasive plant, Heracleum mantegazzianum, across Britain in the 20th Century using geo-referenced covariate information describing local temperature, elevation and habitat type. The use of Markov chain Monte Carlo sampling within a Bayesian framework facilitates statistical assessments of differences in the suitability of different habitat classes for H. mantegazzianum, and enables predictions of future spread to account for parametric uncertainty and system variability. Our results show that ignoring such covariate information may lead to biased estimates of key processes and implausible predictions of future distributions.

Synchronisation signatures in the listening brain: a perspective from non-invasive neuroelectrophysiology.

PubMed

Weisz, Nathan; Obleser, Jonas

2014-01-01

Human magneto- and electroencephalography (M/EEG) are capable of tracking brain activity at millisecond temporal resolution in an entirely non-invasive manner, a feature that offers unique opportunities to uncover the spatiotemporal dynamics of the hearing brain. In general, precise synchronisation of neural activity within as well as across distributed regions is likely to subserve any cognitive process, with auditory cognition being no exception. Brain oscillations, in a range of frequencies, are a putative hallmark of this synchronisation process. Embedded in a larger effort to relate human cognition to brain oscillations, a field of research is emerging on how synchronisation within, as well as between, brain regions may shape auditory cognition. Combined with much improved source localisation and connectivity techniques, it has become possible to study directly the neural activity of auditory cortex with unprecedented spatio-temporal fidelity and to uncover frequency-specific long-range connectivities across the human cerebral cortex. In the present review, we will summarise recent contributions mainly of our laboratories to this emerging domain. We present (1) a more general introduction on how to study local as well as interareal synchronisation in human M/EEG; (2) how these networks may subserve and influence illusory auditory perception (clinical and non-clinical) and (3) auditory selective attention; and (4) how oscillatory networks further reflect and impact on speech comprehension. This article is part of a Special Issue entitled Human Auditory Neuroimaging. Copyright © 2013 Elsevier B.V. All rights reserved.

Historical amphibian declines and extinctions in Brazil linked to chytridiomycosis

PubMed Central

Carvalho, Tamilie; Becker, C. Guilherme

2017-01-01

The recent increase in emerging fungal diseases is causing unprecedented threats to biodiversity. The origin of spread of the frog-killing fungus Batrachochytrium dendrobatidis (Bd) is a matter of continued debate. To date, the historical amphibian declines in Brazil could not be attributed to chytridiomycosis; the high diversity of hosts coupled with the presence of several Bd lineages predating the reported declines raised the hypothesis that a hypervirulent Bd genotype spread from Brazil to other continents causing the recent global amphibian crisis. We tested for a spatio-temporal overlap between Bd and areas of historical amphibian population declines and extinctions in Brazil. A spatio-temporal convergence between Bd and declines would support the hypothesis that Brazilian amphibians were not adapted to Bd prior to the reported declines, thus weakening the hypothesis that Brazil was the global origin of Bd emergence. Alternatively, a lack of spatio-temporal association between Bd and frog declines would indicate an evolution of host resistance in Brazilian frogs predating Bd's global emergence, further supporting Brazil as the potential origin of the Bd panzootic. Here, we Bd-screened over 30 000 museum-preserved tadpoles collected in Brazil between 1930 and 2015 and overlaid spatio-temporal Bd data with areas of historical amphibian declines. We detected an increase in the proportion of Bd-infected tadpoles during the peak of amphibian declines (1979–1987). We also found that clusters of Bd-positive samples spatio-temporally overlapped with most records of amphibian declines in Brazil's Atlantic Forest. Our findings indicate that Brazil is post epizootic for chytridiomycosis and provide another piece to the puzzle to explain the origin of Bd globally. PMID:28179514

Historical amphibian declines and extinctions in Brazil linked to chytridiomycosis.

PubMed

Carvalho, Tamilie; Becker, C Guilherme; Toledo, Luís Felipe

2017-02-08

The recent increase in emerging fungal diseases is causing unprecedented threats to biodiversity. The origin of spread of the frog-killing fungus Batrachochytrium dendrobatidis ( Bd ) is a matter of continued debate. To date, the historical amphibian declines in Brazil could not be attributed to chytridiomycosis; the high diversity of hosts coupled with the presence of several Bd lineages predating the reported declines raised the hypothesis that a hypervirulent Bd genotype spread from Brazil to other continents causing the recent global amphibian crisis. We tested for a spatio-temporal overlap between Bd and areas of historical amphibian population declines and extinctions in Brazil. A spatio-temporal convergence between Bd and declines would support the hypothesis that Brazilian amphibians were not adapted to Bd prior to the reported declines, thus weakening the hypothesis that Brazil was the global origin of Bd emergence. Alternatively, a lack of spatio-temporal association between Bd and frog declines would indicate an evolution of host resistance in Brazilian frogs predating Bd 's global emergence , further supporting Brazil as the potential origin of the Bd panzootic. Here, we Bd -screened over 30 000 museum-preserved tadpoles collected in Brazil between 1930 and 2015 and overlaid spatio-temporal Bd data with areas of historical amphibian declines. We detected an increase in the proportion of Bd -infected tadpoles during the peak of amphibian declines (1979-1987). We also found that clusters of Bd -positive samples spatio-temporally overlapped with most records of amphibian declines in Brazil's Atlantic Forest. Our findings indicate that Brazil is post epizootic for chytridiomycosis and provide another piece to the puzzle to explain the origin of Bd globally. © 2017 The Author(s).

Natural image sequences constrain dynamic receptive fields and imply a sparse code.

PubMed

Häusler, Chris; Susemihl, Alex; Nawrot, Martin P

2013-11-06

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Money Walks: Implicit Mobility Behavior and Financial Well-Being.

PubMed

Singh, Vivek Kumar; Bozkaya, Burcin; Pentland, Alex

2015-01-01

Traditional financial decision systems (e.g. credit) had to rely on explicit individual traits like age, gender, job type, and marital status, while being oblivious to spatio-temporal mobility or the habits of the individual involved. Emerging trends in geo-aware and mobile payment systems, and the resulting "big data," present an opportunity to study human consumption patterns across space and time. Taking inspiration from animal behavior studies that have reported significant interconnections between animal spatio-temporal "foraging" behavior and their life outcomes, we analyzed a corpus of hundreds of thousands of human economic transactions and found that financial outcomes for individuals are intricately linked with their spatio-temporal traits like exploration, engagement, and elasticity. Such features yield models that are 30% to 49% better at predicting future financial difficulties than the comparable demographic models.

How can knowledge discovery methods uncover spatio-temporal patterns in environmental data?

NASA Astrophysics Data System (ADS)

Wachowicz, Monica

2000-04-01

This paper proposes the integration of KDD, GVis and STDB as a long-term strategy, which will allow users to apply knowledge discovery methods for uncovering spatio-temporal patterns in environmental data. The main goal is to combine innovative techniques and associated tools for exploring very large environmental data sets in order to arrive at valid, novel, potentially useful, and ultimately understandable spatio-temporal patterns. The GeoInsight approach is described using the principles and key developments in the research domains of KDD, GVis, and STDB. The GeoInsight approach aims at the integration of these research domains in order to provide tools for performing information retrieval, exploration, analysis, and visualization. The result is a knowledge-based design, which involves visual thinking (perceptual-cognitive process) and automated information processing (computer-analytical process).

Money Walks: Implicit Mobility Behavior and Financial Well-Being

PubMed Central

Singh, Vivek Kumar; Bozkaya, Burcin; Pentland, Alex

2015-01-01

Traditional financial decision systems (e.g. credit) had to rely on explicit individual traits like age, gender, job type, and marital status, while being oblivious to spatio-temporal mobility or the habits of the individual involved. Emerging trends in geo-aware and mobile payment systems, and the resulting “big data,” present an opportunity to study human consumption patterns across space and time. Taking inspiration from animal behavior studies that have reported significant interconnections between animal spatio-temporal “foraging” behavior and their life outcomes, we analyzed a corpus of hundreds of thousands of human economic transactions and found that financial outcomes for individuals are intricately linked with their spatio-temporal traits like exploration, engagement, and elasticity. Such features yield models that are 30% to 49% better at predicting future financial difficulties than the comparable demographic models. PMID:26317339

A Tentative Application Of Morphological Filters To Time-Varying Images

NASA Astrophysics Data System (ADS)

Billard, D.; Poquillon, B.

1989-03-01

In this paper, morphological filters, which are commonly used to process either 2D or multidimensional static images, are generalized to the analysis of time-varying image sequence. The introduction of the time dimension induces then interesting prop-erties when designing such spatio-temporal morphological filters. In particular, the specification of spatio-temporal structuring ele-ments (equivalent to time-varying spatial structuring elements) can be adjusted according to the temporal variations of the image sequences to be processed : this allows to derive specific morphological transforms to perform noise filtering or moving objects discrimination on dynamic images viewed by a non-stationary sensor. First, a brief introduction to the basic principles underlying morphological filters will be given. Then, a straightforward gener-alization of these principles to time-varying images will be pro-posed. This will lead us to define spatio-temporal opening and closing and to introduce some of their possible applications to process dynamic images. At last, preliminary results obtained us-ing a natural forward looking infrared (FUR) image sequence are presented.

Baseline study of the spatio-temporal patterns of reef fish assemblages prior to a major mining project in New Caledonia (South Pacific).

PubMed

Chabanet, Pascale; Guillemot, Nicolas; Kulbicki, Michel; Vigliola, Laurent; Sarramegna, Sébastien

2010-01-01

From 2008 onwards, the coral reefs of Koné (New Caledonia) will be subjected to a major anthropogenic perturbation linked to development of a nickel mine. Dredging and sediment runoff may directly damage the reef environment whereas job creation should generate a large demographic increase and thus a rise in fishing activities. This study analyzed reef fish assemblages between 2002 and 2007 with a focus on spatio-temporal variability. Our results indicate strong spatial structure of fish assemblages through time. Total species richness, density and biomass were highly variable between years but temporal variations were consistent among biotopes. A remarkable spatio-temporal stability was observed for trophic (mean 4.6% piscivores, 53.1% carnivores, 30.8% herbivores and 11.4% planktivores) and home range structures of species abundance contributions. These results are discussed and compared with others sites of the South Pacific. For monitoring perspectives, some indicators related to expected disturbances are proposed. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Emotional modulation of experimental pain: a source imaging study of laser evoked potentials

PubMed Central

Stancak, Andrej; Fallon, Nicholas

2013-01-01

Negative emotions have been shown to augment experimental pain. As induced emotions alter brain activity, it is not clear whether pain augmentation during noxious stimulation would be related to neural activation existing prior to onset of a noxious stimulus or alternatively, whether emotional stimuli would only alter neural activity during the period of nociceptive processing. We analyzed the spatio-temporal patterns of laser evoked potentials (LEPs) occurring prior to and during the period of cortical processing of noxious laser stimuli during passive viewing of negative, positive, or neutral emotional pictures. Independent component analysis (ICA) was applied to series of source activation volumes, reconstructed using local autoregressive average model (LAURA). Pain was the strongest when laser stimuli were associated with negative emotional pictures. Prior to laser stimulus and during the first 100 ms after onset of laser stimulus, activations were seen in the left and right medial temporal cortex, cerebellum, posterior cingulate, and rostral cingulate/prefrontal cortex. In all these regions, positive or neutral pictures showed stronger activations than negative pictures. During laser stimulation, activations in the right and left anterior insula, temporal cortex and right anterior and posterior parietal cortex were stronger during negative than neutral or positive emotional pictures. Results suggest that negative emotional stimuli increase activation in the left and right anterior insula and temporal cortex, and right posterior and anterior parietal cortex only during the period of nociceptive processing. The role of background brain activation in emotional modulation of pain appears to be only permissive, and consisting in attenuation of activation in structures maintaining the resting state of the brain. PMID:24062659

Spatio-Temporal Trends and Identification of Correlated Variables with Water Quality for Drinking-Water Reservoirs

PubMed Central

Gu, Qing; Wang, Ke; Li, Jiadan; Ma, Ligang; Deng, Jinsong; Zheng, Kefeng; Zhang, Xiaobin; Sheng, Li

2015-01-01

It is widely accepted that characterizing the spatio-temporal trends of water quality parameters and identifying correlated variables with water quality are indispensable for the management and protection of water resources. In this study, cluster analysis was used to classify 56 typical drinking water reservoirs in Zhejiang Province into three groups representing different water quality levels, using data of four water quality parameters for the period 2006–2010. Then, the spatio-temporal trends in water quality were analyzed, assisted by geographic information systems (GIS) technology and statistical analysis. The results indicated that the water quality showed a trend of degradation from southwest to northeast, and the overall water quality level was exacerbated during the study period. Correlation analysis was used to evaluate the relationships between water quality parameters and ten independent variables grouped into four categories (land use, socio-economic factors, geographical features, and reservoir attributes). According to the correlation coefficients, land use and socio-economic indicators were identified as the most significant factors related to reservoir water quality. The results offer insights into the spatio-temporal variations of water quality parameters and factors impacting the water quality of drinking water reservoirs in Zhejiang Province, and they could assist managers in making effective strategies to better protect water resources. PMID:26492263

Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

NASA Astrophysics Data System (ADS)

Ruiz-Pérez, Guiomar; Koch, Julian; Manfreda, Salvatore; Caylor, Kelly; Francés, Félix

2017-12-01

Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment - the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

Spatio-Temporal Trends and Identification of Correlated Variables with Water Quality for Drinking-Water Reservoirs.

PubMed

Gu, Qing; Wang, Ke; Li, Jiadan; Ma, Ligang; Deng, Jinsong; Zheng, Kefeng; Zhang, Xiaobin; Sheng, Li

2015-10-20

It is widely accepted that characterizing the spatio-temporal trends of water quality parameters and identifying correlated variables with water quality are indispensable for the management and protection of water resources. In this study, cluster analysis was used to classify 56 typical drinking water reservoirs in Zhejiang Province into three groups representing different water quality levels, using data of four water quality parameters for the period 2006-2010. Then, the spatio-temporal trends in water quality were analyzed, assisted by geographic information systems (GIS) technology and statistical analysis. The results indicated that the water quality showed a trend of degradation from southwest to northeast, and the overall water quality level was exacerbated during the study period. Correlation analysis was used to evaluate the relationships between water quality parameters and ten independent variables grouped into four categories (land use, socio-economic factors, geographical features, and reservoir attributes). According to the correlation coefficients, land use and socio-economic indicators were identified as the most significant factors related to reservoir water quality. The results offer insights into the spatio-temporal variations of water quality parameters and factors impacting the water quality of drinking water reservoirs in Zhejiang Province, and they could assist managers in making effective strategies to better protect water resources.

Predictability of spatio-temporal patterns in a lattice of coupled FitzHugh–Nagumo oscillators

PubMed Central

Grace, Miriam; Hütt, Marc-Thorsten

2013-01-01

In many biological systems, variability of the components can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In pioneering work in the late 1990s, it was hypothesized that a drift of cellular parameters (along a ‘developmental path’), together with differences in cell properties (‘desynchronization’ of cells on the developmental path) can establish self-organized spatio-temporal patterns (in their example, spiral waves of cAMP in a colony of Dictyostelium discoideum cells) starting from a homogeneous state. Here, we embed a generic model of an excitable medium, a lattice of diffusively coupled FitzHugh–Nagumo oscillators, into a developmental-path framework. In this minimal model of spiral wave generation, we can now study the predictability of spatio-temporal patterns from cell properties as a function of desynchronization (or ‘spread’) of cells along the developmental path and the drift speed of cell properties on the path. As a function of drift speed and desynchronization, we observe systematically different routes towards fully established patterns, as well as strikingly different correlations between cell properties and pattern features. We show that the predictability of spatio-temporal patterns from cell properties contains important information on the pattern formation process as well as on the underlying dynamical system. PMID:23349439

Spatio-temporal pattern analysis for evaluation of the spread of human infections with avian influenza A(H7N9) virus in China, 2013-2014.

PubMed

Dong, Wen; Yang, Kun; Xu, Quanli; Liu, Lin; Chen, Juan

2017-10-24

A large number (n = 460) of A(H7N9) human infections have been reported in China from March 2013 through December 2014, and H7N9 outbreaks in humans became an emerging issue for China health, which have caused numerous disease outbreaks in domestic poultry and wild bird populations, and threatened human health severely. The aims of this study were to investigate the directional trend of the epidemic and to identify the significant presence of spatial-temporal clustering of influenza A(H7N9) human cases between March 2013 and December 2014. Three distinct epidemic phases of A(H7N9) human infections were identified in this study. In each phase, standard deviational ellipse analysis was conducted to examine the directional trend of disease spreading, and retrospective space-time permutation scan statistic was then used to identify the spatio-temporal cluster patterns of H7N9 outbreaks in humans. The ever-changing location and the increasing size of the three identified standard deviational ellipses showed that the epidemic moved from east to southeast coast, and hence to some central regions, with a future epidemiological trend of continue dispersing to more central regions of China, and a few new human cases might also appear in parts of the western China. Furthermore, A(H7N9) human infections were clustering in space and time in the first two phases with five significant spatio-temporal clusters (p < 0.05), but there was no significant cluster identified in phase III. There was a new epidemiologic pattern that the decrease in significant spatio-temporal cluster of A(H7N9) human infections was accompanied with an obvious spatial expansion of the outbreaks during the study period, and identification of the spatio-temporal patterns of the epidemic can provide valuable insights for better understanding the spreading dynamics of the disease in China.

Optimal design of a bank of spatio-temporal filters for EEG signal classification.

PubMed

Higashi, Hiroshi; Tanaka, Toshihisa

2011-01-01

The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery based brain computer interfaces (MI-BCI). To achieve accurate classification in CSP, the frequency filter should be properly designed. To this end, several methods for designing the filter have been proposed. However, the existing methods cannot consider plural brain activities described with different frequency bands and different spatial patterns such as activities of mu and beta rhythms. In order to efficiently extract these brain activities, we propose a method to design plural filters and spatial weights which extract desired brain activity. The proposed method designs finite impulse response (FIR) filters and the associated spatial weights by optimization of an objective function which is a natural extension of CSP. Moreover, we show by a classification experiment that the bank of FIR filters which are designed by introducing an orthogonality into the objective function can extract good discriminative features. Moreover, the experiment result suggests that the proposed method can automatically detect and extract brain activities related to motor imagery.

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli.

PubMed

Crosse, Michael J; Di Liberto, Giovanni M; Bednar, Adam; Lalor, Edmund C

2016-01-01

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter-often referred to as a temporal response function-that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli

PubMed Central

Crosse, Michael J.; Di Liberto, Giovanni M.; Bednar, Adam; Lalor, Edmund C.

2016-01-01

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter—often referred to as a temporal response function—that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application. PMID:27965557

Spatio-temporal brain activity related to rotation method during a mental rotation task of three-dimensional objects: an MEG study.

PubMed

Kawamichi, Hiroaki; Kikuchi, Yoshiaki; Ueno, Shoogo

2007-09-01

During mental rotation tasks, subjects perform mental simulation to solve tasks. However, detailed neural mechanisms underlying mental rotation of three-dimensional (3D) objects, particularly, whether higher motor areas related to mental simulation are activated, remain unknown. We hypothesized that environmental monitoring-a process based on environmental information and is included in motor execution-is as a key factor affecting the utilization of higher motor areas. Therefore, using magnetoencephalography (MEG), we measured spatio-temporal brain activities during two types (two-dimensional (2D) and 3D rotation tasks) of mental rotation of 3D objects. Only the 3D rotation tasks required subjects to mentally rotate objects in a depth plane with visualization of hidden parts of the visual stimuli by acquiring and retrieving 3D information. In cases showing significant differences in the averaged activities at 100-ms intervals between the two rotations, the activities were located in the right dorsal premotor (PMd) at approximately 500 ms. In these cases, averaged activities during 3D rotation were greater than those during 2D rotation, implying that the right PMd activities are related to environmental monitoring. During 3D rotation, higher activities were observed from 200 to 300 ms in the left PMd and from 400 to 700 ms in the right PMd. It is considered that the left PMd is related to primary motor control, whereas the right PMd plays a supplementary role during mental simulation. Further, during 3D rotation, late higher activities related to mental simulation are observed in the right superior parietal lobule (SPL), which is connected to PMd.

Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network

PubMed Central

Del Papa, Bruno; Priesemann, Viola

2017-01-01

Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions – matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model’s performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN’s spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences. PMID:28552964

Spatio-temporal coupling of EEG signals in epilepsy

NASA Astrophysics Data System (ADS)

Senger, Vanessa; Müller, Jens; Tetzlaff, Ronald

2011-05-01

Approximately 1% of the world's population suffer from epileptic seizures throughout their lives that mostly come without sign or warning. Thus, epilepsy is the most common chronical disorder of the neurological system. In the past decades, the problem of detecting a pre-seizure state in epilepsy using EEG signals has been addressed in many contributions by various authors over the past two decades. Up to now, the goal of identifying an impending epileptic seizure with sufficient specificity and reliability has not yet been achieved. Cellular Nonlinear Networks (CNN) are characterized by local couplings of dynamical systems of comparably low complexity. Thus, they are well suited for an implementation as highly parallel analogue processors. Programmable sensor-processor realizations of CNN combine high computational power comparable to tera ops of digital processors with low power consumption. An algorithm allowing an automated and reliable detection of epileptic seizure precursors would be a"huge step" towards the vision of an implantable seizure warning device that could provide information to patients and for a time/event specific treatment directly in the brain. Recent contributions have shown that modeling of brain electrical activity by solutions of Reaction-Diffusion-CNN as well as the application of a CNN predictor taking into account values of neighboring electrodes may contribute to the realization of a seizure warning device. In this paper, a CNN based predictor corresponding to a spatio-temporal filter is applied to multi channel EEG data in order to identify mutual couplings for different channels which lead to a enhanced prediction quality. Long term EEG recordings of different patients are considered. Results calculated for these recordings with inter-ictal phases as well as phases with seizures will be discussed in detail.

Identification of Vibrotactile Patterns Encoding Obstacle Distance Information.

PubMed

Kim, Yeongmi; Harders, Matthias; Gassert, Roger

2015-01-01

Delivering distance information of nearby obstacles from sensors embedded in a white cane-in addition to the intrinsic mechanical feedback from the cane-can aid the visually impaired in ambulating independently. Haptics is a common modality for conveying such information to cane users, typically in the form of vibrotactile signals. In this context, we investigated the effect of tactile rendering methods, tactile feedback configurations and directions of tactile flow on the identification of obstacle distance. Three tactile rendering methods with temporal variation only, spatio-temporal variation and spatial/temporal/intensity variation were investigated for two vibration feedback configurations. Results showed a significant interaction between tactile rendering method and feedback configuration. Spatio-temporal variation generally resulted in high correct identification rates for both feedback configurations. In the case of the four-finger vibration, tactile rendering with spatial/temporal/intensity variation also resulted in high distance identification rate. Further, participants expressed their preference for the four-finger vibration over the single-finger vibration in a survey. Both preferred rendering methods with spatio-temporal variation and spatial/temporal/intensity variation for the four-finger vibration could convey obstacle distance information with low workload. Overall, the presented findings provide valuable insights and guidance for the design of haptic displays for electronic travel aids for the visually impaired.

Spatio-temporal source cluster analysis reveals fronto-temporal auditory change processing differences within a shared autistic and schizotypal trait phenotype.

PubMed

Ford, Talitha C; Woods, Will; Crewther, David P

2017-01-01

Social Disorganisation (SD) is a shared autistic and schizotypal phenotype that is present in the subclinical population. Auditory processing deficits, particularly in mismatch negativity/field (MMN/F) have been reported across both spectrum disorders. This study investigates differences in MMN/F cortical spatio-temporal source activity between higher and lower quintiles of the SD spectrum. Sixteen low (9 female) and 19 high (9 female) SD subclinical adults (18-40years) underwent magnetoencephalography (MEG) during an MMF paradigm where standard tones (50ms) were interrupted by infrequent duration deviants (100ms). Spatio-temporal source cluster analysis with permutation testing revealed no difference between the groups in source activation to the standard tone. To the deviant tone however, there was significantly reduced right hemisphere fronto-temporal and insular cortex activation for the high SD group ( p = 0.038). The MMF, as a product of the cortical response to the deviant minus that to the standard, did not differ significantly between the high and low Social Disorganisation groups. These data demonstrate a deficit in right fronto-temporal processing of an auditory change for those with more of the shared SD phenotype, indicating that right fronto-temporal auditory processing may be associated with psychosocial functioning.

Assessing spatio-temporal eruption forecasts in a monogenetic volcanic field

NASA Astrophysics Data System (ADS)

Bebbington, Mark S.

2013-02-01

Many spatio-temporal models have been proposed for forecasting the location and timing of the next eruption in a monogenetic volcanic field. These have almost invariably been fitted retrospectively. That is, the model has been tuned to all of the data, and hence an assessment of the goodness of fit has not been carried out on independent data. The low rate of eruptions in monogenetic fields means that there is not the opportunity to carry out a purely prospective test, as thousands of years would be required to accumulate the necessary data. This leaves open the possibility of a retrospective sequential test, where the parameters are calculated only on the basis of prior events and the resulting forecast compared statistically with the location and time of the next eruption. In general, events in volcanic fields are not dated with sufficient accuracy and precision to pursue this line of investigation; An exception is the Auckland Volcanic Field (New Zealand), consisting of c. 50 centers formed during the last c. 250 kyr, for which an age-order model exists in the form of a Monte Carlo sampling algorithm, facilitating repeated sequential testing. I examine a suite of spatial, temporal and spatio-temporal hazard models, comparing the degree of fit, and attempt to draw lessons from how and where each model is particularly successful or unsuccessful. A relatively simple (independent) combination of a renewal model (temporal term) and a spatially uniform ellipse (spatial term) performs as well as any other model. Both avoid over fitting the data, and hence large errors, when the spatio-temporal occurrence pattern changes.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

PubMed

Albers, Christian; Westkott, Maren; Pawelzik, Klaus

2016-01-01

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

PubMed Central

Albers, Christian; Westkott, Maren; Pawelzik, Klaus

2016-01-01

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns. PMID:26900845

Single-trial EEG-informed fMRI reveals spatial dependency of BOLD signal on early and late IC-ERP amplitudes during face recognition.

PubMed

Wirsich, Jonathan; Bénar, Christian; Ranjeva, Jean-Philippe; Descoins, Médéric; Soulier, Elisabeth; Le Troter, Arnaud; Confort-Gouny, Sylviane; Liégeois-Chauvel, Catherine; Guye, Maxime

2014-10-15

Simultaneous EEG-fMRI has opened up new avenues for improving the spatio-temporal resolution of functional brain studies. However, this method usually suffers from poor EEG quality, especially for evoked potentials (ERPs), due to specific artifacts. As such, the use of EEG-informed fMRI analysis in the context of cognitive studies has particularly focused on optimizing narrow ERP time windows of interest, which ignores the rich diverse temporal information of the EEG signal. Here, we propose to use simultaneous EEG-fMRI to investigate the neural cascade occurring during face recognition in 14 healthy volunteers by using the successive ERP peaks recorded during the cognitive part of this process. N170, N400 and P600 peaks, commonly associated with face recognition, were successfully and reproducibly identified for each trial and each subject by using a group independent component analysis (ICA). For the first time we use this group ICA to extract several independent components (IC) corresponding to the sequence of activation and used single-trial peaks as modulation parameters in a general linear model (GLM) of fMRI data. We obtained an occipital-temporal-frontal stream of BOLD signal modulation, in accordance with the three successive IC-ERPs providing an unprecedented spatio-temporal characterization of the whole cognitive process as defined by BOLD signal modulation. By using this approach, the pattern of EEG-informed BOLD modulation provided improved characterization of the network involved than the fMRI-only analysis or the source reconstruction of the three ERPs; the latter techniques showing only two regions in common localized in the occipital lobe. Copyright © 2014 Elsevier Inc. All rights reserved.

Real-time three-dimensional temperature mapping in photothermal therapy with optoacoustic tomography

NASA Astrophysics Data System (ADS)

Oyaga Landa, Francisco Javier; Deán-Ben, Xosé Luís.; Sroka, Ronald; Razansky, Daniel

2017-07-01

Ablation and photothermal therapy are widely employed medical protocols where the selective destruction of tissue is a necessity as in cancerous tissue removal or vascular and brain abnormalities. Tissue denaturation takes place when the temperature reaches a threshold value while the time of exposure determines the lesion size. Therefore, the spatio-temporal distribution of temperature plays a crucial role in the outcome of these clinical interventions. We demonstrate fast volumetric temperature mapping with optoacoustic tomography based on real-time optoacoustic readings from the treated region. The performance of the method was investigated in tissue-mimicking phantom experiments. The new ability to non-invasively measure temperature volumetrically in an entire treated region with high spatial and temporal resolutions holds potential for improving safety and efficacy of thermal ablation and to advance the general applicability of laser-based therapy.

Distributional patterns in an insect community inhabiting a sandy beach of Uruguay

NASA Astrophysics Data System (ADS)

Mourglia, Virginia; González-Vainer, Patricia; Defeo, Omar

2015-12-01

Most studies of sandy beach macrofauna have been restricted to semiterrestrial species and do not include insects when providing species richness and abundance estimates. Particularly, spatio-temporal patterns of community structure of the entomofauna inhabiting these ecosystems have been scarcely documented. This study assessed spatio-temporal distributional patterns of the night active entomofauna on a beach-dune system of Uruguay, including variations in species richness, abundance and diversity, and their relationship with environmental factors. A deconstructive taxonomic analysis was also performed, considering richness and abundance patterns separately for the most abundant insect Orders (Hymenoptera and Coleoptera) to better understand the factors which drive their patterns. We found clear temporal and across-shore patterns in the insect community inhabiting a land-ocean interface, which matched spatiotemporal variations in the environment. Abundance and species richness were highest in spring and summer, concurrently with high temperatures and low values of sediment moisture and compaction. Multivariate ordinations showed two well-defined species groups, which separated summer, autumn and spring samples from winter ones. Generalized Linear Models allowed us to describe a clear segregation in space of the most important orders of the insect community, with specific preferences for the terrestrial (Hymenoptera) and beach (Coleoptera) fringes. Hymenoptera preferred the dune zone, characterized by high elevation and low sand moisture and compaction levels, whereas Coleoptera preferred gentle slopes and fine and humid sands of the beach. Our results suggest that beach and dune ecosystems operate as two separate components in regard to their physical and biological features. The high values of species richness and abundance of insects reveal that this group has a more significant ecological role than that originally considered so far in sandy beach ecology.

The spatio-temporal dynamics of deviance and target detection in the passive and active auditory oddball paradigm: a sLORETA study.

PubMed

Justen, Christoph; Herbert, Cornelia

2018-04-19

Numerous studies have investigated the neural underpinnings of passive and active deviance and target detection in the well-known auditory oddball paradigm by means of event-related potentials (ERPs) or functional magnetic resonance imaging (fMRI). The present auditory oddball study investigates the spatio-temporal dynamics of passive versus active deviance and target detection by analyzing amplitude modulations of early and late ERPs while at the same time exploring the neural sources underling this modulation with standardized low-resolution brain electromagnetic tomography (sLORETA) . A 64-channel EEG was recorded from twelve healthy right-handed participants while listening to 'standards' and 'deviants' (500 vs. 1000 Hz pure tones) during a passive (block 1) and an active (block 2) listening condition. During passive listening, participants had to simply listen to the tones. During active listening they had to attend and press a key in response to the deviant tones. Passive and active listening elicited an N1 component, a mismatch negativity (MMN) as difference potential (whose amplitudes were temporally overlapping with the N1) and a P3 component. N1/MMN and P3 amplitudes were significantly more pronounced for deviants as compared to standards during both listening conditions. Active listening augmented P3 modulation to deviants significantly compared to passive listening, whereas deviance detection as indexed by N1/MMN modulation was unaffected by the task. During passive listening, sLORETA contrasts (deviants > standards) revealed significant activations in the right superior temporal gyrus (STG) and the lingual gyri bilaterally (N1/MMN) as well as in the left and right insulae (P3). During active listening, significant activations were found for the N1/MMN in the right inferior parietal lobule (IPL) and for the P3 in multiple cortical regions (e.g., precuneus). The results provide evidence for the hypothesis that passive as well as active deviance and target detection elicit cortical activations in spatially distributed brain regions and neural networks including the ventral attention network (VAN), dorsal attention network (DAN) and salience network (SN). Based on the temporal activation of the neural sources underlying ERP modulations, a neurophysiological model of passive and active deviance and target detection is proposed which can be tested in future studies.

Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar

DOE PAGES

Sen, Satyabrata

2015-08-04

We develop space-time adaptive processing (STAP) methods by leveraging the advantages of sparse signal processing techniques in order to detect a slowly-moving target. We observe that the inherent sparse characteristics of a STAP problem can be formulated as the low-rankness of clutter covariance matrix when compared to the total adaptive degrees-of-freedom, and also as the sparse interference spectrum on the spatio-temporal domain. By exploiting these sparse properties, we propose two approaches for estimating the interference covariance matrix. In the first approach, we consider a constrained matrix rank minimization problem (RMP) to decompose the sample covariance matrix into a low-rank positivemore » semidefinite and a diagonal matrix. The solution of RMP is obtained by applying the trace minimization technique and the singular value decomposition with matrix shrinkage operator. Our second approach deals with the atomic norm minimization problem to recover the clutter response-vector that has a sparse support on the spatio-temporal plane. We use convex relaxation based standard sparse-recovery techniques to find the solutions. With extensive numerical examples, we demonstrate the performances of proposed STAP approaches with respect to both the ideal and practical scenarios, involving Doppler-ambiguous clutter ridges, spatial and temporal decorrelation effects. As a result, the low-rank matrix decomposition based solution requires secondary measurements as many as twice the clutter rank to attain a near-ideal STAP performance; whereas the spatio-temporal sparsity based approach needs a considerably small number of secondary data.« less

Spatio-Temporal Process Variability in Watershed Scale Wetland Restoration Planning

NASA Astrophysics Data System (ADS)

Evenson, G. R.

