spatio-temporal encoding technique: Topics by Science.gov

Sample records for spatio-temporal encoding technique

Patient-Adaptive Reconstruction and Acquisition in Dynamic Imaging with Sensitivity Encoding (PARADISE)

PubMed Central

Sharif, Behzad; Derbyshire, J. Andrew; Faranesh, Anthony Z.; Bresler, Yoram

2010-01-01

MR imaging of the human heart without explicit cardiac synchronization promises to extend the applicability of cardiac MR to a larger patient population and potentially expand its diagnostic capabilities. However, conventional non-gated imaging techniques typically suffer from low image quality or inadequate spatio-temporal resolution and fidelity. Patient-Adaptive Reconstruction and Acquisition in Dynamic Imaging with Sensitivity Encoding (PARADISE) is a highly-accelerated non-gated dynamic imaging method that enables artifact-free imaging with high spatio-temporal resolutions by utilizing novel computational techniques to optimize the imaging process. In addition to using parallel imaging, the method gains acceleration from a physiologically-driven spatio-temporal support model; hence, it is doubly accelerated. The support model is patient-adaptive, i.e., its geometry depends on dynamics of the imaged slice, e.g., subject’s heart-rate and heart location within the slice. The proposed method is also doubly adaptive as it adapts both the acquisition and reconstruction schemes. Based on the theory of time-sequential sampling, the proposed framework explicitly accounts for speed limitations of gradient encoding and provides performance guarantees on achievable image quality. The presented in-vivo results demonstrate the effectiveness and feasibility of the PARADISE method for high resolution non-gated cardiac MRI during a short breath-hold. PMID:20665794
Semi-supervised tracking of extreme weather events in global spatio-temporal climate datasets

NASA Astrophysics Data System (ADS)

Kim, S. K.; Prabhat, M.; Williams, D. N.

2017-12-01

Deep neural networks have been successfully applied to solve problem to detect extreme weather events in large scale climate datasets and attend superior performance that overshadows all previous hand-crafted methods. Recent work has shown that multichannel spatiotemporal encoder-decoder CNN architecture is able to localize events in semi-supervised bounding box. Motivated by this work, we propose new learning metric based on Variational Auto-Encoders (VAE) and Long-Short-Term-Memory (LSTM) to track extreme weather events in spatio-temporal dataset. We consider spatio-temporal object tracking problems as learning probabilistic distribution of continuous latent features of auto-encoder using stochastic variational inference. For this, we assume that our datasets are i.i.d and latent features is able to be modeled by Gaussian distribution. In proposed metric, we first train VAE to generate approximate posterior given multichannel climate input with an extreme climate event at fixed time. Then, we predict bounding box, location and class of extreme climate events using convolutional layers given input concatenating three features including embedding, sampled mean and standard deviation. Lastly, we train LSTM with concatenated input to learn timely information of dataset by recurrently feeding output back to next time-step's input of VAE. Our contribution is two-fold. First, we show the first semi-supervised end-to-end architecture based on VAE to track extreme weather events which can apply to massive scaled unlabeled climate datasets. Second, the information of timely movement of events is considered for bounding box prediction using LSTM which can improve accuracy of localization. To our knowledge, this technique has not been explored neither in climate community or in Machine Learning community.
SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

PubMed

Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

2017-01-01

This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.
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).
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.
Space-time encoding for high frame rate ultrasound imaging.

PubMed

Misaridis, Thanassis X; Jensen, Jørgen A

2002-05-01

Frame rate in ultrasound imaging can be dramatically increased by using sparse synthetic transmit aperture (STA) beamforming techniques. The two main drawbacks of the method are the low signal-to-noise ratio (SNR) and the motion artifacts, that degrade the image quality. In this paper we propose a spatio-temporal encoding for STA imaging based on simultaneous transmission of two quasi-orthogonal tapered linear FM signals. The excitation signals are an up- and a down-chirp with frequency division and a cross-talk of -55 dB. The received signals are first cross-correlated with the appropriate code, then spatially decoded and finally beamformed for each code, yielding two images per emission. The spatial encoding is a Hadamard encoding previously suggested by Chiao et al. [in: Proceedings of the IEEE Ultrasonics Symposium, 1997, p. 1679]. The Hadamard matrix has half the size of the transmit element groups, due to the orthogonality of the temporal encoded wavefronts. Thus, with this method, the frame rate is doubled compared to previous systems. Another advantage is the utilization of temporal codes which are more robust to attenuation. With the proposed technique it is possible to obtain images dynamically focused in both transmit and receive with only two firings. This reduces the problem of motion artifacts. The method has been tested with extensive simulations using Field II. Resolution and SNR are compared with uncoded STA imaging and conventional phased-array imaging. The range resolution remains the same for coded STA imaging with four emissions and is slightly degraded for STA imaging with two emissions due to the -55 dB cross-talk between the signals. The additional proposed temporal encoding adds more than 15 dB on the SNR gain, yielding a SNR at the same order as in phased-array imaging.
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
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.
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.
Optogenetics: a new enlightenment age for zebrafish neurobiology.

PubMed

Del Bene, Filippo; Wyart, Claire

2012-03-01

Zebrafish became a model of choice for neurobiology because of the transparency of its brain and because of its amenability to genetic manipulation. In particular, at early stages of development the intact larva is an ideal system to apply optical techniques for deep imaging in the nervous system, as well as genetically encoded tools for targeting subsets of neurons and monitoring and manipulating their activity. For these applications,new genetically encoded optical tools, fluorescent sensors, and light-gated channels have been generated,creating the field of "optogenetics." It is now possible to monitor and control neuronal activity with minimal perturbation and unprecedented spatio-temporal resolution.We describe here the main achievements that have occurred in the last decade in imaging and manipulating neuronal activity in intact zebrafish larvae. We provide also examples of functional dissection of neuronal circuits achieved with the applications of these techniques in the visual and locomotor systems.
A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

PubMed Central

Gilbert, David

2016-01-01

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems. PMID:27187178
A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking.

PubMed

Pârvu, Ovidiu; Gilbert, David

2016-01-01

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems.
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.
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).
Encoding dependence in Bayesian causal networks

USDA-ARS?s Scientific Manuscript database

Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...
Memory on time

PubMed Central

Eichenbaum, Howard

2013-01-01

Considerable recent work has shown that the hippocampus is critical for remembering the order of events in distinct experiences, a defining feature of episodic memory. Correspondingly, hippocampal neuronal activity can ‘replay’ sequential events in memories and hippocampal neuronal ensembles represent a gradually changing temporal context signal. Most strikingly, single hippocampal neurons – called time cells – encode moments in temporally structured experiences much as the well-known place cells encode locations in spatially structured experiences. These observations bridge largely disconnected literatures on the role of the hippocampus in episodic memory and spatial mapping, and suggest that the fundamental function of the hippocampus is to establish spatio-temporal frameworks for organizing memories. PMID:23318095
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.
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.
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

Visual Processing of Object Velocity and Acceleration

DTIC Science & Technology

1991-12-13

more recently, Dr. Grzywacz’s applications of filtering models to the psychophysics of speed discrimination; 3) the McKee-Welch studies on the...population of spatio-temporally oriented filters to encode velocity. Dr. Grzywacz has attempted to reconcile his model with a variety of psychophysical...by many authors.23 In these models , the image is tectors have different sizes and spatial positions, but they all spatially and temporally filtered
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.
Localized Spatio-Temporal Constraints for Accelerated CMR Perfusion

PubMed Central

Akçakaya, Mehmet; Basha, Tamer A.; Pflugi, Silvio; Foppa, Murilo; Kissinger, Kraig V.; Hauser, Thomas H.; Nezafat, Reza

2013-01-01

Purpose To develop and evaluate an image reconstruction technique for cardiac MRI (CMR)perfusion that utilizes localized spatio-temporal constraints. Methods CMR perfusion plays an important role in detecting myocardial ischemia in patients with coronary artery disease. Breath-hold k-t based image acceleration techniques are typically used in CMR perfusion for superior spatial/temporal resolution, and improved coverage. In this study, we propose a novel compressed sensing based image reconstruction technique for CMR perfusion, with applicability to free-breathing examinations. This technique uses local spatio-temporal constraints by regularizing image patches across a small number of dynamics. The technique is compared to conventional dynamic-by-dynamic reconstruction, and sparsity regularization using a temporal principal-component (pc) basis, as well as zerofilled data in multi-slice 2D and 3D CMR perfusion. Qualitative image scores are used (1=poor, 4=excellent) to evaluate the technique in 3D perfusion in 10 patients and 5 healthy subjects. On 4 healthy subjects, the proposed technique was also compared to a breath-hold multi-slice 2D acquisition with parallel imaging in terms of signal intensity curves. Results The proposed technique results in images that are superior in terms of spatial and temporal blurring compared to the other techniques, even in free-breathing datasets. The image scores indicate a significant improvement compared to other techniques in 3D perfusion (2.8±0.5 vs. 2.3±0.5 for x-pc regularization, 1.7±0.5 for dynamic-by-dynamic, 1.1±0.2 for zerofilled). Signal intensity curves indicate similar dynamics of uptake between the proposed method with a 3D acquisition and the breath-hold multi-slice 2D acquisition with parallel imaging. Conclusion The proposed reconstruction utilizes sparsity regularization based on localized information in both spatial and temporal domains for highly-accelerated CMR perfusion with potential utility in free-breathing 3D acquisitions. PMID:24123058
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.
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
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.
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.
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.
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.
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.
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.
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.
Motion-adaptive model-assisted compatible coding with spatiotemporal scalability

NASA Astrophysics Data System (ADS)

Lee, JaeBeom; Eleftheriadis, Alexandros

1997-01-01

We introduce the concept of motion adaptive spatio-temporal model-assisted compatible (MA-STMAC) coding, a technique to selectively encode areas of different importance to the human eye in terms of space and time in moving images with the consideration of object motion. PRevious STMAC was proposed base don the fact that human 'eye contact' and 'lip synchronization' are very important in person-to-person communication. Several areas including the eyes and lips need different types of quality, since different areas have different perceptual significance to human observers. The approach provides a better rate-distortion tradeoff than conventional image coding techniques base don MPEG-1, MPEG- 2, H.261, as well as H.263. STMAC coding is applied on top of an encoder, taking full advantage of its core design. Model motion tracking in our previous STMAC approach was not automatic. The proposed MA-STMAC coding considers the motion of the human face within the STMAC concept using automatic area detection. Experimental results are given using ITU-T H.263, addressing very low bit-rate compression.
Deriving video content type from HEVC bitstream semantics

NASA Astrophysics Data System (ADS)

Nightingale, James; Wang, Qi; Grecos, Christos; Goma, Sergio R.

2014-05-01

As network service providers seek to improve customer satisfaction and retention levels, they are increasingly moving from traditional quality of service (QoS) driven delivery models to customer-centred quality of experience (QoE) delivery models. QoS models only consider metrics derived from the network however, QoE models also consider metrics derived from within the video sequence itself. Various spatial and temporal characteristics of a video sequence have been proposed, both individually and in combination, to derive methods of classifying video content either on a continuous scale or as a set of discrete classes. QoE models can be divided into three broad categories, full reference, reduced reference and no-reference models. Due to the need to have the original video available at the client for comparison, full reference metrics are of limited practical value in adaptive real-time video applications. Reduced reference metrics often require metadata to be transmitted with the bitstream, while no-reference metrics typically operate in the decompressed domain at the client side and require significant processing to extract spatial and temporal features. This paper proposes a heuristic, no-reference approach to video content classification which is specific to HEVC encoded bitstreams. The HEVC encoder already makes use of spatial characteristics to determine partitioning of coding units and temporal characteristics to determine the splitting of prediction units. We derive a function which approximates the spatio-temporal characteristics of the video sequence by using the weighted averages of the depth at which the coding unit quadtree is split and the prediction mode decision made by the encoder to estimate spatial and temporal characteristics respectively. Since the video content type of a sequence is determined by using high level information parsed from the video stream, spatio-temporal characteristics are identified without the need for full decoding and can be used in a timely manner to aid decision making in QoE oriented adaptive real time streaming.
Similarity-Based Fusion of MEG and fMRI Reveals Spatio-Temporal Dynamics in Human Cortex During Visual Object Recognition

PubMed Central

Cichy, Radoslaw Martin; Pantazis, Dimitrios; Oliva, Aude

2016-01-01

Every human cognitive function, such as visual object recognition, is realized in a complex spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation cannot resolve the brain's spatio-temporal dynamics, because they provide either high spatial or temporal resolution but not both. To overcome this limitation, we developed an integration approach that uses representational similarities to combine measurements of magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to yield a spatially and temporally integrated characterization of neuronal activation. Applying this approach to 2 independent MEG–fMRI data sets, we observed that neural activity first emerged in the occipital pole at 50–80 ms, before spreading rapidly and progressively in the anterior direction along the ventral and dorsal visual streams. Further region-of-interest analyses established that dorsal and ventral regions showed MEG–fMRI correspondence in representations later than early visual cortex. Together, these results provide a novel and comprehensive, spatio-temporally resolved view of the rapid neural dynamics during the first few hundred milliseconds of object vision. They further demonstrate the feasibility of spatially unbiased representational similarity-based fusion of MEG and fMRI, promising new insights into how the brain computes complex cognitive functions. PMID:27235099
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.
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).
Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes

PubMed Central

Hurtado, Rafael G.; Floría, Luis Mario

2016-01-01

We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized. PMID:27853531

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.
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.
Qubits, qutrits, and ququads stored in single photons from an atom-cavity system

NASA Astrophysics Data System (ADS)