2012-12-01

Watershed scale restoration decision making processes are increasingly informed by quantitative methodologies providing site-specific restoration recommendations - sometimes referred to as "systematic planning." The more advanced of these methodologies are characterized by a coupling of search algorithms and ecological models to discover restoration plans that optimize environmental outcomes. Yet while these methods have exhibited clear utility as decision support toolsets, they may be critiqued for flawed evaluations of spatio-temporally variable processes fundamental to watershed scale restoration. Hydrologic and non-hydrologic mediated process connectivity along with post-restoration habitat dynamics, for example, are commonly ignored yet known to appreciably affect restoration outcomes. This talk will present a methodology to evaluate such spatio-temporally complex processes in the production of watershed scale wetland restoration plans. Using the Tuscarawas Watershed in Eastern Ohio as a case study, a genetic algorithm will be coupled with the Soil and Water Assessment Tool (SWAT) to reveal optimal wetland restoration plans as measured by their capacity to maximize nutrient reductions. Then, a so-called "graphical" representation of the optimization problem will be implemented in-parallel to promote hydrologic and non-hydrologic mediated connectivity amongst existing wetlands and sites selected for restoration. Further, various search algorithm mechanisms will be discussed as a means of accounting for temporal complexities such as post-restoration habitat dynamics. Finally, generalized patterns of restoration plan optimality will be discussed as an alternative and possibly superior decision support toolset given the complexity and stochastic nature of spatio-temporal process variability.

A class of cellular automata modeling winnerless competition

NASA Astrophysics Data System (ADS)

Afraimovich, V.; Ordaz, F. C.; Urías, J.

2002-06-01

Neural units introduced by Rabinovich et al. ("Sensory coding with dynamically competitive networks," UCSD and CIT, February 1999) motivate a class of cellular automata (CA) where spatio-temporal encoding is feasible. The spatio-temporal information capacity of a CA is estimated by the information capacity of the attractor set, which happens to be finitely specified. Two-dimensional CA are studied in detail. An example is given for which the attractor is not a subshift.

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

DOE PAGES

Kaufeld, Kimberly Ann; Fuentes, Montse; Reich, Brian J.; ...

2017-09-11

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated tomore » the pregnant women’s homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.« less

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaufeld, Kimberly Ann; Fuentes, Montse; Reich, Brian J.

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated tomore » the pregnant women’s homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.« less

Visual pattern recognition based on spatio-temporal patterns of retinal ganglion cells’ activities

PubMed Central

Jing, Wei; Liu, Wen-Zhong; Gong, Xin-Wei; Gong, Hai-Qing

2010-01-01

Neural information is processed based on integrated activities of relevant neurons. Concerted population activity is one of the important ways for retinal ganglion cells to efficiently organize and process visual information. In the present study, the spike activities of bullfrog retinal ganglion cells in response to three different visual patterns (checker-board, vertical gratings and horizontal gratings) were recorded using multi-electrode arrays. A measurement of subsequence distribution discrepancy (MSDD) was applied to identify the spatio-temporal patterns of retinal ganglion cells’ activities in response to different stimulation patterns. The results show that the population activity patterns were different in response to different stimulation patterns, such difference in activity pattern was consistently detectable even when visual adaptation occurred during repeated experimental trials. Therefore, the stimulus pattern can be reliably discriminated according to the spatio-temporal pattern of the neuronal activities calculated using the MSDD algorithm. PMID:21886670

Learning of spatio-temporal codes in a coupled oscillator system.

PubMed

Orosz, Gábor; Ashwin, Peter; Townley, Stuart

2009-07-01

In this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modeled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomogeneous inputs produces cyclic sequences of transitions between the cluster states, that is, information about inputs is encoded via a "winnerless competition" process into spatio-temporal codes. The large variety of codes can be learned by the learning system that adapts its frequencies to those of the teaching system. We visualize the dynamics using "weighted order parameters (WOPs)" that are analogous to "local field potentials" in neural systems. Since spatio-temporal coding is a mechanism that appears in olfactory systems, the developed learning rules may help to extract information from these neural ensembles.

Towards human behavior recognition based on spatio temporal features and support vector machines

NASA Astrophysics Data System (ADS)

Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

2017-03-01

Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

Bayesian spatio-temporal modeling of particulate matter concentrations in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Manga, Edna; Awang, Norhashidah

2016-06-01

This article presents an application of a Bayesian spatio-temporal Gaussian process (GP) model on particulate matter concentrations from Peninsular Malaysia. We analyze daily PM10 concentration levels from 35 monitoring sites in June and July 2011. The spatiotemporal model set in a Bayesian hierarchical framework allows for inclusion of informative covariates, meteorological variables and spatiotemporal interactions. Posterior density estimates of the model parameters are obtained by Markov chain Monte Carlo methods. Preliminary data analysis indicate information on PM10 levels at sites classified as industrial locations could explain part of the space time variations. We include the site-type indicator in our modeling efforts. Results of the parameter estimates for the fitted GP model show significant spatio-temporal structure and positive effect of the location-type explanatory variable. We also compute some validation criteria for the out of sample sites that show the adequacy of the model for predicting PM10 at unmonitored sites.

Real time eye tracking using Kalman extended spatio-temporal context learning

NASA Astrophysics Data System (ADS)

Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

2017-06-01

Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

Dynamic design of ecological monitoring networks for non-Gaussian spatio-temporal data

USGS Publications Warehouse

Wikle, C.K.; Royle, J. Andrew

2005-01-01

Many ecological processes exhibit spatial structure that changes over time in a coherent, dynamical fashion. This dynamical component is often ignored in the design of spatial monitoring networks. Furthermore, ecological variables related to processes such as habitat are often non-Gaussian (e.g. Poisson or log-normal). We demonstrate that a simulation-based design approach can be used in settings where the data distribution is from a spatio-temporal exponential family. The key random component in the conditional mean function from this distribution is then a spatio-temporal dynamic process. Given the computational burden of estimating the expected utility of various designs in this setting, we utilize an extended Kalman filter approximation to facilitate implementation. The approach is motivated by, and demonstrated on, the problem of selecting sampling locations to estimate July brood counts in the prairie pothole region of the U.S.

Interocular suppression in normal and amblyopic vision: spatio-temporal properties.

PubMed

Huang, Pi-Chun; Baker, Daniel H; Hess, Robert F

2012-10-31

We measured the properties of interocular suppression in strabismic amblyopes and compared these to dichoptic masking in binocularly normal observers. We used a dichoptic version of the well-established probed-sinewave paradigm that measured sensitivity to a brief target stimulus (one of four letters to be discriminated) in the amblyopic eye at different times relative to a suppression-inducing mask in the fixing eye. This was done using both sinusoidal steady state and transient approaches. The suppression-inducing masks were either modulations of luminance or contrast (full field, just overlaying the target, or just surrounding the target). Our results were interpreted using a descriptive model that included contrast gain control and spatio-temporal filtering prior to excitatory binocular combination. The suppression we measured, other than in magnitude, was not fundamentally different from normal dichoptic masking: lowpass spatio-temporal properties with similar contributions from both surround and overlay suppression.

Joint level-set and spatio-temporal motion detection for cell segmentation.

PubMed

Boukari, Fatima; Makrogiannis, Sokratis

2016-08-10

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images. In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result. We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques, and 4 % compared to the nonlinear spatio-temporal diffusion method. Despite the wide variation in cell shape, density, mitotic events, and image quality among the datasets, our proposed method produced promising segmentation results. These results indicate the efficiency and robustness of this method especially for mitotic events and low SNR imaging, enabling the application of subsequent quantification tasks.

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

PubMed Central

Jehee, Janneke F. M.; Ballard, Dana H.

2009-01-01

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain. PMID:19412529

Spatio-Temporal Variation in Length-Weight Relationships and Condition of the Ribbonfish Trichiurus lepturus (Linnaeus, 1758): Implications for Fisheries Management

PubMed Central

Garcia de Leaniz, Carlos; King, Andrew J.

2016-01-01

Knowledge of length-weight relationships for commercially exploited fish is an important tool for assessing and managing of fish stocks. However, analyses of length-weight relationship fisheries data typically do not consider the inherent differences in length-weight relationships for fish caught from different habitats, seasons, or years, and this can affect the utility of these data for developing condition indices or calculating fisheries biomass. Here, we investigated length-weight relationships for ribbonfish Trichiurus lepturus in the waters of the Arabian Sea off Oman collected during three periods (2001–02, 2007–08, and 2014–15) and showed that a multivariate modelling approach that considers the areas and seasons in which ribbonfish were caught improved estimation of length-weight relationships. We used the outputs of these models to explore spatio-temporal variations in condition indices and relative weights among ribbonfish, revealing fish of 85–125 cm were in the best overall condition. We also found that condition differed according to where and when fish were caught, with condition lowest during spring and pre-south-west monsoon periods and highest during and after the south-west monsoons. We interpret these differences to be a consequence of variability in temperature and food availability. Based on our findings, we suggest fishing during seasons that have the lowest impact on fish condition and which are commercially most viable; such fishery management would enhance fisheries conservation and economic revenue in the region. PMID:27579485

Spatio-Temporal Variation in Length-Weight Relationships and Condition of the Ribbonfish Trichiurus lepturus (Linnaeus, 1758): Implications for Fisheries Management.

PubMed

Al Nahdi, Abdullah; Garcia de Leaniz, Carlos; King, Andrew J

2016-01-01

Knowledge of length-weight relationships for commercially exploited fish is an important tool for assessing and managing of fish stocks. However, analyses of length-weight relationship fisheries data typically do not consider the inherent differences in length-weight relationships for fish caught from different habitats, seasons, or years, and this can affect the utility of these data for developing condition indices or calculating fisheries biomass. Here, we investigated length-weight relationships for ribbonfish Trichiurus lepturus in the waters of the Arabian Sea off Oman collected during three periods (2001-02, 2007-08, and 2014-15) and showed that a multivariate modelling approach that considers the areas and seasons in which ribbonfish were caught improved estimation of length-weight relationships. We used the outputs of these models to explore spatio-temporal variations in condition indices and relative weights among ribbonfish, revealing fish of 85-125 cm were in the best overall condition. We also found that condition differed according to where and when fish were caught, with condition lowest during spring and pre-south-west monsoon periods and highest during and after the south-west monsoons. We interpret these differences to be a consequence of variability in temperature and food availability. Based on our findings, we suggest fishing during seasons that have the lowest impact on fish condition and which are commercially most viable; such fishery management would enhance fisheries conservation and economic revenue in the region.

Spatio-temporal variational characteristics analysis of heavy metals pollution in water of the typical northern rivers, China

NASA Astrophysics Data System (ADS)

Lu, Hongwei; Yu, Sen

2018-04-01

The rapid urbanization and industrialization in developing countries have increased pollution by heavy metals, which is a concern for human health and the environment. In this study, according to the data obtained from the monitoring stations in the Songhua River basin, the multivariate statistical analysis methods are applied to the hydrological data of the Songhua River basin in order to examine the relation between human activities and the spatio-temporal change of heavy metals (Pb and Cu) in water. By comparing the concentrations at different flow periods, the minimum Pb concentrations are found to have occurred most frequently in low flow periods while the maximum values mostly appeared in average flow periods. Moreover, the minimum Cu concentration in the water frequently occurred in high flow periods. The results show there are low Pb and Cu concentrations in upstream and downstream sections and high concentrations in mid-stream sections, and high concentrations are most frequently measured in the sections of Ashihe' downstream and estuary. Moreover, we have predicted the future (during 2018-2025) trend of the change for the heavy metals pollution in the rivers. The results demonstrated intense human activities are the most important factor causing jump features of typical heavy metal pollution in the different periods for different sections of this study area. The research would provide decision-making and planning for the Songhua River basin during the period of China's 13th Five-Year Plan.

Analysis of the features of untrained human movements based on the multichannel EEG for controlling anthropomorphic robotic arm

NASA Astrophysics Data System (ADS)

Maksimenko, Vladimir; Runnova, Anastasia; Pchelintseva, Svetlana; Efremova, Tatiana; Zhuravlev, Maksim; Pisarchik, Alexander

2018-04-01

We have considered time-frequency and spatio-temporal structure of electrical brain activity, associated with real and imaginary movements based on the multichannel EEG recordings. We have found that along with wellknown effects of event-related desynchronization (ERD) in α/μ - rhythms and β - rhythm, these types of activity are accompanied by the either ERS (for real movement) or ERD (for imaginary movement) in low-frequency δ - band, located mostly in frontal lobe. This may be caused by the associated processes of decision making, which take place when subject is deciding either perform the movement or imagine it. Obtained features have been found in untrained subject which it its turn gives the possibility to use our results in the development of brain-computer interfaces for controlling anthropomorphic robotic arm.

Spatio-temporal modelling for assessing air pollution in Santiago de Chile

NASA Astrophysics Data System (ADS)

Nicolis, Orietta; Camaño, Christian; Mařın, Julio C.; Sahu, Sujit K.

2017-01-01

In this work, we propose a space-time approach for studying the PM2.5 concentration in the city of Santiago de Chile. In particular, we apply the autoregressive hierarchical model proposed by [1] using the PM2.5 observations collected by a monitoring network as a response variable and numerical weather forecasts from the Weather Research and Forecasting (WRF) model as covariate together with spatial and temporal (periodic) components. The approach is able to provide short-term spatio-temporal predictions of PM2.5 concentrations on a fine spatial grid (at 1km × 1km horizontal resolution.)

Spatio-temporal organization of replication in bacteria and eukaryotes (nucleoids and nuclei).

PubMed

Jackson, Dean; Wang, Xindan; Rudner, David Z

2012-08-01

Here we discuss the spatio-temporal organization of replication in eubacteria and eukaryotes. Although there are significant differences in how replication is organized in cells that contain nuclei from those that do not, you will see that organization of replication in all organisms is principally dictated by the structured arrangement of the chromosome. We will begin with how replication is organized in eubacteria with particular emphasis on three well studied model organisms. We will then discuss spatial and temporal organization of replication in eukaryotes highlighting the similarities and differences between these two domains of life.

Spatio-Temporal Organization of Replication in Bacteria and Eukaryotes (Nucleoids and Nuclei)

PubMed Central

Jackson, Dean; Wang, Xindan; Rudner, David Z.

2012-01-01

Here we discuss the spatio-temporal organization of replication in eubacteria and eukaryotes. Although there are significant differences in how replication is organized in cells that contain nuclei from those that do not, you will see that organization of replication in all organisms is principally dictated by the structured arrangement of the chromosome. We will begin with how replication is organized in eubacteria with particular emphasis on three well studied model organisms. We will then discuss spatial and temporal organization of replication in eukaryotes highlighting the similarities and differences between these two domains of life. PMID:22855726

Copula Multivariate analysis of Gross primary production and its hydro-environmental driver; A BIOME-BGC model applied to the Antisana páramos

NASA Astrophysics Data System (ADS)

Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur

2014-05-01

Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos

Electrical Neuroimaging of Music Processing Reveals Mid-Latency Changes with Level of Musical Expertise

PubMed Central

James, Clara E.; Oechslin, Mathias S.; Michel, Christoph M.; De Pretto, Michael

2017-01-01

This original research focused on the effect of musical training intensity on cerebral and behavioral processing of complex music using high-density event-related potential (ERP) approaches. Recently we have been able to show progressive changes with training in gray and white matter, and higher order brain functioning using (f)MRI [(functional) Magnetic Resonance Imaging], as well as changes in musical and general cognitive functioning. The current study investigated the same population of non-musicians, amateur pianists and expert pianists using spatio-temporal ERP analysis, by means of microstate analysis, and ERP source imaging. The stimuli consisted of complex musical compositions containing three levels of transgression of musical syntax at closure that participants appraised. ERP waveforms, microstates and underlying brain sources revealed gradual differences according to musical expertise in a 300–500 ms window after the onset of the terminal chords of the pieces. Within this time-window, processing seemed to concern context-based memory updating, indicated by a P3b-like component or microstate for which underlying sources were localized in the right middle temporal gyrus, anterior cingulate and right parahippocampal areas. Given that the 3 expertise groups were carefully matched for demographic factors, these results provide evidence of the progressive impact of training on brain and behavior. PMID:29163017

Electrical Neuroimaging of Music Processing Reveals Mid-Latency Changes with Level of Musical Expertise.

PubMed

James, Clara E; Oechslin, Mathias S; Michel, Christoph M; De Pretto, Michael

2017-01-01

This original research focused on the effect of musical training intensity on cerebral and behavioral processing of complex music using high-density event-related potential (ERP) approaches. Recently we have been able to show progressive changes with training in gray and white matter, and higher order brain functioning using (f)MRI [(functional) Magnetic Resonance Imaging], as well as changes in musical and general cognitive functioning. The current study investigated the same population of non-musicians, amateur pianists and expert pianists using spatio-temporal ERP analysis, by means of microstate analysis, and ERP source imaging. The stimuli consisted of complex musical compositions containing three levels of transgression of musical syntax at closure that participants appraised. ERP waveforms, microstates and underlying brain sources revealed gradual differences according to musical expertise in a 300-500 ms window after the onset of the terminal chords of the pieces. Within this time-window, processing seemed to concern context-based memory updating, indicated by a P3b-like component or microstate for which underlying sources were localized in the right middle temporal gyrus, anterior cingulate and right parahippocampal areas. Given that the 3 expertise groups were carefully matched for demographic factors, these results provide evidence of the progressive impact of training on brain and behavior.

Encoding of Spatio-Temporal Input Characteristics by a CA1 Pyramidal Neuron Model

PubMed Central

Pissadaki, Eleftheria Kyriaki; Sidiropoulou, Kyriaki; Reczko, Martin; Poirazi, Panayiota

2010-01-01

The in vivo activity of CA1 pyramidal neurons alternates between regular spiking and bursting, but how these changes affect information processing remains unclear. Using a detailed CA1 pyramidal neuron model, we investigate how timing and spatial arrangement variations in synaptic inputs to the distal and proximal dendritic layers influence the information content of model responses. We find that the temporal delay between activation of the two layers acts as a switch between excitability modes: short delays induce bursting while long delays decrease firing. For long delays, the average firing frequency of the model response discriminates spatially clustered from diffused inputs to the distal dendritic tree. For short delays, the onset latency and inter-spike-interval succession of model responses can accurately classify input signals as temporally close or distant and spatially clustered or diffused across different stimulation protocols. These findings suggest that a CA1 pyramidal neuron may be capable of encoding and transmitting presynaptic spatiotemporal information about the activity of the entorhinal cortex-hippocampal network to higher brain regions via the selective use of either a temporal or a rate code. PMID:21187899

Highly efficient intracellular transduction in three-dimensional gradients for programming cell fate.

PubMed

Eltaher, Hoda M; Yang, Jing; Shakesheff, Kevin M; Dixon, James E

2016-09-01

Fundamental behaviour such as cell fate, growth and death are mediated through the control of key genetic transcriptional regulators. These regulators are activated or repressed by the integration of multiple signalling molecules in spatio-temporal gradients. Engineering these gradients is complex but considered key in controlling tissue formation in regenerative medicine approaches. Direct programming of cells using exogenously delivered transcription factors can by-pass growth factor complexity but there is still a requirement to deliver such activity spatio-temporally. We previously developed a technology termed GAG-binding enhanced transduction (GET) to efficiently deliver a variety of cargoes intracellularly using GAG-binding domains to promote cell targeting, and cell penetrating peptides (CPPs) to allow cell entry. Herein we demonstrate that GET can be used in a three dimensional (3D) hydrogel matrix to produce gradients of intracellular transduction of mammalian cells. Using a compartmentalised diffusion model with a source-gel-sink (So-G-Si) assembly, we created gradients of reporter proteins (mRFP1-tagged) and a transcription factor (TF, myogenic master regulator MyoD) and showed that GET can be used to deliver molecules into cells spatio-temporally by monitoring intracellular transduction and gene expression programming as a function of location and time. The ability to spatio-temporally control the intracellular delivery of functional proteins will allow the establishment of gradients of cell programming in hydrogels and approaches to direct cellular behaviour for many regenerative medicine applications. Regenerative medicine aims to reform functional biological tissues by controlling cell behaviour. Growth factors (GFs) are soluble cues presented to cells in spatio-temporal gradients and play important roles programming cell fate and gene expression. The efficient transduction of cells by GET (Glycosaminoglycan-enhanced transducing)-tagged transcription factors (TFs) can be used to by-pass GF-stimulation and directly program cells. For the first time we demonstrate diffusion of GET proteins generate stable protein transduction gradients. We demonstrated the feasibility of creating spatio-temporal gradients of GET-MyoD and show differential programing of myogenic differentiation. We believe that GET could provide a powerful tool to program cell behaviour using gradients of recombinant proteins that allow tissue generation directly by programming gene expression with TFs. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

PubMed

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

Visual representation of spatiotemporal structure

NASA Astrophysics Data System (ADS)

Schill, Kerstin; Zetzsche, Christoph; Brauer, Wilfried; Eisenkolb, A.; Musto, A.

1998-07-01

The processing and representation of motion information is addressed from an integrated perspective comprising low- level signal processing properties as well as higher-level cognitive aspects. For the low-level processing of motion information we argue that a fundamental requirement is the existence of a spatio-temporal memory. Its key feature, the provision of an orthogonal relation between external time and its internal representation, is achieved by a mapping of temporal structure into a locally distributed activity distribution accessible in parallel by higher-level processing stages. This leads to a reinterpretation of the classical concept of `iconic memory' and resolves inconsistencies on ultra-short-time processing and visual masking. The spatial-temporal memory is further investigated by experiments on the perception of spatio-temporal patterns. Results on the direction discrimination of motion paths provide evidence that information about direction and location are not processed and represented independent of each other. This suggests a unified representation on an early level, in the sense that motion information is internally available in form of a spatio-temporal compound. For the higher-level representation we have developed a formal framework for the qualitative description of courses of motion that may occur with moving objects.

Keeping their distance? Odor response patterns along the concentration range

PubMed Central

Strauch, Martin; Ditzen, Mathias; Galizia, C. Giovanni

2012-01-01

We investigate the interplay of odor identity and concentration coding in the antennal lobe (AL) of the honeybee Apis mellifera. In this primary olfactory center of the honeybee brain, odors are encoded by the spatio-temporal response patterns of olfactory glomeruli. With rising odor concentration, further glomerular responses are recruited into the patterns, which affects distances between the patterns. Based on calcium-imaging recordings, we found that such pattern broadening renders distances between glomerular response patterns closer to chemical distances between the corresponding odor molecules. Our results offer an explanation for the honeybee's improved odor discrimination performance at higher odor concentrations. PMID:23087621

The Ecuadorian Artisanal Fishery for Large Pelagics: Species Composition and Spatio-Temporal Dynamics

PubMed Central

Martínez-Ortiz, Jimmy; Aires-da-Silva, Alexandre M.; Lennert-Cody, Cleridy E.; Maunder, Mark N.

2015-01-01

The artisanal fisheries of Ecuador operate within one of the most dynamic and productive marine ecosystems of the world. This study investigates the catch composition of the Ecuadorian artisanal fishery for large pelagic fishes, including aspects of its spatio-temporal dynamics. The analyses of this study are based on the most extensive dataset available to date for this fishery: a total of 106,963 trip-landing inspection records collected at its five principal ports during 2008 ‒ 2012. Ecuadorian artisanal fisheries remove a substantial amount of biomass from the upper trophic-level predatory fish community of the eastern tropical Pacific Ocean. It is estimated that at least 135 thousand metric tons (mt) (about 15.5 million fish) were landed in the five principal ports during the study period. The great novelty of Ecuadorian artisanal fisheries is the “oceanic-artisanal” fleet component, which consists of mother-ship (nodriza) boats with their towed fiber-glass skiffs (fibras) operating with pelagic longlines. This fleet has fully expanded into oceanic waters as far offshore as 100°W, west of the Galapagos Archipelago. It is estimated that nodriza operations produce as much as 80% of the total catches of the artisanal fishery. The remainder is produced by independent fibras operating in inshore waters with pelagic longlines and/or surface gillnets. A multivariate regression tree analysis was used to investigate spatio-environmental effects on the nodriza fleet (n = 6,821 trips). The catch species composition of the nodriza fleet is strongly influenced by the northwesterly circulation of the Humboldt Current along the coast of Peru and its associated cold waters masses. The target species and longline gear-type used by nodrizas change seasonally with the incursion of cool waters (< 25°C) from the south and offshore. During this season, dolphinfish (Coryphaena hippurus) dominates the catches. However, in warmer waters, the fishery changes to tuna-billfish-shark longline gear and the catch composition becomes much more diverse. PMID:26317751

The Ecuadorian Artisanal Fishery for Large Pelagics: Species Composition and Spatio-Temporal Dynamics.

PubMed

Martínez-Ortiz, Jimmy; Aires-da-Silva, Alexandre M; Lennert-Cody, Cleridy E; Maunder, Mark N

2015-01-01

The artisanal fisheries of Ecuador operate within one of the most dynamic and productive marine ecosystems of the world. This study investigates the catch composition of the Ecuadorian artisanal fishery for large pelagic fishes, including aspects of its spatio-temporal dynamics. The analyses of this study are based on the most extensive dataset available to date for this fishery: a total of 106,963 trip-landing inspection records collected at its five principal ports during 2008 ‒ 2012. Ecuadorian artisanal fisheries remove a substantial amount of biomass from the upper trophic-level predatory fish community of the eastern tropical Pacific Ocean. It is estimated that at least 135 thousand metric tons (mt) (about 15.5 million fish) were landed in the five principal ports during the study period. The great novelty of Ecuadorian artisanal fisheries is the "oceanic-artisanal" fleet component, which consists of mother-ship (nodriza) boats with their towed fiber-glass skiffs (fibras) operating with pelagic longlines. This fleet has fully expanded into oceanic waters as far offshore as 100°W, west of the Galapagos Archipelago. It is estimated that nodriza operations produce as much as 80% of the total catches of the artisanal fishery. The remainder is produced by independent fibras operating in inshore waters with pelagic longlines and/or surface gillnets. A multivariate regression tree analysis was used to investigate spatio-environmental effects on the nodriza fleet (n = 6,821 trips). The catch species composition of the nodriza fleet is strongly influenced by the northwesterly circulation of the Humboldt Current along the coast of Peru and its associated cold waters masses. The target species and longline gear-type used by nodrizas change seasonally with the incursion of cool waters (< 25°C) from the south and offshore. During this season, dolphinfish (Coryphaena hippurus) dominates the catches. However, in warmer waters, the fishery changes to tuna-billfish-shark longline gear and the catch composition becomes much more diverse.

Joint Spatio-Temporal Shared Component Model with an Application in Iran Cancer Data

PubMed

Mahaki, Behzad; Mehrabi, Yadollah; Kavousi, Amir; Schmid, Volker J

2018-06-25

Background: Among the proposals for joint disease mapping, the shared component model has become more popular. Another advance to strengthen inference of disease data is the extension of purely spatial models to include time aspect. We aim to combine the idea of multivariate shared components with spatio-temporal modelling in a joint disease mapping model and apply it for incidence rates of seven prevalent cancers in Iran which together account for approximately 50% of all cancers. Methods: In the proposed model, each component is shared by different subsets of diseases, spatial and temporal trends are considered for each component, and the relative weight of these trends for each component for each relevant disease can be estimated. Results: For esophagus and stomach cancers the Northern provinces was the area of high risk. For colorectal cancer Gilan, Semnan, Fars, Isfahan, Yazd and East-Azerbaijan were the highest risk provinces. For bladder and lung cancer, the northwest were the highest risk area. For prostate and breast cancers, Isfahan, Yazd, Fars, Tehran, Semnan, Mazandaran and Khorasane-Razavi were the highest risk part. The smoking component, shared by esophagus, stomach, bladder and lung, had more effect in Gilan, Mazandaran, Chaharmahal and Bakhtiari, Kohgilouyeh and Boyerahmad, Ardebil and Tehran provinces, in turn. For overweight and obesity component, shared by esophagus, colorectal, prostate and breast cancers the largest effect was found for Tehran, Khorasane-Razavi, Semnan, Yazd, Isfahan, Fars, Mazandaran and Gilan, in turn. For low physical activity component, shared by colorectal and breast cancers North-Khorasan, Ardebil, Golestan, Ilam, Khorasane-Razavi and South-Khorasan had the largest effects, in turn. The smoking component is significantly more important for stomach than for esophagus, bladder and lung. The overweight and obesity had significantly more effect for colorectal than of esophagus cancer. Conclusions: The presented model is a valuable model to model geographical and temporal variation among diseases and has some interesting potential features and benefits over other joint models. Creative Commons Attribution License

Sickle cell disease diagnosis based on spatio-temporal cell dynamics analysis using 3D printed shearing digital holographic microscopy.

PubMed

Javidi, Bahram; Markman, Adam; Rawat, Siddharth; O'Connor, Timothy; Anand, Arun; Andemariam, Biree

2018-05-14

We present a spatio-temporal analysis of cell membrane fluctuations to distinguish healthy patients from patients with sickle cell disease. A video hologram containing either healthy red blood cells (h-RBCs) or sickle cell disease red blood cells (SCD-RBCs) was recorded using a low-cost, compact, 3D printed shearing interferometer. Reconstructions were created for each hologram frame (time steps), forming a spatio-temporal data cube. Features were extracted by computing the standard deviations and the mean of the height fluctuations over time and for every location on the cell membrane, resulting in two-dimensional standard deviation and mean maps, followed by taking the standard deviations of these maps. The optical flow algorithm was used to estimate the apparent motion fields between subsequent frames (reconstructions). The standard deviation of the magnitude of the optical flow vectors across all frames was then computed. In addition, seven morphological cell (spatial) features based on optical path length were extracted from the cells to further improve the classification accuracy. A random forest classifier was trained to perform cell identification to distinguish between SCD-RBCs and h-RBCs. To the best of our knowledge, this is the first report of machine learning assisted cell identification and diagnosis of sickle cell disease based on cell membrane fluctuations and morphology using both spatio-temporal and spatial analysis.

An adaptive spatio-temporal Gaussian filter for processing cardiac optical mapping data.

PubMed

Pollnow, S; Pilia, N; Schwaderlapp, G; Loewe, A; Dössel, O; Lenis, G

2018-06-04

Optical mapping is widely used as a tool to investigate cardiac electrophysiology in ex vivo preparations. Digital filtering of fluorescence-optical data is an important requirement for robust subsequent data analysis and still a challenge when processing data acquired from thin mammalian myocardium. Therefore, we propose and investigate the use of an adaptive spatio-temporal Gaussian filter for processing optical mapping signals from these kinds of tissue usually having low signal-to-noise ratio (SNR). We demonstrate how filtering parameters can be chosen automatically without additional user input. For systematic comparison of this filter with standard filtering methods from the literature, we generated synthetic signals representing optical recordings from atrial myocardium of a rat heart with varying SNR. Furthermore, all filter methods were applied to experimental data from an ex vivo setup. Our developed filter outperformed the other filter methods regarding local activation time detection at SNRs smaller than 3 dB which are typical noise ratios expected in these signals. At higher SNRs, the proposed filter performed slightly worse than the methods from literature. In conclusion, the proposed adaptive spatio-temporal Gaussian filter is an appropriate tool for investigating fluorescence-optical data with low SNR. The spatio-temporal filter parameters were automatically adapted in contrast to the other investigated filters. Copyright © 2018 Elsevier Ltd. All rights reserved.

Dynamic CT perfusion imaging of the myocardium: a technical note on improvement of image quality.

PubMed

Muenzel, Daniela; Kabus, Sven; Gramer, Bettina; Leber, Vivian; Vembar, Mani; Schmitt, Holger; Wildgruber, Moritz; Fingerle, Alexander A; Rummeny, Ernst J; Huber, Armin; Noël, Peter B

2013-01-01

To improve image and diagnostic quality in dynamic CT myocardial perfusion imaging (MPI) by using motion compensation and a spatio-temporal filter. Dynamic CT MPI was performed using a 256-slice multidetector computed tomography scanner (MDCT). Data from two different patients-with and without myocardial perfusion defects-were evaluated to illustrate potential improvements for MPI (institutional review board approved). Three datasets for each patient were generated: (i) original data (ii) motion compensated data and (iii) motion compensated data with spatio-temporal filtering performed. In addition to the visual assessment of the tomographic slices, noise and contrast-to-noise-ratio (CNR) were measured for all data. Perfusion analysis was performed using time-density curves with regions-of-interest (ROI) placed in normal and hypoperfused myocardium. Precision in definition of normal and hypoperfused areas was determined in corresponding coloured perfusion maps. The use of motion compensation followed by spatio-temporal filtering resulted in better alignment of the cardiac volumes over time leading to a more consistent perfusion quantification and improved detection of the extend of perfusion defects. Additionally image noise was reduced by 78.5%, with CNR improvements by a factor of 4.7. The average effective radiation dose estimate was 7.1±1.1 mSv. The use of motion compensation and spatio-temporal smoothing will result in improved quantification of dynamic CT MPI using a latest generation CT scanner.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Swati, F. N. U.; Stein, Michael L.

Regional climate models (RCMs) are a standard tool for downscaling climate forecasts to finer spatial scales. The evaluation of RCMs against observational data is an important step in building confidence in the use of RCMs for future prediction. In addition to model performance in climatological means and marginal distributions, a model’s ability to capture spatio-temporal relationships is important. This study develops two approaches: (1) spatial correlation/variogram for a range of spatial lags, with total monthly precipitation and non-seasonal precipitation components used to assess the spatial variations of precipitation; and (2) spatio-temporal correlation for a wide range of distances, directions, andmore » time lags, with daily precipitation occurrence used to detect the dynamic features of precipitation. These measures of spatial and spatio-temporal dependence are applied to a high-resolution RCM run and to the National Center for Environmental Prediction (NCEP)-U.S. Department of Energy (DOE) AMIP II reanalysis data (NCEP-R2), which provides initial and lateral boundary conditions for the RCM. The RCM performs better than NCEP-R2 in capturing both the spatial variations of total and non-seasonal precipitation components and the spatio-temporal correlations of daily precipitation occurrences, which are related to dynamic behaviors of precipitating systems. The improvements are apparent not just at resolutions finer than that of NCEP-R2, but also when the RCM and observational data are aggregated to the resolution of NCEP-R2.« less

Evaluating the Spatio-Temporal Factors that Structure Network Parameters of Plant-Herbivore Interactions

PubMed Central

López-Carretero, Antonio; Díaz-Castelazo, Cecilia; Boege, Karina; Rico-Gray, Víctor

2014-01-01

Despite the dynamic nature of ecological interactions, most studies on species networks offer static representations of their structure, constraining our understanding of the ecological mechanisms involved in their spatio-temporal stability. This is the first study to evaluate plant-herbivore interaction networks on a small spatio-temporal scale. Specifically, we simultaneously assessed the effect of host plant availability, habitat complexity and seasonality on the structure of plant-herbivore networks in a coastal tropical ecosystem. Our results revealed that changes in the host plant community resulting from seasonality and habitat structure are reflected not only in the herbivore community, but also in the emergent properties (network parameters) of the plant-herbivore interaction network such as connectance, selectiveness and modularity. Habitat conditions and periods that are most stressful favored the presence of less selective and susceptible herbivore species, resulting in increased connectance within networks. In contrast, the high degree of selectivennes (i.e. interaction specialization) and modularity of the networks under less stressful conditions was promoted by the diversification in resource use by herbivores. By analyzing networks at a small spatio-temporal scale we identified the ecological factors structuring this network such as habitat complexity and seasonality. Our research offers new evidence on the role of abiotic and biotic factors in the variation of the properties of species interaction networks. PMID:25340790

Facilitating insights with a user adaptable dashboard, illustrated by airport connectivity data

NASA Astrophysics Data System (ADS)

Dobraja, Ieva; Kraak, Menno-Jan; Engelhardt, Yuri

2018-05-01

Since the movement data exist, there have been approaches to collect and analyze them to get insights. This kind of data is often heterogeneous, multiscale and multi-temporal. Those interested in spatio-temporal patterns of movement data do not gain insights from textual descriptions. Therefore, visualization is required. As spatio-temporal movement data can be complex because size and characteristics, it is even challenging to create an overview of it. Plotting all the data on the screen will not be the solution as it likely will result into cluttered images where no data exploration is possible. To ensure that users will receive the information they are interested in, it is important to provide a graphical data representation environment where exploration to gain insights are possible not only in the overall level but at sub-levels as well. A dashboard would be a solution the representation of heterogeneous spatio- temporal data. It provides an overview and helps to unravel the complexity of data by splitting data in multiple data representation views. The adaptability of dashboard will help to reveal the information which cannot be seen in the overview.