Holleczek, Annemarie; Barter, Oliver; Langfahl-Klabes, Gunnar; Kuhn, Axel

2015-03-01

One of today's challenge to realize computing based on quantum mechanics is to reliably and scalably encode information in quantum systems. Here, we present a photon source to on-demand deliver photonic quantum bits of information based on a strongly coupled atom-cavity system. It operates intermittently for periods of up to 100μs, with a single-photon repetition rate of 1MHz, and an intra-cavity production e!ciency of up to 85%. Due to the photons inherent coherence time of 500ns and our ability to arbitrarily shape their amplitude and phase profile we time-bin encode information within one photon. To do so, the spatio-temporal envelope of a single photon is sub-divided in d time bins which allows for the delivery of arbitrary qu-d-its. The latter is done with a fidelity of > 95% for qubits, and 94% for qutrits verified using a newly developed time-resolved quantum-homodyne technique.
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.
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.
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.
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 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.
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.
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
Spatio-temporal behaviour of medium-range ensemble forecasts

NASA Astrophysics Data System (ADS)

Kipling, Zak; Primo, Cristina; Charlton-Perez, Andrew

2010-05-01

Using the recently-developed mean-variance of logarithms (MVL) diagram, together with the TIGGE archive of medium-range ensemble forecasts from nine different centres, we present an analysis of the spatio-temporal dynamics of their perturbations, and show how the differences between models and perturbation techniques can explain the shape of their characteristic MVL curves. We also consider the use of the MVL diagram to compare the growth of perturbations within the ensemble with the growth of the forecast error, showing that there is a much closer correspondence for some models than others. We conclude by looking at how the MVL technique might assist in selecting models for inclusion in a multi-model ensemble, and suggest an experiment to test its potential in this context.
H.264/AVC digital fingerprinting based on spatio-temporal just noticeable distortion

NASA Astrophysics Data System (ADS)

Ait Saadi, Karima; Bouridane, Ahmed; Guessoum, Abderrezak

2014-01-01

This paper presents a robust adaptive embedding scheme using a modified Spatio-Temporal noticeable distortion (JND) model that is designed for tracing the distribution of the H.264/AVC video content and protecting them from unauthorized redistribution. The Embedding process is performed during coding process in selected macroblocks type Intra 4x4 within I-Frame. The method uses spread-spectrum technique in order to obtain robustness against collusion attacks and the JND model to dynamically adjust the embedding strength and control the energy of the embedded fingerprints so as to ensure their imperceptibility. Linear and non linear collusion attacks are performed to show the robustness of the proposed technique against collusion attacks while maintaining visual quality unchanged.
Shot boundary detection and label propagation for spatio-temporal video segmentation

NASA Astrophysics Data System (ADS)

Piramanayagam, Sankaranaryanan; Saber, Eli; Cahill, Nathan D.; Messinger, David

2015-02-01

This paper proposes a two stage algorithm for streaming video segmentation. In the first stage, shot boundaries are detected within a window of frames by comparing dissimilarity between 2-D segmentations of each frame. In the second stage, the 2-D segments are propagated across the window of frames in both spatial and temporal direction. The window is moved across the video to find all shot transitions and obtain spatio-temporal segments simultaneously. As opposed to techniques that operate on entire video, the proposed approach consumes significantly less memory and enables segmentation of lengthy videos. We tested our segmentation based shot detection method on the TRECVID 2007 video dataset and compared it with block-based technique. Cut detection results on the TRECVID 2007 dataset indicate that our algorithm has comparable results to the best of the block-based methods. The streaming video segmentation routine also achieves promising results on a challenging video segmentation benchmark database.
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.
Measuring Memory and Attention to Preview in Motion.

PubMed

Jagacinski, Richard J; Hammond, Gordon M; Rizzi, Emanuele

2017-08-01

Objective Use perceptual-motor responses to perturbations to reveal the spatio-temporal detail of memory for the recent past and attention to preview when participants track a winding roadway. Background Memory of the recently passed roadway can be inferred from feedback control models of the participants' manual movement patterns. Similarly, attention to preview of the upcoming roadway can be inferred from feedforward control models of manual movement patterns. Method Perturbation techniques were used to measure these memory and attention functions. Results In a laboratory tracking task, the bandwidth of lateral roadway deviations was found to primarily influence memory for the past roadway rather than attention to preview. A secondary auditory/verbal/vocal memory task resulted in higher velocity error and acceleration error in the tracking task but did not affect attention to preview. Attention to preview was affected by the frequency pattern of sinusoidal perturbations of the roadway. Conclusion Perturbation techniques permit measurement of the spatio-temporal span of memory and attention to preview that affect tracking a winding roadway. They also provide new ways to explore goal-directed forgetting and spatially distributed attention in the context of movement. More generally, these techniques provide sensitive measures of individual differences in cognitive aspects of action. Application Models of driving behavior and assessment of driving skill may benefit from more detailed spatio-temporal measurement of attention to preview.
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.
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.
Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns

PubMed Central

Matsubara, Takashi

2017-01-01

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning. PMID:29209191

Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

PubMed

Matsubara, Takashi

2017-01-01

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.
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.
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
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.
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 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
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.
Modeling of Aerosol Optical Depth Variability during the 1998 Canadian Forest Fire Smoke Event

NASA Astrophysics Data System (ADS)

Aubé, M.; O`Neill, N. T.; Royer, A.; Lavoué, D.

2003-04-01

Monitoring of aerosol optical depth (AOD) is of particular importance due to the significant role of aerosols in the atmospheric radiative budget. 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 based DDV (Dense Dark Vegetation) inversion algorithms which extract 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 methodology that links AOD measurements and particulate matter Transport Model using a data assimilation approach. This modelling package (AODSEM for Aerosol Optical Depth Spatio-temporal Evolution Model) uses a size and aerosol type segregated semi-Lagrangian-Eulerian trajectory algorithm driven by analysed meteorological data. Its novelty resides in the fact that the model evolution is tied to both ground based and satellite level AOD measurement and all physical processes have been optimized to track this important but crude parameter. We applied this methodology to a significant smoke event that occurred over Canada in august 1998. The results show the potential of this approach inasmuch as residuals between AODSEM assimilated analysis and measurements are smaller than typical errors associated to remotely sensed AOD (satellite or ground based). The AODSEM assimilation approach also gives better results than classical interpolation techniques. This improvement is especially evident when the available number of AOD measurements is small.
A quantitative image cytometry technique for time series or population analyses of signaling networks.