Spatio-Temporal Pattern Analysis for Regional Climate Change Using Mathematical Morphology

NASA Astrophysics Data System (ADS)

Das, M.; Ghosh, S. K.

2015-07-01

Of late, significant changes in climate with their grave consequences have posed great challenges on humankind. Thus, the detection and assessment of climatic changes on a regional scale is gaining importance, since it helps to adopt adequate mitigation and adaptation measures. In this paper, we have presented a novel approach for detecting spatio-temporal pattern of regional climate change by exploiting the theory of mathematical morphology. At first, the various climatic zones in the region have been identified by using multifractal cross-correlation analysis (MF-DXA) of different climate variables of interest. Then, the directional granulometry with four different structuring elements has been studied to detect the temporal changes in spatial distribution of the identified climatic zones in the region and further insights have been drawn with respect to morphological uncertainty index and Hurst exponent. The approach has been evaluated with the daily time series data of land surface temperature (LST) and precipitation rate, collected from Microsoft Research - Fetch Climate Explorer, to analyze the spatio-temporal climatic pattern-change in the Eastern and North-Eastern regions of India throughout four quarters of the 20th century.

Spatio-Temporal Variability of Water Vapor in the Free Troposphere Investigated by Dial and Ftir Vertical Soundings

NASA Astrophysics Data System (ADS)

Vogelmann, H.; Sussmann, R.; Trickl, T.; Reichert, A.

2016-06-01

We report on the free tropospheric spatio-temporal variability of water vapor investigated by the analysis of a five-year period of water vapor vertical soundings above Mt. Zugspitze (2962 m a.s.l., Germany). Our results are obtained from a combination of measurements of vertically integrated water vapor (IWV), recorded with a solar Fourier Transform InfraRed (FTIR) spectrometer and of water vapor profiles recorded with the nearby differential absorption lidar (DIAL). The special geometrical arrangement of one zenith-viewing and one sun-pointing instrument and the temporal resolution of both optical instruments allow for an investigation of the spatio-temporal variability of IWV on a spatial scale of less than one kilometer and on a time scale of less than one hour. We investigated the short-term variability of both IWV and water vapor profiles from statistical analyses. The latter was also examined by case studies with a clear assignment to certain atmospheric processes as local convection or long-range transport. This study is described in great detail in our recent publication [1].

Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

DOE PAGES

Murugesan, Sugeerth; Bouchard, Kristofer; Chang, Edward; ...

2016-10-02

Here, we present ECoG ClusterFlow, a novel interactive visual analysis tool for the exploration of high-resolution Electrocorticography (ECoG) data. Our system detects and visualizes dynamic high-level structures, such as communities, using the time-varying spatial connectivity network derived from the high-resolution ECoG data. ECoG ClusterFlow provides a multi-scale visualization of the spatio-temporal patterns underlying the time-varying communities using two views: 1) an overview summarizing the evolution of clusters over time and 2) a hierarchical glyph-based technique that uses data aggregation and small multiples techniques to visualize the propagation of clusters in their spatial domain. ECoG ClusterFlow makes it possible 1) tomore » compare the spatio-temporal evolution patterns across various time intervals, 2) to compare the temporal information at varying levels of granularity, and 3) to investigate the evolution of spatial patterns without occluding the spatial context information. Lastly, we present case studies done in collaboration with neuroscientists on our team for both simulated and real epileptic seizure data aimed at evaluating the effectiveness of our approach.« less

A spatial-temporal system for dynamic cadastral management.

PubMed

Nan, Liu; Renyi, Liu; Guangliang, Zhu; Jiong, Xie

2006-03-01

A practical spatio-temporal database (STDB) technique for dynamic urban land management is presented. One of the STDB models, the expanded model of Base State with Amendments (BSA), is selected as the basis for developing the dynamic cadastral management technique. Two approaches, the Section Fast Indexing (SFI) and the Storage Factors of Variable Granularity (SFVG), are used to improve the efficiency of the BSA model. Both spatial graphic data and attribute data, through a succinct engine, are stored in standard relational database management systems (RDBMS) for the actual implementation of the BSA model. The spatio-temporal database is divided into three interdependent sub-databases: present DB, history DB and the procedures-tracing DB. The efficiency of database operation is improved by the database connection in the bottom layer of the Microsoft SQL Server. The spatio-temporal system can be provided at a low-cost while satisfying the basic needs of urban land management in China. The approaches presented in this paper may also be of significance to countries where land patterns change frequently or to agencies where financial resources are limited.

Review of complex networks application in hydroclimatic extremes with an implementation to characterize spatio-temporal drought propagation in continental USA

NASA Astrophysics Data System (ADS)

Konapala, Goutam; Mishra, Ashok

2017-12-01

The quantification of spatio-temporal hydroclimatic extreme events is a key variable in water resources planning, disaster mitigation, and preparing climate resilient society. However, quantification of these extreme events has always been a great challenge, which is further compounded by climate variability and change. Recently complex network theory was applied in earth science community to investigate spatial connections among hydrologic fluxes (e.g., rainfall and streamflow) in water cycle. However, there are limited applications of complex network theory for investigating hydroclimatic extreme events. This article attempts to provide an overview of complex networks and extreme events, event synchronization method, construction of networks, their statistical significance and the associated network evaluation metrics. For illustration purpose, we apply the complex network approach to study the spatio-temporal evolution of droughts in Continental USA (CONUS). A different drought threshold leads to a new drought event as well as different socio-economic implications. Therefore, it would be interesting to explore the role of thresholds on spatio-temporal evolution of drought through network analysis. In this study, long term (1900-2016) Palmer drought severity index (PDSI) was selected for spatio-temporal drought analysis using three network-based metrics (i.e., strength, direction and distance). The results indicate that the drought events propagate differently at different thresholds associated with initiation of drought events. The direction metrics indicated that onset of mild drought events usually propagate in a more spatially clustered and uniform approach compared to onsets of moderate droughts. The distance metric shows that the drought events propagate for longer distance in western part compared to eastern part of CONUS. We believe that the network-aided metrics utilized in this study can be an important tool in advancing our knowledge on drought propagation as well as other hydroclimatic extreme events. Although the propagation of droughts is investigated using the network approach, however process (physics) based approaches is essential to further understand the dynamics of hydroclimatic extreme events.

Different horse's paces during hippotherapy on spatio-temporal parameters of gait in children with bilateral spastic cerebral palsy: A feasibility study.

PubMed

Antunes, Fabiane Nunes; Pinho, Alexandre Severo do; Kleiner, Ana Francisca Rozin; Salazar, Ana Paula; Eltz, Giovana Duarte; de Oliveira Junior, Alcyr Alves; Cechetti, Fernanda; Galli, Manuela; Pagnussat, Aline Souza

2016-12-01

Hippotherapy is often carried out for the rehabilitation of children with Cerebral Palsy (CP), with the horse riding at a walking pace. This study aimed to explore the immediate effects of a hippotherapy protocol using a walk-trot pace on spatio-temporal gait parameters and muscle tone in children with Bilateral Spastic CP (BS-CP). Ten children diagnosed with BS-CP and 10 healthy aged-matched children (reference group) took part in this study. The children with BS-CP underwent two sessions of hippotherapy for one week of washout between them. Two protocols (lasting 30min) were applied on separate days: Protocol 1: the horse's pace was a walking pace; and Protocol 2: the horse's pace was a walk-trot pace. Children from the reference group were not subjected to treatment. A wireless inertial measurement unit measured gait spatio-temporal parameters before and after each session. The Modified Ashworth Scale was applied for muscle tone measurement of hip adductors. The participants underwent the gait assessment on a path with surface irregularities (ecological context). The comparisons between BS-CP and the reference group found differences in all spatio-temporal parameters, except for gait velocity. Within-group analysis of children with BS-CP showed that the swing phase did not change after the walk pace and after the walk-trot pace. The percentage of rolling phase and double support improved after the walk-trot. The spasticity of the hip adductors was significantly reduced as an immediate result of both protocols, but this decrease was more evident after the walk-trot. The walk-trot protocol is feasible and is able to induce an immediate effect that improves the gait spatio-temporal parameters and the hip adductors spasticity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Wholly Patient-tailored Ablation of Atrial Fibrillation Guided by Spatio-Temporal Dispersion of Electrograms in the Absence of Pulmonary Veins Isolation

PubMed Central

Seitz, Julien; Bars, Clément; Théodore, Guillaume; Beurtheret, Sylvain; Lellouche, Nicolas; Bremondy, Michel; Ferracci, Ange; Faure, Jacques; Penaranda, Guillaume; Yamazaki, Masatoshi; Avula, Uma Mahesh R.; Curel, Laurence; Siame, Sabrina; Berenfeld, Omer; Pisapia, André; Kalifa, Jérôme

2017-01-01

Background The use of intra-cardiac electrograms to guide atrial fibrillation (AF) ablation has yielded conflicting results. We evaluated an electrogram marker of AF drivers: the clustering of electrograms exhibiting spatio-temporal dispersion — regardless of whether such electrograms were fractionated or not. Objective To evaluate the usefulness of spatio-temporal dispersion, a visually recognizable electric footprint of AF drivers, for the ablation of all forms of AF. Methods We prospectively enrolled 105 patients admitted for AF ablation. AF was sequentially mapped in both atria with a 20-pole PentaRay catheter. We tagged and ablated only regions displaying electrogram dispersion during AF. Results were compared to a validation set in which a conventional ablation approach was used (pulmonary vein isolation/stepwise approach). To establish the mechanism underlying spatio-temporal dispersion of AF electrograms, we conducted realistic numerical simulations of AF drivers in a 2-dimensional model and optical mapping of ovine atrial scar-related AF. Results Ablation at dispersion areas terminated AF in 95%. After ablation of 17±10% of the left atrial surface and 18 months of follow-up, the atrial arrhythmia recurrence rate was 15% after 1.4±0.5 procedure/patient vs 41% in the validation set after 1.5±0.5 procedure/patient (arrhythmia free-survival rates: 85% vs 59%, log rank P<0.001). In comparison with the validation set, radiofrequency times (49 ± 21 minutes vs 85 ± 34.5 minutes, p=0.001) and procedure times (168 ± 42 minutes vs. 230 ± 67 minutes, p<.0001) were shorter. In simulations and optical mapping experiments, virtual PentaRay recordings demonstrated that electrogram dispersion is mostly recorded in the vicinity of a driver. Conclusions The clustering of intra-cardiac electrograms exhibiting spatio-temporal dispersion is indicative of AF drivers. Their ablation allows for a non-extensive and patient-tailored approach to AF ablation. Clinical trial.gov number: NCT02093949 PMID:28104073

Neuroanatomical and Cognitive Mediators of Age-Related Differences in Episodic Memory

PubMed Central

Head, Denise; Rodrigue, Karen M.; Kennedy, Kristen M.; Raz, Naftali

2009-01-01

Aging is associated with declines in episodic memory. In this study, the authors used a path analysis framework to explore the mediating role of differences in brain structure, executive functions, and processing speed in age-related differences in episodic memory. Measures of regional brain volume (prefrontal gray and white matter, caudate, hippocampus, visual cortex), executive functions (working memory, inhibitory control, task switching, temporal processing), processing speed, and episodic memory were obtained in a sample of young and older adults. As expected, age was linked to reduction in regional brain volumes and cognitive performance. Moreover, neural and cognitive factors completely mediated age differences in episodic memory. Whereas hippocampal shrinkage directly affected episodic memory, prefrontal volumetric reductions influenced episodic memory via limitations in working memory and inhibitory control. Age-related slowing predicted reduced efficiency in temporal processing, working memory, and inhibitory control. Lastly, poorer temporal processing directly affected episodic memory. No direct effects of age on episodic memory remained once these factors were taken into account. These analyses highlight the value of a multivariate approach with the understanding of complex relationships in cognitive and brain aging. PMID:18590361

Resting state brain dynamics and its transients: a combined TMS-EEG study.

PubMed

Bonnard, Mireille; Chen, Sophie; Gaychet, Jérôme; Carrere, Marcel; Woodman, Marmaduke; Giusiano, Bernard; Jirsa, Viktor

2016-08-04

The brain at rest exhibits a spatio-temporally rich dynamics which adheres to systematic behaviours that persist in task paradigms but appear altered in disease. Despite this hypothesis, many rest state paradigms do not act directly upon the rest state and therefore cannot confirm hypotheses about its mechanisms. To address this challenge, we combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to study brain's relaxation toward rest following a transient perturbation. Specifically, TMS targeted either the medial prefrontal cortex (MPFC), i.e. part of the Default Mode Network (DMN) or the superior parietal lobule (SPL), involved in the Dorsal Attention Network. TMS was triggered by a given brain state, namely an increase in occipital alpha rhythm power. Following the initial TMS-Evoked Potential, TMS at MPFC enhances the induced occipital alpha rhythm, called Event Related Synchronisation, with a longer transient lifetime than TMS at SPL, and a higher amplitude. Our findings show a strong coupling between MPFC and the occipital alpha power. Although the rest state is organized around a core of resting state networks, the DMN functionally takes a special role among these resting state networks.

A highly accurate symmetric optical flow based high-dimensional nonlinear spatial normalization of brain images.

PubMed

Wen, Ying; Hou, Lili; He, Lianghua; Peterson, Bradley S; Xu, Dongrong

2015-05-01

Spatial normalization plays a key role in voxel-based analyses of brain images. We propose a highly accurate algorithm for high-dimensional spatial normalization of brain images based on the technique of symmetric optical flow. We first construct a three dimension optical model with the consistency assumption of intensity and consistency of the gradient of intensity under a constraint of discontinuity-preserving spatio-temporal smoothness. Then, an efficient inverse consistency optical flow is proposed with aims of higher registration accuracy, where the flow is naturally symmetric. By employing a hierarchical strategy ranging from coarse to fine scales of resolution and a method of Euler-Lagrange numerical analysis, our algorithm is capable of registering brain images data. Experiments using both simulated and real datasets demonstrated that the accuracy of our algorithm is not only better than that of those traditional optical flow algorithms, but also comparable to other registration methods used extensively in the medical imaging community. Moreover, our registration algorithm is fully automated, requiring a very limited number of parameters and no manual intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

Cortex-based inter-subject analysis of iEEG and fMRI data sets: application to sustained task-related BOLD and gamma responses.

PubMed

Esposito, Fabrizio; Singer, Neomi; Podlipsky, Ilana; Fried, Itzhak; Hendler, Talma; Goebel, Rainer

2013-02-01

Linking regional metabolic changes with fluctuations in the local electromagnetic fields directly on the surface of the human cerebral cortex is of tremendous importance for a better understanding of detailed brain processes. Functional magnetic resonance imaging (fMRI) and intra-cranial electro-encephalography (iEEG) measure two technically unrelated but spatially and temporally complementary sets of functional descriptions of human brain activity. In order to allow fine-grained spatio-temporal human brain mapping at the population-level, an effective comparative framework for the cortex-based inter-subject analysis of iEEG and fMRI data sets is needed. We combined fMRI and iEEG recordings of the same patients with epilepsy during alternated intervals of passive movie viewing and music listening to explore the degree of local spatial correspondence and temporal coupling between blood oxygen level dependent (BOLD) fMRI changes and iEEG spectral power modulations across the cortical surface after cortex-based inter-subject alignment. To this purpose, we applied a simple model of the iEEG activity spread around each electrode location and the cortex-based inter-subject alignment procedure to transform discrete iEEG measurements into cortically distributed group patterns by establishing a fine anatomic correspondence of many iEEG cortical sites across multiple subjects. Our results demonstrate the feasibility of a multi-modal inter-subject cortex-based distributed analysis for combining iEEG and fMRI data sets acquired from multiple subjects with the same experimental paradigm but with different iEEG electrode coverage. The proposed iEEG-fMRI framework allows for improved group statistics in a common anatomical space and preserves the dynamic link between the temporal features of the two modalities. Copyright © 2012 Elsevier Inc. All rights reserved.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ(2) = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.

PubMed

Wu, Zhaohua; Feng, Jiaxin; Qiao, Fangli; Tan, Zhe-Min

2016-04-13

In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders. © 2016 The Authors.

A bio-inspired system for spatio-temporal recognition in static and video imagery

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Moore, Christopher K.; Chelian, Suhas

2007-04-01

This paper presents a bio-inspired method for spatio-temporal recognition in static and video imagery. It builds upon and extends our previous work on a bio-inspired Visual Attention and object Recognition System (VARS). The VARS approach locates and recognizes objects in a single frame. This work presents two extensions of VARS. The first extension is a Scene Recognition Engine (SCE) that learns to recognize spatial relationships between objects that compose a particular scene category in static imagery. This could be used for recognizing the category of a scene, e.g., office vs. kitchen scene. The second extension is the Event Recognition Engine (ERE) that recognizes spatio-temporal sequences or events in sequences. This extension uses a working memory model to recognize events and behaviors in video imagery by maintaining and recognizing ordered spatio-temporal sequences. The working memory model is based on an ARTSTORE1 neural network that combines an ART-based neural network with a cascade of sustained temporal order recurrent (STORE)1 neural networks. A series of Default ARTMAP classifiers ascribes event labels to these sequences. Our preliminary studies have shown that this extension is robust to variations in an object's motion profile. We evaluated the performance of the SCE and ERE on real datasets. The SCE module was tested on a visual scene classification task using the LabelMe2 dataset. The ERE was tested on real world video footage of vehicles and pedestrians in a street scene. Our system is able to recognize the events in this footage involving vehicles and pedestrians.

Dispersive optical solitons and modulation instability analysis of Schrödinger-Hirota equation with spatio-temporal dispersion and Kerr law nonlinearity

NASA Astrophysics Data System (ADS)

Inc, Mustafa; Aliyu, Aliyu Isa; Yusuf, Abdullahi; Baleanu, Dumitru

2018-01-01

This paper obtains the dark, bright, dark-bright or combined optical and singular solitons to the perturbed nonlinear Schrödinger-Hirota equation (SHE) with spatio-temporal dispersion (STD) and Kerr law nonlinearity in optical fibers. The integration algorithm is the Sine-Gordon equation method (SGEM). Furthermore, the modulation instability analysis (MI) of the equation is studied based on the standard linear-stability analysis and the MI gain spectrum is got.

The 4-D approach to visual control of autonomous systems

NASA Technical Reports Server (NTRS)

Dickmanns, Ernst D.

1994-01-01

Development of a 4-D approach to dynamic machine vision is described. Core elements of this method are spatio-temporal models oriented towards objects and laws of perspective projection in a foward mode. Integration of multi-sensory measurement data was achieved through spatio-temporal models as invariants for object recognition. Situation assessment and long term predictions were allowed through maintenance of a symbolic 4-D image of processes involving objects. Behavioral capabilities were easily realized by state feedback and feed-foward control.

High-throughput analysis of spatio-temporal dynamics in Dictyostelium

PubMed Central

Sawai, Satoshi; Guan, Xiao-Juan; Kuspa, Adam; Cox, Edward C

2007-01-01

We demonstrate a time-lapse video approach that allows rapid examination of the spatio-temporal dynamics of Dictyostelium cell populations. Quantitative information was gathered by sampling life histories of more than 2,000 mutant clones from a large mutagenesis collection. Approximately 4% of the clonal lines showed a mutant phenotype at one stage. Many of these could be ordered by clustering into functional groups. The dataset allows one to search and retrieve movies on a gene-by-gene and phenotype-by-phenotype basis. PMID:17659086

Space-time airborne disease mapping applied to detect specific behaviour of varicella in Valencia, Spain.

PubMed

Iftimi, Adina; Montes, Francisco; Santiyán, Ana Míguez; Martínez-Ruiz, Francisco

2015-01-01

Airborne diseases are one of humanity's most feared sicknesses and have regularly caused concern among specialists. Varicella is an airborne disease which usually affects children before the age of 10. Because of its nature, varicella gives rise to interesting spatial, temporal and spatio-temporal patterns. This paper studies spatio-temporal exploratory analysis tools to detect specific behaviour of varicella in the city of Valencia, Spain, from 2008 to 2013. These methods have shown a significant association between the spatial and the temporal component, confirmed by the space-time models applied to the data. High relative risk of varicella is observed in economically disadvantaged regions, areas less involved in vaccination programmes. Copyright © 2015 Elsevier Ltd. All rights reserved.

A spatio-temporally compensated acousto-optic scanner for two-photon microscopy providing large field of view.

PubMed

Kremer, Y; Léger, J-F; Lapole, R; Honnorat, N; Candela, Y; Dieudonné, S; Bourdieu, L

2008-07-07

Acousto-optic deflectors (AOD) are promising ultrafast scanners for non-linear microscopy. Their use has been limited until now by their small scanning range and by the spatial and temporal dispersions of the laser beam going through the deflectors. We show that the use of AOD of large aperture (13mm) compared to standard deflectors allows accessing much larger field of view while minimizing spatio-temporal distortions. An acousto-optic modulator (AOM) placed at distance of the AOD is used to compensate spatial and temporal dispersions. Fine tuning of the AOM-AOD setup using a frequency-resolved optical gating (GRENOUILLE) allows elimination of pulse front tilt whereas spatial chirp is minimized thanks to the large aperture AOD.

How to Link Brain and Experience? Spatiotemporal Psychopathology of the Lived Body

PubMed Central

Northoff, Georg; Stanghellini, Giovanni

2016-01-01

The focus of the present article is on sketching a psychopathology of the body in schizophrenia and linking it to brain activity. This is done providing converging data from psychopathological evidence (phenomenal), phenomenological contructs (trans-phenomenal) and neuroscientific measures (pre-phenomenal). The phenomenal level is the detailed documentation of the patients’ subjective anomalous experiences. These phenomena are explicit contents in the patients’ field of consciousness. The trans-phenomenal level targets the implicit yet operative matrix that underlies these anomalous subjective experiences. Abnormal phenomena are viewed as expressions of a modification of trans-phenomenal matrix, that is, in terms of an abnormal synthesis or integration through time of intero-, proprio- and extero-ceptive stimuli. Finally, we link the abnormalities of the trans-phenomenal matrix to pre-phenomenal alterations of the brain resting state and of its spatio-temporal organization, as documented by neurobiological methods providing spatial and temporal resolution of intrinsic brain activity (with many features of the resting state remaining yet unclear though). Based on phenomenological research, the body in schizophrenia is typically experienced in an itemized way as an object external to one’s self and unrelated to events in the external world. Based on neurobiological data, we tentatively hypothesize that such anomalies of the lived body are related to decreased integration between intero-, extero- and proprioceptive experiences by the brain’s spontaneous activity and its temporal structure. Taken all together, this suggests that we view abnormalities of bodily experience in terms of their underlying abnormal spatiotemporal features which, as we suppose, can be traced back to the spatiotemporal features of the brain’s spontaneous activity. PMID:27199695

Alcohol Dose Effects on Brain Circuits During Simulated Driving: An fMRI Study

PubMed Central

Meda, Shashwath A.; Calhoun, Vince D.; Astur, Robert S.; Turner, Beth M.; Ruopp, Kathryn; Pearlson, Godfrey D.

2009-01-01

Driving while intoxicated remains a major public health hazard. Driving is a complex task involving simultaneous recruitment of multiple cognitive functions. The investigators studied the neural substrates of driving and their response to different blood alcohol concentrations (BACs), using functional magnetic resonance imaging (fMRI) and a virtual reality driving simulator. We used independent component analysis (ICA) to isolate spatially independent and temporally correlated driving-related brain circuits in 40 healthy, adult moderate social drinkers. Each subject received three individualized, separate single-blind doses of beverage alcohol to produce BACs of 0.05% (moderate), 0.10% (high), or 0% (placebo). 3 T fMRI scanning and continuous behavioral measurement occurred during simulated driving. Brain function was assessed and compared using both ICA and a conventional general linear model (GLM) analysis. ICA results replicated and significantly extended our previous 1.5T study (Calhoun et al. [2004a]: Neuropsychopharmacology 29:2097–2017). GLM analysis revealed significant dose-related functional differences, complementing ICA data. Driving behaviors including opposite white line crossings and mean speed independently demonstrated significant dose-dependent changes. Behavior-based factors also predicted a frontal-basal-temporal circuit to be functionally impaired with alcohol dosage across baseline scaled, good versus poorly performing drivers. We report neural correlates of driving behavior and found dose-related spatio-temporal disruptions in critical driving-associated regions including the superior, middle and orbito frontal gyri, anterior cingulate, primary/supplementary motor areas, basal ganglia, and cerebellum. Overall, results suggest that alcohol (especially at high doses) causes significant impairment of both driving behavior and brain functionality related to motor planning and control, goal directedness, error monitoring, and memory. PMID:18571794

Laser Speckle Imaging of Cerebral Blood Flow

NASA Astrophysics Data System (ADS)

Luo, Qingming; Jiang, Chao; Li, Pengcheng; Cheng, Haiying; Wang, Zhen; Wang, Zheng; Tuchin, Valery V.

Monitoring the spatio-temporal characteristics of cerebral blood flow (CBF) is crucial for studying the normal and pathophysiologic conditions of brain metabolism. By illuminating the cortex with laser light and imaging the resulting speckle pattern, relative CBF images with tens of microns spatial and millisecond temporal resolution can be obtained. In this chapter, a laser speckle imaging (LSI) method for monitoring dynamic, high-resolution CBF is introduced. To improve the spatial resolution of current LSI, a modified LSI method is proposed. To accelerate the speed of data processing, three LSI data processing frameworks based on graphics processing unit (GPU), digital signal processor (DSP), and field-programmable gate array (FPGA) are also presented. Applications for detecting the changes in local CBF induced by sensory stimulation and thermal stimulation, the influence of a chemical agent on CBF, and the influence of acute hyperglycemia following cortical spreading depression on CBF are given.

Spatio-temporal Analysis for New York State SPARCS Data

PubMed Central

Chen, Xin; Wang, Yu; Schoenfeld, Elinor; Saltz, Mary; Saltz, Joel; Wang, Fusheng

2017-01-01

Increased accessibility of health data provides unique opportunities to discover spatio-temporal patterns of diseases. For example, New York State SPARCS (Statewide Planning and Research Cooperative System) data collects patient level detail on patient demographics, diagnoses, services, and charges for each hospital inpatient stay and outpatient visit. Such data also provides home addresses for each patient. This paper presents our preliminary work on spatial, temporal, and spatial-temporal analysis of disease patterns for New York State using SPARCS data. We analyzed spatial distribution patterns of typical diseases at ZIP code level. We performed temporal analysis of common diseases based on 12 years’ historical data. We then compared the spatial variations for diseases with different levels of clustering tendency, and studied the evolution history of such spatial patterns. Case studies based on asthma demonstrated that the discovered spatial clusters are consistent with prior studies. We visualized our spatial-temporal patterns as animations through videos. PMID:28815148

Whole-animal imaging with high spatio-temporal resolution

NASA Astrophysics Data System (ADS)

Chhetri, Raghav; Amat, Fernando; Wan, Yinan; Höckendorf, Burkhard; Lemon, William C.; Keller, Philipp J.

2016-03-01

We developed isotropic multiview (IsoView) light-sheet microscopy in order to image fast cellular dynamics, such as cell movements in an entire developing embryo or neuronal activity throughput an entire brain or nervous system, with high resolution in all dimensions, high imaging speeds, good physical coverage and low photo-damage. To achieve high temporal resolution and high spatial resolution at the same time, IsoView microscopy rapidly images large specimens via simultaneous light-sheet illumination and fluorescence detection along four orthogonal directions. In a post-processing step, these four views are then combined by means of high-throughput multiview deconvolution to yield images with a system resolution of ≤ 450 nm in all three dimensions. Using IsoView microscopy, we performed whole-animal functional imaging of Drosophila embryos and larvae at a spatial resolution of 1.1-2.5 μm and at a temporal resolution of 2 Hz for up to 9 hours. We also performed whole-brain functional imaging in larval zebrafish and multicolor imaging of fast cellular dynamics across entire, gastrulating Drosophila embryos with isotropic, sub-cellular resolution. Compared with conventional (spatially anisotropic) light-sheet microscopy, IsoView microscopy improves spatial resolution at least sevenfold and decreases resolution anisotropy at least threefold. Compared with existing high-resolution light-sheet techniques, such as lattice lightsheet microscopy or diSPIM, IsoView microscopy effectively doubles the penetration depth and provides subsecond temporal resolution for specimens 400-fold larger than could previously be imaged.

Spatio-temporal analysis of Modified Omori law in Bayesian framework

NASA Astrophysics Data System (ADS)

Rezanezhad, V.; Narteau, C.; Shebalin, P.; Zoeller, G.; Holschneider, M.

2017-12-01

This work presents a study of the spatio temporal evolution of the modified Omori parameters in southern California in then time period of 1981-2016. A nearest-neighbor approach is applied for earthquake clustering. This study targets small mainshocks and corresponding big aftershocks ( 2.5 ≤ mmainshocks ≤ 4.5 and 1.8 ≤ maftershocks ≤ 2.8 ). We invert for the spatio temporal behavior of c and p values (especially c) all over the area using a MCMC based maximum likelihood estimator. As parameterizing families we use Voronoi cells with randomly distributed cell centers. Considering that c value represents a physical character like stress change we expect to see a coherent c value pattern over seismologically coacting areas. This correlation of c valus can actually be seen for the San Andreas, San Jacinto and Elsinore faults. Moreover, the depth dependency of c value is studied which shows a linear behavior of log(c) with respect to aftershock's depth within 5 to 15 km depth.

A dense array stimulator to generate arbitrary spatio-temporal tactile stimuli

PubMed Central

Killebrew, Justin H.; Bensmaïa, Sliman J.; Dammann, John F.; Denchev, Peter; Hsiao, Steven S.; Craig, James C.

2007-01-01

The generation and presentation of tactile stimuli presents a unique challenge. Unlike vision and audition, in which standard equipment such as monitors and audio systems can be used for most experiments, tactile stimuli and/or stimulators often have to be tailor-made for a given study. Here, we present a novel tactile stimulator designed to present arbitrary spatio-temporal stimuli to the skin. The stimulator consists of 400 pins, arrayed over a 1 cm2 area, each under independent computer control. The dense array allows for an unprecedented number of stimuli to be presented within an experimental session (e.g., up to 1200 stimuli per minute) and for stimuli to be generated adaptively. The stimulator can be used in a variety of modes and can deliver indented and scanned patterns as well as stimuli defined by mathematical spatio-temporal functions (e.g., drifting sinusoids). We describe the hardware and software of the system, and discuss previous and prospective applications. PMID:17134760

Spatio-temporal dynamics of a fish spawning aggregation and its fishery in the Gulf of California

PubMed Central

Erisman, Brad; Aburto-Oropeza, Octavio; Gonzalez-Abraham, Charlotte; Mascareñas-Osorio, Ismael; Moreno-Báez, Marcia; Hastings, Philip A.

2012-01-01

We engaged in cooperative research with fishers and stakeholders to characterize the fine-scale, spatio-temporal characteristics of spawning behavior in an aggregating marine fish (Cynoscion othonopterus: Sciaenidae) and coincident activities of its commercial fishery in the Upper Gulf of California. Approximately 1.5–1.8 million fish are harvested annually from spawning aggregations of C. othonopterus during 21–25 days of fishing and within an area of 1,149 km2 of a biosphere reserve. Spawning and fishing are synchronized on a semi-lunar cycle, with peaks in both occurring 5 to 2 days before the new and full moon, and fishing intensity and catch are highest at the spawning grounds within a no-take reserve. Results of this study demonstrate the benefits of combining GPS data loggers, fisheries data, biological surveys, and cooperative research with fishers to produce spatio-temporally explicit information relevant to the science and management of fish spawning aggregations and the spatial planning of marine reserves. PMID:22359736

Retrieval of Spatio-temporal Distributions of Particle Parameters from Multiwavelength Lidar Measurements Using the Linear Estimation Technique and Comparison with AERONET

NASA Technical Reports Server (NTRS)

Veselovskii, I.; Whiteman, D. N.; Korenskiy, M.; Kolgotin, A.; Dubovik, O.; Perez-Ramirez, D.; Suvorina, A.

2013-01-01

The results of the application of the linear estimation technique to multiwavelength Raman lidar measurements performed during the summer of 2011 in Greenbelt, MD, USA, are presented. We demonstrate that multiwavelength lidars are capable not only of providing vertical profiles of particle properties but also of revealing the spatio-temporal evolution of aerosol features. The nighttime 3 Beta + 1 alpha lidar measurements on 21 and 22 July were inverted to spatio-temporal distributions of particle microphysical parameters, such as volume, number density, effective radius and the complex refractive index. The particle volume and number density show strong variation during the night, while the effective radius remains approximately constant. The real part of the refractive index demonstrates a slight decreasing tendency in a region of enhanced extinction coefficient. The linear estimation retrievals are stable and provide time series of particle parameters as a function of height at 4 min resolution. AERONET observations are compared with multiwavelength lidar retrievals showing good agreement.

Spatio-temporal modelling of wind speed variations and extremes in the Caribbean and the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Rychlik, Igor; Mao, Wengang

2018-02-01

The wind speed variability in the North Atlantic has been successfully modelled using a spatio-temporal transformed Gaussian field. However, this type of model does not correctly describe the extreme wind speeds attributed to tropical storms and hurricanes. In this study, the transformed Gaussian model is further developed to include the occurrence of severe storms. In this new model, random components are added to the transformed Gaussian field to model rare events with extreme wind speeds. The resulting random field is locally stationary and homogeneous. The localized dependence structure is described by time- and space-dependent parameters. The parameters have a natural physical interpretation. To exemplify its application, the model is fitted to the ECMWF ERA-Interim reanalysis data set. The model is applied to compute long-term wind speed distributions and return values, e.g., 100- or 1000-year extreme wind speeds, and to simulate random wind speed time series at a fixed location or spatio-temporal wind fields around that location.

Evaluation of spatio-temporal Bayesian models for the spread of infectious diseases in oil palm.

PubMed

Denis, Marie; Cochard, Benoît; Syahputra, Indra; de Franqueville, Hubert; Tisné, Sébastien

2018-02-01

In the field of epidemiology, studies are often focused on mapping diseases in relation to time and space. Hierarchical modeling is a common flexible and effective tool for modeling problems related to disease spread. In the context of oil palm plantations infected by the fungal pathogen Ganoderma boninense, we propose and compare two spatio-temporal hierarchical Bayesian models addressing the lack of information on propagation modes and transmission vectors. We investigate two alternative process models to study the unobserved mechanism driving the infection process. The models help gain insight into the spatio-temporal dynamic of the infection by identifying a genetic component in the disease spread and by highlighting a spatial component acting at the end of the experiment. In this challenging context, we propose models that provide assumptions on the unobserved mechanism driving the infection process while making short-term predictions using ready-to-use software. Copyright © 2018 Elsevier Ltd. All rights reserved.

Exploring the Spatio-Temporal Dynamics of Reservoir Hosts, Vectors, and Human Hosts of West Nile Virus: A Review of the Recent Literature

PubMed Central

Ozdenerol, Esra; Taff, Gregory N.; Akkus, Cem

2013-01-01

Over the last two decades West Nile Virus (WNV) has been responsible for significant disease outbreaks in humans and animals in many parts of the World. Its extremely rapid global diffusion argues for a better understanding of its geographic extent. The purpose of this inquiry was to explore spatio-temporal patterns of WNV using geospatial technologies to study populations of the reservoir hosts, vectors, and human hosts, in addition to the spatio-temporal interactions among these populations. Review of the recent literature on spatial WNV disease risk modeling led to the conclusion that numerous environmental factors might be critical for its dissemination. New Geographic Information Systems (GIS)-based studies are monitoring occurrence at the macro-level, and helping pinpoint areas of occurrence at the micro-level, where geographically-targeted, species-specific control measures are sometimes taken and more sophisticated methods of surveillance have been used. PMID:24284356

The evolution of meaning: spatio-temporal dynamics of visual object recognition.

PubMed

Clarke, Alex; Taylor, Kirsten I; Tyler, Lorraine K

2011-08-01

Research on the spatio-temporal dynamics of visual object recognition suggests a recurrent, interactive model whereby an initial feedforward sweep through the ventral stream to prefrontal cortex is followed by recurrent interactions. However, critical questions remain regarding the factors that mediate the degree of recurrent interactions necessary for meaningful object recognition. The novel prediction we test here is that recurrent interactivity is driven by increasing semantic integration demands as defined by the complexity of semantic information required by the task and driven by the stimuli. To test this prediction, we recorded magnetoencephalography data while participants named living and nonliving objects during two naming tasks. We found that the spatio-temporal dynamics of neural activity were modulated by the level of semantic integration required. Specifically, source reconstructed time courses and phase synchronization measures showed increased recurrent interactions as a function of semantic integration demands. These findings demonstrate that the cortical dynamics of object processing are modulated by the complexity of semantic information required from the visual input.

Violence in the prehistoric period of Japan: the spatio-temporal pattern of skeletal evidence for violence in the Jomon period.

PubMed

Nakao, Hisashi; Tamura, Kohei; Arimatsu, Yui; Nakagawa, Tomomi; Matsumoto, Naoko; Matsugi, Takehiko

2016-03-01

Whether man is predisposed to lethal violence, ranging from homicide to warfare, and how that may have impacted human evolution, are among the most controversial topics of debate on human evolution. Although recent studies on the evolution of warfare have been based on various archaeological and ethnographic data, they have reported mixed results: it is unclear whether or not warfare among prehistoric hunter-gatherers was common enough to be a component of human nature and a selective pressure for the evolution of human behaviour. This paper reports the mortality attributable to violence, and the spatio-temporal pattern of violence thus shown among ancient hunter-gatherers using skeletal evidence in prehistoric Japan (the Jomon period: 13 000 cal BC-800 cal BC). Our results suggest that the mortality due to violence was low and spatio-temporally highly restricted in the Jomon period, which implies that violence including warfare in prehistoric Japan was not common. © 2016 The Author(s).

Violence in the prehistoric period of Japan: the spatio-temporal pattern of skeletal evidence for violence in the Jomon period

PubMed Central

Nakao, Hisashi; Tamura, Kohei; Arimatsu, Yui; Nakagawa, Tomomi; Matsumoto, Naoko; Matsugi, Takehiko

2016-01-01

Whether man is predisposed to lethal violence, ranging from homicide to warfare, and how that may have impacted human evolution, are among the most controversial topics of debate on human evolution. Although recent studies on the evolution of warfare have been based on various archaeological and ethnographic data, they have reported mixed results: it is unclear whether or not warfare among prehistoric hunter–gatherers was common enough to be a component of human nature and a selective pressure for the evolution of human behaviour. This paper reports the mortality attributable to violence, and the spatio-temporal pattern of violence thus shown among ancient hunter–gatherers using skeletal evidence in prehistoric Japan (the Jomon period: 13 000 cal BC–800 cal BC). Our results suggest that the mortality due to violence was low and spatio-temporally highly restricted in the Jomon period, which implies that violence including warfare in prehistoric Japan was not common. PMID:27029838

Spontaneous formation of spiral-like patterns with distinct periodic physical properties by confined electrodeposition of Co-In disks

NASA Astrophysics Data System (ADS)

Golvano-Escobal, Irati; Gonzalez-Rosillo, Juan Carlos; Domingo, Neus; Illa, Xavi; López-Barberá, José Francisco; Fornell, Jordina; Solsona, Pau; Aballe, Lucia; Foerster, Michael; Suriñach, Santiago; Baró, Maria Dolors; Puig, Teresa; Pané, Salvador; Nogués, Josep; Pellicer, Eva; Sort, Jordi

2016-07-01

Spatio-temporal patterns are ubiquitous in different areas of materials science and biological systems. However, typically the motifs in these types of systems present a random distribution with many possible different structures. Herein, we demonstrate that controlled spatio-temporal patterns, with reproducible spiral-like shapes, can be obtained by electrodeposition of Co-In alloys inside a confined circular geometry (i.e., in disks that are commensurate with the typical size of the spatio-temporal features). These patterns are mainly of compositional nature, i.e., with virtually no topographic features. Interestingly, the local changes in composition lead to a periodic modulation of the physical (electric, magnetic and mechanical) properties. Namely, the Co-rich areas show higher saturation magnetization and electrical conductivity and are mechanically harder than the In-rich ones. Thus, this work reveals that confined electrodeposition of this binary system constitutes an effective procedure to attain template-free magnetic, electric and mechanical surface patterning with specific and reproducible shapes.

Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice.

PubMed

Centeno, Tonatiuh Pena; Shomroni, Orr; Hennion, Magali; Halder, Rashi; Vidal, Ramon; Rahman, Raza-Ur; Bonn, Stefan

2016-10-11

Recent evidence suggests that the formation and maintenance of memory requires epigenetic changes. In an effort to understand the spatio-temporal extent of learning and memory-related epigenetic changes we have charted genome-wide histone and DNA methylation profiles, in two different brain regions, two cell types, and three time-points, before and after learning. In this data descriptor we provide detailed information on data generation, give insights into the rationale of experiments, highlight necessary steps to assess data quality, offer guidelines for future use of the data and supply ready-to-use code to replicate the analysis results. The data provides a blueprint of the gene regulatory network underlying short- and long-term memory formation and maintenance. This 'healthy' gene regulatory network of learning can now be compared to changes in neurological or psychiatric diseases, providing mechanistic insights into brain disorders and highlighting potential therapeutic avenues.

Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture.

PubMed

Gholami Doborjeh, Zohreh; Kasabov, Nikola; Gholami Doborjeh, Maryam; Sumich, Alexander

2018-06-11

Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network (SNN) approach that reveals the complexity of peri-perceptual processes of familiarity. The method is applied to data from 20 participants viewing familiar and unfamiliar logos. The results support the potential of SNN models as novel tools in the exploration of peri-perceptual mechanisms that respond differentially to familiar and unfamiliar stimuli. Specifically, the activation pattern of the time-locked response identified by the proposed SNN model at approximately 200 milliseconds post-stimulus suggests greater connectivity and more widespread dynamic spatio-temporal patterns for familiar than unfamiliar logos. The proposed SNN approach can be applied to study other peri-perceptual or perceptual brain processes in cognitive and computational neuroscience.

Geometric Metamorphosis

PubMed Central

Niethammer, Marc; Hart, Gabriel L.; Pace, Danielle F.; Vespa, Paul M.; Irimia, Andrei; Van Horn, John D.; Aylward, Stephen R.

2013-01-01

Standard image registration methods do not account for changes in image appearance. Hence, metamorphosis approaches have been developed which jointly estimate a space deformation and a change in image appearance to construct a spatio-temporal trajectory smoothly transforming a source to a target image. For standard metamorphosis, geometric changes are not explicitly modeled. We propose a geometric metamorphosis formulation, which explains changes in image appearance by a global deformation, a deformation of a geometric model, and an image composition model. This work is motivated by the clinical challenge of predicting the long-term effects of traumatic brain injuries based on time-series images. This work is also applicable to the quantification of tumor progression (e.g., estimating its infiltrating and displacing components) and predicting chronic blood perfusion changes after stroke. We demonstrate the utility of the method using simulated data as well as scans from a clinical traumatic brain injury patient. PMID:21995083

Noise covariance incorporated MEG-MUSIC algorithm: a method for multiple-dipole estimation tolerant of the influence of background brain activity.

PubMed

Sekihara, K; Poeppel, D; Marantz, A; Koizumi, H; Miyashita, Y

1997-09-01

This paper proposes a method of localizing multiple current dipoles from spatio-temporal biomagnetic data. The method is based on the multiple signal classification (MUSIC) algorithm and is tolerant of the influence of background brain activity. In this method, the noise covariance matrix is estimated using a portion of the data that contains noise, but does not contain any signal information. Then, a modified noise subspace projector is formed using the generalized eigenvectors of the noise and measured-data covariance matrices. The MUSIC localizer is calculated using this noise subspace projector and the noise covariance matrix. The results from a computer simulation have verified the effectiveness of the method. The method was then applied to source estimation for auditory-evoked fields elicited by syllable speech sounds. The results strongly suggest the method's effectiveness in removing the influence of background activity.

Spatio-temporal Analysis of the Genetic Diversity of Arctic Rabies Viruses and Their Reservoir Hosts in Greenland

PubMed Central

Hanke, Dennis; Freuling, Conrad M.; Fischer, Susanne; Hueffer, Karsten; Hundertmark, Kris; Nadin-Davis, Susan; Marston, Denise; Fooks, Anthony R.; Bøtner, Anette; Mettenleiter, Thomas C.; Beer, Martin; Rasmussen, Thomas B.; Müller, Thomas F.; Höper, Dirk

2016-01-01

There has been limited knowledge on spatio-temporal epidemiology of zoonotic arctic fox rabies among countries bordering the Arctic, in particular Greenland. Previous molecular epidemiological studies have suggested the occurrence of one particular arctic rabies virus (RABV) lineage (arctic-3), but have been limited by a low number of available samples preventing in-depth high resolution phylogenetic analysis of RABVs at that time. However, an improved knowledge of the evolution, at a molecular level, of the circulating RABVs and a better understanding of the historical perspective of the disease in Greenland is necessary for better direct control measures on the island. These issues have been addressed by investigating the spatio-temporal genetic diversity of arctic RABVs and their reservoir host, the arctic fox, in Greenland using both full and partial genome sequences. Using a unique set of 79 arctic RABV full genome sequences from Greenland, Canada, USA (Alaska) and Russia obtained between 1977 and 2014, a description of the historic context in relation to the genetic diversity of currently circulating RABV in Greenland and neighboring Canadian Northern territories has been provided. The phylogenetic analysis confirmed delineation into four major arctic RABV lineages (arctic 1–4) with viruses from Greenland exclusively grouping into the circumpolar arctic-3 lineage. High resolution analysis enabled distinction of seven geographically distinct subclades (3.I – 3.VII) with two subclades containing viruses from both Greenland and Canada. By combining analysis of full length RABV genome sequences and host derived sequences encoding mitochondrial proteins obtained simultaneously from brain tissues of 49 arctic foxes, the interaction of viruses and their hosts was explored in detail. Such an approach can serve as a blueprint for analysis of infectious disease dynamics and virus-host interdependencies. The results showed a fine-scale spatial population structure in Greenland arctic foxes based on mitochondrial sequences, but provided no evidence for independent isolated evolutionary development of RABV in different arctic fox lineages. These data are invaluable to support future initiatives for arctic fox rabies control and elimination in Greenland. PMID:27459154

Spatio-temporal Analysis of the Genetic Diversity of Arctic Rabies Viruses and Their Reservoir Hosts in Greenland.

PubMed

Hanke, Dennis; Freuling, Conrad M; Fischer, Susanne; Hueffer, Karsten; Hundertmark, Kris; Nadin-Davis, Susan; Marston, Denise; Fooks, Anthony R; Bøtner, Anette; Mettenleiter, Thomas C; Beer, Martin; Rasmussen, Thomas B; Müller, Thomas F; Höper, Dirk

2016-07-01

There has been limited knowledge on spatio-temporal epidemiology of zoonotic arctic fox rabies among countries bordering the Arctic, in particular Greenland. Previous molecular epidemiological studies have suggested the occurrence of one particular arctic rabies virus (RABV) lineage (arctic-3), but have been limited by a low number of available samples preventing in-depth high resolution phylogenetic analysis of RABVs at that time. However, an improved knowledge of the evolution, at a molecular level, of the circulating RABVs and a better understanding of the historical perspective of the disease in Greenland is necessary for better direct control measures on the island. These issues have been addressed by investigating the spatio-temporal genetic diversity of arctic RABVs and their reservoir host, the arctic fox, in Greenland using both full and partial genome sequences. Using a unique set of 79 arctic RABV full genome sequences from Greenland, Canada, USA (Alaska) and Russia obtained between 1977 and 2014, a description of the historic context in relation to the genetic diversity of currently circulating RABV in Greenland and neighboring Canadian Northern territories has been provided. The phylogenetic analysis confirmed delineation into four major arctic RABV lineages (arctic 1-4) with viruses from Greenland exclusively grouping into the circumpolar arctic-3 lineage. High resolution analysis enabled distinction of seven geographically distinct subclades (3.I - 3.VII) with two subclades containing viruses from both Greenland and Canada. By combining analysis of full length RABV genome sequences and host derived sequences encoding mitochondrial proteins obtained simultaneously from brain tissues of 49 arctic foxes, the interaction of viruses and their hosts was explored in detail. Such an approach can serve as a blueprint for analysis of infectious disease dynamics and virus-host interdependencies. The results showed a fine-scale spatial population structure in Greenland arctic foxes based on mitochondrial sequences, but provided no evidence for independent isolated evolutionary development of RABV in different arctic fox lineages. These data are invaluable to support future initiatives for arctic fox rabies control and elimination in Greenland.

Development of functional in vivo imaging of cerebral lenticulostriate artery using novel synchrotron radiation angiography

NASA Astrophysics Data System (ADS)

Lin, Xiaojie; Miao, Peng; Mu, Zhihao; Jiang, Zhen; Lu, Yifan; Guan, Yongjing; Chen, Xiaoyan; Xiao, Tiqiao; Wang, Yongting; Yang, Guo-Yuan

2015-02-01

The lenticulostriate artery plays a vital role in the onset and development of cerebral ischemia. However, current imaging techniques cannot assess the in vivo functioning of small arteries such as the lenticulostriate artery in the brain of rats. Here, we report a novel method to achieve a high resolution multi-functional imaging of the cerebrovascular system using synchrotron radiation angiography, which is based on spatio-temporal analysis of contrast density in the arterial cross section. This method provides a unique tool for studying the sub-cortical vascular elasticity after cerebral ischemia in rats. Using this technique, we demonstrated that the vascular elasticity of the lenticulostriate artery decreased from day 1 to day 7 after transient middle cerebral artery occlusion in rats and recovered from day 7 to day 28 compared to the controls (p < 0.001), which paralleled with brain edema formation and inversely correlated with blood flow velocity (p < 0.05). Our results demonstrated that the change of vascular elasticity was related to the levels of brain edema and the velocity of focal blood flow, suggesting that reducing brain edema is important for the improvement of the function of the lenticulostriate artery in the ischemic brain.

Characteristics, processes, and causes of the spatio-temporal variabilities of the East Asian monsoon system

NASA Astrophysics Data System (ADS)

Huang, Ronghui; Chen, Jilong; Wang, Lin; Lin, Zhongda

2012-09-01

Recent advances in the study of the characteristics, processes, and causes of spatio-temporal variabilities of the East Asian monsoon (EAM) system are reviewed in this paper. The understanding of the EAM system has improved in many aspects: the basic characteristics of horizontal and vertical structures, the annual cycle of the East Asian summer monsoon (EASM) system and the East Asian winter monsoon (EAWM) system, the characteristics of the spatio-temporal variabilities of the EASM system and the EAWM system, and especially the multiple modes of the EAM system and their spatio-temporal variabilities. Some new results have also been achieved in understanding the atmosphere-ocean interaction and atmosphere-land interaction processes that affect the variability of the EAM system. Based on recent studies, the EAM system can be seen as more than a circulation system, it can be viewed as an atmosphere-ocean-land coupled system, namely, the EAM climate system. In addition, further progress has been made in diagnosing the internal physical mechanisms of EAM climate system variability, especially regarding the characteristics and properties of the East Asia-Pacific (EAP) teleconnection over East Asia and the North Pacific, the "Silk Road" teleconnection along the westerly jet stream in the upper troposphere over the Asian continent, and the dynamical effects of quasi-stationary planetary wave activity on EAM system variability. At the end of the paper, some scientific problems regarding understanding the EAM system variability are proposed for further study.

Spatio-Temporal Regression Based Clustering of Precipitation Extremes in a Presence of Systematically Missing Covariates

NASA Astrophysics Data System (ADS)

Kaiser, Olga; Martius, Olivia; Horenko, Illia

2017-04-01

Regression based Generalized Pareto Distribution (GPD) models are often used to describe the dynamics of hydrological threshold excesses relying on the explicit availability of all of the relevant covariates. But, in real application the complete set of relevant covariates might be not available. In this context, it was shown that under weak assumptions the influence coming from systematically missing covariates can be reflected by a nonstationary and nonhomogenous dynamics. We present a data-driven, semiparametric and an adaptive approach for spatio-temporal regression based clustering of threshold excesses in a presence of systematically missing covariates. The nonstationary and nonhomogenous behavior of threshold excesses is describes by a set of local stationary GPD models, where the parameters are expressed as regression models, and a non-parametric spatio-temporal hidden switching process. Exploiting nonparametric Finite Element time-series analysis Methodology (FEM) with Bounded Variation of the model parameters (BV) for resolving the spatio-temporal switching process, the approach goes beyond strong a priori assumptions made is standard latent class models like Mixture Models and Hidden Markov Models. Additionally, the presented FEM-BV-GPD provides a pragmatic description of the corresponding spatial dependence structure by grouping together all locations that exhibit similar behavior of the switching process. The performance of the framework is demonstrated on daily accumulated precipitation series over 17 different locations in Switzerland from 1981 till 2013 - showing that the introduced approach allows for a better description of the historical data.

Gait functional assessment: Spatio-temporal analysis and classification of barefoot plantar pressure in a group of 11-12-year-old children.

PubMed

Latour, Ewa; Latour, Marek; Arlet, Jarosław; Adach, Zdzisław; Bohatyrewicz, Andrzej

2011-07-01

Analysis of pedobarographical data requires geometric identification of specific anatomical areas extracted from recorded plantar pressures. This approach has led to ambiguity in measurements that may underlie the inconsistency of conclusions reported in pedobarographical studies. The goal of this study was to design a new analysis method less susceptible to the projection accuracy of anthropometric points and distance estimation, based on rarely used spatio-temporal indices. Six pedobarographic records per person (three per foot) from a group of 60 children aged 11-12 years were obtained and analyzed. The basis of the analysis was a mutual relationship between two spatio-temporal indices created by excursion of the peak pressure point and the center-of-pressure point on the dynamic pedobarogram. Classification of weight-shift patterns was elaborated and performed, and their frequencies of occurrence were assessed. This new method allows an assessment of body weight shift through the plantar pressure surface based on distribution analysis of spatio-temporal indices not affected by the shape of this surface. Analysis of the distribution of the created index confirmed the existence of typical ways of weight shifting through the plantar surface of the foot during gait, as well as large variability of the intrasubject occurrence. This method may serve as the basis for interpretation of foot functional features and may extend the clinical usefulness of pedobarography. Copyright © 2011 Elsevier B.V. All rights reserved.

Self-organized mechano-chemical dynamics in amoeboid locomotion of Physarum fragments

NASA Astrophysics Data System (ADS)

Zhang, Shun; Guy, Robert D.; Lasheras, Juan C.; del Álamo, Juan C.

2017-05-01

The aim of this work is to quantify the spatio-temporal dynamics of flow-driven amoeboid locomotion in small (∼100 μm) fragments of the true slime mold Physarum polycephalum. In this model organism, cellular contraction drives intracellular flows, and these flows transport the chemical signals that regulate contraction in the first place. As a consequence of these non-linear interactions, a diversity of migratory behaviors can be observed in migrating Physarum fragments. To study these dynamics, we measure the spatio-temporal distributions of the velocities of the endoplasm and ectoplasm of each migrating fragment, the traction stresses it generates on the substratum, and the concentration of free intracellular calcium. Using these unprecedented experimental data, we classify migrating Physarum fragments according to their dynamics, finding that they often exhibit spontaneously coordinated waves of flow, contractility and chemical signaling. We show that Physarum fragments exhibiting symmetric spatio-temporal patterns of endoplasmic flow migrate significantly slower than fragments with asymmetric patterns. In addition, our joint measurements of ectoplasm velocity and traction stress at the substratum suggest that forward motion of the ectoplasm is enabled by a succession of stick-slip transitions, which we conjecture are also organized in the form of waves. Combining our experiments with a simplified convection-diffusion model, we show that the convective transport of calcium ions may be key for establishing and maintaining the spatio-temporal patterns of calcium concentration that regulate the generation of contractile forces.

Spatio-temporal Granger causality: a new framework

PubMed Central

Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A.; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng

2015-01-01

That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924

Mushroom biomass and diversity are driven by different spatio-temporal scales along Mediterranean elevation gradients

NASA Astrophysics Data System (ADS)

Alday, Josu G.; Martínez de Aragón, Juan; de-Miguel, Sergio; Bonet, José Antonio

2017-04-01

Mushrooms are important non-wood-forest-products in many Mediterranean ecosystems, being highly vulnerable to climate change. However, the ecological scales of variation of mushroom productivity and diversity, and climate dependence has been usually overlooked due to a lack of available data. We determined the spatio-temporal variability of epigeous sporocarps and the climatic factors driving their fruiting to plan future sustainable management of wild mushrooms production. We collected fruiting bodies in Pinus sylvestris stands along an elevation gradient for 8 consecutive years. Overall, sporocarp biomass was mainly dependent on inter-annual variations, whereas richness was more spatial-scale dependent. Elevation was not significant, but there were clear elevational differences in biomass and richness patterns between ectomycorrhizal and saprotrophic guilds. The main driver of variation was late-summer-early-autumn precipitation. Thus, different scale processes (inter-annual vs. spatial-scale) drive sporocarp biomass and diversity patterns; temporal effects for biomass and ectomycorrhizal fungi vs. spatial scale for diversity and saprotrophic fungi. The significant role of precipitation across fungal guilds and spatio-temporal scales indicates that it is a limiting resource controlling sporocarp production and diversity in Mediterranean regions. The high spatial and temporal variability of mushrooms emphasize the need for long-term datasets of multiple spatial points to effectively characterize fungal fruiting patterns.

Dorsal and ventral stream contributions to form-from-motion perception in a patient with form-from motion deficit: a case report.

PubMed

Mercier, Manuel R; Schwartz, Sophie; Spinelli, Laurent; Michel, Christoph M; Blanke, Olaf

2017-03-01

The main model of visual processing in primates proposes an anatomo-functional distinction between the dorsal stream, specialized in spatio-temporal information, and the ventral stream, processing essentially form information. However, these two pathways also communicate to share much visual information. These dorso-ventral interactions have been studied using form-from-motion (FfM) stimuli, revealing that FfM perception first activates dorsal regions (e.g., MT+/V5), followed by successive activations of ventral regions (e.g., LOC). However, relatively little is known about the implications of focal brain damage of visual areas on these dorso-ventral interactions. In the present case report, we investigated the dynamics of dorsal and ventral activations related to FfM perception (using topographical ERP analysis and electrical source imaging) in a patient suffering from a deficit in FfM perception due to right extrastriate brain damage in the ventral stream. Despite the patient's FfM impairment, both successful (observed for the highest level of FfM signal) and absent/failed FfM perception evoked the same temporal sequence of three processing states observed previously in healthy subjects. During the first period, brain source localization revealed cortical activations along the dorsal stream, currently associated with preserved elementary motion processing. During the latter two periods, the patterns of activity differed from normal subjects: activations were observed in the ventral stream (as reported for normal subjects), but also in the dorsal pathway, with the strongest and most sustained activity localized in the parieto-occipital regions. On the other hand, absent/failed FfM perception was characterized by weaker brain activity, restricted to the more lateral regions. This study shows that in the present case report, successful FfM perception, while following the same temporal sequence of processing steps as in normal subjects, evoked different patterns of brain activity. By revealing a brain circuit involving the most rostral part of the dorsal pathway, this study provides further support for neuro-imaging studies and brain lesion investigations that have suggested the existence of different brain circuits associated with different profiles of interaction between the dorsal and the ventral streams.

Spatio-Temporal EEG Models for Brain Interfaces

PubMed Central

Gonzalez-Navarro, P.; Moghadamfalahi, M.; Akcakaya, M.; Erdogmus, D.

2016-01-01

Multichannel electroencephalography (EEG) is widely used in non-invasive brain computer interfaces (BCIs) for user intent inference. EEG can be assumed to be a Gaussian process with unknown mean and autocovariance, and the estimation of parameters is required for BCI inference. However, the relatively high dimensionality of the EEG feature vectors with respect to the number of labeled observations lead to rank deficient covariance matrix estimates. In this manuscript, to overcome ill-conditioned covariance estimation, we propose a structure for the covariance matrices of the multichannel EEG signals. Specifically, we assume that these covariances can be modeled as a Kronecker product of temporal and spatial covariances. Our results over the experimental data collected from the users of a letter-by-letter typing BCI show that with less number of parameter estimations, the system can achieve higher classification accuracies compared to a method that uses full unstructured covariance estimation. Moreover, in order to illustrate that the proposed Kronecker product structure could enable shortening the BCI calibration data collection sessions, using Cramer-Rao bound analysis on simulated data, we demonstrate that a model with structured covariance matrices will achieve the same estimation error as a model with no covariance structure using fewer labeled EEG observations. PMID:27713590

Plasticity of brain wave network interactions and evolution across physiologic states

PubMed Central

Liu, Kang K. L.; Bartsch, Ronny P.; Lin, Aijing; Mantegna, Rosario N.; Ivanov, Plamen Ch.

2015-01-01

Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability (TDS) to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very different degree of network connectivity and link strength, while at the same time each frequency-specific network is characterized by a different signature pattern of sleep-stage stratification, reflecting a remarkable flexibility in response to change in physiologic state. These new aspects of neural plasticity demonstrate that in addition to dominant brain waves, the network of brain wave interactions is a previously unrecognized hallmark of physiologic state and function. PMID:26578891

Whole-head functional brain imaging of neonates at cot-side using time-resolved diffuse optical tomography

NASA Astrophysics Data System (ADS)

Dempsey, Laura A.; Cooper, Robert J.; Powell, Samuel; Edwards, Andrea; Lee, Chuen-Wai; Brigadoi, Sabrina; Everdell, Nick; Arridge, Simon; Gibson, Adam P.; Austin, Topun; Hebden, Jeremy C.

2015-07-01

We present a method for acquiring whole-head images of changes in blood volume and oxygenation from the infant brain at cot-side using time-resolved diffuse optical tomography (TR-DOT). At UCL, we have built a portable TR-DOT device, known as MONSTIR II, which is capable of obtaining a whole-head (1024 channels) image sequence in 75 seconds. Datatypes extracted from the temporal point spread functions acquired by the system allow us to determine changes in absorption and reduced scattering coefficients within the interrogated tissue. This information can then be used to define clinically relevant measures, such as oxygen saturation, as well as to reconstruct images of relative changes in tissue chromophore concentration, notably those of oxy- and deoxyhaemoglobin. Additionally, the effective temporal resolution of our system is improved with spatio-temporal regularisation implemented through a Kalman filtering approach, allowing us to image transient haemodynamic changes. By using this filtering technique with intensity and mean time-of-flight datatypes, we have reconstructed images of changes in absorption and reduced scattering coefficients in a dynamic 2D phantom. These results demonstrate that MONSTIR II is capable of resolving slow changes in tissue optical properties within volumes that are comparable to the preterm head. Following this verification study, we are progressing to imaging a 3D dynamic phantom as well as the neonatal brain at cot-side. Our current study involves scanning healthy babies to demonstrate the quality of recordings we are able to achieve in this challenging patient population, with the eventual goal of imaging functional activation and seizures.

Improving exposure assessment in environmental epidemiology: Application of spatio-temporal visualization tools

NASA Astrophysics Data System (ADS)

Meliker, Jaymie R.; Slotnick, Melissa J.; Avruskin, Gillian A.; Kaufmann, Andrew; Jacquez, Geoffrey M.; Nriagu, Jerome O.

2005-05-01

A thorough assessment of human exposure to environmental agents should incorporate mobility patterns and temporal changes in human behaviors and concentrations of contaminants; yet the temporal dimension is often under-emphasized in exposure assessment endeavors, due in part to insufficient tools for visualizing and examining temporal datasets. Spatio-temporal visualization tools are valuable for integrating a temporal component, thus allowing for examination of continuous exposure histories in environmental epidemiologic investigations. An application of these tools to a bladder cancer case-control study in Michigan illustrates continuous exposure life-lines and maps that display smooth, continuous changes over time. Preliminary results suggest increased risk of bladder cancer from combined exposure to arsenic in drinking water (>25 μg/day) and heavy smoking (>30 cigarettes/day) in the 1970s and 1980s, and a possible cancer cluster around automotive, paint, and organic chemical industries in the early 1970s. These tools have broad application for examining spatially- and temporally-specific relationships between exposures to environmental risk factors and disease.

Probing the Spatio-Temporal Characteristics of Temporal Aliasing Errors and their Impact on Satellite Gravity Retrievals

NASA Astrophysics Data System (ADS)

Wiese, D. N.; McCullough, C. M.

2017-12-01

Studies have shown that both single pair low-low satellite-to-satellite tracking (LL-SST) and dual-pair LL-SST hypothetical future satellite gravimetry missions utilizing improved onboard measurement systems relative to the Gravity Recovery and Climate Experiment (GRACE) will be limited by temporal aliasing errors; that is, the error introduced through deficiencies in models of high frequency mass variations required for the data processing. Here, we probe the spatio-temporal characteristics of temporal aliasing errors to understand their impact on satellite gravity retrievals using high fidelity numerical simulations. We find that while aliasing errors are dominant at long wavelengths and multi-day timescales, improving knowledge of high frequency mass variations at these resolutions translates into only modest improvements (i.e. spatial resolution/accuracy) in the ability to measure temporal gravity variations at monthly timescales. This result highlights the reliance on accurate models of high frequency mass variations for gravity processing, and the difficult nature of reducing temporal aliasing errors and their impact on satellite gravity retrievals.

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

PubMed Central

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430

Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements

PubMed Central

Riehle, Alexa; Wirtssohn, Sarah; Grün, Sonja; Brochier, Thomas

2013-01-01

Grasping an object involves shaping the hand and fingers in relation to the object’s physical properties. Following object contact, it also requires a fine adjustment of grasp forces for secure manipulation. Earlier studies suggest that the control of hand shaping and grasp force involve partially segregated motor cortical networks. However, it is still unclear how information originating from these networks is processed and integrated. We addressed this issue by analyzing massively parallel signals from population measures (local field potentials, LFPs) and single neuron spiking activities recorded simultaneously during a delayed reach-to-grasp task, by using a 100-electrode array chronically implanted in monkey motor cortex. Motor cortical LFPs exhibit a large multi-component movement-related potential (MRP) around movement onset. Here, we show that the peak amplitude of each MRP component and its latency with respect to movement onset vary along the cortical surface covered by the array. Using a comparative mapping approach, we suggest that the spatio-temporal structure of the MRP reflects the complex physical properties of the reach-to-grasp movement. In addition, we explored how the spatio-temporal structure of the MRP relates to two other measures of neuronal activity: the temporal profile of single neuron spiking activity at each electrode site and the somatosensory receptive field properties of single neuron activities. We observe that the spatial representations of LFP and spiking activities overlap extensively and relate to the spatial distribution of proximal and distal representations of the upper limb. Altogether, these data show that, in motor cortex, a precise spatio-temporal pattern of activation is involved for the control of reach-to-grasp movements and provide some new insight about the functional organization of motor cortex during reaching and object manipulation. PMID:23543888

Spatio-Temporal Variation in Landscape Composition May Speed Resistance Evolution of Pests to Bt Crops.