PubMed

Ozaki, Yu-ichi; Uda, Shinsuke; Saito, Takeshi H; Chung, Jaehoon; Kubota, Hiroyuki; Kuroda, Shinya

2010-04-01

Modeling of cellular functions on the basis of experimental observation is increasingly common in the field of cellular signaling. However, such modeling requires a large amount of quantitative data of signaling events with high spatio-temporal resolution. A novel technique which allows us to obtain such data is needed for systems biology of cellular signaling. We developed a fully automatable assay technique, termed quantitative image cytometry (QIC), which integrates a quantitative immunostaining technique and a high precision image-processing algorithm for cell identification. With the aid of an automated sample preparation system, this device can quantify protein expression, phosphorylation and localization with subcellular resolution at one-minute intervals. The signaling activities quantified by the assay system showed good correlation with, as well as comparable reproducibility to, western blot analysis. Taking advantage of the high spatio-temporal resolution, we investigated the signaling dynamics of the ERK pathway in PC12 cells. The QIC technique appears as a highly quantitative and versatile technique, which can be a convenient replacement for the most conventional techniques including western blot, flow cytometry and live cell imaging. Thus, the QIC technique can be a powerful tool for investigating the systems biology of cellular signaling.
Point Target Detection in IR Image Sequences using Spatio-Temporal Hypotheses Testing

DTIC Science & Technology

1999-02-01

incorporate temporal as well as spatial infor- mation, they are often referred to as \\ track before detect " algorithms. The standard approach was to pose the...6, 3]. A drawback of these track - before - detect techniques is that they are very computationally intensive since the entire 3-D space must be ltered
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.
Spatial and temporal dynamics of cortical networks engaged in memory encoding and retrieval

PubMed Central

Miller, Brian T.; D'Esposito, Mark

2012-01-01

Memory operations such as encoding and retrieval require the coordinated interplay of cortical regions with distinct functional contributions. The mechanistic nature of these interactions, however, remains unspecified. During the performance of a face memory task during fMRI scanning, we measured the magnitude (a measure of the strength of coupling between areas) and phase (a measure of the relative timing across areas) of coherence between regions of interest and the rest of the brain. The fusiform face area (FFA) showed robust coherence with a distributed network of subregions in the prefrontal cortex (PFC), posterior parietal cortex (PPC), precuneus, and hippocampus across both memory operations. While these findings reveal significant overlap in the cortical networks underlying mnemonic encoding and retrieval, coherence phase analyses revealed context-dependent differences in cortical dynamics. During both encoding and retrieval, PFC and PPC exhibited earlier activity than in the FFA and hippocampus. Also, during retrieval, PFC activity preceded PPC activity. These findings are consistent with prior physiology studies suggesting an early contribution of PFC and PPC in mnemonic control. Together, these findings contribute to the growing literature exploring the spatio-temporal dynamics of basic memory operations. PMID:22557959
Modulation-frequency encoded multi-color fluorescent DNA analysis in an optofluidic chip.

PubMed

Dongre, Chaitanya; van Weerd, Jasper; Besselink, Geert A J; Vazquez, Rebeca Martinez; Osellame, Roberto; Cerullo, Giulio; van Weeghel, Rob; van den Vlekkert, Hans H; Hoekstra, Hugo J W M; Pollnau, Markus

2011-02-21

We introduce a principle of parallel optical processing to an optofluidic lab-on-a-chip. During electrophoretic separation, the ultra-low limit of detection achieved with our set-up allows us to record fluorescence from covalently end-labeled DNA molecules. Different sets of exclusively color-labeled DNA fragments-otherwise rendered indistinguishable by spatio-temporal coincidence-are traced back to their origin by modulation-frequency-encoded multi-wavelength laser excitation, fluorescence detection with a single ultrasensitive, albeit color-blind photomultiplier, and Fourier analysis decoding. As a proof of principle, fragments obtained by multiplex ligation-dependent probe amplification from independent human genomic segments, associated with genetic predispositions to breast cancer and anemia, are simultaneously analyzed.
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.
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...

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.
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).
A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States.

PubMed

Watson, Stella C; Liu, Yan; Lund, Robert B; Gettings, Jenna R; Nordone, Shila K; McMahan, Christopher S; Yabsley, Michael J

2017-01-01

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge.
A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States

PubMed Central

Watson, Stella C.; Liu, Yan; Lund, Robert B.; Gettings, Jenna R.; Nordone, Shila K.; McMahan, Christopher S.

2017-01-01

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge. PMID:28472096
Insight into others' minds: spatio-temporal representations by intrinsic frame of reference.

PubMed

Sun, Yanlong; Wang, Hongbin

2014-01-01

Recent research has seen a growing interest in connections between domains of spatial and social cognition. Much evidence indicates that processes of representing space in distinct frames of reference (FOR) contribute to basic spatial abilities as well as sophisticated social abilities such as tracking other's intention and belief. Argument remains, however, that belief reasoning in social domain requires an innately dedicated system and cannot be reduced to low-level encoding of spatial relationships. Here we offer an integrated account advocating the critical roles of spatial representations in intrinsic frame of reference. By re-examining the results from a spatial task (Tamborello etal., 2012) and a false-belief task (Onishi and Baillargeon, 2005), we argue that spatial and social abilities share a common origin at the level of spatio-temporal association and predictive learning, where multiple FOR-based representations provide the basic building blocks for efficient and flexible partitioning of the environmental statistics. We also discuss neuroscience evidence supporting these mechanisms. We conclude that FOR-based representations may bridge the conceptual as well as the implementation gaps between the burgeoning fields of social and spatial cognition.
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.
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.
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.
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.
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.
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.
Event Networks and the Identification of Crime Pattern Motifs

PubMed Central

2015-01-01

In this paper we demonstrate the use of network analysis to characterise patterns of clustering in spatio-temporal events. Such clustering is of both theoretical and practical importance in the study of crime, and forms the basis for a number of preventative strategies. However, existing analytical methods show only that clustering is present in data, while offering little insight into the nature of the patterns present. Here, we show how the classification of pairs of events as close in space and time can be used to define a network, thereby generalising previous approaches. The application of graph-theoretic techniques to these networks can then offer significantly deeper insight into the structure of the data than previously possible. In particular, we focus on the identification of network motifs, which have clear interpretation in terms of spatio-temporal behaviour. Statistical analysis is complicated by the nature of the underlying data, and we provide a method by which appropriate randomised graphs can be generated. Two datasets are used as case studies: maritime piracy at the global scale, and residential burglary in an urban area. In both cases, the same significant 3-vertex motif is found; this result suggests that incidents tend to occur not just in pairs, but in fact in larger groups within a restricted spatio-temporal domain. In the 4-vertex case, different motifs are found to be significant in each case, suggesting that this technique is capable of discriminating between clustering patterns at a finer granularity than previously possible. PMID:26605544
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).
An Innovative Infrastructure with a Universal Geo-Spatiotemporal Data Representation Supporting Cost-Effective Integration of Diverse Earth Science Data

NASA Technical Reports Server (NTRS)

Rilee, Michael Lee; Kuo, Kwo-Sen

2017-01-01

The SpatioTemporal Adaptive Resolution Encoding (STARE) is a unifying scheme encoding geospatial and temporal information for organizing data on scalable computing/storage resources, minimizing expensive data transfers. STARE provides a compact representation that turns set-logic functions into integer operations, e.g. conditional sub-setting, taking into account representative spatiotemporal resolutions of the data in the datasets. STARE geo-spatiotemporally aligns data placements of diverse data on massive parallel resources to maximize performance. Automating important scientific functions (e.g. regridding) and computational functions (e.g. data placement) allows scientists to focus on domain-specific questions instead of expending their efforts and expertise on data processing. With STARE-enabled automation, SciDB (Scientific Database) plus STARE provides a database interface, reducing costly data preparation, increasing the volume and variety of interoperable data, and easing result sharing. Using SciDB plus STARE as part of an integrated analysis infrastructure dramatically eases combining diametrically different datasets.
Community ecology in 3D: Tensor decomposition reveals spatio-temporal dynamics of large ecological communities.