PubMed

Ives, Anthony R; Paull, Cate; Hulthen, Andrew; Downes, Sharon; Andow, David A; Haygood, Ralph; Zalucki, Myron P; Schellhorn, Nancy A

2017-01-01

Transgenic crops that express insecticide genes from Bacillus thuringiensis (Bt) are used worldwide against moth and beetle pests. Because these engineered plants can kill over 95% of susceptible larvae, they can rapidly select for resistance. Here, we use a model for a pyramid two-toxin Bt crop to explore the consequences of spatio-temporal variation in the area of Bt crop and non-Bt refuge habitat. We show that variability over time in the proportion of suitable non-Bt breeding habitat, Q, or in the total area of Bt and suitable non-Bt habitat, K, can increase the overall rate of resistance evolution by causing short-term surges of intense selection. These surges can be exacerbated when temporal variation in Q and/or K cause high larval densities in refuges that increase density-dependent mortality; this will give resistant larvae in Bt fields a relative advantage over susceptible larvae that largely depend on refuges. We address the effects of spatio-temporal variation in a management setting for two bollworm pests of cotton, Helicoverpa armigera and H. punctigera, and field data on landscape crop distributions from Australia. Even a small proportion of Bt fields available to egg-laying females when refuges are sparse may result in high exposure to Bt for just a single generation per year and cause a surge in selection. Therefore, rapid resistance evolution can occur when Bt crops are rare rather than common in the landscape. These results highlight the need to understand spatio-temporal fluctuations in the landscape composition of Bt crops and non-Bt habitats in order to design effective resistance management strategies.

Spatio-temporal Change Patterns of Tropical Forests from 2000 to 2014 Using MOD09A1 Dataset

NASA Astrophysics Data System (ADS)

Qin, Y.; Xiao, X.; Dong, J.

2016-12-01

Large-scale deforestation and forest degradation in the tropical region have resulted in extensive carbon emissions and biodiversity loss. However, restricted by the availability of good-quality observations, large uncertainty exists in mapping the spatial distribution of forests and their spatio-temporal changes. In this study, we proposed a pixel- and phenology-based algorithm to identify and map annual tropical forests from 2000 to 2014, using the 8-day, 500-m MOD09A1 (v005) product, under the support of Google cloud computing (Google Earth Engine). A temporal filter was applied to reduce the random noises and to identify the spatio-temporal changes of forests. We then built up a confusion matrix and assessed the accuracy of the annual forest maps based on the ground reference interpreted from high spatial resolution images in Google Earth. The resultant forest maps showed the consistent forest/non-forest, forest loss, and forest gain in the pan-tropical zone during 2000 - 2014. The proposed algorithm showed the potential for tropical forest mapping and the resultant forest maps are important for the estimation of carbon emission and biodiversity loss.

Spatio-temporal features for tracking and quadruped/biped discrimination

NASA Astrophysics Data System (ADS)

Rickman, Rick; Copsey, Keith; Bamber, David C.; Page, Scott F.

2012-05-01

Techniques such as SIFT and SURF facilitate efficient and robust image processing operations through the use of sparse and compact spatial feature descriptors and show much potential for defence and security applications. This paper considers the extension of such techniques to include information from the temporal domain, to improve utility in applications involving moving imagery within video data. In particular, the paper demonstrates how spatio-temporal descriptors can be used very effectively as the basis of a target tracking system and as target discriminators which can distinguish between bipeds and quadrupeds. Results using sequences of video imagery of walking humans and dogs are presented, and the relative merits of the approach are discussed.

Prospects for Electron Imaging with Ultrafast Time Resolution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, M R; Reed, B W; Torralva, B R

2007-01-26

Many pivotal aspects of material science, biomechanics, and chemistry would benefit from nanometer imaging with ultrafast time resolution. Here we demonstrate the feasibility of short-pulse electron imaging with t10 nanometer/10 picosecond spatio-temporal resolution, sufficient to characterize phenomena that propagate at the speed of sound in materials (1-10 kilometer/second) without smearing. We outline resolution-degrading effects that occur at high current density followed by strategies to mitigate these effects. Finally, we present a model electron imaging system that achieves 10 nanometer/10 picosecond spatio-temporal resolution.

Storyline Visualizations of Eye Tracking of Movie Viewing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balint, John T.; Arendt, Dustin L.; Blaha, Leslie M.

Storyline visualizations offer an approach that promises to capture the spatio-temporal characteristics of individual observers and simultaneously illustrate emerging group behaviors. We develop a visual analytics approach to parsing, aligning, and clustering fixation sequences from eye tracking data. Visualization of the results captures the similarities and differences across a group of observers performing a common task. We apply our storyline approach to visualize gaze patterns of people watching dynamic movie clips. Storylines mitigate some of the shortcomings of existent spatio-temporal visualization techniques and, importantly, continue to highlight individual observer behavioral dynamics.

Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Kong, Shish-Jeng; Chang, Chih-Hua

2018-03-01

The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space-time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis.

The Features and Functions of Neuronal Assemblies: Possible Dependency on Mechanisms beyond Synaptic Transmission.

PubMed

Badin, Antoine-Scott; Fermani, Francesco; Greenfield, Susan A

2016-01-01

"Neuronal assemblies" are defined here as coalitions within the brain of millions of neurons extending in space up to 1-2 mm, and lasting for hundreds of milliseconds: as such they could potentially link bottom-up, micro-scale with top-down, macro-scale events. The perspective first compares the features in vitro versus in vivo of this underappreciated "meso-scale" level of brain processing, secondly considers the various diverse functions in which assemblies may play a pivotal part, and thirdly analyses whether the surprisingly spatially extensive and prolonged temporal properties of assemblies can be described exclusively in terms of classic synaptic transmission or whether additional, different types of signaling systems are likely to operate. Based on our own voltage-sensitive dye imaging (VSDI) data acquired in vitro we show how restriction to only one signaling process, i.e., synaptic transmission, is unlikely to be adequate for modeling the full profile of assemblies. Based on observations from VSDI with its protracted spatio-temporal scales, we suggest that two other, distinct processes are likely to play a significant role in assembly dynamics: "volume" transmission (the passive diffusion of diverse bioactive transmitters, hormones, and modulators), as well as electrotonic spread via gap junctions. We hypothesize that a combination of all three processes has the greatest potential for deriving a realistic model of assemblies and hence elucidating the various complex brain functions that they may mediate.

Looking for a precursor of spontaneous Sleep Slow Oscillations in human sleep: The role of the sigma activity

PubMed Central

Allegrini, Paolo; Bedini, Remo; Bergamasco, Massimo; Laurino, Marco; Sebastiani, Laura; Gemignani, Angelo

2016-01-01

Sleep Slow Oscillations (SSOs), paradigmatic EEG markers of cortical bistability (alternation between cellular downstates and upstates), and sleep spindles, paradigmatic EEG markers of thalamic rhythm, are two hallmarks of sleeping brain. Selective thalamic lesions are reportedly associated to reductions of spindle activity and its spectrum ~14 Hz (sigma), and to alterations of SSO features. This apparent, parallel behavior suggests that thalamo-cortical entrainment favors cortical bistability. Here we investigate temporally-causal associations between thalamic sigma activity and shape, topology, and dynamics of SSOs. We recorded sleep EEG and studied whether spatio-temporal variability of SSO amplitude, negative slope (synchronization in downstate falling) and detection rate are driven by cortical-sigma-activity expression (12–18 Hz), in 3 consecutive 1 s-EEG-epochs preceding each SSO event (Baselines). We analyzed: (i) spatial variability, comparing maps of baseline sigma power and of SSO features, averaged over the first sleep cycle; (ii) event-by-event shape variability, computing for each electrode correlations between baseline sigma power and amplitude/slope of related SSOs; (iii) event-by-event spreading variability, comparing baseline sigma power in electrodes showing an SSO event with the homologous ones, spared by the event. The scalp distribution of baseline sigma power mirrored those of SSO amplitude and slope; event-by-event variability in baseline sigma power was associated with that in SSO amplitude in fronto-central areas; within each SSO event, electrodes involved in cortical bistability presented higher baseline sigma activity than those free of SSO. In conclusion, spatio-temporal variability of thalamocortical entrainment, measured by background sigma activity, is a reliable estimate of the cortical proneness to bistability. PMID:26003553

Spatio-temporal filtering techniques for the detection of disaster-related communication.

PubMed

Fitzhugh, Sean M; Ben Gibson, C; Spiro, Emma S; Butts, Carter T

2016-09-01

Individuals predominantly exchange information with one another through informal, interpersonal channels. During disasters and other disrupted settings, information spread through informal channels regularly outpaces official information provided by public officials and the press. Social scientists have long examined this kind of informal communication in the rumoring literature, but studying rumoring in disrupted settings has posed numerous methodological challenges. Measuring features of informal communication-timing, content, location-with any degree of precision has historically been extremely challenging in small studies and infeasible at large scales. We address this challenge by using online, informal communication from a popular microblogging website and for which we have precise spatial and temporal metadata. While the online environment provides a new means for observing rumoring, the abundance of data poses challenges for parsing hazard-related rumoring from countless other topics in numerous streams of communication. Rumoring about disaster events is typically temporally and spatially constrained to places where that event is salient. Accordingly, we use spatio and temporal subsampling to increase the resolution of our detection techniques. By filtering out data from known sources of error (per rumor theories), we greatly enhance the signal of disaster-related rumoring activity. We use these spatio-temporal filtering techniques to detect rumoring during a variety of disaster events, from high-casualty events in major population centers to minimally destructive events in remote areas. We consistently find three phases of response: anticipatory excitation where warnings and alerts are issued ahead of an event, primary excitation in and around the impacted area, and secondary excitation which frequently brings a convergence of attention from distant locales onto locations impacted by the event. Our results demonstrate the promise of spatio-temporal filtering techniques for "tuning" measurement of hazard-related rumoring to enable observation of rumoring at scales that have long been infeasible. Copyright © 2016 Elsevier Inc. All rights reserved.

Demand-supply dynamics in tourism systems: A spatio-temporal GIS analysis. The Alberta ski industry case study

NASA Astrophysics Data System (ADS)

Bertazzon, Stefania

The present research focuses on the interaction of supply and demand of down-hill ski tourism in the province of Alberta. The main hypothesis is that the demand for skiing depends on the socio-economic and demographic characteristics of the population living in the province and outside it. A second, consequent hypothesis is that the development of ski resorts (supply) is a response to the demand for skiing. From the latter derives the hypothesis of a dynamic interaction between supply (ski resorts) and demand (skiers). Such interaction occurs in space, within a range determined by physical distance and the means available to overcome it. The above hypotheses implicitly define interactions that take place in space and evolve over time. The hypotheses are tested by temporal, spatial, and spatio-temporal regression models, using the best available data and the latest commercially available software. The main purpose of this research is to explore analytical techniques to model spatial, temporal, and spatio-temporal dynamics in the context of regional science. The completion of the present research has produced more significant contributions than was originally expected. Many of the unexpected contributions resulted from theoretical and applied needs arising from the application of spatial regression models. Spatial regression models are a new and largely under-applied technique. The models are fairly complex and a considerable amount of preparatory work is needed, prior to their specification and estimation. Most of this work is specific to the field of application. The originality of the solutions devised is increased by the lack of applications in the field of tourism. The scarcity of applications in other fields adds to their value for other applications. The estimation of spatio-temporal models has been only partially attained in the present research. This apparent limitation is due to the novelty and complexity of the analytical methods applied. This opens new directions for further work in the field of spatial analysis, in conjunction with the development of specific software.

A general science-based framework for dynamical spatio-temporal models

USGS Publications Warehouse

Wikle, C.K.; Hooten, M.B.

2010-01-01

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic nonlinearity and demonstrate that it accommodates many different classes of scientific-based parameterizations as special cases. The model is presented in a hierarchical Bayesian framework and is illustrated with examples from ecology and oceanography. ?? 2010 Sociedad de Estad??stica e Investigaci??n Operativa.

Multivariate Spatio-Temporal Clustering: A Framework for Integrating Disparate Data to Understand Network Representativeness and Scaling Up Sparse Ecosystem Measurements

NASA Astrophysics Data System (ADS)

Hoffman, F. M.; Kumar, J.; Maddalena, D. M.; Langford, Z.; Hargrove, W. W.

2014-12-01

Disparate in situ and remote sensing time series data are being collected to understand the structure and function of ecosystems and how they may be affected by climate change. However, resource and logistical constraints limit the frequency and extent of observations, particularly in the harsh environments of the arctic and the tropics, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent variability at desired scales. These regions host large areas of potentially vulnerable ecosystems that are poorly represented in Earth system models (ESMs), motivating two new field campaigns, called Next Generation Ecosystem Experiments (NGEE) for the Arctic and Tropics, funded by the U.S. Department of Energy. Multivariate Spatio-Temporal Clustering (MSTC) provides a quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks. We applied MSTC to down-scaled general circulation model results and data for the State of Alaska at a 4 km2 resolution to define maps of ecoregions for the present (2000-2009) and future (2090-2099), showing how combinations of 37 bioclimatic characteristics are distributed and how they may shift in the future. Optimal representative sampling locations were identified on present and future ecoregion maps, and representativeness maps for candidate sampling locations were produced. We also applied MSTC to remotely sensed LiDAR measurements and multi-spectral imagery from the WorldView-2 satellite at a resolution of about 5 m2 within the Barrow Environmental Observatory (BEO) in Alaska. At this resolution, polygonal ground features—such as centers, edges, rims, and troughs—can be distinguished. Using these remote sensing data, we up-scaled vegetation distribution data collected on these polygonal ground features to a large area of the BEO to provide distributions of plant functional types that can be used to parameterize ESMs. In addition, we applied MSTC to 4 km2 global bioclimate data to define global ecoregions and understand the representativeness of CTFS-ForestGEO, Fluxnet, and RAINFOR sampling networks. These maps identify tropical forests underrepresented in existing observations of individual and combined networks.

Contrast affects flicker and speed perception differently

NASA Technical Reports Server (NTRS)

Thompson, P.; Stone, L. S.

1997-01-01

We have previously shown that contrast affects speed perception, with lower-contrast, drifting gratings perceived as moving slower. In a recent study, we examined the implications of this result on models of speed perception that use the amplitude of the response of linear spatio-temporal filters to determine speed. In this study, we investigate whether the contrast dependence of speed can be understood within the context of models in which speed estimation is made using the temporal frequency of the response of linear spatio-temporal filters. We measured the effect of contrast on flicker perception and found that contrast manipulations produce opposite effects on perceived drift rate and perceived flicker rate, i.e., reducing contrast increases the apparent temporal frequency of counterphase modulated gratings. This finding argues that, if a temporal frequency-based algorithm underlies speed perception, either flicker and speed perception must not be based on the output of the same mechanism or contrast effects on perceived spatial frequency reconcile the disparate effects observed for perceived temporal frequency and speed.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

PubMed

Ince, Robin A A; Giordano, Bruno L; Kayser, Christoph; Rousselet, Guillaume A; Gross, Joachim; Schyns, Philippe G

2017-03-01

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc. 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

SVM-based classification of LV wall motion in cardiac MRI with the assessment of STE

NASA Astrophysics Data System (ADS)

Mantilla, Juan; Garreau, Mireille; Bellanger, Jean-Jacques; Paredes, José Luis

2015-01-01

In this paper, we propose an automated method to classify normal/abnormal wall motion in Left Ventricle (LV) function in cardiac cine-Magnetic Resonance Imaging (MRI), taking as reference, strain information obtained from 2D Speckle Tracking Echocardiography (STE). Without the need of pre-processing and by exploiting all the images acquired during a cardiac cycle, spatio-temporal profiles are extracted from a subset of radial lines from the ventricle centroid to points outside the epicardial border. Classical Support Vector Machines (SVM) are used to classify features extracted from gray levels of the spatio-temporal profile as well as their representations in the Wavelet domain under the assumption that the data may be sparse in that domain. Based on information obtained from radial strain curves in 2D-STE studies, we label all the spatio-temporal profiles that belong to a particular segment as normal if the peak systolic radial strain curve of this segment presents normal kinesis, or abnormal if the peak systolic radial strain curve presents hypokinesis or akinesis. For this study, short-axis cine- MR images are collected from 9 patients with cardiac dyssynchrony for which we have the radial strain tracings at the mid-papilary muscle obtained by 2D STE; and from one control group formed by 9 healthy subjects. The best classification performance is obtained with the gray level information of the spatio-temporal profiles using a RBF kernel with 91.88% of accuracy, 92.75% of sensitivity and 91.52% of specificity.

An Online Atlas for Exploring Spatio-Temporal Patterns of Cancer Mortality (1972–2011) and Incidence (1995–2008) in Taiwan

PubMed Central

Ku, Wen-Yuan; Liaw, Yung-Po; Huang, Jing-Yang; Nfor, Oswald Ndi; Hsu, Shu-Yi; Ko, Pei-Chieh; Lee, Wen-Chung; Chen, Chien-Jen

2016-01-01

Abstract Public health mapping and Geographical Information Systems (GIS) are already being used to locate the geographical spread of diseases. This study describes the construction of an easy-to-use online atlas of cancer mortality (1972–2011) and incidence (1995–2008) in Taiwan. Two sets of color maps were made based on “age-adjusted mortality by rate” and “age-adjusted mortality by rank.” AJAX (Asynchronous JavaScript and XML), JSON (JavaScript Object Notation), and SVG (Scaling Vector Graphic) were used to create the online atlas. Spatio-temporal patterns of cancer mortality and incidence in Taiwan over the period from 1972 to 2011 and from 1995 to 2008. The constructed online atlas contains information on cancer mortality and incidence (http://taiwancancermap.csmu-liawyp.tw/). The common GIS functions include zoom and pan and identity tools. Users can easily customize the maps to explore the spatio-temporal trends of cancer mortality and incidence using different devices (such as personal computers, mobile phone, or pad). This study suggests an easy- to-use, low-cost, and independent platform for exploring cancer incidence and mortality. It is expected to serve as a reference tool for cancer prevention and risk assessment. This online atlas is a cheap and fast tool that integrates various cancer maps. Therefore, it can serve as a powerful tool that allows users to examine and compare spatio-temporal patterns of various maps. Furthermore, it is an-easy-to use tool for updating data and assessing risk factors of cancer in Taiwan. PMID:27227915

Imaging the Spatio-Temporal Dynamics of Supragranular Activity in the Rat Somatosensory Cortex in Response to Stimulation of the Paws

PubMed Central

Morales-Botello, M. L.; Aguilar, J.; Foffani, G.

2012-01-01

We employed voltage-sensitive dye (VSD) imaging to investigate the spatio-temporal dynamics of the responses of the supragranular somatosensory cortex to stimulation of the four paws in urethane-anesthetized rats. We obtained the following main results. (1) Stimulation of the contralateral forepaw evoked VSD responses with greater amplitude and smaller latency than stimulation of the contralateral hindpaw, and ipsilateral VSD responses had a lower amplitude and greater latency than contralateral responses. (2) While the contralateral stimulation initially activated only one focus, the ipsilateral stimulation initially activated two foci: one focus was typically medial to the focus activated by contralateral stimulation and was stereotaxically localized in the motor cortex; the other focus was typically posterior to the focus activated by contralateral stimulation and was stereotaxically localized in the somatosensory cortex. (3) Forepaw and hindpaw somatosensory stimuli activated large areas of the sensorimotor cortex, well beyond the forepaw and hindpaw somatosensory areas of classical somatotopic maps, and forepaw stimuli activated larger cortical areas with greater activation velocity than hindpaw stimuli. (4) Stimulation of the forepaw and hindpaw evoked different cortical activation dynamics: forepaw responses displayed a clear medial directionality, whereas hindpaw responses were much more uniform in all directions. In conclusion, this work offers a complete spatio-temporal map of the supragranular VSD cortical activation in response to stimulation of the paws, showing important somatotopic differences between contralateral and ipsilateral maps as well as differences in the spatio-temporal activation dynamics in response to forepaw and hindpaw stimuli. PMID:22829873

Gait characteristics and spatio-temporal variables of climbing in bonobos (Pan paniscus).

PubMed

Schoonaert, Kirsten; D'Août, Kristiaan; Samuel, Diana; Talloen, Willem; Nauwelaerts, Sandra; Kivell, Tracy L; Aerts, Peter

2016-11-01

Although much is known about the terrestrial locomotion of great apes, their arboreal locomotion has been studied less extensively. This study investigates arboreal locomotion in bonobos (Pan paniscus), focusing on the gait characteristics and spatio-temporal variables associated with locomotion on a pole. These features are compared across different substrate inclinations (0°, 30°, 45°, 60°, and 90°), and horizontal quadrupedal walking is compared between an arboreal and a terrestrial substrate. Our results show greater variation in footfall patterns with increasing incline, resulting in more lateral gait sequences. During climbing on arboreal inclines, smaller steps and strides but higher stride frequencies and duty factors are found compared to horizontal arboreal walking. This may facilitate better balance control and dynamic stability on the arboreal substrate. We found no gradual change in spatio-temporal variables with increasing incline; instead, the results for all inclines were clustered together. Bonobos take larger strides at lower stride frequencies and lower duty factors on a horizontal arboreal substrate than on a flat terrestrial substrate. We suggest that these changes are the result of the better grip of the grasping feet on an arboreal substrate. Speed modulation of the spatio-temporal variables is similar across substrate inclinations and between substrate types, suggesting a comparable underlying motor control. Finally, we contrast these variables of arboreal inclined climbing with those of terrestrial bipedal locomotion, and briefly discuss the results with respect to the origin of habitual bipedalism. Am. J. Primatol. 78:1165-1177, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A Fresh Look at Spatio-Temporal Remote Sensing Data: Data Formats, Processing Flow, and Visualization

NASA Astrophysics Data System (ADS)

Gens, R.

2017-12-01

With increasing number of experimental and operational satellites in orbit, remote sensing based mapping and monitoring of the dynamic Earth has entered into the realm of `big data'. Just the Landsat series of satellites provide a near continuous archive of 45 years of data. The availability of such spatio-temporal datasets has created opportunities for long-term monitoring diverse features and processes operating on the Earth's terrestrial and aquatic systems. Processes such as erosion, deposition, subsidence, uplift, evapotranspiration, urbanization, land-cover regime shifts can not only be monitored and change can be quantified using time-series data analysis. This unique opportunity comes with new challenges in management, analysis, and visualization of spatio-temporal datasets. Data need to be stored in a user-friendly format, and relevant metadata needs to be recorded, to allow maximum flexibility for data exchange and use. Specific data processing workflows need to be defined to support time-series analysis for specific applications. Value-added data products need to be generated keeping in mind the needs of the end-users, and using best practices in complex data visualization. This presentation systematically highlights the various steps for preparing spatio-temporal remote sensing data for time series analysis. It showcases a prototype workflow for remote sensing based change detection that can be generically applied while preserving the application-specific fidelity of the datasets. The prototype includes strategies for visualizing change over time. This has been exemplified using a time-series of optical and SAR images for visualizing the changing glacial, coastal, and wetland landscapes in parts of Alaska.

Similarities and differences among half-marathon runners according to their performance level

PubMed Central

Morante, Juan Carlos; Gómez-Molina, Josué; García-López, Juan

2018-01-01

This study aimed to identify the similarities and differences among half-marathon runners in relation to their performance level. Forty-eight male runners were classified into 4 groups according to their performance level in a half-marathon (min): Group 1 (n = 11, < 70 min), Group 2 (n = 13, < 80 min), Group 3 (n = 13, < 90 min), Group 4 (n = 11, < 105 min). In two separate sessions, training-related, anthropometric, physiological, foot strike pattern and spatio-temporal variables were recorded. Significant differences (p<0.05) between groups (ES = 0.55–3.16) and correlations with performance were obtained (r = 0.34–0.92) in training-related (experience and running distance per week), anthropometric (mass, body mass index and sum of 6 skinfolds), physiological (VO2max, RCT and running economy), foot strike pattern and spatio-temporal variables (contact time, step rate and length). At standardized submaximal speeds (11, 13 and 15 km·h-1), no significant differences between groups were observed in step rate and length, neither in contact time when foot strike pattern was taken into account. In conclusion, apart from training-related, anthropometric and physiological variables, foot strike pattern and step length were the only biomechanical variables sensitive to half-marathon performance, which are essential to achieve high running speeds. However, when foot strike pattern and running speeds were controlled (submaximal test), the spatio-temporal variables were similar. This indicates that foot strike pattern and running speed are responsible for spatio-temporal differences among runners of different performance level. PMID:29364940

An Online Atlas for Exploring Spatio-Temporal Patterns of Cancer Mortality (1972-2011) and Incidence (1995-2008) in Taiwan.

PubMed

Ku, Wen-Yuan; Liaw, Yung-Po; Huang, Jing-Yang; Nfor, Oswald Ndi; Hsu, Shu-Yi; Ko, Pei-Chieh; Lee, Wen-Chung; Chen, Chien-Jen

2016-05-01

Public health mapping and Geographical Information Systems (GIS) are already being used to locate the geographical spread of diseases. This study describes the construction of an easy-to-use online atlas of cancer mortality (1972-2011) and incidence (1995-2008) in Taiwan.Two sets of color maps were made based on "age-adjusted mortality by rate" and "age-adjusted mortality by rank." AJAX (Asynchronous JavaScript and XML), JSON (JavaScript Object Notation), and SVG (Scaling Vector Graphic) were used to create the online atlas. Spatio-temporal patterns of cancer mortality and incidence in Taiwan over the period from 1972 to 2011 and from 1995 to 2008.The constructed online atlas contains information on cancer mortality and incidence (http://taiwancancermap.csmu-liawyp.tw/). The common GIS functions include zoom and pan and identity tools. Users can easily customize the maps to explore the spatio-temporal trends of cancer mortality and incidence using different devices (such as personal computers, mobile phone, or pad). This study suggests an easy- to-use, low-cost, and independent platform for exploring cancer incidence and mortality. It is expected to serve as a reference tool for cancer prevention and risk assessment.This online atlas is a cheap and fast tool that integrates various cancer maps. Therefore, it can serve as a powerful tool that allows users to examine and compare spatio-temporal patterns of various maps. Furthermore, it is an-easy-to use tool for updating data and assessing risk factors of cancer in Taiwan.

A new space-time characterization of Northern Hemisphere drought in model simulations of the past and future as compared to the paleoclimate record

NASA Astrophysics Data System (ADS)

Coats, S.; Smerdon, J. E.; Stevenson, S.; Fasullo, J.; Otto-Bliesner, B. L.

2017-12-01

The observational record, which provides only limited sampling of past climate variability, has made it difficult to quantitatively analyze the complex spatio-temporal character of drought. To provide a more complete characterization of drought, machine learning based methods that identify drought in three-dimensional space-time are applied to climate model simulations of the last millennium and future, as well as tree-ring based reconstructions of hydroclimate over the Northern Hemisphere extratropics. A focus is given to the most persistent and severe droughts of the past 1000 years. Analyzing reconstructions and simulations in this context allows for a validation of the spatio-temporal character of persistent and severe drought in climate model simulations. Furthermore, the long records provided by the reconstructions and simulations, allows for sufficient sampling to constrain projected changes to the spatio-temporal character of these features using the reconstructions. Along these lines, climate models suggest that there will be large increases in the persistence and severity of droughts over the coming century, but little change in their spatial extent. These models, however, exhibit biases in the spatio-temporal character of persistent and severe drought over parts of the Northern Hemisphere, which may undermine their usefulness for future projections. Despite these limitations, and in contrast to previous claims, there are no systematic changes in the character of persistent and severe droughts in simulations of the historical interval. This suggests that climate models are not systematically overestimating the hydroclimate response to anthropogenic forcing over this period, with critical implications for confidence in hydroclimate projections.

Gene Expression Analyses of the Spatio-Temporal Relationships of Human Medulloblastoma Subgroups during Early Human Neurogenesis

PubMed Central

Hooper, Cornelia M.; Hawes, Susan M.; Kees, Ursula R.; Gottardo, Nicholas G.; Dallas, Peter B.

2014-01-01

Medulloblastoma is the most common form of malignant paediatric brain tumour and is the leading cause of childhood cancer related mortality. The four molecular subgroups of medulloblastoma that have been identified – WNT, SHH, Group 3 and Group 4 - have molecular and topographical characteristics suggestive of different cells of origin. Definitive identification of the cell(s) of origin of the medulloblastoma subgroups, particularly the poorer prognosis Group 3 and Group 4 medulloblastoma, is critical to understand the pathogenesis of the disease, and ultimately for the development of more effective treatment options. To address this issue, the gene expression profiles of normal human neural tissues and cell types representing a broad neuro-developmental continuum, were compared to those of two independent cohorts of primary human medulloblastoma specimens. Clustering, co-expression network, and gene expression analyses revealed that WNT and SHH medulloblastoma may be derived from distinct neural stem cell populations during early embryonic development, while the transcriptional profiles of Group 3 and Group 4 medulloblastoma resemble cerebellar granule neuron precursors at weeks 10–15 and 20–30 of embryogenesis, respectively. Our data indicate that Group 3 medulloblastoma may arise through abnormal neuronal differentiation, whereas deregulation of synaptic pruning-associated apoptosis may be driving Group 4 tumorigenesis. Overall, these data provide significant new insight into the spatio-temporal relationships and molecular pathogenesis of the human medulloblastoma subgroups, and provide an important framework for the development of more refined model systems, and ultimately improved therapeutic strategies. PMID:25412507

Gene expression analyses of the spatio-temporal relationships of human medulloblastoma subgroups during early human neurogenesis.

PubMed

Hooper, Cornelia M; Hawes, Susan M; Kees, Ursula R; Gottardo, Nicholas G; Dallas, Peter B

2014-01-01

Medulloblastoma is the most common form of malignant paediatric brain tumour and is the leading cause of childhood cancer related mortality. The four molecular subgroups of medulloblastoma that have been identified - WNT, SHH, Group 3 and Group 4 - have molecular and topographical characteristics suggestive of different cells of origin. Definitive identification of the cell(s) of origin of the medulloblastoma subgroups, particularly the poorer prognosis Group 3 and Group 4 medulloblastoma, is critical to understand the pathogenesis of the disease, and ultimately for the development of more effective treatment options. To address this issue, the gene expression profiles of normal human neural tissues and cell types representing a broad neuro-developmental continuum, were compared to those of two independent cohorts of primary human medulloblastoma specimens. Clustering, co-expression network, and gene expression analyses revealed that WNT and SHH medulloblastoma may be derived from distinct neural stem cell populations during early embryonic development, while the transcriptional profiles of Group 3 and Group 4 medulloblastoma resemble cerebellar granule neuron precursors at weeks 10-15 and 20-30 of embryogenesis, respectively. Our data indicate that Group 3 medulloblastoma may arise through abnormal neuronal differentiation, whereas deregulation of synaptic pruning-associated apoptosis may be driving Group 4 tumorigenesis. Overall, these data provide significant new insight into the spatio-temporal relationships and molecular pathogenesis of the human medulloblastoma subgroups, and provide an important framework for the development of more refined model systems, and ultimately improved therapeutic strategies.

Ultra-low-power hybrid light–matter solitons

PubMed Central

Walker, P. M.; Tinkler, L.; Skryabin, D. V.; Yulin, A.; Royall, B.; Farrer, I.; Ritchie, D. A.; Skolnick, M. S.; Krizhanovskii, D. N.

2015-01-01

New functionalities in nonlinear optics will require systems with giant optical nonlinearity as well as compatibility with photonic circuit fabrication techniques. Here we introduce a platform based on strong light–matter coupling between waveguide photons and quantum-well excitons. On a sub-millimetre length scale we generate picosecond bright temporal solitons at a pulse energy of only 0.5 pJ. From this we deduce a nonlinear refractive index three orders of magnitude larger than in any other ultrafast system. We study both temporal and spatio-temporal nonlinear effects and observe dark–bright spatio-temporal polariton solitons. Theoretical modelling of soliton formation in the strongly coupled system confirms the experimental observations. These results show the promise of our system as a high speed, low power, integrated platform for physics and devices based on strong interactions between photons. PMID:26400748

Ultra-low-power hybrid light-matter solitons.

PubMed

Walker, P M; Tinkler, L; Skryabin, D V; Yulin, A; Royall, B; Farrer, I; Ritchie, D A; Skolnick, M S; Krizhanovskii, D N

2015-09-24

New functionalities in nonlinear optics will require systems with giant optical nonlinearity as well as compatibility with photonic circuit fabrication techniques. Here we introduce a platform based on strong light-matter coupling between waveguide photons and quantum-well excitons. On a sub-millimetre length scale we generate picosecond bright temporal solitons at a pulse energy of only 0.5 pJ. From this we deduce a nonlinear refractive index three orders of magnitude larger than in any other ultrafast system. We study both temporal and spatio-temporal nonlinear effects and observe dark-bright spatio-temporal polariton solitons. Theoretical modelling of soliton formation in the strongly coupled system confirms the experimental observations. These results show the promise of our system as a high speed, low power, integrated platform for physics and devices based on strong interactions between photons.

RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint

PubMed Central

Piao, Xinglin; Zhang, Yong; Li, Tingshu; Hu, Yongli; Liu, Hao; Zhang, Ke; Ge, Yun

2016-01-01

The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or temporal correlation existing in fingerprint data and measurement data, which is helpful for improving localization accuracy. In this paper, we propose an RSS fingerprint-based indoor localization method by integrating the spatio-temporal constraints into the sparse representation model. The proposed model utilizes the inherent spatial correlation of fingerprint data in the fingerprint matching and uses the temporal continuity of the RSS measurement data in the localization phase. Experiments on the simulated data and the localization tests in the real scenes show that the proposed method improves the localization accuracy and stability effectively compared with state-of-the-art indoor localization methods. PMID:27827882

Agreement between the spatio-temporal gait parameters from treadmill-based photoelectric cell and the instrumented treadmill system in healthy young adults and stroke patients.

PubMed

Lee, Myungmo; Song, Changho; Lee, Kyoungjin; Shin, Doochul; Shin, Seungho

2014-07-14

Treadmill gait analysis was more advantageous than over-ground walking because it allowed continuous measurements of the gait parameters. The purpose of this study was to investigate the concurrent validity and the test-retest reliability of the OPTOGait photoelectric cell system against the treadmill-based gait analysis system by assessing spatio-temporal gait parameters. Twenty-six stroke patients and 18 healthy adults were asked to walk on the treadmill at their preferred speed. The concurrent validity was assessed by comparing data obtained from the 2 systems, and the test-retest reliability was determined by comparing data obtained from the 1st and the 2nd session of the OPTOGait system. The concurrent validity, identified by the intra-class correlation coefficients (ICC [2, 1]), coefficients of variation (CVME), and 95% limits of agreement (LOA) for the spatial-temporal gait parameters, were excellent but the temporal parameters expressed as a percentage of the gait cycle were poor. The test-retest reliability of the OPTOGait System, identified by ICC (3, 1), CVME, 95% LOA, standard error of measurement (SEM), and minimum detectable change (MDC95%) for the spatio-temporal gait parameters, was high. These findings indicated that the treadmill-based OPTOGait System had strong concurrent validity and test-retest reliability. This portable system could be useful for clinical assessments.