PubMed

Frelat, Romain; 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.
Adaptive OFDM Waveform Design for Spatio-Temporal-Sparsity Exploited STAP Radar

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

Sen, Satyabrata

In this chapter, we describe a sparsity-based space-time adaptive processing (STAP) algorithm to detect a slowly moving target using an orthogonal frequency division multiplexing (OFDM) radar. The motivation of employing an OFDM signal is that it improves the target-detectability from the interfering signals by increasing the frequency diversity of the system. However, due to the addition of one extra dimension in terms of frequency, the adaptive degrees-of-freedom in an OFDM-STAP also increases. Therefore, to avoid the construction a fully adaptive OFDM-STAP, we develop a sparsity-based STAP algorithm. We observe that the interference spectrum is inherently sparse in the spatio-temporal domain,more » as the clutter responses occupy only a diagonal ridge on the spatio-temporal plane and the jammer signals interfere only from a few spatial directions. Hence, we exploit that sparsity to develop an efficient STAP technique that utilizes considerably lesser number of secondary data compared to the other existing STAP techniques, and produces nearly optimum STAP performance. In addition to designing the STAP filter, we optimally design the transmit OFDM signals by maximizing the output signal-to-interference-plus-noise ratio (SINR) in order to improve the STAP performance. The computation of output SINR depends on the estimated value of the interference covariance matrix, which we obtain by applying the sparse recovery algorithm. Therefore, we analytically assess the effects of the synthesized OFDM coefficients on the sparse recovery of the interference covariance matrix by computing the coherence measure of the sparse measurement matrix. Our numerical examples demonstrate the achieved STAP-performance due to sparsity-based technique and adaptive waveform design.« less
[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.
Comparative analysis of 2D spatio-temporal visualisation techniques for the pulsed THz-radiation field using an electro-optic crystal

NASA Astrophysics Data System (ADS)

Ushakov, A. A.; Chizhov, P. A.; Bukin, V. V.; Garnov, S. V.; Savel'ev, A. B.

2018-05-01

Two 2D techniques for visualising the field of pulsed THz radiation ('shadow' and 'interferometric'), which are based on the linear electro-optical effect with application of a ZnTe detector crystal 1 × 1 cm in size, are compared. The noise level and dynamic range for the aforementioned techniques are analysed and their applicability limits are discussed.
Mapping historical landscape changes with the use of a space-time cube

NASA Astrophysics Data System (ADS)

Bogucka, Edyta P.; Jahnke, Mathias

2018-05-01

In this contribution, we introduce geographic concepts in the humanities and present the results of a spacetime visualization of ancient buildings over the last centuries. The techniques and approaches used were based on cartographic research to visualize spatio-temporal information. As a case study, we applied cartographic styling techniques to a model of the Royal Castle in Warsaw and its different spatial elements, which were constructed and destroyed during their eventful history. In our case, the space-time cube approach seems to be the most suitable representation of this spatio-temporal information. Therefore, we digitized the different footprints of the castle during the ancient centuries as well as the landscape structure around, and annotated them with monarchies, epochs and time. During the digitization process, we had to cope with difficulties like sources in various scales and map projections, which resulted in varying accuracies. The results were stored in KML to support a wide variety of visualization platforms.
Spatio-Temporal Brain Mapping of Motion-Onset VEPs Combined with fMRI and Retinotopic Maps

PubMed Central

Pitzalis, Sabrina; Strappini, Francesca; De Gasperis, Marco; Bultrini, Alessandro; Di Russo, Francesco

2012-01-01

Neuroimaging studies have identified several motion-sensitive visual areas in the human brain, but the time course of their activation cannot be measured with these techniques. In the present study, we combined electrophysiological and neuroimaging methods (including retinotopic brain mapping) to determine the spatio-temporal profile of motion-onset visual evoked potentials for slow and fast motion stimuli and to localize its neural generators. We found that cortical activity initiates in the primary visual area (V1) for slow stimuli, peaking 100 ms after the onset of motion. Subsequently, activity in the mid-temporal motion-sensitive areas, MT+, peaked at 120 ms, followed by peaks in activity in the more dorsal area, V3A, at 160 ms and the lateral occipital complex at 180 ms. Approximately 250 ms after stimulus onset, activity fast motion stimuli was predominant in area V6 along the parieto-occipital sulcus. Finally, at 350 ms (100 ms after the motion offset) brain activity was visible again in area V1. For fast motion stimuli, the spatio-temporal brain pattern was similar, except that the first activity was detected at 70 ms in area MT+. Comparing functional magnetic resonance data for slow vs. fast motion, we found signs of slow-fast motion stimulus topography along the posterior brain in at least three cortical regions (MT+, V3A and LOR). PMID:22558222
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.
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 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
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
Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques.

PubMed

Chang, Fi-John; Chen, Pin-An; Chang, Li-Chiu; Tsai, Yu-Hsuan

2016-08-15

This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling process equipped with a dynamic neural network and three refined statistical methods, for reliably predicting the TP concentrations along a river simultaneously. Two different types of artificial neural network (BPNN-static neural network; NARX network-dynamic neural network) are constructed in modeling the dynamic system. The Dahan River in Taiwan is used as a study case, where ten-year seasonal water quality data collected at seven monitoring stations along the river are used for model training and validation. Results demonstrate that the NARX network can suitably capture the important dynamic features and remarkably outperforms the BPNN model, and the SMS can effectively identify key input factors, suitably overcome data scarcity, significantly increase model reliability, satisfactorily estimate site-specific TP concentration at seven monitoring stations simultaneously, and adequately reconstruct seasonal TP data into a monthly scale. The proposed SMS can reliably model the dynamic spatio-temporal water pollution variation in a river system for missing, hazardous or costly data of interest. Copyright © 2016 Elsevier B.V. All rights reserved.
Sleep benefits in parallel implicit and explicit measures of episodic memory.

PubMed

Weber, Frederik D; Wang, Jing-Yi; Born, Jan; Inostroza, Marion

2014-03-14

Research in rats using preferences during exploration as a measure of memory has indicated that sleep is important for the consolidation of episodic-like memory, i.e., memory for an event bound into specific spatio-temporal context. How these findings relate to human episodic memory is unclear. We used spontaneous preferences during visual exploration and verbal recall as, respectively, implicit and explicit measures of memory, to study effects of sleep on episodic memory consolidation in humans. During encoding before 10-h retention intervals that covered nighttime sleep or daytime wakefulness, two groups of young adults were presented with two episodes that were 1-h apart. Each episode entailed a spatial configuration of four different faces in a 3 × 3 grid of locations. After the retention interval, implicit spatio-temporal recall performance was assessed by eye-tracking visual exploration of another configuration of four faces of which two were from the first and second episode, respectively; of the two faces one was presented at the same location as during encoding and the other at another location. Afterward explicit verbal recall was assessed. Measures of implicit and explicit episodic memory retention were positively correlated (r = 0.57, P < 0.01), and were both better after nighttime sleep than daytime wakefulness (P < 0.05). In the sleep group, implicit episodic memory recall was associated with increased fast spindles during nonrapid eye movement (NonREM) sleep (r = 0.62, P < 0.05). Together with concordant observations in rats our results indicate that consolidation of genuinely episodic memory benefits from sleep.
Sleep benefits in parallel implicit and explicit measures of episodic memory

PubMed Central

Weber, Frederik D.; Wang, Jing-Yi; Born, Jan; Inostroza, Marion

2014-01-01

Research in rats using preferences during exploration as a measure of memory has indicated that sleep is important for the consolidation of episodic-like memory, i.e., memory for an event bound into specific spatio-temporal context. How these findings relate to human episodic memory is unclear. We used spontaneous preferences during visual exploration and verbal recall as, respectively, implicit and explicit measures of memory, to study effects of sleep on episodic memory consolidation in humans. During encoding before 10-h retention intervals that covered nighttime sleep or daytime wakefulness, two groups of young adults were presented with two episodes that were 1-h apart. Each episode entailed a spatial configuration of four different faces in a 3 × 3 grid of locations. After the retention interval, implicit spatio-temporal recall performance was assessed by eye-tracking visual exploration of another configuration of four faces of which two were from the first and second episode, respectively; of the two faces one was presented at the same location as during encoding and the other at another location. Afterward explicit verbal recall was assessed. Measures of implicit and explicit episodic memory retention were positively correlated (r = 0.57, P < 0.01), and were both better after nighttime sleep than daytime wakefulness (P < 0.05). In the sleep group, implicit episodic memory recall was associated with increased fast spindles during nonrapid eye movement (NonREM) sleep (r = 0.62, P < 0.05). Together with concordant observations in rats our results indicate that consolidation of genuinely episodic memory benefits from sleep. PMID:24634354
Spatio-temporal distribution of energy radiation from low frequency tremor

NASA Astrophysics Data System (ADS)

Maeda, T.; Obara, K.

2007-12-01

Recent fine-scale hypocenter locations of low frequency tremors (LFTs) estimated by cross-correlation technique (Shelly et al. 2006; Maeda et al. 2006) and new finding of very low frequency earthquake (Ito et al. 2007) suggest that these slow events occur at the plate boundary associated with slow slip events (Obara and Hirose, 2006). However, the number of tremor detected by above technique is limited since continuous tremor waveforms are too complicated. Although an envelope correlation method (ECM) (Obara, 2002) enables us to locate epicenters of LFT without arrival time picks, however, ECM fails to locate LFTs precisely especially on the most active stage of tremor activity because of the low-correlation of envelope amplitude. To reveal total energy release of LFT, here we propose a new method for estimating the location of LFTs together with radiated energy from the tremor source by using envelope amplitude. The tremor amplitude observed at NIED Hi-net stations in western Shikoku simply decays in proportion to the reciprocal of the source-receiver distance after the correction of site- amplification factor even though the phases of the tremor are very complicated. So, we model the observed mean square envelope amplitude by time-dependent energy radiation with geometrical spreading factor. In the model, we do not have origin time of the tremor since we assume that the source of the tremor continuously radiates the energy. Travel-time differences between stations estimated by the ECM technique also incorporated in our locating algorithm together with the amplitude information. Three-component 1-hour Hi-net velocity continuous waveforms with a pass-band of 2-10 Hz are used for the inversion after the correction of site amplification factors at each station estimated by coda normalization method (Takahashi et al. 2005) applied to normal earthquakes in the region. The source location and energy are estimated by applying least square inversion to the 1-min window iteratively. As a first application of our method, we estimated the spatio-temporal distribution of energy radiation for 2006 May episodic tremor and slip event occurred in western Shikoku, Japan, region. Tremor location and their radiated energy are estimated for every 1 minute. We counted the number of located LFTs and summed up their total energy at each grid having 0.05-degree spacing at each day to figure out the spatio-temporal distribution of energy release of tremors. The resultant spatial distribution of radiated energy is concentrated at a specific region. Additionally, we see the daily change of released energy, both of location and amount, which corresponds to the migration of tremor activity. The spatio-temporal distribution of energy radiation of tremors is in good agreement with a spatio-temporal slip distribution of slow slip event estimated from Hi-net tiltmeter record (Hirose et al. 2007). This suggests that small continuous tremors occur associated with a rupture process of slow slip.
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.
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.
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.
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.
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.
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.
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.
Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan

NASA Astrophysics Data System (ADS)

Hussain, Ijaz; Spöck, Gunter; Pilz, Jürgen; Yu, Hwa-Lung

2010-08-01

Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space-time heterogeneity of rainfall observations make space-time estimation of precipitation a challenging task. In this paper we propose a Box-Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space-time monthly precipitation in the monsoon periods during 1974-2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space-time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.
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.
Advancement on Visualization Techniques

DTIC Science & Technology

1980-10-01

proposed STOL airport , a missed approach requires a go-around path = that must simultaneously (1) avoid existing reserved flight corridors ( JFK and Newark...absent but the aim is still to produce a particular colour sensation at a given spatio-temporal position on the display. Economical representation of...and p, q, r ... respectively. 1.5.1 Selection techniques An element is selected by applying a suitable signal between one of the row and one of the
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.
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.
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.
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)...

Wide-field two-photon microscopy with temporal focusing and HiLo background rejection

NASA Astrophysics Data System (ADS)

Yew, Elijah Y. S.; Choi, Heejin; Kim, Daekeun; So, Peter T. C.

2011-03-01

Scanningless depth-resolved microscopy is achieved through spatial-temporal focusing and has been demonstrated previously. The advantage of this method is that a large area may be imaged without scanning resulting in higher throughput of the imaging system. Because it is a widefield technique, the optical sectioning effect is considerably poorer than with conventional spatial focusing two-photon microscopy. Here we propose wide-field two-photon microscopy based on spatio-temporal focusing and employing background rejection based on the HiLo microscope principle. We demonstrate the effects of applying HiLo microscopy to widefield temporally focused two-photon microscopy.
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...
Adaptation to changes in higher-order stimulus statistics in the salamander retina.