Attempting to physically explain space-time correlation of extremes

NASA Astrophysics Data System (ADS)

Bernardara, Pietro; Gailhard, Joel

2010-05-01

Spatial and temporal clustering of hydro-meteorological extreme events is scientific evidence. Moreover, the statistical parameters characterizing their local frequencies of occurrence show clear spatial patterns. Thus, in order to robustly assess the hydro-meteorological hazard, statistical models need to be able to take into account spatial and temporal dependencies. Statistical models considering long term correlation for quantifying and qualifying temporal and spatial dependencies are available, such as multifractal approach. Furthermore, the development of regional frequency analysis techniques allows estimating the frequency of occurrence of extreme events taking into account spatial patterns on the extreme quantiles behaviour. However, in order to understand the origin of spatio-temporal clustering, an attempt to find physical explanation should be done. Here, some statistical evidences of spatio-temporal correlation and spatial patterns of extreme behaviour are given on a large database of more than 400 rainfall and discharge series in France. In particular, the spatial distribution of multifractal and Generalized Pareto distribution parameters shows evident correlation patterns in the behaviour of frequency of occurrence of extremes. It is then shown that the identification of atmospheric circulation pattern (weather types) can physically explain the temporal clustering of extreme rainfall events (seasonality) and the spatial pattern of the frequency of occurrence. Moreover, coupling this information with the hydrological modelization of a watershed (as in the Schadex approach) an explanation of spatio-temporal distribution of extreme discharge can also be provided. We finally show that a hydro-meteorological approach (as the Schadex approach) can explain and take into account space and time dependencies of hydro-meteorological extreme events.

Stability Switches, Hopf Bifurcations, and Spatio-temporal Patterns in a Delayed Neural Model with Bidirectional Coupling

NASA Astrophysics Data System (ADS)

Song, Yongli; Zhang, Tonghua; Tadé, Moses O.

2009-12-01

The dynamical behavior of a delayed neural network with bi-directional coupling is investigated by taking the delay as the bifurcating parameter. Some parameter regions are given for conditional/absolute stability and Hopf bifurcations by using the theory of functional differential equations. As the propagation time delay in the coupling varies, stability switches for the trivial solution are found. Conditions ensuring the stability and direction of the Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. We also discuss the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay differential equations combined with representation theory of Lie groups. In particular, we obtain that the spatio-temporal patterns of bifurcating periodic oscillations will alternate according to the change of the propagation time delay in the coupling, i.e., different ranges of delays correspond to different patterns of neural activities. Numerical simulations are given to illustrate the obtained results and show the existence of bursts in some interval of the time for large enough delay.

Geovisualization of Local and Regional Migration Using Web-mined Demographics

NASA Astrophysics Data System (ADS)

Schuermann, R. T.; Chow, T. E.

2014-11-01

The intent of this research was to augment and facilitate analyses, which gauges the feasibility of web-mined demographics to study spatio-temporal dynamics of migration. As a case study, we explored the spatio-temporal dynamics of Vietnamese Americans (VA) in Texas through geovisualization of mined demographic microdata from the World Wide Web. Based on string matching across all demographic attributes, including full name, address, date of birth, age and phone number, multiple records of the same entity (i.e. person) over time were resolved and reconciled into a database. Migration trajectories were geovisualized through animated sprites by connecting the different addresses associated with the same person and segmenting the trajectory into small fragments. Intra-metropolitan migration patterns appeared at the local scale within many metropolitan areas. At the scale of metropolitan area, varying degrees of immigration and emigration manifest different types of migration clusters. This paper presents a methodology incorporating GIS methods and cartographic design to produce geovisualization animation, enabling the cognitive identification of migration patterns at multiple scales. Identification of spatio-temporal patterns often stimulates further research to better understand the phenomenon and enhance subsequent modeling.

Building a Billion Spatio-Temporal Object Search and Visualization Platform

NASA Astrophysics Data System (ADS)

Kakkar, D.; Lewis, B.

2017-10-01

With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA) has developed a prototype spatio-temporal visualization platform called the Billion Object Platform or BOP. The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. The BOP is now loaded with the latest billion geo-tweets, and is fed a real-time stream of about 1 million tweets per day. The geo-tweets are enriched with sentiment and census/admin boundary codes when they enter the system. The system is open source and is currently hosted on Massachusetts Open Cloud (MOC), an OpenStack environment with all components deployed in Docker orchestrated by Kontena. This paper will provide an overview of the BOP architecture, which is built on an open source stack consisting of Apache Lucene, Solr, Kafka, Zookeeper, Swagger, scikit-learn, OpenLayers, and AngularJS. The paper will further discuss the approach used for harvesting, enriching, streaming, storing, indexing, visualizing and querying a billion streaming geo-tweets.

Multiscale recurrence analysis of spatio-temporal data

NASA Astrophysics Data System (ADS)

Riedl, M.; Marwan, N.; Kurths, J.

2015-12-01

The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for this purpose, the mapogram approach allows the specific focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics. We illustrate this approach by application on mixed dynamics, such as traveling parallel wave fronts with additive noise, as well as more complicate examples, pseudo-random numbers and coupled map lattices with a semi-logistic mapping rule. Especially the complicate examples show the usefulness of the multi-scale consideration in order to take spatial pattern of different scales and with different rhythms into account. So, this mapogram approach promises new insights in problems of climatology, ecology, or medicine.

Multiscale recurrence analysis of spatio-temporal data.

PubMed

Riedl, M; Marwan, N; Kurths, J

2015-12-01

The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for this purpose, the mapogram approach allows the specific focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics. We illustrate this approach by application on mixed dynamics, such as traveling parallel wave fronts with additive noise, as well as more complicate examples, pseudo-random numbers and coupled map lattices with a semi-logistic mapping rule. Especially the complicate examples show the usefulness of the multi-scale consideration in order to take spatial pattern of different scales and with different rhythms into account. So, this mapogram approach promises new insights in problems of climatology, ecology, or medicine.

Visualizing and communicating uncertainty in the earth and environmental sciences: a review

NASA Astrophysics Data System (ADS)

Pebesma, Edzer

2014-05-01

I will review past attempts to visualising uncertainty in spatial or spatio-temporal predictions of groundwater quality, quality predictions, sea bed sediment, bird densities, air quality measurements, and exposure to air quality of individuals and populations. The attempts involved software development (aguila [1], greenland [2]), the development of standards for communicating uncertain spatial and spatio-temporal information (UncertML, [3]), and have been illustrated by applications in a number of EU projects (Apmosphere [4], INTAMAP [5], UncertWeb [6] and GeoViQua [7]). I will also report on usability studies that were carried out (e.g. [8]). [1] http://pcraster.geo.uu.nl/projects/developments/aguila/ [2] https://wiki.52north.org/bin/view/Geostatistics/Greenland [3] http://www.uncertml.org/ [4] http://www.apmosphere.org/ [5] http://www.intamap.org/ [6] http://www.uncertweb.org/ [7] http://www.geoviqua.org/ [8] Senaratne, H. L. Gerharz, E. Pebesma, A. Schwering, 2012. Usability of Spatio-Temporal Uncertainty Visualisation Methods. In: Bridging the Geographic Information Sciences, Lecture Notes in Geoinformation and Cartography, J. Gensel, D. Josselin and D. Vandenbroucke. Springer Berlin Heidelberg.

Spatio-Temporal Dynamics of Fructan Metabolism in Developing Barley Grains[W

PubMed Central

Peukert, Manuela; Thiel, Johannes; Peshev, Darin; Weschke, Winfriede; Van den Ende, Wim; Mock, Hans-Peter; Matros, Andrea

2014-01-01

Barley (Hordeum vulgare) grain development follows a series of defined morphological and physiological stages and depends on the supply of assimilates (mainly sucrose) from the mother plant. Here, spatio-temporal patterns of sugar distributions were investigated by mass spectrometric imaging, targeted metabolite analyses, and transcript profiling of microdissected grain tissues. Distinct spatio-temporal sugar balances were observed, which may relate to differentiation and grain filling processes. Notably, various types of oligofructans showed specific distribution patterns. Levan- and graminan-type oligofructans were synthesized in the cellularized endosperm prior to the commencement of starch biosynthesis, while during the storage phase, inulin-type oligofructans accumulated to a high concentration in and around the nascent endosperm cavity. In the shrunken endosperm mutant seg8, with a decreased sucrose flux toward the endosperm, fructan accumulation was impaired. The tight partitioning of oligofructan biosynthesis hints at distinct functions of the various fructan types in the young endosperm prior to starch accumulation and in the endosperm transfer cells that accomplish the assimilate supply toward the endosperm at the storage phase. PMID:25271242

Decoding the time-course of object recognition in the human brain: From visual features to categorical decisions.

PubMed

Contini, Erika W; Wardle, Susan G; Carlson, Thomas A

2017-10-01

Visual object recognition is a complex, dynamic process. Multivariate pattern analysis methods, such as decoding, have begun to reveal how the brain processes complex visual information. Recently, temporal decoding methods for EEG and MEG have offered the potential to evaluate the temporal dynamics of object recognition. Here we review the contribution of M/EEG time-series decoding methods to understanding visual object recognition in the human brain. Consistent with the current understanding of the visual processing hierarchy, low-level visual features dominate decodable object representations early in the time-course, with more abstract representations related to object category emerging later. A key finding is that the time-course of object processing is highly dynamic and rapidly evolving, with limited temporal generalisation of decodable information. Several studies have examined the emergence of object category structure, and we consider to what degree category decoding can be explained by sensitivity to low-level visual features. Finally, we evaluate recent work attempting to link human behaviour to the neural time-course of object processing. Copyright © 2017 Elsevier Ltd. All rights reserved.

VAUD: A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data.

PubMed

Chen, Wei; Huang, Zhaosong; Wu, Feiran; Zhu, Minfeng; Guan, Huihua; Maciejewski, Ross

2017-10-02

Urban data is massive, heterogeneous, and spatio-temporal, posing a substantial challenge for visualization and analysis. In this paper, we design and implement a novel visual analytics approach, Visual Analyzer for Urban Data (VAUD), that supports the visualization, querying, and exploration of urban data. Our approach allows for cross-domain correlation from multiple data sources by leveraging spatial-temporal and social inter-connectedness features. Through our approach, the analyst is able to select, filter, aggregate across multiple data sources and extract information that would be hidden to a single data subset. To illustrate the effectiveness of our approach, we provide case studies on a real urban dataset that contains the cyber-, physical-, and socialinformation of 14 million citizens over 22 days.

Concurrent temporal stability of the apparent electrical conductivity and soil water content

USDA-ARS?s Scientific Manuscript database

Knowledge of spatio-temporal soil water content (SWC) variability within agricultural fields is useful to improve crop management. Spatial patterns of soil water contents can be characterized using the temporal stability analysis, however high density sampling is required. Soil apparent electrical c...

Flexible Neural Electrode Array Based-on Porous Graphene for Cortical Microstimulation and Sensing

NASA Astrophysics Data System (ADS)

Lu, Yichen; Lyu, Hongming; Richardson, Andrew G.; Lucas, Timothy H.; Kuzum, Duygu

2016-09-01

Neural sensing and stimulation have been the backbone of neuroscience research, brain-machine interfaces and clinical neuromodulation therapies for decades. To-date, most of the neural stimulation systems have relied on sharp metal microelectrodes with poor electrochemical properties that induce extensive damage to the tissue and significantly degrade the long-term stability of implantable systems. Here, we demonstrate a flexible cortical microelectrode array based on porous graphene, which is capable of efficient electrophysiological sensing and stimulation from the brain surface, without penetrating into the tissue. Porous graphene electrodes show superior impedance and charge injection characteristics making them ideal for high efficiency cortical sensing and stimulation. They exhibit no physical delamination or degradation even after 1 million biphasic stimulation cycles, confirming high endurance. In in vivo experiments with rodents, same array is used to sense brain activity patterns with high spatio-temporal resolution and to control leg muscles with high-precision electrical stimulation from the cortical surface. Flexible porous graphene array offers a minimally invasive but high efficiency neuromodulation scheme with potential applications in cortical mapping, brain-computer interfaces, treatment of neurological disorders, where high resolution and simultaneous recording and stimulation of neural activity are crucial.

On the potentials of multiple climate variables in assessing the spatio-temporal characteristics of hydrological droughts over the Volta Basin.

PubMed

Ndehedehe, Christopher E; Awange, Joseph L; Corner, Robert J; Kuhn, Michael; Okwuashi, Onuwa

2016-07-01

Multiple drought episodes over the Volta basin in recent reports may lead to food insecurity and loss of revenue. However, drought studies over the Volta basin are rather generalised and largely undocumented due to sparse ground observations and unsuitable framework to determine their space-time occurrence. In this study, we examined the utility of standardised indicators (standardised precipitation index (SPI), standardised runoff index (SRI), standardised soil moisture index (SSI), and multivariate standardised drought index (MSDI)) and Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water storage to assess hydrological drought characteristics over the basin. In order to determine the space-time patterns of hydrological drought in the basin, Independent Component Analysis (ICA), a higher order statistical technique was employed. The results show that SPI and SRI exhibit inconsistent behaviour in observed wet years presupposing a non-linear relationship that reflects the slow response of river discharge to precipitation especially after a previous extreme dry period. While the SPI and SSI show a linear relationship with a correlation of 0.63, the correlation between the MSDIs derived from combining precipitation/river discharge and precipitation/soil moisture indicates a significant value of 0.70 and shows an improved skill in hydrological drought monitoring over the Volta basin during the study period. The ICA-derived spatio-temporal hydrological drought patterns show Burkina Faso and the Lake Volta areas as predominantly drought zones. Further, the statistically significant negative correlations of pacific decadal oscillations (0.39 and 0.25) with temporal evolutions of drought in Burkina Faso and Ghana suggest the possible influence of low frequency large scale oscillations in the observed wet and dry regimes over the basin. Finally, our approach in drought assessment over the Volta basin contributes to a broad framework for hydrological drought monitoring that will complement existing methods while looking forward to a longer record of GRACE observations. Copyright © 2016 Elsevier B.V. All rights reserved.

A new framework to increase the efficiency of large-scale solar power plants.

NASA Astrophysics Data System (ADS)

Alimohammadi, Shahrouz; Kleissl, Jan P.

2015-11-01

A new framework to estimate the spatio-temporal behavior of solar power is introduced, which predicts the statistical behavior of power output at utility scale Photo-Voltaic (PV) power plants. The framework is based on spatio-temporal Gaussian Processes Regression (Kriging) models, which incorporates satellite data with the UCSD version of the Weather and Research Forecasting model. This framework is designed to improve the efficiency of the large-scale solar power plants. The results are also validated from measurements of the local pyranometer sensors, and some improvements in different scenarios are observed. Solar energy.

Transition from complete synchronization to spatio-temporal chaos in coupled chaotic systems with nonhyperbolic and hyperbolic attractors

NASA Astrophysics Data System (ADS)

Rybalova, Elena; Semenova, Nadezhda; Strelkova, Galina; Anishchenko, Vadim

2017-06-01

We study the transition from coherence (complete synchronization) to incoherence (spatio-temporal chaos) in ensembles of nonlocally coupled chaotic maps with nonhyperbolic and hyperbolic attractors. As basic models of a partial element we use the Henon map and the Lozi map. We show that the transition to incoherence in a ring of coupled Henon maps occurs through the appearance of phase and amplitude chimera states. An ensemble of coupled Lozi maps demonstrates the coherence-incoherence transition via solitary states and no chimera states are observed in this case.

Early-warning signals for catastrophic soil degradation

NASA Astrophysics Data System (ADS)

Karssenberg, Derek

2010-05-01

Many earth systems have critical thresholds at which the system shifts abruptly from one state to another. Such critical transitions have been described, among others, for climate, vegetation, animal populations, and geomorphology. Predicting the timing of critical transitions before they are reached is of importance because of the large impact on nature and society associated with the transition. However, it is notably difficult to predict the timing of a transition. This is because the state variables of the system show little change before the threshold is reached. As a result, the precision of field observations is often too low to provide predictions of the timing of a transition. A possible solution is the use of spatio-temporal patterns in state variables as leading indicators of a transition. It is becoming clear that the critically slowing down of a system causes spatio-temporal autocorrelation and variance to increase before the transition. Thus, spatio-temporal patterns are important candidates for early-warning signals. In this research we will show that these early-warning signals also exist in geomorphological systems. We consider a modelled vegetation-soil system under a gradually increasing grazing pressure causing an abrupt shift towards extensive soil degradation. It is shown that changes in spatio-temporal patterns occur well ahead of this catastrophic transition. A distributed model describing the coupled processes of vegetation growth and geomorphological denudation is adapted. The model uses well-studied simple process representations for vegetation and geomorphology. A logistic growth model calculates vegetation cover as a function of grazing pressure and vegetation growth rate. Evolution of the soil thickness is modelled by soil creep and wash processes, as a function of net rain reaching the surface. The vegetation and soil system are coupled by 1) decreasing vegetation growth with decreasing soil thickness and 2) increasing soil wash with decreasing vegetation cover. The model describes a critical, catastrophic transition of an underexploited system with low grazing pressure towards an overexploited system. The underexploited state has high vegetation cover and well developed soils, while the overexploited state has low vegetation cover and largely degraded soils. We first show why spatio-temporal patterns in vegetation cover, morphology, erosion rate, and sediment load should be expected to change well before the critical transition towards the overexploited state. Subsequently, spatio-temporal patterns are quantified by calculating statistics, in particular first order statistics and autocorrelation in space and time. It is shown that these statistics gradually change before the transition is reached. This indicates that the statistics may serve as early-warning signals in real-world applications. We also discuss the potential use of remote sensing to predict the critical transition in real-world landscapes.

Small-Scale Spatio-Temporal Distribution of Bactrocera minax (Enderlein) (Diptera: Tephritidae) Using Probability Kriging.

PubMed

Wang, S Q; Zhang, H Y; Li, Z L

2016-10-01

Understanding spatio-temporal distribution of pest in orchards can provide important information that could be used to design monitoring schemes and establish better means for pest control. In this study, the spatial and temporal distribution of Bactrocera minax (Enderlein) (Diptera: Tephritidae) was assessed, and activity trends were evaluated by using probability kriging. Adults of B. minax were captured in two successive occurrences in a small-scale citrus orchard by using food bait traps, which were placed both inside and outside the orchard. The weekly spatial distribution of B. minax within the orchard and adjacent woods was examined using semivariogram parameters. The edge concentration was discovered during the most weeks in adult occurrence, and the population of the adults aggregated with high probability within a less-than-100-m-wide band on both of the sides of the orchard and the woods. The sequential probability kriged maps showed that the adults were estimated in the marginal zone with higher probability, especially in the early and peak stages. The feeding, ovipositing, and mating behaviors of B. minax are possible explanations for these spatio-temporal patterns. Therefore, spatial arrangement and distance to the forest edge of traps or spraying spot should be considered to enhance pest control on B. minax in small-scale orchards.

A modeling approach for aerosol optical depth analysis during forest fire events

NASA Astrophysics Data System (ADS)

Aube, Martin P.; O'Neill, Normand T.; Royer, Alain; Lavoue, David

2004-10-01

Measurements of aerosol optical depth (AOD) are important indicators of aerosol particle behavior. Up to now the two standard techniques used for retrieving AOD are; (i) sun photometry which provides measurements of high temporal frequency and sparse spatial frequency, and (ii) satellite based approaches such as DDV (Dense Dark Vegetation) based inversion algorithms which yield AOD over dark targets in remotely sensed imagery. Although the latter techniques allow AOD retrieval over appreciable spatial domains, the irregular spatial pattern of dark targets and the typically low repeat frequencies of imaging satellites exclude the acquisition of AOD databases on a continuous spatio-temporal basis. We attempt to fill gaps in spatio-temporal AOD measurements using a new assimilation methodology that links AOD measurements and the predictions of a particulate matter Transport Model. This modelling package (AODSEM V2.0 for Aerosol Optical Depth Spatio-temporal Evolution Model) uses a size and aerosol type segregated semi-Lagrangian trajectory algorithm driven by analysed meteorological data. Its novelty resides in the fact that the model evolution may be tied to both ground based and satellite level AOD measurement and all physical processes have been optimized to track this important and robust parameter. We applied this methodology to a significant smoke event that occurred over the eastern part of North America in July 2002.

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

NASA Astrophysics Data System (ADS)

Sampson, Paul D.; Szpiro, Adam A.; Sheppard, Lianne; Lindström, Johan; Kaufman, Joel D.

2011-11-01

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM 2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.

Topodynamics of metastable brains

NASA Astrophysics Data System (ADS)

Tozzi, Arturo; Peters, James F.; Fingelkurts, Andrew A.; Fingelkurts, Alexander A.; Marijuán, Pedro C.

2017-07-01

The brain displays both the anatomical features of a vast amount of interconnected topological mappings as well as the functional features of a nonlinear, metastable system at the edge of chaos, equipped with a phase space where mental random walks tend towards lower energetic basins. Nevertheless, with the exception of some advanced neuro-anatomic descriptions and present-day connectomic research, very few studies have been addressing the topological path of a brain embedded or embodied in its external and internal environment. Herein, by using new formal tools derived from algebraic topology, we provide an account of the metastable brain, based on the neuro-scientific model of Operational Architectonics of brain-mind functioning. We introduce a ;topodynamic; description that shows how the relationships among the countless intertwined spatio-temporal levels of brain functioning can be assessed in terms of projections and mappings that take place on abstract structures, equipped with different dimensions, curvatures and energetic constraints. Such a topodynamical approach, apart from providing a biologically plausible model of brain function that can be operationalized, is also able to tackle the issue of a long-standing dichotomy: it throws indeed a bridge between the subjective, immediate datum of the naïve complex of sensations and mentations and the objective, quantitative, data extracted from experimental neuro-scientific procedures. Importantly, it opens the door to a series of new predictions and future directions of advancement for neuroscientific research.

What kind of noise is brain noise: anomalous scaling behavior of the resting brain activity fluctuations

PubMed Central

Fraiman, Daniel; Chialvo, Dante R.

2012-01-01

The study of spontaneous fluctuations of brain activity, often referred as brain noise, is getting increasing attention in functional magnetic resonance imaging (fMRI) studies. Despite important efforts, much of the statistical properties of such fluctuations remain largely unknown. This work scrutinizes these fluctuations looking at specific statistical properties which are relevant to clarify its dynamical origins. Here, three statistical features which clearly differentiate brain data from naive expectations for random processes are uncovered: First, the variance of the fMRI mean signal as a function of the number of averaged voxels remains constant across a wide range of observed clusters sizes. Second, the anomalous behavior of the variance is originated by bursts of synchronized activity across regions, regardless of their widely different sizes. Finally, the correlation length (i.e., the length at which the correlation strength between two regions vanishes) as well as mutual information diverges with the cluster's size considered, such that arbitrarily large clusters exhibit the same collective dynamics than smaller ones. These three properties are known to be exclusive of complex systems exhibiting critical dynamics, where the spatio-temporal dynamics show these peculiar type of fluctuations. Thus, these findings are fully consistent with previous reports of brain critical dynamics, and are relevant for the interpretation of the role of fluctuations and variability in brain function in health and disease. PMID:22934058

Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders

PubMed Central

Lord, Louis-David; Stevner, Angus B.; Kringelbach, Morten L.

2017-01-01

To survive in an ever-changing environment, the brain must seamlessly integrate a rich stream of incoming information into coherent internal representations that can then be used to efficiently plan for action. The brain must, however, balance its ability to integrate information from various sources with a complementary capacity to segregate information into modules which perform specialized computations in local circuits. Importantly, evidence suggests that imbalances in the brain's ability to bind together and/or segregate information over both space and time is a common feature of several neuropsychiatric disorders. Most studies have, however, until recently strictly attempted to characterize the principles of integration and segregation in static (i.e. time-invariant) representations of human brain networks, hence disregarding the complex spatio-temporal nature of these processes. In the present Review, we describe how the emerging discipline of whole-brain computational connectomics may be used to study the causal mechanisms of the integration and segregation of information on behaviourally relevant timescales. We emphasize how novel methods from network science and whole-brain computational modelling can expand beyond traditional neuroimaging paradigms and help to uncover the neurobiological determinants of the abnormal integration and segregation of information in neuropsychiatric disorders. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’. PMID:28507228

Spatio-temporal patterns of Campylobacter colonization in Danish broilers.

PubMed

Chowdhury, S; Themudo, G E; Sandberg, M; Ersbøll, A K

2013-05-01

Despite a number of risk-factor studies in different countries, the epidemiology of Campylobacter colonization in broilers, particularly spatial dependencies, is still not well understood. A series of analyses (visualization and exploratory) were therefore conducted in order to obtain a better understanding of the spatial and temporal distribution of Campylobacter in the Danish broiler population. In this study, we observed a non-random temporal occurrence of Campylobacter, with high prevalence during summer and low during winter. Significant spatio-temporal clusters were identified in the same areas in the summer months from 2007 to 2009. Range of influence between broiler farms were estimated at distances of 9.6 km and 13.5 km in different years. Identification of areas and time with greater risk indicates variable presence of risk factors with space and time. Implementation of safety measures on farms within high-risk clusters during summer could have an impact in reducing prevalence.

Four-dimensional optoacoustic temperature mapping in laser-induced thermotherapy

NASA Astrophysics Data System (ADS)

Oyaga Landa, Francisco Javier; Deán-Ben, Xosé Luís.; Sroka, Ronald; Razansky, Daniel

2018-02-01

Photoablative laser therapy is in common use for selective destruction of malignant masses, vascular and brain abnormalities. Tissue ablation and coagulation are irreversible processes occurring shortly after crossing a certain thermal exposure threshold. As a result, accurate mapping of the temperature field is essential for optimizing the outcome of these clinical interventions. Here we demonstrate four-dimensional optoacoustic temperature mapping of the entire photoablated region. Accuracy of the method is investigated in tissue-mimicking phantom experiments. Deviations of the volumetric optoacoustic temperature readings provided at 40ms intervals remained below 10% for temperature elevations above 3°C, as validated by simultaneous thermocouple measurements. The excellent spatio-temporal resolution of the new temperature monitoring approach aims at improving safety and efficacy of laser-based photothermal procedures.

EEG-based "serious" games and monitoring tools for pain management.

PubMed

Sourina, Olga; Wang, Qiang; Nguyen, Minh Khoa

2011-01-01

EEG-based "serious games" for medical applications attracted recently more attention from the research community and industry as wireless EEG reading devices became easily available on the market. EEG-based technology has been applied in anesthesiology, psychology, etc. In this paper, we proposed and developed EEG-based "serious" games and doctor's monitoring tools that could be used for pain management. As EEG signal is considered to have a fractal nature, we proposed and develop a novel spatio-temporal fractal based algorithm for brain state quantification. The algorithm is implemented with blobby visualization tools for patient monitoring and in EEG-based "serious" games. Such games could be used by patient even at home convenience for pain management as an alternative to traditional drug treatment.

Haptic fMRI: Reliability and performance of electromagnetic haptic interfaces for motion and force neuroimaging experiments.

PubMed

Menon, Samir; Zhu, Jack; Goyal, Deeksha; Khatib, Oussama

2017-07-01

Haptic interfaces compatible with functional magnetic resonance imaging (Haptic fMRI) promise to enable rich motor neuroscience experiments that study how humans perform complex manipulation tasks. Here, we present a large-scale study (176 scans runs, 33 scan sessions) that characterizes the reliability and performance of one such electromagnetically actuated device, Haptic fMRI Interface 3 (HFI-3). We outline engineering advances that ensured HFI-3 did not interfere with fMRI measurements. Observed fMRI temporal noise levels with HFI-3 operating were at the fMRI baseline (0.8% noise to signal). We also present results from HFI-3 experiments demonstrating that high resolution fMRI can be used to study spatio-temporal patterns of fMRI blood oxygenation dependent (BOLD) activation. These experiments include motor planning, goal-directed reaching, and visually-guided force control. Observed fMRI responses are consistent with existing literature, which supports Haptic fMRI's effectiveness at studying the brain's motor regions.

A Kinect based sign language recognition system using spatio-temporal features

NASA Astrophysics Data System (ADS)

Memiş, Abbas; Albayrak, Songül

2013-12-01

This paper presents a sign language recognition system that uses spatio-temporal features on RGB video images and depth maps for dynamic gestures of Turkish Sign Language. Proposed system uses motion differences and accumulation approach for temporal gesture analysis. Motion accumulation method, which is an effective method for temporal domain analysis of gestures, produces an accumulated motion image by combining differences of successive video frames. Then, 2D Discrete Cosine Transform (DCT) is applied to accumulated motion images and temporal domain features transformed into spatial domain. These processes are performed on both RGB images and depth maps separately. DCT coefficients that represent sign gestures are picked up via zigzag scanning and feature vectors are generated. In order to recognize sign gestures, K-Nearest Neighbor classifier with Manhattan distance is performed. Performance of the proposed sign language recognition system is evaluated on a sign database that contains 1002 isolated dynamic signs belongs to 111 words of Turkish Sign Language (TSL) in three different categories. Proposed sign language recognition system has promising success rates.

Has the economic crisis widened the intraurban socioeconomic inequalities in mortality? The case of Barcelona, Spain.

PubMed

Maynou, Laia; Saez, Marc; Lopez-Casasnovas, Guillem

2016-02-01

There is considerable evidence demonstrating socioeconomic inequalities in mortality, some of which focuses on intraurban inequalities. However, all the studies assume that the spatial variation of inequalities is stable over the time. We challenge this assumption and propose two hypotheses: (i) have spatial variations in socioeconomic inequalities in mortality at an intraurban level changed over time? and (ii) as a result of the economic crisis, has the gap between such disparities widened? In this paper, our objective is to assess the effect of the economic recession on the spatio-temporal variation of socioeconomic inequalities in mortality in Barcelona (Catalonia, Spain). We used a spatio-temporal ecological design to analyse mortality inequalities at small area level in Barcelona. Mortality data and socioeconomic indicators correspond to the years 2005 and 2008-2011. We specified spatio-temporal ecological mixed regressions for both men and women using two indicators, neighbourhood and year. We allowed the coefficients of the socioeconomic variables to differ according to the levels and explicitly took into account spatio-temporal adjustment. For men and women both absolute and, above all, relative risks for mortality have increased since 2009. In relative terms, this means that the risk of dying has increased much more in the most economically deprived neighbourhoods than in the more affluent ones. Although the geographical pattern in relative risks for mortality in neighbourhoods in Barcelona remained very stable between 2005 and 2011, socioeconomic inequalities in mortality at an intraurban level have surged since 2009. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

How innate is locomotion in precocial animals? A study on the early development of spatio-temporal gait variables and gait symmetry in piglets.

PubMed

Vanden Hole, Charlotte; Goyens, Jana; Prims, Sara; Fransen, Erik; Ayuso Hernando, Miriam; Van Cruchten, Steven; Aerts, Peter; Van Ginneken, Chris

2017-08-01

Locomotion is one of the most important ecological functions in animals. Precocial animals, such as pigs, are capable of independent locomotion shortly after birth. This raises the question whether coordinated movement patterns and the underlying muscular control in these animals is fully innate or whether there still exists a rapid maturation. We addressed this question by studying gait development in neonatal pigs through the analysis of spatio-temporal gait characteristics during locomotion at self-selected speed. To this end, we made video recordings of piglets walking along a corridor at several time points (from 0 h to 96 h). After digitization of the footfalls, we analysed self-selected speed and spatio-temporal characteristics (e.g. stride and step lengths, stride frequency and duty factor) to study dynamic similarity, intralimb coordination and interlimb coordination. To assess the variability of the gait pattern, left-right asymmetry was studied. To distinguish neuromotor maturation from effects caused by growth, both absolute and normalized data (according to the dynamic similarity concept) were included in the analysis. All normalized spatio-temporal variables reached stable values within 4 h of birth, with most of them showing little change after the age of 2 h. Most asymmetry indices showed stable values, hovering around 10%, within 8 h of birth. These results indicate that coordinated movement patterns are not entirely innate, but that a rapid neuromotor maturation, potentially also the result of the rearrangement or recombination of existing motor modules, takes place in these precocial animals. © 2017. Published by The Company of Biologists Ltd.

Spatio-temporal distribution of soil nitrogen in Poyang lake ecological economic zone (South-China).

PubMed

Jiang, Yefeng; Rao, Lei; Sun, Kai; Han, Yi; Guo, Xi

2018-06-01

Revealing the spatio-temporal distribution of soil nitrogen (N) contributes to N management and prevention of N pollution. The objective of this work is to study the spatio-temporal distribution of soil N and their driving factors in the topsoil (0-20 cm) of farmland in Yugan county, China in 1982 and 2012. Data were collected from 200 sampling sites of the second national soil survey in Yugan in 1982 and 423 sampling sites of the soil testing and formula fertilization project in 2012. On average total N (TN) and available N (AN) significantly increased from 1.50 g kg -1 and 153.04 mg kg -1 in 1982 to 1.58 g kg -1 and 179.75 mg kg -1 in 2012, respectively. The distance of spatial autocorrelation for TN increased from 2.79 to 6.18 km and from 2.97 to 18.00 km for AN from 1982 to 2012. The nugget/sill ratio for TN (0.472 in 1982 and 0.581 in 2012) indicated that soil TN driving by natural characteristics in 1982 to human activities in 2012. The nugget/sill ratio for soil AN (0.471 in 1982 and 0.688 in 2012) indicated that soil AN is more influenced by human activities. The major factors driving the spatio-temporal distribution of soil N was N application rate. To promote the sustainable development of agriculture and eco-environment, we should improve the awareness of farmers on chemical fertilizers (particularly N) and the level of N fertilizer management, increase the use of manure and organic fertilizer and facilitate rational fertilization by farmers. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Spatio-temporal characterization imaging of Ca2+ oscillations in rat hippocampal neurons

NASA Astrophysics Data System (ADS)

Zhang, Zhihong; Lu, Jinling; Zhou, Wei; Liu, Rengang; Zeng, Shaoqun; Luo, Qingming

2001-08-01

Ca2+ is the most common signal transduction element in cells and plays critical rolls in neuronal development and plasticity. Ca2+ signals encode information in their oscillation frequency or amplitude and response time to regular cellular function. In this study, in order to reveal the spatio-temporal characterization of Ca2+ oscillations in rat hippocampal neurons, two kinds of Ca2+ fluorescent probes, yellow cameleons 2.1 (YC2.1) and Fluo-3, were used to monitor the change of the intracellular free Ca2+ concentration (]Ca2+[i). Spontaneous Ca2+ oscillations and glutamate elicited Ca2+ oscillations were observed with multi-photon excitation laser scan microscope (MPELSM) and confocal laser scan microscope (CLSM). The observation showed that the spatio- temporal characterization of either spontaneous or glutamate provoked Ca2+ oscillations had difference between the neurites and somata in individual nerons, especially in some distal end of neurites. The result indicated that Ca2+ oscillations were most important signal transduction pattern in neuronal development and activation. The spatio-temporal characterization of difference of Ca2+ signals between the distal endo of neurites and the somata might be associated with the distribution of ionotropic receptor and metabotropic glutamate receptors, and Ca2+ response mechanism mediated by two kinds of glutamate receptor. Ca2+ signal elicited by glutamate in the distal end of neurites appeared more complex and generated faster than that in the somata. It was suggested that Ca2+ signal in glutamate stimulated hippacamal neurons first generated from the distal end of neurites and then transduted to the somata. The complicated Ca2+ signal characterization in the distal end of neurites might be associated with neuronal activitation, neurotransmitter releasing, and other functions of neurons.

beta-diversity and species accumulation in antarctic coastal benthos: influence of habitat, distance and productivity on ecological connectivity.

PubMed

Thrush, Simon F; Hewitt, Judi E; Cummings, Vonda J; Norkko, Alf; Chiantore, Mariachiara

2010-07-30

High Antarctic coastal marine environments are comparatively pristine with strong environmental gradients, which make them important places to investigate biodiversity relationships. Defining how different environmental features contribute to shifts in beta-diversity is especially important as these shifts reflect both spatio-temporal variations in species richness and the degree of ecological separation between local and regional species pools. We used complementary techniques (species accumulation models, multivariate variance partitioning and generalized linear models) to assess how the roles of productivity, bio-physical habitat heterogeneity and connectivity change with spatial scales from metres to 100's of km. Our results demonstrated that the relative importance of specific processes influencing species accumulation and beta-diversity changed with increasing spatial scale, and that patterns were never driven by only one factor. Bio-physical habitat heterogeneity had a strong influence on beta-diversity at scales <290 km, while the effects of productivity were low and significant only at scales >40 km. Our analysis supports the emphasis on the analysis of diversity relationships across multiple spatial scales and highlights the unequal connectivity of individual sites to the regional species pool. This has important implications for resilience to habitat loss and community homogenisation, especially for Antarctic benthic communities where rates of recovery from disturbance are slow, there is a high ratio of poor-dispersing and brooding species, and high biogenic habitat heterogeneity and spatio-temporal variability in primary production make the system vulnerable to disturbance. Consequently, large areas need to be included within marine protected areas for effective management and conservation of these special ecosystems in the face of increasing anthropogenic disturbance.

Spatio-Temporal Dynamics of Cholera during the First Year of the Epidemic in Haiti

PubMed Central

Gaudart, Jean; Rebaudet, Stanislas; Barrais, Robert; Boncy, Jacques; Faucher, Benoit; Piarroux, Martine; Magloire, Roc; Thimothe, Gabriel; Piarroux, Renaud

2013-01-01

Background In October 2010, cholera importation in Haiti triggered an epidemic that rapidly proved to be the world's largest epidemic of the seventh cholera pandemic. To establish effective control and elimination policies, strategies rely on the analysis of cholera dynamics. In this report, we describe the spatio-temporal dynamics of cholera and the associated environmental factors. Methodology/Principal findings Cholera-associated morbidity and mortality data were prospectively collected at the commune level according to the World Health Organization standard definition. Attack and mortality rates were estimated and mapped to assess epidemic clusters and trends. The relationships between environmental factors were assessed at the commune level using multivariate analysis. The global attack and mortality rates were 488.9 cases/10,000 inhabitants and 6.24 deaths/10,000 inhabitants, respectively. Attack rates displayed a significantly high level of spatial heterogeneity (varying from 64.7 to 3070.9 per 10,000 inhabitants), thereby suggesting disparate outbreak processes. The epidemic course exhibited two principal outbreaks. The first outbreak (October 16, 2010–January 30, 2011) displayed a centrifugal spread of a damping wave that suddenly emerged from Mirebalais. The second outbreak began at the end of May 2011, concomitant with the onset of the rainy season, and displayed a highly fragmented epidemic pattern. Environmental factors (river and rice fields: p<0.003) played a role in disease dynamics exclusively during the early phases of the epidemic. Conclusion Our findings demonstrate that the epidemic is still evolving, with a changing transmission pattern as time passes. Such an evolution could have hardly been anticipated, especially in a country struck by cholera for the first time. These results argue for the need for control measures involving intense efforts in rapid and exhaustive case tracking. PMID:23593516

Mapping of Human FOXP2 Enhancers Reveals Complex Regulation.

PubMed

Becker, Martin; Devanna, Paolo; Fisher, Simon E; Vernes, Sonja C

2018-01-01

Mutations of the FOXP2 gene cause a severe speech and language disorder, providing a molecular window into the neurobiology of language. Individuals with FOXP2 mutations have structural and functional alterations affecting brain circuits that overlap with sites of FOXP2 expression, including regions of the cortex, striatum, and cerebellum. FOXP2 displays complex patterns of expression in the brain, as well as in non-neuronal tissues, suggesting that sophisticated regulatory mechanisms control its spatio-temporal expression. However, to date, little is known about the regulation of FOXP2 or the genomic elements that control its expression. Using chromatin conformation capture (3C), we mapped the human FOXP2 locus to identify putative enhancer regions that engage in long-range interactions with the promoter of this gene. We demonstrate the ability of the identified enhancer regions to drive gene expression. We also show regulation of the FOXP2 promoter and enhancer regions by candidate regulators - FOXP family and TBR1 transcription factors. These data point to regulatory elements that may contribute to the temporal- or tissue-specific expression patterns of human FOXP2 . Understanding the upstream regulatory pathways controlling FOXP2 expression will bring new insight into the molecular networks contributing to human language and related disorders.

Mapping of Human FOXP2 Enhancers Reveals Complex Regulation

PubMed Central

Becker, Martin; Devanna, Paolo; Fisher, Simon E.; Vernes, Sonja C.

2018-01-01

Mutations of the FOXP2 gene cause a severe speech and language disorder, providing a molecular window into the neurobiology of language. Individuals with FOXP2 mutations have structural and functional alterations affecting brain circuits that overlap with sites of FOXP2 expression, including regions of the cortex, striatum, and cerebellum. FOXP2 displays complex patterns of expression in the brain, as well as in non-neuronal tissues, suggesting that sophisticated regulatory mechanisms control its spatio-temporal expression. However, to date, little is known about the regulation of FOXP2 or the genomic elements that control its expression. Using chromatin conformation capture (3C), we mapped the human FOXP2 locus to identify putative enhancer regions that engage in long-range interactions with the promoter of this gene. We demonstrate the ability of the identified enhancer regions to drive gene expression. We also show regulation of the FOXP2 promoter and enhancer regions by candidate regulators – FOXP family and TBR1 transcription factors. These data point to regulatory elements that may contribute to the temporal- or tissue-specific expression patterns of human FOXP2. Understanding the upstream regulatory pathways controlling FOXP2 expression will bring new insight into the molecular networks contributing to human language and related disorders. PMID:29515369

The future of human cerebral cartography: a novel approach

PubMed Central

Frackowiak, Richard; Markram, Henry

2015-01-01

Cerebral cartography can be understood in a limited, static, neuroanatomical sense. Temporal information from electrical recordings contributes information on regional interactions adding a functional dimension. Selective tagging and imaging of molecules adds biochemical contributions. Cartographic detail can also be correlated with normal or abnormal psychological or behavioural data. Modern cerebral cartography is assimilating all these elements. Cartographers continue to collect ever more precise data in the hope that general principles of organization will emerge. However, even detailed cartographic data cannot generate knowledge without a multi-scale framework making it possible to relate individual observations and discoveries. We propose that, in the next quarter century, advances in cartography will result in progressively more accurate drafts of a data-led, multi-scale model of human brain structure and function. These blueprints will result from analysis of large volumes of neuroscientific and clinical data, by a process of reconstruction, modelling and simulation. This strategy will capitalize on remarkable recent developments in informatics and computer science and on the existence of much existing, addressable data and prior, though fragmented, knowledge. The models will instantiate principles that govern how the brain is organized at different levels and how different spatio-temporal scales relate to each other in an organ-centred context. PMID:25823868

Ongoing activity in temporally coherent networks predicts intra-subject fluctuation of response time to sporadic executive control demands.

PubMed

Nozawa, Takayuki; Sugiura, Motoaki; Yokoyama, Ryoichi; Ihara, Mizuki; Kotozaki, Yuka; Miyauchi, Carlos Makoto; Kanno, Akitake; Kawashima, Ryuta

2014-01-01

Can ongoing fMRI BOLD signals predict fluctuations in swiftness of a person's response to sporadic cognitive demands? This is an important issue because it clarifies whether intrinsic brain dynamics, for which spatio-temporal patterns are expressed as temporally coherent networks (TCNs), have effects not only on sensory or motor processes, but also on cognitive processes. Predictivity has been affirmed, although to a limited extent. Expecting a predictive effect on executive performance for a wider range of TCNs constituting the cingulo-opercular, fronto-parietal, and default mode networks, we conducted an fMRI study using a version of the color-word Stroop task that was specifically designed to put a higher load on executive control, with the aim of making its fluctuations more detectable. We explored the relationships between the fluctuations in ongoing pre-trial activity in TCNs and the task response time (RT). The results revealed the existence of TCNs in which fluctuations in activity several seconds before the onset of the trial predicted RT fluctuations for the subsequent trial. These TCNs were distributed in the cingulo-opercular and fronto-parietal networks, as well as in perceptual and motor networks. Our results suggest that intrinsic brain dynamics in these networks constitute "cognitive readiness," which plays an active role especially in situations where information for anticipatory attention control is unavailable. Fluctuations in these networks lead to fluctuations in executive control performance.

Spatial, temporal and spatio-temporal clusters of measles incidence at the county level in Guangxi, China during 2004-2014: flexibly shaped scan statistics.

PubMed

Tang, Xianyan; Geater, Alan; McNeil, Edward; Deng, Qiuyun; Dong, Aihu; Zhong, Ge

2017-04-04

Outbreaks of measles re-emerged in Guangxi province during 2013-2014, where measles again became a major public health concern. A better understanding of the patterns of measles cases would help in identifying high-risk areas and periods for optimizing preventive strategies, yet these patterns remain largely unknown. Thus, this study aimed to determine the patterns of measles clusters in space, time and space-time at the county level over the period 2004-2014 in Guangxi. Annual data on measles cases and population sizes for each county were obtained from Guangxi CDC and Guangxi Bureau of Statistics, respectively. Epidemic curves and Kulldorff's temporal scan statistics were used to identify seasonal peaks and high-risk periods. Tango's flexible scan statistics were implemented to determine irregular spatial clusters. Spatio-temporal clusters in elliptical cylinder shapes were detected by Kulldorff's scan statistics. Population attributable risk percent (PAR%) of children aged ≤24 months was used to identify regions with a heavy burden of measles. Seasonal peaks occurred between April and June, and a temporal measles cluster was detected in 2014. Spatial clusters were identified in West, Southwest and North Central Guangxi. Three phases of spatio-temporal clusters with high relative risk were detected: Central Guangxi during 2004-2005, Midwest Guangxi in 2007, and West and Southwest Guangxi during 2013-2014. Regions with high PAR% were mainly clustered in West, Southwest, North and Central Guangxi. A temporal uptrend of measles incidence existed in Guangxi between 2010 and 2014, while downtrend during 2004-2009. The hotspots shifted from Central to West and Southwest Guangxi, regions overburdened with measles. Thus, intensifying surveillance of timeliness and completeness of routine vaccination and implementing supplementary immunization activities for measles should prioritized in these regions.

Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures

NASA Astrophysics Data System (ADS)

Deilami, Kaveh; Kamruzzaman, Md.; Liu, Yan

2018-05-01

Despite research on urban heat island (UHI) effect has increased exponentially over the last few decades, a systematic review of factors contributing to UHI effect has scarcely been reported in the literature. This paper provides a systematic and overarching review of different spatial and temporal factors affecting the UHI effect. UHI is a phenomenon when urban areas experience a higher temperature than their surrounding non-urban areas and is considered as a critical factor contributing to global warming, heat related mortalities, and unpredictable climatic changes. Therefore, there is a pressing need to identify the spatio-temporal factors that contribute to (or mitigate) the UHI effect in order to develop a thorough understanding of their causal mechanism so that these are addressed through urban planning policies. This paper systematically identified 75 eligible studies on UHI effect and reviews the nature and type of satellite images used, the techniques applied to classify land cover/use changes, the models to assess the link between spatio-temporal factors and UHI effect, and the effects of these factors on UHI. The review results show that: a) 54% of the studies used Landsat TM images for modelling the UHI effect followed by Landsat ETM (34%), and MODIS (28%); b) land cover indices (46%), followed by supervised classification (17%) were the dominant methods to derive land cover/use changes associated with UHI effect; c) ordinary least square regression is the most commonly applied method (68%) to investigate the link between different spatio-temporal factors and the UHI effect followed by comparative analysis (33%); and d) the most common factors affecting the UHI effect as reported in the reviewed studies, include vegetation cover (44%), season (33%), built-up area (28%), day/night (25%), population density (14%), water body (12%) together with others. This research discusses the findings in policy terms and provides directions for future research.

Spatio-temporal genetic variation of the biting midge vector species Culicoides imicola (Ceratopogonidae) Kieffer in France.

PubMed

Jacquet, Stéphanie; Huber, Karine; Guis, Hélène; Setier-Rio, Marie-Laure; Goffredo, Maria; Allène, Xavier; Rakotoarivony, Ignace; Chevillon, Christine; Bouyer, Jérémy; Baldet, Thierry; Balenghien, Thomas; Garros, Claire

2016-03-11

Introduction of vector species into new areas represents a main driver for the emergence and worldwide spread of vector-borne diseases. This poses a substantial threat to livestock economies and public health. Culicoides imicola Kieffer, a major vector species of economically important animal viruses, is described with an apparent range expansion in Europe where it has been recorded in south-eastern continental France, its known northern distribution edge. This questioned on further C. imicola population extension and establishment into new territories. Studying the spatio-temporal genetic variation of expanding populations can provide valuable information for the design of reliable models of future spread. Entomological surveys and population genetic approaches were used to assess the spatio-temporal population dynamics of C. imicola in France. Entomological surveys (2-3 consecutive years) were used to evaluate population abundances and local spread in continental France (28 sites in the Var department) and in Corsica (4 sites). We also genotyped at nine microsatellite loci insects from 3 locations in the Var department over 3 years (2008, 2010 and 2012) and from 6 locations in Corsica over 4 years (2002, 2008, 2010 and 2012). Entomological surveys confirmed the establishment of C. imicola populations in Var department, but indicated low abundances and no apparent expansion there within the studied period. Higher population abundances were recorded in Corsica. Our genetic data suggested the absence of spatio-temporal genetic changes within each region but a significant increase of the genetic differentiation between Corsican and Var populations through time. The lack of intra-region population structure may result from strong gene flow among populations. We discussed the observed temporal variation between Corsica and Var as being the result of genetic drift following introduction, and/or the genetic characteristics of populations at their range edge. Our results suggest that local range expansion of C. imicola in continental France may be slowed by the low population abundances and unsuitable climatic and environmental conditions.

Spatio-temporal patterns in land use and management affecting surface runoff response of agricultural catchments - a review

NASA Astrophysics Data System (ADS)

Fiener, P.; Auerswald, K.; van Oost, K.

2009-04-01

In many landscapes, land use creates a complex pattern in addition to the patterns resulting from soil, topography and rain. Despite the static layout of fields, a spatio-temporally highly variable situation regarding the surface runoff and erosion processes results from the asynchronous seasonal variation associated with different land uses. While the behaviour of individual land-uses and their seasonal variation is analyzed in many studies, the spatio-temporal interaction related to this pattern is rarely studied despite its crucial influence on hydrological and geomorphic response of catchments. The difficulty in studying such interactions mainly results from the fact that it is impossible to set up a replicated experiment on the landscape scale. The purpose of this review is to present the advances made thus far in quantifying the effects of patchiness of land use and management on surface runoff response in agricultural catchments. We will focus on the effects of spatio-temporal patterns in land use patches on hydraulic connectivity between patches and within catchments. This will include the temporal patterns in land management affecting infiltration, surface roughness and hence runoff concentration within single fields or land use patches insofar as these effects must be known to evaluate the combined effect of patch behaviour in space and time on catchment connectivity and surface runoff. Surface runoff effects of patchiness and connectivity between patches or within a catchment, can either be addressed by modelling studies or by comprehensive catchment field measurements, e.g. paired-watershed experiments or landscape scale studies on different scales. This limits our review to studies at the scale of small catchments < 10 km², where the time constant of the network (i.e. travel time through it) is smaller than the infiltration phase. Despite this limitation, these small catchments are important as they constitute 2/3 of the total surface of large water drainage networks.

Spatio-Temporal Equalizer for a Receiving-Antenna Feed Array

NASA Technical Reports Server (NTRS)

Mukai, Ryan; Lee, Dennis; Vilnrotter, Victor

2010-01-01

A spatio-temporal equalizer has been conceived as an improved means of suppressing multipath effects in the reception of aeronautical telemetry signals, and may be adaptable to radar and aeronautical communication applications as well. This equalizer would be an integral part of a system that would also include a seven-element planar array of receiving feed horns centered at the focal point of a paraboloidal antenna that would be nominally aimed at or near the aircraft that would be the source of the signal that one seeks to receive (see Figure 1). This spatio-temporal equalizer would consist mostly of a bank of seven adaptive finite-impulse-response (FIR) filters one for each element in the array - and the outputs of the filters would be summed (see Figure 2). The combination of the spatial diversity of the feedhorn array and the temporal diversity of the filter bank would afford better multipath-suppression performance than is achievable by means of temporal equalization alone. The seven-element feed array would supplant the single feed horn used in a conventional paraboloidal ground telemetry-receiving antenna. The radio-frequency telemetry signals re ceiv ed by the seven elements of the array would be digitized, converted to complex baseband form, and sent to the FIR filter bank, which would adapt itself in real time to enable reception of telemetry at a low bit error rate, even in the presence of multipath of the type found at many flight test ranges.

Componential Network for the Recognition of Tool-Associated Actions: Evidence from Voxel-based Lesion-Symptom Mapping in Acute Stroke Patients.

PubMed

Martin, Markus; Dressing, Andrea; Bormann, Tobias; Schmidt, Charlotte S M; Kümmerer, Dorothee; Beume, Lena; Saur, Dorothee; Mader, Irina; Rijntjes, Michel; Kaller, Christoph P; Weiller, Cornelius

2017-08-01

The study aimed to elucidate areas involved in recognizing tool-associated actions, and to characterize the relationship between recognition and active performance of tool use.We performed voxel-based lesion-symptom mapping in a prospective cohort of 98 acute left-hemisphere ischemic stroke patients (68 male, age mean ± standard deviation, 65 ± 13 years; examination 4.4 ± 2 days post-stroke). In a video-based test, patients distinguished correct tool-related actions from actions with spatio-temporal (incorrect grip, kinematics, or tool orientation) or conceptual errors (incorrect tool-recipient matching, e.g., spreading jam on toast with a paintbrush). Moreover, spatio-temporal and conceptual errors were determined during actual tool use.Deficient spatio-temporal error discrimination followed lesions within a dorsal network in which the inferior parietal lobule (IPL) and the lateral temporal cortex (sLTC) were specifically relevant for assessing functional hand postures and kinematics, respectively. Conversely, impaired recognition of conceptual errors resulted from damage to ventral stream regions including anterior temporal lobe. Furthermore, LTC and IPL lesions impacted differently on action recognition and active tool use, respectively.In summary, recognition of tool-associated actions relies on a componential network. Our study particularly highlights the dissociable roles of LTC and IPL for the recognition of action kinematics and functional hand postures, respectively. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Subjective visual perception: from local processing to emergent phenomena of brain activity.

PubMed

Panagiotaropoulos, Theofanis I; Kapoor, Vishal; Logothetis, Nikos K

2014-05-05

The combination of electrophysiological recordings with ambiguous visual stimulation made possible the detection of neurons that represent the content of subjective visual perception and perceptual suppression in multiple cortical and subcortical brain regions. These neuronal populations, commonly referred to as the neural correlates of consciousness, are more likely to be found in the temporal and prefrontal cortices as well as the pulvinar, indicating that the content of perceptual awareness is represented with higher fidelity in higher-order association areas of the cortical and thalamic hierarchy, reflecting the outcome of competitive interactions between conflicting sensory information resolved in earlier stages. However, despite the significant insights into conscious perception gained through monitoring the activities of single neurons and small, local populations, the immense functional complexity of the brain arising from correlations in the activity of its constituent parts suggests that local, microscopic activity could only partially reveal the mechanisms involved in perceptual awareness. Rather, the dynamics of functional connectivity patterns on a mesoscopic and macroscopic level could be critical for conscious perception. Understanding these emergent spatio-temporal patterns could be informative not only for the stability of subjective perception but also for spontaneous perceptual transitions suggested to depend either on the dynamics of antagonistic ensembles or on global intrinsic activity fluctuations that may act upon explicit neural representations of sensory stimuli and induce perceptual reorganization. Here, we review the most recent results from local activity recordings and discuss the potential role of effective, correlated interactions during perceptual awareness.

Subjective visual perception: from local processing to emergent phenomena of brain activity

PubMed Central

Panagiotaropoulos, Theofanis I.; Kapoor, Vishal; Logothetis, Nikos K.

2014-01-01

The combination of electrophysiological recordings with ambiguous visual stimulation made possible the detection of neurons that represent the content of subjective visual perception and perceptual suppression in multiple cortical and subcortical brain regions. These neuronal populations, commonly referred to as the neural correlates of consciousness, are more likely to be found in the temporal and prefrontal cortices as well as the pulvinar, indicating that the content of perceptual awareness is represented with higher fidelity in higher-order association areas of the cortical and thalamic hierarchy, reflecting the outcome of competitive interactions between conflicting sensory information resolved in earlier stages. However, despite the significant insights into conscious perception gained through monitoring the activities of single neurons and small, local populations, the immense functional complexity of the brain arising from correlations in the activity of its constituent parts suggests that local, microscopic activity could only partially reveal the mechanisms involved in perceptual awareness. Rather, the dynamics of functional connectivity patterns on a mesoscopic and macroscopic level could be critical for conscious perception. Understanding these emergent spatio-temporal patterns could be informative not only for the stability of subjective perception but also for spontaneous perceptual transitions suggested to depend either on the dynamics of antagonistic ensembles or on global intrinsic activity fluctuations that may act upon explicit neural representations of sensory stimuli and induce perceptual reorganization. Here, we review the most recent results from local activity recordings and discuss the potential role of effective, correlated interactions during perceptual awareness. PMID:24639588

The ultimate picture-the combination of live cell superresolution microscopy and single molecule tracking yields highest spatio-temporal resolution.

PubMed

Dersch, Simon; Graumann, Peter L

2018-06-01

We are witnessing a breathtaking development in light (fluorescence) microscopy, where structures can be resolved down to the size of a ribosome within cells. This has already yielded surprising insight into the subcellular structure of cells, including the smallest cells, bacteria. Moreover, it has become possible to visualize and track single fluorescent protein fusions in real time, and quantify molecule numbers within individual cells. Combined, super resolution and single molecule tracking are pushing the limits of our understanding of the spatio-temporal organization even of the smallest cells to an unprecedented depth. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatio-Temporal Patterns in Colonies of Rod-Shaped Bacteria

NASA Astrophysics Data System (ADS)

Kitsunezaki, S.

In incubation experiments of bacterial colonies of Proteus Mirabilis, macroscopic spatio-temporal patterns, such as turbulent and unidirectional spiral patterns, appear in colonies. Considering only kinetic propeties of rod-shaped bacteria, we propose a phenomenological model for the directional and positional distributions. As the average density increases, homogeneous states bifurcate sub-critically into nonuniform states exhibiting localized collective motion, and spiral patterns appear for sufficiently large density. These patterns result from interactions between the local bacteria densities and the order parameter representing collective motion. Our model can be described by reduced equations using a perturbative method for large density. The unidirectionality of sprial rotation is also discussed.

Optical solitons, explicit solutions and modulation instability analysis with second-order spatio-temporal dispersion

NASA Astrophysics Data System (ADS)

Inc, Mustafa; Isa Aliyu, Aliyu; Yusuf, Abdullahi; Baleanu, Dumitru

2017-12-01

This paper obtains the dark, bright, dark-bright or combined optical and singular solitons to the nonlinear Schrödinger equation (NLSE) with group velocity dispersion coefficient and second-order spatio-temporal dispersion coefficient, which arises in photonics and waveguide optics and in optical fibers. The integration algorithm is the sine-Gordon equation method (SGEM). Furthermore, the explicit solutions of the equation are derived by considering the power series solutions (PSS) theory and the convergence of the solutions is guaranteed. Lastly, the modulation instability analysis (MI) is studied based on the standard linear-stability analysis and the MI gain spectrum is obtained.

Spatio-temporal dynamics in the origin of genetic information

NASA Astrophysics Data System (ADS)

Kim, Pan-Jun; Jeong, Hawoong

2005-04-01

We study evolutionary processes induced by spatio-temporal dynamics in prebiotic evolution. Using numerical simulations, we demonstrate that hypercycles emerge from complex interaction structures in multispecies systems. In this work, we also find that ‘hypercycle hybrid’ protects the hypercycle from its environment during the growth process. There is little selective advantage for one hypercycle to maintain coexistence with others. This brings the possibility of the outcompetition between hypercycles resulting in the negative effect on information diversity. To enrich the information in hypercycles, symbiosis with parasites is suggested. It is shown that symbiosis with parasites can play an important role in the prebiotic immunology.

Health impact assessment of industrial development projects: a spatio-temporal visualization.

PubMed

Winkler, Mirko S; Krieger, Gary R; Divall, Mark J; Singer, Burton H; Utzinger, Jürg

2012-05-01

Development and implementation of large-scale industrial projects in complex eco-epidemiological settings typically require combined environmental, social and health impact assessments. We present a generic, spatio-temporal health impact assessment (HIA) visualization, which can be readily adapted to specific projects and key stakeholders, including poorly literate communities that might be affected by consequences of a project. We illustrate how the occurrence of a variety of complex events can be utilized for stakeholder communication, awareness creation, interactive learning as well as formulating HIA research and implementation questions. Methodological features are highlighted in the context of an iron ore development in a rural part of Africa.

Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever.

PubMed

Raghavan, Ram K; Goodin, Douglas G; Neises, Daniel; Anderson, Gary A; Ganta, Roman R

2016-01-01

This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region. Our results show a steady increase in RMSF incidence over the study period to newer geographic areas, and the posterior probabilities of county-specific trends indicate clustering of high risk counties in the central and southern parts of the study region. At the spatial scale of a county, the prevalence levels of RMSF is influenced by poverty status, average relative humidity, and average land surface temperature (>35°C) in the region, and the relevance of these factors in the context of climate-change impacts on tick-borne diseases are discussed.

Spatio-temporal dynamics of security investments in an interdependent risk environment

NASA Astrophysics Data System (ADS)

Shafi, Kamran; Bender, Axel; Zhong, Weicai; Abbass, Hussein A.

2012-10-01

In a globalised world where risks spread through contagion, the decision of an entity to invest in securing its premises from stochastic risks no longer depends solely on its own actions but also on the actions of other interacting entities in the system. This phenomenon is commonly seen in many domains including airline, logistics and computer security and is referred to as Interdependent Security (IDS). An IDS game models this decision problem from a game-theoretic perspective and deals with the behavioural dynamics of risk-reduction investments in such settings. This paper enhances this model and investigates the spatio-temporal aspects of the IDS games. The spatio-temporal dynamics are studied using simple replicator dynamics on a variety of network structures and for various security cost tradeoffs that lead to different Nash equilibria in an IDS game. The simulation results show that the neighbourhood configuration has a greater effect on the IDS game dynamics than network structure. An in-depth empirical analysis of game dynamics is carried out on regular graphs, which leads to the articulation of necessary and sufficient conditions for dominance in IDS games under spatial constraints.

Numerical solution of a spatio-temporal gender-structured model for hantavirus infection in rodents.

PubMed

Bürger, Raimund; Chowell, Gerardo; Gavilán, Elvis; Mulet, Pep; Villada, Luis M

2018-02-01

In this article we describe the transmission dynamics of hantavirus in rodents using a spatio-temporal susceptible-exposed-infective-recovered (SEIR) compartmental model that distinguishes between male and female subpopulations [L.J.S. Allen, R.K. McCormack and C.B. Jonsson, Bull. Math. Biol. 68 (2006), 511--524]. Both subpopulations are assumed to differ in their movement with respect to local variations in the densities of their own and the opposite gender group. Three alternative models for the movement of the male individuals are examined. In some cases the movement is not only directed by the gradient of a density (as in the standard diffusive case), but also by a non-local convolution of density values as proposed, in another context, in [R.M. Colombo and E. Rossi, Commun. Math. Sci., 13 (2015), 369--400]. An efficient numerical method for the resulting convection-diffusion-reaction system of partial differential equations is proposed. This method involves techniques of weighted essentially non-oscillatory (WENO) reconstructions in combination with implicit-explicit Runge-Kutta (IMEX-RK) methods for time stepping. The numerical results demonstrate significant differences in the spatio-temporal behavior predicted by the different models, which suggest future research directions.

A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions

PubMed Central

Koorehdavoudi, Hana; Bogdan, Paul

2016-01-01

Biological systems are frequently categorized as complex systems due to their capabilities of generating spatio-temporal structures from apparent random decisions. In spite of research on analyzing biological systems, we lack a quantifiable framework for measuring their complexity. To fill this gap, in this paper, we develop a new paradigm to study a collective group of N agents moving and interacting in a three-dimensional space. Our paradigm helps to identify the spatio-temporal states of the motion of the group and their associated transition probabilities. This framework enables the estimation of the free energy landscape corresponding to the identified states. Based on the energy landscape, we quantify missing information, emergence, self-organization and complexity for a collective motion. We show that the collective motion of the group of agents evolves to reach the most probable state with relatively lowest energy level and lowest missing information compared to other possible states. Our analysis demonstrates that the natural group of animals exhibit a higher degree of emergence, self-organization and complexity over time. Consequently, this algorithm can be integrated into new frameworks to engineer collective motions to achieve certain degrees of emergence, self-organization and complexity. PMID:27297496

Cellular automata rule characterization and classification using texture descriptors

NASA Astrophysics Data System (ADS)

Machicao, Jeaneth; Ribas, Lucas C.; Scabini, Leonardo F. S.; Bruno, Odermir M.

2018-05-01

The cellular automata (CA) spatio-temporal patterns have attracted the attention from many researchers since it can provide emergent behavior resulting from the dynamics of each individual cell. In this manuscript, we propose an approach of texture image analysis to characterize and classify CA rules. The proposed method converts the CA spatio-temporal patterns into a gray-scale image. The gray-scale is obtained by creating a binary number based on the 8-connected neighborhood of each dot of the CA spatio-temporal pattern. We demonstrate that this technique enhances the CA rule characterization and allow to use different texture image analysis algorithms. Thus, various texture descriptors were evaluated in a supervised training approach aiming to characterize the CA's global evolution. Our results show the efficiency of the proposed method for the classification of the elementary CA (ECAs), reaching a maximum of 99.57% of accuracy rate according to the Li-Packard scheme (6 classes) and 94.36% for the classification of the 88 rules scheme. Moreover, within the image analysis context, we found a better performance of the method by means of a transformation of the binary states to a gray-scale.

A Statistical Physics Characterization of the Complex Systems Dynamics: Quantifying Complexity from Spatio-Temporal Interactions

NASA Astrophysics Data System (ADS)

Koorehdavoudi, Hana; Bogdan, Paul

2016-06-01

Biological systems are frequently categorized as complex systems due to their capabilities of generating spatio-temporal structures from apparent random decisions. In spite of research on analyzing biological systems, we lack a quantifiable framework for measuring their complexity. To fill this gap, in this paper, we develop a new paradigm to study a collective group of N agents moving and interacting in a three-dimensional space. Our paradigm helps to identify the spatio-temporal states of the motion of the group and their associated transition probabilities. This framework enables the estimation of the free energy landscape corresponding to the identified states. Based on the energy landscape, we quantify missing information, emergence, self-organization and complexity for a collective motion. We show that the collective motion of the group of agents evolves to reach the most probable state with relatively lowest energy level and lowest missing information compared to other possible states. Our analysis demonstrates that the natural group of animals exhibit a higher degree of emergence, self-organization and complexity over time. Consequently, this algorithm can be integrated into new frameworks to engineer collective motions to achieve certain degrees of emergence, self-organization and complexity.

Spatio-temporal correlations in models of collective motion ruled by different dynamical laws.

PubMed

Cavagna, Andrea; Conti, Daniele; Giardina, Irene; Grigera, Tomas S; Melillo, Stefania; Viale, Massimiliano

2016-11-15

Information transfer is an essential factor in determining the robustness of biological systems with distributed control. The most direct way to study the mechanisms ruling information transfer is to experimentally observe the propagation across the system of a signal triggered by some perturbation. However, this method may be inefficient for experiments in the field, as the possibilities to perturb the system are limited and empirical observations must rely on natural events. An alternative approach is to use spatio-temporal correlations to probe the information transfer mechanism directly from the spontaneous fluctuations of the system, without the need to have an actual propagating signal on record. Here we test this method on models of collective behaviour in their deeply ordered phase by using ground truth data provided by numerical simulations in three dimensions. We compare two models characterized by very different dynamical equations and information transfer mechanisms: the classic Vicsek model, describing an overdamped noninertial dynamics and the inertial spin model, characterized by an underdamped inertial dynamics. By using dynamic finite-size scaling, we show that spatio-temporal correlations are able to distinguish unambiguously the diffusive information transfer mechanism of the Vicsek model from the linear mechanism of the inertial spin model.

Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system

PubMed Central

Lall, Ramona; Levin-Rector, Alison; Sell, Jessica; Paladini, Marc; Konty, Kevin J.; Olson, Don; Weiss, Don

2017-01-01

The New York City Department of Health and Mental Hygiene has operated an emergency department syndromic surveillance system since 2001, using temporal and spatial scan statistics run on a daily basis for cluster detection. Since the system was originally implemented, a number of new methods have been proposed for use in cluster detection. We evaluated six temporal and four spatial/spatio-temporal detection methods using syndromic surveillance data spiked with simulated injections. The algorithms were compared on several metrics, including sensitivity, specificity, positive predictive value, coherence, and timeliness. We also evaluated each method’s implementation, programming time, run time, and the ease of use. Among the temporal methods, at a set specificity of 95%, a Holt-Winters exponential smoother performed the best, detecting 19% of the simulated injects across all shapes and sizes, followed by an autoregressive moving average model (16%), a generalized linear model (15%), a modified version of the Early Aberration Reporting System’s C2 algorithm (13%), a temporal scan statistic (11%), and a cumulative sum control chart (<2%). Of the spatial/spatio-temporal methods we tested, a spatial scan statistic detected 3% of all injects, a Bayes regression found 2%, and a generalized linear mixed model and a space-time permutation scan statistic detected none at a specificity of 95%. Positive predictive value was low (<7%) for all methods. Overall, the detection methods we tested did not perform well in identifying the temporal and spatial clusters of cases in the inject dataset. The spatial scan statistic, our current method for spatial cluster detection, performed slightly better than the other tested methods across different inject magnitudes and types. Furthermore, we found the scan statistics, as applied in the SaTScan software package, to be the easiest to program and implement for daily data analysis. PMID:28886112

Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system.