PubMed

Tkačik, Gašper; Ghosh, Anandamohan; Schneidman, Elad; Segev, Ronen

2014-01-01

Adaptation in the retina is thought to optimize the encoding of natural light signals into sequences of spikes sent to the brain. While adaptive changes in retinal processing to the variations of the mean luminance level and second-order stimulus statistics have been documented before, no such measurements have been performed when higher-order moments of the light distribution change. We therefore measured the ganglion cell responses in the tiger salamander retina to controlled changes in the second (contrast), third (skew) and fourth (kurtosis) moments of the light intensity distribution of spatially uniform temporally independent stimuli. The skew and kurtosis of the stimuli were chosen to cover the range observed in natural scenes. We quantified adaptation in ganglion cells by studying linear-nonlinear models that capture well the retinal encoding properties across all stimuli. We found that the encoding properties of retinal ganglion cells change only marginally when higher-order statistics change, compared to the changes observed in response to the variation in contrast. By analyzing optimal coding in LN-type models, we showed that neurons can maintain a high information rate without large dynamic adaptation to changes in skew or kurtosis. This is because, for uncorrelated stimuli, spatio-temporal summation within the receptive field averages away non-gaussian aspects of the light intensity distribution.
Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE.

PubMed

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.
Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

PubMed Central

Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M.; Grün, Sonja

2017-01-01

Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis. PMID:28596729
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.
Mean field analysis of a spatial stochastic model of a gene regulatory network.

PubMed

Sturrock, M; Murray, P J; Matzavinos, A; Chaplain, M A J

2015-10-01

A gene regulatory network may be defined as a collection of DNA segments which interact with each other indirectly through their RNA and protein products. Such a network is said to contain a negative feedback loop if its products inhibit gene transcription, and a positive feedback loop if a gene product promotes its own production. Negative feedback loops can create oscillations in mRNA and protein levels while positive feedback loops are primarily responsible for signal amplification. It is often the case in real biological systems that both negative and positive feedback loops operate in parameter regimes that result in low copy numbers of gene products. In this paper we investigate the spatio-temporal dynamics of a single feedback loop in a eukaryotic cell. We first develop a simplified spatial stochastic model of a canonical feedback system (either positive or negative). Using a Gillespie's algorithm, we compute sample trajectories and analyse their corresponding statistics. We then derive a system of equations that describe the spatio-temporal evolution of the stochastic means. Subsequently, we examine the spatially homogeneous case and compare the results of numerical simulations with the spatially explicit case. Finally, using a combination of steady-state analysis and data clustering techniques, we explore model behaviour across a subregion of the parameter space that is difficult to access experimentally and compare the parameter landscape of our spatio-temporal and spatially-homogeneous models.
High spatio-temporal resolution observations of crater-lake temperatures at Kawah Ijen volcano, East Java, Indonesia

USGS Publications Warehouse

Lewicki, Jennifer L.; Corentin Caudron,; Vincent van Hinsberg,; George Hilley,

2016-01-01

The crater lake of Kawah Ijen volcano, East Java, Indonesia, has displayed large and rapid changes in temperature at point locations during periods of unrest, but measurement techniques employed to-date have not resolved how the lake’s thermal regime has evolved over both space and time. We applied a novel approach for mapping and monitoring variations in crater-lake apparent surface (“skin”) temperatures at high spatial (~32 cm) and temporal (every two minutes) resolution at Kawah Ijen on 18 September 2014. We used a ground-based FLIR T650sc camera with digital and thermal infrared (TIR) sensors from the crater rim to collect (1) a set of visible imagery around the crater during the daytime and (2) a time series of co-located visible and TIR imagery at one location from pre-dawn to daytime. We processed daytime visible imagery with the Structure-from-Motion photogrammetric method to create a digital elevation model onto which the time series of TIR imagery was orthorectified and georeferenced. Lake apparent skin temperatures typically ranged from ~21 to 33oC. At two locations, apparent skin temperatures were ~ 4 and 7 oC less than in-situ lake temperature measurements at 1.5 and 5 m depth, respectively. These differences, as well as the large spatio-temporal variations observed in skin temperatures, were likely largely associated with atmospheric effects such as evaporative cooling of the lake surface and infrared absorption by water vapor and SO2. Calculations based on orthorectified TIR imagery thus yielded underestimates of volcanic heat fluxes into the lake, whereas volcanic heat fluxes estimated based on in-situ temperature measurements (68 to 111 MW) were likely more representative of Kawah Ijen in a quiescent state. The ground-based imaging technique should provide a valuable tool to continuously monitor crater-lake temperatures and contribute insight into the spatio-temporal evolution of these temperatures associated with volcanic activity.
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
Spatio-temporal Event Classification using Time-series Kernel based Structured Sparsity

PubMed Central

Jeni, László A.; Lőrincz, András; Szabó, Zoltán; Cohn, Jeffrey F.; Kanade, Takeo

2016-01-01

In many behavioral domains, such as facial expression and gesture, sparse structure is prevalent. This sparsity would be well suited for event detection but for one problem. Features typically are confounded by alignment error in space and time. As a consequence, high-dimensional representations such as SIFT and Gabor features have been favored despite their much greater computational cost and potential loss of information. We propose a Kernel Structured Sparsity (KSS) method that can handle both the temporal alignment problem and the structured sparse reconstruction within a common framework, and it can rely on simple features. We characterize spatio-temporal events as time-series of motion patterns and by utilizing time-series kernels we apply standard structured-sparse coding techniques to tackle this important problem. We evaluated the KSS method using both gesture and facial expression datasets that include spontaneous behavior and differ in degree of difficulty and type of ground truth coding. KSS outperformed both sparse and non-sparse methods that utilize complex image features and their temporal extensions. In the case of early facial event classification KSS had 10% higher accuracy as measured by F1 score over kernel SVM methods1. PMID:27830214
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.
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).
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...
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.
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.
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.
Air pollution exposure and adverse pregnancy outcomes in a large UK birth cohort: use of a novel spatio-temporal modelling technique.

PubMed

Hannam, Kimberly; McNamee, Roseanne; Baker, Philip; Sibley, Colin; Agius, Raymond

2014-09-01

Previous work suggests an association between air pollution exposure and adverse pregnancy outcomes, even at relatively low concentrations. Our aim was to quantify the effect of air pollution having an adverse effect on preterm birth (PTB) and fetal growth in a large UK cohort using a novel exposure estimation technique [spatio-temporal (S-T) model] alongside a traditional nearest stationary monitor technique (NSTAT). All available postcodes from a Northwest England birth outcome dataset during 2004-2008 were geocoded (N=203 562 deliveries). Pollution estimates were linked to corresponding pregnancy periods using temporally adjusted background modelled concentrations as well as NSTAT. Associations with PTB, small for gestational age (SGA), and birth weight were investigated using regression models adjusting for maternal age, ethnicity, parity, birth season, socioeconomic status (SES), body mass index (BMI), and smoking. Based on the novel S-T model, a small statistically significant association was observed for particulate matter (PM10) and SGA, particularly with exposure in the first and third trimesters. Similar effects on SGA were also found for nitrogen dioxide (NO 2), particulate matter (PM 2,5), and carbon monoxide (CO) in later pregnancy, but no overall increased risk was observed. No associations were found with NO xor the outcomes PTB and reduction in birth weight. Our findings suggest an association between air pollution exposure and birth of a SGA infant, particularly in the later stages of pregnancy but not with PTB or mean birth weight change.
Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation.