PubMed

Mathes, Robert W; Lall, Ramona; Levin-Rector, Alison; Sell, Jessica; Paladini, Marc; Konty, Kevin J; Olson, Don; Weiss, Don

2017-01-01

The New York City Department of Health and Mental Hygiene has operated an emergency department syndromic surveillance system since 2001, using temporal and spatial scan statistics run on a daily basis for cluster detection. Since the system was originally implemented, a number of new methods have been proposed for use in cluster detection. We evaluated six temporal and four spatial/spatio-temporal detection methods using syndromic surveillance data spiked with simulated injections. The algorithms were compared on several metrics, including sensitivity, specificity, positive predictive value, coherence, and timeliness. We also evaluated each method's implementation, programming time, run time, and the ease of use. Among the temporal methods, at a set specificity of 95%, a Holt-Winters exponential smoother performed the best, detecting 19% of the simulated injects across all shapes and sizes, followed by an autoregressive moving average model (16%), a generalized linear model (15%), a modified version of the Early Aberration Reporting System's C2 algorithm (13%), a temporal scan statistic (11%), and a cumulative sum control chart (<2%). Of the spatial/spatio-temporal methods we tested, a spatial scan statistic detected 3% of all injects, a Bayes regression found 2%, and a generalized linear mixed model and a space-time permutation scan statistic detected none at a specificity of 95%. Positive predictive value was low (<7%) for all methods. Overall, the detection methods we tested did not perform well in identifying the temporal and spatial clusters of cases in the inject dataset. The spatial scan statistic, our current method for spatial cluster detection, performed slightly better than the other tested methods across different inject magnitudes and types. Furthermore, we found the scan statistics, as applied in the SaTScan software package, to be the easiest to program and implement for daily data analysis.

Genomic connectivity networks based on the BrainSpan atlas of the developing human brain

NASA Astrophysics Data System (ADS)

Mahfouz, Ahmed; Ziats, Mark N.; Rennert, Owen M.; Lelieveldt, Boudewijn P. F.; Reinders, Marcel J. T.

2014-03-01

The human brain comprises systems of networks that span the molecular, cellular, anatomic and functional levels. Molecular studies of the developing brain have focused on elucidating networks among gene products that may drive cellular brain development by functioning together in biological pathways. On the other hand, studies of the brain connectome attempt to determine how anatomically distinct brain regions are connected to each other, either anatomically (diffusion tensor imaging) or functionally (functional MRI and EEG), and how they change over development. A global examination of the relationship between gene expression and connectivity in the developing human brain is necessary to understand how the genetic signature of different brain regions instructs connections to other regions. Furthermore, analyzing the development of connectivity networks based on the spatio-temporal dynamics of gene expression provides a new insight into the effect of neurodevelopmental disease genes on brain networks. In this work, we construct connectivity networks between brain regions based on the similarity of their gene expression signature, termed "Genomic Connectivity Networks" (GCNs). Genomic connectivity networks were constructed using data from the BrainSpan Transcriptional Atlas of the Developing Human Brain. Our goal was to understand how the genetic signatures of anatomically distinct brain regions relate to each other across development. We assessed the neurodevelopmental changes in connectivity patterns of brain regions when networks were constructed with genes implicated in the neurodevelopmental disorder autism (autism spectrum disorder; ASD). Using graph theory metrics to characterize the GCNs, we show that ASD-GCNs are relatively less connected later in development with the cerebellum showing a very distinct expression of ASD-associated genes compared to other brain regions.

Spatio-Temporal Characteristics of Resident Trip Based on Poi and OD Data of Float CAR in Beijing

NASA Astrophysics Data System (ADS)

Mou, N.; Li, J.; Zhang, L.; Liu, W.; Xu, Y.

2017-09-01

Due to the influence of the urban inherent regional functional distribution, the daily activities of the residents presented some spatio-temporal patterns (periodic patterns, gathering patterns, etc.). In order to further understand the spatial and temporal characteristics of urban residents, this paper research takes the taxi trajectory data of Beijing as a sample data and studies the spatio-temporal characteristics of the residents' activities on the weekdays. At first, according to the characteristics of the taxi trajectory data distributed along the road network, it takes the Voronoi generated by the road nodes as the research unit. This paper proposes a hybrid clustering method - based on grid density, which is used to cluster the OD (origin and destination) data of taxi at different times. Then combining with the POI data of Beijing, this research calculated the density of the POI data in the clustering results, and analyzed the relationship between the activities of residents in different periods and the functional types of the region. The final results showed that the residents were mainly commuting on weekdays. And it found that the distribution of travel density showed a concentric circle of the characteristics, focusing on residential areas and work areas. The results of cluster analysis and POI analysis showed that the residents' travel had experienced the process of "spatial relative dispersion - spatial aggregation - spatial relative dispersion" in one day.

Spatio-temporal conditional inference and hypothesis tests for neural ensemble spiking precision

PubMed Central

Harrison, Matthew T.; Amarasingham, Asohan; Truccolo, Wilson

2014-01-01

The collective dynamics of neural ensembles create complex spike patterns with many spatial and temporal scales. Understanding the statistical structure of these patterns can help resolve fundamental questions about neural computation and neural dynamics. Spatio-temporal conditional inference (STCI) is introduced here as a semiparametric statistical framework for investigating the nature of precise spiking patterns from collections of neurons that is robust to arbitrarily complex and nonstationary coarse spiking dynamics. The main idea is to focus statistical modeling and inference, not on the full distribution of the data, but rather on families of conditional distributions of precise spiking given different types of coarse spiking. The framework is then used to develop families of hypothesis tests for probing the spatio-temporal precision of spiking patterns. Relationships among different conditional distributions are used to improve multiple hypothesis testing adjustments and to design novel Monte Carlo spike resampling algorithms. Of special note are algorithms that can locally jitter spike times while still preserving the instantaneous peri-stimulus time histogram (PSTH) or the instantaneous total spike count from a group of recorded neurons. The framework can also be used to test whether first-order maximum entropy models with possibly random and time-varying parameters can account for observed patterns of spiking. STCI provides a detailed example of the generic principle of conditional inference, which may be applicable in other areas of neurostatistical analysis. PMID:25380339

Life on Earth is an individual.

PubMed

Hermida, Margarida

2016-06-01

Life is a self-maintaining process based on metabolism. Something is said to be alive when it exhibits organization and is actively involved in its own continued existence through carrying out metabolic processes. A life is a spatio-temporally restricted event, which continues while the life processes are occurring in a particular chunk of matter (or, arguably, when they are temporally suspended, but can be restarted at any moment), even though there is continuous replacement of parts. Life is organized in discrete packages, particular cells and multicellular organisms with differing degrees of individuality. Biological species, too, have been shown to be individuals, and not classes, as these collections of organisms are spatio-temporally localized, restricted, continuous, and somewhat cohesive entities, with a definite beginning and end. Assuming that all life on Earth has a common origin, all living organisms, cells, and tissues descending from this origin exhibit continuity of the life processes at the cellular level, as well as many of the features that define the individual character of species: spatio-temporal localization and restriction, continuity, historicity, and cohesiveness. Therefore, life on Earth is an ontological individual. Independent origins of life will have produced other such individuals. These provisionally called 'life-individuals' constitute a category of organization of life which has seldom been recognized. The discovery of at least one independent life-individual would go a long way toward the project of the universality of biology.

Multivariate Pattern Analysis Reveals Category-Related Organization of Semantic Representations in Anterior Temporal Cortex.

PubMed

Malone, Patrick S; Glezer, Laurie S; Kim, Judy; Jiang, Xiong; Riesenhuber, Maximilian

2016-09-28

The neural substrates of semantic representation have been the subject of much controversy. The study of semantic representations is complicated by difficulty in disentangling perceptual and semantic influences on neural activity, as well as in identifying stimulus-driven, "bottom-up" semantic selectivity unconfounded by top-down task-related modulations. To address these challenges, we trained human subjects to associate pseudowords (TPWs) with various animal and tool categories. To decode semantic representations of these TPWs, we used multivariate pattern classification of fMRI data acquired while subjects performed a semantic oddball detection task. Crucially, the classifier was trained and tested on disjoint sets of TPWs, so that the classifier had to use the semantic information from the training set to correctly classify the test set. Animal and tool TPWs were successfully decoded based on fMRI activity in spatially distinct subregions of the left medial anterior temporal lobe (LATL). In addition, tools (but not animals) were successfully decoded from activity in the left inferior parietal lobule. The tool-selective LATL subregion showed greater functional connectivity with left inferior parietal lobule and ventral premotor cortex, indicating that each LATL subregion exhibits distinct patterns of connectivity. Our findings demonstrate category-selective organization of semantic representations in LATL into spatially distinct subregions, continuing the lateral-medial segregation of activation in posterior temporal cortex previously observed in response to images of animals and tools, respectively. Together, our results provide evidence for segregation of processing hierarchies for different classes of objects and the existence of multiple, category-specific semantic networks in the brain. The location and specificity of semantic representations in the brain are still widely debated. We trained human participants to associate specific pseudowords with various animal and tool categories, and used multivariate pattern classification of fMRI data to decode the semantic representations of the trained pseudowords. We found that: (1) animal and tool information was organized in category-selective subregions of medial left anterior temporal lobe (LATL); (2) tools, but not animals, were encoded in left inferior parietal lobe; and (3) LATL subregions exhibited distinct patterns of functional connectivity with category-related regions across cortex. Our findings suggest that semantic knowledge in LATL is organized in category-related subregions, providing evidence for the existence of multiple, category-specific semantic representations in the brain. Copyright © 2016 the authors 0270-6474/16/3610089-08$15.00/0.

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

PubMed

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

Information spreading by a combination of MEG source estimation and multivariate pattern classification.

PubMed

Sato, Masashi; Yamashita, Okito; Sato, Masa-Aki; Miyawaki, Yoichi

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of "information spreading" may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

Information spreading by a combination of MEG source estimation and multivariate pattern classification

PubMed Central

Sato, Masashi; Yamashita, Okito; Sato, Masa-aki

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined. PMID:29912968

Video repairing under variable illumination using cyclic motions.

PubMed

Jia, Jiaya; Tai, Yu-Wing; Wu, Tai-Pang; Tang, Chi-Keung

2006-05-01

This paper presents a complete system capable of synthesizing a large number of pixels that are missing due to occlusion or damage in an uncalibrated input video. These missing pixels may correspond to the static background or cyclic motions of the captured scene. Our system employs user-assisted video layer segmentation, while the main processing in video repair is fully automatic. The input video is first decomposed into the color and illumination videos. The necessary temporal consistency is maintained by tensor voting in the spatio-temporal domain. Missing colors and illumination of the background are synthesized by applying image repairing. Finally, the occluded motions are inferred by spatio-temporal alignment of collected samples at multiple scales. We experimented on our system with some difficult examples with variable illumination, where the capturing camera can be stationary or in motion.

Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means

PubMed Central

Sabit, Hakilo; Al-Anbuky, Adnan

2014-01-01

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495

Spatio-temporal and kinematic gait analysis in patients with Frontotemporal dementia and Alzheimer's disease through 3D motion capture.

PubMed

Rucco, Rosaria; Agosti, Valeria; Jacini, Francesca; Sorrentino, Pierpaolo; Varriale, Pasquale; De Stefano, Manuela; Milan, Graziella; Montella, Patrizia; Sorrentino, Giuseppe

2017-02-01

Alzheimer's disease (AD) and behavioral variant of Frontotemporal Dementia (bvFTD) are characterized respectively by atrophy in the medial temporal lobe with memory loss and prefrontal and anterior temporal degeneration with dysexecutive syndrome. In this study, we hypothesized that specific gait patterns are induced by either frontal or temporal degeneration. To test this hypothesis, we studied the gait pattern in bvFTD (23) and AD (22) patients in single and dual task ("motor" and "cognitive") conditions. To detect subtle alterations, we performed motion analysis estimating both spatio-temporal parameters and joint excursions. In the single task condition, the bvFTD group was more unstable and slower compared to healthy subjects, while only two stability parameters were compromised in the AD group. During the motor dual task, both velocity and stability parameters worsened further in the bvFTD group. In the same experimental conditions, AD patients showed a significantly lower speed and stride length than healthy subjects. During the cognitive dual task, a further impairment of velocity and stability parameters was observed in the bvFTD group. Interestingly, during the cognitive dual task, the gait performance of the AD group markedly deteriorated, as documented by the impairment of more indices of velocity and stability. Finally, the kinematic data of thigh, knee, and ankle were more helpful in revealing gait impairment than the spatio-temporal parameters alone. In conclusion, our data showed that the dysexecutive syndrome induces specific gait alterations. Furthermore, our results suggest that the gait worsens in the AD patients when the cognitive resources are stressed. Copyright © 2016 Elsevier B.V. All rights reserved.

The Immediate and Chronic Influence of Spatio-Temporal Metaphors on the Mental Representations of Time in English, Mandarin, and Mandarin-English Speakers

PubMed Central

Lai, Vicky Tzuyin; Boroditsky, Lera

2013-01-01

In this paper we examine whether experience with spatial metaphors for time has an influence on people’s representation of time. In particular we ask whether spatio-temporal metaphors can have both chronic and immediate effects on temporal thinking. In Study 1, we examine the prevalence of ego-moving representations for time in Mandarin speakers, English speakers, and Mandarin-English (ME) bilinguals. As predicted by observations in linguistic analyses, we find that Mandarin speakers are less likely to take an ego-moving perspective than are English speakers. Further, we find that ME bilinguals tested in English are less likely to take an ego-moving perspective than are English monolinguals (an effect of L1 on meaning-making in L2), and also that ME bilinguals tested in Mandarin are more likely to take an ego-moving perspective than are Mandarin monolinguals (an effect of L2 on meaning-making in L1). These findings demonstrate that habits of metaphor use in one language can influence temporal reasoning in another language, suggesting the metaphors can have a chronic effect on patterns in thought. In Study 2 we test Mandarin speakers using either horizontal or vertical metaphors in the immediate context of the task. We find that Mandarin speakers are more likely to construct front-back representations of time when understanding front-back metaphors, and more likely to construct up-down representations of time when understanding up-down metaphors. These findings demonstrate that spatio-temporal metaphors can also have an immediate influence on temporal reasoning. Taken together, these findings demonstrate that the metaphors we use to talk about time have both immediate and long-term consequences for how we conceptualize and reason about this fundamental domain of experience. PMID:23630505

Spatio-temporal variability of soil water content on the local scale in a Mediterranean mountain area (Vallcebre, North Eastern Spain). How different spatio-temporal scales reflect mean soil water content

NASA Astrophysics Data System (ADS)

Molina, Antonio J.; Latron, Jérôme; Rubio, Carles M.; Gallart, Francesc; Llorens, Pilar

2014-08-01

As a result of complex human-land interactions and topographic variability, many Mediterranean mountain catchments are covered by agricultural terraces that have locally modified the soil water content dynamic. Understanding these local-scale dynamics helps us grasp better how hydrology behaves on the catchment scale. Thus, this study examined soil water content variability in the upper 30 cm of the soil on a Mediterranean abandoned terrace in north-east Spain. Using a dataset of high spatial (regular grid of 128 automatic TDR probes at 2.5 m intervals) and temporal (20-min time step) resolution, gathered throughout a 84-day period, the spatio-temporal variability of soil water content at the local scale and the way that different spatio-temporal scales reflect the mean soil water content were investigated. Soil water content spatial variability and its relation to wetness conditions were examined, along with the spatial structuring of the soil water content within the terrace. Then, the ability of single probes and of different combinations of spatial measurements (transects and grids) to provide a good estimate of mean soil water content on the terrace scale was explored by means of temporal stability analyses. Finally, the effect of monitoring frequency on the magnitude of detectable daily soil water content variations was studied. Results showed that soil water content spatial variability followed a bimodal pattern of increasing absolute variability with increasing soil water content. In addition, a linear trend of decreasing soil water content as the distance from the inner part of the terrace increased was identified. Once this trend was subtracted, resulting semi-variograms suggested that the spatial resolution examined was too high to appreciate spatial structuring in the data. Thus, the spatial pattern should be considered as random. Of all the spatial designs tested, the 10 × 10 m mesh grid (9 probes) was considered the most suitable option for a good, time-stable estimate of mean soil water content, as no improvement was obtained with the 5 × 5 m mesh grid (30 probes). Finally, the results of temporal aggregation showed that decreasing the monitoring frequency down to 8 h during wetting-up periods and to 1 day during drying-down ones did not result in a loss of information on daily soil water content variations.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

PubMed Central

Giordano, Bruno L.; Kayser, Christoph; Rousselet, Guillaume A.; Gross, Joachim; Schyns, Philippe G.

2016-01-01

Abstract We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc. PMID:27860095

Spatio-temporal dynamics of brain mechanisms in aversive classical conditioning: high-density event-related potential and brain electrical tomography analyses.

PubMed

Pizzagalli, Diego A; Greischar, Lawrence L; Davidson, Richard J

2003-01-01

Social cognition, including complex social judgments and attitudes, is shaped by individual learning experiences, where affect often plays a critical role. Aversive classical conditioning-a form of associative learning involving a relationship between a neutral event (conditioned stimulus, CS) and an aversive event (unconditioned stimulus, US)-represents a well-controlled paradigm to study how the acquisition of socially relevant knowledge influences behavior and the brain. Unraveling the temporal unfolding of brain mechanisms involved appears critical for an initial understanding about how social cognition operates. Here, 128-channel ERPs were recorded in 50 subjects during the acquisition phase of a differential aversive classical conditioning paradigm. The CS+ (two fearful faces) were paired 50% of the time with an aversive noise (CS upward arrow + /Paired), whereas in the remaining 50% they were not (CS upward arrow + /Unpaired); the CS- (two different fearful faces) were never paired with the noise. Scalp ERP analyses revealed differences between CS upward arrow + /Unpaired and CS- as early as approximately 120 ms post-stimulus. Tomographic source localization analyses revealed early activation modulated by the CS+ in the ventral visual pathway (e.g. fusiform gyrus, approximately 120 ms), right middle frontal gyrus (approximately 176 ms), and precuneus (approximately 240 ms). At approximately 120 ms, the CS- elicited increased activation in the left insula and left middle frontal gyrus. These findings not only confirm a critical role of prefrontal, insular, and precuneus regions in aversive conditioning, but they also suggest that biologically and socially salient information modulates activation at early stages of the information processing flow, and thus furnish initial insight about how affect and social judgments operate.

Differential recruitment of brain networks in single-digit addition and multiplication: Evidence from EEG oscillations in theta and lower alpha bands.

PubMed

Wang, Lihan; Gan, John Q; Zhang, Li; Wang, Haixian

2018-06-01

Previous neuroimaging research investigating dissociation between single-digit addition and multiplication has suggested that the former placed more reliance on the visuo-spatial processing whereas the latter on the verbal processing. However, there has been little exploration into the disassociation in spatio-temporal dynamics of the oscillatory brain activity in specific frequency bands during the two arithmetic operations. To address this issue, the electroencephalogram (EEG) data were recorded from 19 participants engaged in a delayed verification arithmetic task. By analyzing oscillatory EEG activity in theta (5-7 Hz) and lower alpha frequency (9-10 Hz) bands, we found different patterns of oscillatory brain activity between single-digit addition and multiplication during the early processing stage (0-400 ms post-operand onset). Experiment results in this study showed a larger phasic increase of theta-band power for addition than for multiplication in the midline and the right frontal and central regions during the operator and operands presentation intervals, which was extended to the right parietal and the right occipito-temporal regions during the interval immediately after the operands presentation. In contrast, during multiplication higher phase-locking in lower alpha band was evident in the centro-parietal regions during the operator presentation, which was extended to the left fronto-central and anterior regions during the operands presentation. Besides, we found stronger theta phase synchrony between the parietal areas and the right occipital areas for single-digit addition than for multiplication during operands encoding. These findings of oscillatory brain activity extend the previous observations on functional dissociation between the two arithmetic operations. Copyright © 2018 Elsevier B.V. All rights reserved.

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach

PubMed Central

Teng, Santani

2017-01-01

In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research. This article is part of the themed issue ‘Auditory and visual scene analysis’. PMID:28044019

Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach.

PubMed

Cichy, Radoslaw Martin; Teng, Santani

2017-02-19

In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research.This article is part of the themed issue 'Auditory and visual scene analysis'. © 2017 The Authors.

Spatio-temporal evolution of female lung cancer mortality in a region of Spain, is it worth taking migration into account?

PubMed

Zurriaga, Oscar; Vanaclocha, Hermelinda; Martinez-Beneito, Miguel A; Botella-Rocamora, Paloma

2008-01-31

The Comunitat Valenciana (CV) is a tourist region on the Mediterranean coast of Spain with a high rate of retirement migration. Lung cancer in women is the cancer mortality cause that has increased most in the CV during the period 1991 to 2000. Moreover, the geographical distribution of risk from this cause in the CV has been previously described and a non-homogenous pattern was determined. The present paper studies the spatio-temporal distribution of lung cancer mortality for women in the CV during the period 1987-2004, in order to gain some insight into the factors, such as migration, that have had an influence on these changes. A novel methodology, consisting of a Bayesian hierarchical model, is used in this paper. Such a model allows the handling of data with a very high disaggregation, while at the same time taking advantage of its spatial and temporal structure. The spatio-temporal pattern which was found points to geographical differences in the time trends of risk. In fact, the southern coastal side of the CV has had a higher increase in risk, coinciding with the settlement of a large foreign community in that area, mainly comprised of elderly people from the European Union. Migration has frequently been ignored as a risk factor in the description of the geographical risk of lung cancer and it is suggested that this factor should be considered, especially in tourist regions. The temporal component in disease mapping provides a more accurate depiction of risk factors acting on the population.

Spatio-temporal statistical models for river monitoring networks.

PubMed

Clement, L; Thas, O; Vanrolleghem, P A; Ottoy, J P

2006-01-01

When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream.

Spatial and Temporal Variation of Water Quality in the Bertam Catchment, Cameron Highlands, Malaysia.

PubMed

Rasul, M G; Islam, Mir Sujaul; Yunus, Rosli Bin Mohd; Mokhtar, Mazlin Bin; Alam, Lubna; Yahaya, F M

2017-12-01

  The spatio-temporal variability of water quality associated with anthropogenic activities was studied for the Bertam River and its main tributaries within the Bertam Catchment, Cameron Highlands, Malaysia. A number of physico-chemical parameters of collected samples were analyzed to evaluate their spatio-temporal variability. Nonparametric statistical analysis showed significant temporal and spatial differences (p < 0.05) in most of the parameters across the catchment. Parameters except dissolved oxygen and chemical oxygen demand displayed higher values in rainy season. The higher concentration of total suspended solids was caused by massive soil erosion and sedimentation. Seasonal variations in contaminant concentrations are largely affected by precipitation and anthropogenic influences. Untreated domestic wastewater discharge as well as agricultural runoff significantly influenced the water quality. Poor agricultural practices and development activities at slope areas also affected the water quality within the catchment. The analytical results provided a basis for protection of river environments and ecological restoration in mountainous Bertam Catchment.

Spatio-temporal variability of faunal and floral assemblages in Mediterranean temporary wetlands.

PubMed

Rouissi, Maya; Boix, Dani; Muller, Serge D; Gascón, Stéphanie; Ruhí, Albert; Sala, Jordi; Bouattour, Ali; Ben Haj Jilani, Imtinen; Ghrabi-Gammar, Zeineb; Ben Saad-Limam, Samia; Daoud-Bouattour, Amina

2014-12-01

Six temporary wetlands in the region of Sejenane (Mogods, NW Tunisia) were studied in order to characterize the aquatic flora and fauna and to quantify their spatio-temporal variability. Samplings of aquatic fauna, phytosociological relevés, and measurements of the physicochemical parameters of water were taken during four different field visits carried out during the four seasons of the year (November 2009-July 2010). Despite the strong anthropic pressures on them, these temporary wetlands are home to rich and diversified biodiversity, including rare and endangered species. Spatial and temporal variations affect fauna and flora differently, as temporal variability influences the fauna rather more than the plants, which are relatively more dependent on spatial factors. These results demonstrate the interest of small water bodies for maintaining biodiversity at the regional level, and thus underscore the conservation issues of Mediterranean temporary wetlands that are declining on an ongoing basis currently. Copyright © 2014 Académie des sciences. Published by Elsevier SAS. All rights reserved.

Spatio-temporal patterns of gun violence in Syracuse, New York 2009-2015.

PubMed

Larsen, David A; Lane, Sandra; Jennings-Bey, Timothy; Haygood-El, Arnett; Brundage, Kim; Rubinstein, Robert A

2017-01-01

Gun violence in the United States of America is a large public health problem that disproportionately affects urban areas. The epidemiology of gun violence reflects various aspects of an infectious disease including spatial and temporal clustering. We examined the spatial and temporal trends of gun violence in Syracuse, New York, a city of 145,000. We used a spatial scan statistic to reveal spatio-temporal clusters of gunshots investigated and corroborated by Syracuse City Police Department for the years 2009-2015. We also examined predictors of areas with increased gun violence using a multi-level zero-inflated Poisson regression with data from the 2010 census. Two space-time clusters of gun violence were revealed in the city. Higher rates of segregation, poverty and the summer months were all associated with increased risk of gun violence. Previous gunshots in the area were associated with a 26.8% increase in the risk of gun violence. Gun violence in Syracuse, NY is both spatially and temporally stable, with some neighborhoods of the city greatly afflicted.

Spatio-temporal patterns of gun violence in Syracuse, New York 2009-2015

PubMed Central

Lane, Sandra; Jennings-Bey, Timothy; Haygood-El, Arnett; Brundage, Kim; Rubinstein, Robert A.

2017-01-01

Gun violence in the United States of America is a large public health problem that disproportionately affects urban areas. The epidemiology of gun violence reflects various aspects of an infectious disease including spatial and temporal clustering. We examined the spatial and temporal trends of gun violence in Syracuse, New York, a city of 145,000. We used a spatial scan statistic to reveal spatio-temporal clusters of gunshots investigated and corroborated by Syracuse City Police Department for the years 2009–2015. We also examined predictors of areas with increased gun violence using a multi-level zero-inflated Poisson regression with data from the 2010 census. Two space-time clusters of gun violence were revealed in the city. Higher rates of segregation, poverty and the summer months were all associated with increased risk of gun violence. Previous gunshots in the area were associated with a 26.8% increase in the risk of gun violence. Gun violence in Syracuse, NY is both spatially and temporally stable, with some neighborhoods of the city greatly afflicted. PMID:28319125

Disrupted Cerebro-cerebellar Intrinsic Functional Connectivity in Young Adults with High-functioning Autism Spectrum Disorder: A Data-driven, Whole-brain, High Temporal Resolution fMRI Study.

PubMed

Arnold Anteraper, Sheeba; Guell, Xavier; D'Mello, Anila; Joshi, Neha; Whitfield-Gabrieli, Susan; Joshi, Gagan

2018-06-13

To examine the resting-state functional-connectivity (RsFc) in young adults with high-functioning autism spectrum disorder (HF-ASD) using state-of-the-art fMRI data acquisition and analysis techniques. Simultaneous multi-slice, high temporal resolution fMRI acquisition; unbiased whole-brain connectome-wide multivariate pattern analysis (MVPA) techniques for assessing RsFc; and post-hoc whole-brain seed-to-voxel analyses using MVPA results as seeds. MVPA revealed two clusters of abnormal connectivity in the cerebellum. Whole-brain seed-based functional connectivity analyses informed by MVPA-derived clusters showed significant under connectivity between the cerebellum and social, emotional, and language brain regions in the HF-ASD group compared to healthy controls. The results we report are coherent with existing structural, functional, and RsFc literature in autism, extend previous literature reporting cerebellar abnormalities in the neuropathology of autism, and highlight the cerebellum as a potential target for therapeutic, diagnostic, predictive, and prognostic developments in ASD. The description of functional connectivity abnormalities using whole-brain, data-driven analyses as reported in the present study may crucially advance the development of ASD biomarkers, targets for therapeutic interventions, and neural predictors for measuring treatment response.

Spatio-temporal error growth in the multi-scale Lorenz'96 model

NASA Astrophysics Data System (ADS)

Herrera, S.; Fernández, J.; Rodríguez, M. A.; Gutiérrez, J. M.

2010-07-01

The influence of multiple spatio-temporal scales on the error growth and predictability of atmospheric flows is analyzed throughout the paper. To this aim, we consider the two-scale Lorenz'96 model and study the interplay of the slow and fast variables on the error growth dynamics. It is shown that when the coupling between slow and fast variables is weak the slow variables dominate the evolution of fluctuations whereas in the case of strong coupling the fast variables impose a non-trivial complex error growth pattern on the slow variables with two different regimes, before and after saturation of fast variables. This complex behavior is analyzed using the recently introduced Mean-Variance Logarithmic (MVL) diagram.

The Distribution and Abundance of Bird Species: Towards a Satellite, Data Driven Avian Energetics and Species Richness Model

NASA Technical Reports Server (NTRS)

Smith, James A.

2003-01-01

This paper addresses the fundamental question of why birds occur where and when they do, i.e., what are the causative factors that determine the spatio-temporal distributions, abundance, or richness of bird species? In this paper we outline the first steps toward building a satellite, data-driven model of avian energetics and species richness based on individual bird physiology, morphology, and interaction with the spatio-temporal habitat. To evaluate our model, we will use the North American Breeding Bird Survey and Christmas Bird Count data for species richness, wintering and breeding range. Long term and current satellite data series include AVHRR, Landsat, and MODIS.

Is timber insurable? A study of wildfire Risks in the U.S. forest sector using spatio-temporal models.

Treesearch

Xuan Chen; Barry K. Goodwin; Jeffrey P. Prestemon

2014-01-01

In the U.S. forest products industry, wildfire is one of the leading causes of damage and economic losses. While individual wildfire behavior is well studied, new literature is emerging on broad-scale (e.g., county-level) wildfire risks. Our paper studies wildfire risks using crucial informational vari­ ables across both spatio units and time periods....

Decoding word and category-specific spatiotemporal representations from MEG and EEG

PubMed Central

Chan, Alexander M.; Halgren, Eric; Marinkovic, Ksenija; Cash, Sydney S.

2010-01-01

The organization and localization of lexico-semantic information in the brain has been debated for many years. Specifically, lesion and imaging studies have attempted to map the brain areas representing living versus non-living objects, however, results remain variable. This may be due, in part, to the fact that the univariate statistical mapping analyses used to detect these brain areas are typically insensitive to subtle, but widespread, effects. Decoding techniques, on the other hand, allow for a powerful multivariate analysis of multichannel neural data. In this study, we utilize machine-learning algorithms to first demonstrate that semantic category, as well as individual words, can be decoded from EEG and MEG recordings of subjects performing a language task. Mean accuracies of 76% (chance = 50%) and 83% (chance = 20%) were obtained for the decoding of living vs. non-living category or individual words respectively. Furthermore, we utilize this decoding analysis to demonstrate that the representations of words and semantic category are highly distributed both spatially and temporally. In particular, bilateral anterior temporal, bilateral inferior frontal, and left inferior temporal-occipital sensors are most important for discrimination. Successful intersubject and intermodality decoding shows that semantic representations between stimulus modalities and individuals are reasonably consistent. These results suggest that both word and category-specific information are present in extracranially recorded neural activity and that these representations may be more distributed, both spatially and temporally, than previous studies suggest. PMID:21040796

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

Analysis of factors driving stream water composition and synthesis of management tools--a case study on small/medium Greek catchments.

PubMed

Skoulikidis, N Th; Amaxidis, Y; Bertahas, I; Laschou, S; Gritzalis, K

2006-06-01

Twenty-nine small- and mid-sized permanent rivers (thirty-six sites) scattered throughout Greece and equally distributed within three geo-chemical-climatic zones, have been investigated in a seasonal base. Hydrochemical types have been determined and spatio-temporal variations have been interpreted in relation to environmental characteristics and anthropogenic pressures. Multivariate statistical techniques have been used to identify the factors and processes affecting hydrochemical variability and the driving forces that control aquatic composition. It has been shown that spatial variation of aquatic quality is mainly governed by geological and hydrogeological factors. Due to geological and climatic variability, the three zones have different hydrochemical characteristics. Temporal hydrological variations in combination with hydrogeological factors control seasonal hydrochemical trends. Respiration processes due to municipal wastewaters, dominate in summer, and enhance nutrient, chloride and sodium concentrations, while nitrate originates primarily from agriculture. Photosynthetic processes dominate in spring. Carbonate chemistry is controlled by hydrogeological factors and biological activity. A possible enrichment of surface waters with nutrients in "pristine" forested catchments is attributed to soil leaching and mineralisation processes. Two management tools have been developed: a nutrient classification system and a rapid prediction of aquatic composition tool.

Investigation of the 3D temperature distribution patterns above the Antarctic Peninsula using remote sensing data - A contribution for polar climate monitoring

NASA Astrophysics Data System (ADS)

Wachter, Paul; Höppner, Kathrin; Jacobeit, Jucundus; Diedrich, Erhard

2015-04-01

West Antarctica and the Antarctic Peninsula are in the focus of current studies on a changing environment and climate of the polar regions. A recently founded Junior Researchers Group at the German Aerospace Center (DLR) is studying changing processes in cryosphere and atmosphere above the Antarctic Peninsula. It is the aim of the group to make use of long-term remote sensing data sets of the land and ice surfaces and the atmosphere in order to characterize environmental changes in this highly sensitive region. One of the PhD projects focuses on the investigation of the 3D temperature distribution patterns above the Antarctic Peninsula. Temperature data sets ranging from MODIS land surface temperatures up to middle atmosphere data of AURA/MLS will be evaluated over the last approx. 12 years. This 3-dimensional view allows comprehensive investigations of the thermal structure and spatio-temporal characteristics of the southern polar atmosphere. Tropospheric data sets will be analyzed by multivariate statistical methods and will allow the identification of dominant atmospheric circulation patterns as well as their temporal variability. An overview of the data sets and first results will be presented.