PubMed

Lee, Min Seok; Park, Sang Wook; Kang, Moon Gi

2017-05-28

In this paper, a spatio-spectral-temporal filter considering an inter-channel correlation is proposed for the denoising of a color filter array (CFA) sequence acquired by CCD/CMOS image sensors. Owing to the alternating under-sampled grid of the CFA pattern, the inter-channel correlation must be considered in the direct denoising process. The proposed filter is applied in the spatial, spectral, and temporal domain, considering the spatio-tempo-spectral correlation. First, nonlocal means (NLM) spatial filtering with patch-based difference (PBD) refinement is performed by considering both the intra-channel correlation and inter-channel correlation to overcome the spatial resolution degradation occurring with the alternating under-sampled pattern. Second, a motion-compensated temporal filter that employs inter-channel correlated motion estimation and compensation is proposed to remove the noise in the temporal domain. Then, a motion adaptive detection value controls the ratio of the spatial filter and the temporal filter. The denoised CFA sequence can thus be obtained without motion artifacts. Experimental results for both simulated and real CFA sequences are presented with visual and numerical comparisons to several state-of-the-art denoising methods combined with a demosaicing method. Experimental results confirmed that the proposed frameworks outperformed the other techniques in terms of the objective criteria and subjective visual perception in CFA sequences.
[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.
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.
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.
Spatial and temporal patterns of locally-acquired dengue transmission in northern Queensland, Australia, 1993-2012.

PubMed

Naish, Suchithra; Dale, Pat; Mackenzie, John S; McBride, John; Mengersen, Kerrie; Tong, Shilu

2014-01-01

Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992-1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993-2012. Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ(2) = 15.17, d.f. = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (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 northern Queensland. Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas.
Spatial and Temporal Patterns of Locally-Acquired Dengue Transmission in Northern Queensland, Australia, 1993–2012

PubMed Central

Naish, Suchithra; Dale, Pat; Mackenzie, John S.; McBride, John; Mengersen, Kerrie; Tong, Shilu

2014-01-01

Background Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992–1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993–2012. Methods Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. Results 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ2 = 15.17, d.f. = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (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 northern Queensland. Conclusions Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas. PMID:24691549
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...
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.
Normalization Strategies for Enhancing Spatio-Temporal Analysis of Social Media Responses during Extreme Events: A Case Study based on Analysis of Four Extreme Events using Socio-Environmental Data Explorer (SEDE)

NASA Astrophysics Data System (ADS)

Ajayakumar, J.; Shook, E.; Turner, V. K.

2017-10-01

With social media becoming increasingly location-based, there has been a greater push from researchers across various domains including social science, public health, and disaster management, to tap in the spatial, temporal, and textual data available from these sources to analyze public response during extreme events such as an epidemic outbreak or a natural disaster. Studies based on demographics and other socio-economic factors suggests that social media data could be highly skewed based on the variations of population density with respect to place. To capture the spatio-temporal variations in public response during extreme events we have developed the Socio-Environmental Data Explorer (SEDE). SEDE collects and integrates social media, news and environmental data to support exploration and assessment of public response to extreme events. For this study, using SEDE, we conduct spatio-temporal social media response analysis on four major extreme events in the United States including the "North American storm complex" in December 2015, the "snowstorm Jonas" in January 2016, the "West Virginia floods" in June 2016, and the "Hurricane Matthew" in October 2016. Analysis is conducted on geo-tagged social media data from Twitter and warnings from the storm events database provided by National Centers For Environmental Information (NCEI) for analysis. Results demonstrate that, to support complex social media analyses, spatial and population-based normalization and filtering is necessary. The implications of these results suggests that, while developing software solutions to support analysis of non-conventional data sources such as social media, it is quintessential to identify the inherent biases associated with the data sources, and adapt techniques and enhance capabilities to mitigate the bias. The normalization strategies that we have developed and incorporated to SEDE will be helpful in reducing the population bias associated with social media data and will be useful for researchers and decision makers to enhance their analysis on spatio-temporal social media responses during extreme events.
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
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
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.
Decoding memory features from hippocampal spiking activities using sparse classification models.

PubMed

Dong Song; Hampson, Robert E; Robinson, Brian S; Marmarelis, Vasilis Z; Deadwyler, Sam A; Berger, Theodore W

2016-08-01

To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.
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.
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.
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.
Impact of large-scale atmospheric refractive structures on optical wave propagation

NASA Astrophysics Data System (ADS)

Nunalee, Christopher G.; He, Ping; Basu, Sukanta; Vorontsov, Mikhail A.; Fiorino, Steven T.

2014-10-01

Conventional techniques used to model optical wave propagation through the Earth's atmosphere typically as- sume flow fields based on various empirical relationships. Unfortunately, these synthetic refractive index fields do not take into account the influence of transient macroscale and mesoscale (i.e. larger than turbulent microscale) atmospheric phenomena. Nevertheless, a number of atmospheric structures that are characterized by various spatial and temporal scales exist which have the potential to significantly impact refractive index fields, thereby resulting dramatic impacts on optical wave propagation characteristics. In this paper, we analyze a subset of spatio-temporal dynamics found to strongly affect optical waves propagating through these atmospheric struc- tures. Analysis of wave propagation was performed in the geometrical optics approximation using a standard ray tracing technique. Using a numerical weather prediction (NWP) approach, we simulate multiple realistic atmospheric events (e.g., island wakes, low-level jets, etc.), and estimate the associated refractivity fields prior to performing ray tracing simulations. By coupling NWP model output with ray tracing simulations, we demon- strate the ability to quantitatively assess the potential impacts of coherent atmospheric phenomena on optical ray propagation. Our results show a strong impact of spatio-temporal characteristics of the refractive index field on optical ray trajectories. Such correlations validate the effectiveness of NWP models as they offer a more comprehensive representation of atmospheric refractivity fields compared to conventional methods based on the assumption of horizontal homogeneity.
Computation of the spectrum of spatial Lyapunov exponents for the spatially extended beam-plasma systems and electron-wave devices

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

Hramov, Alexander E.; Saratov State Technical University, Politechnicheskaja str., 77, Saratov 410054; Koronovskii, Alexey A.

2012-08-15

The spectrum of Lyapunov exponents is powerful tool for the analysis of the complex system dynamics. In the general framework of nonlinear dynamics, a number of the numerical techniques have been developed to obtain the spectrum of Lyapunov exponents for the complex temporal behavior of the systems with a few degree of freedom. Unfortunately, these methods cannot be applied directly to analysis of complex spatio-temporal dynamics of plasma devices which are characterized by the infinite phase space, since they are the spatially extended active media. In the present paper, we propose the method for the calculation of the spectrum ofmore » the spatial Lyapunov exponents (SLEs) for the spatially extended beam-plasma systems. The calculation technique is applied to the analysis of chaotic spatio-temporal oscillations in three different beam-plasma model: (1) simple plasma Pierce diode, (2) coupled Pierce diodes, and (3) electron-wave system with backward electromagnetic wave. We find an excellent agreement between the system dynamics and the behavior of the spectrum of the spatial Lyapunov exponents. Along with the proposed method, the possible problems of SLEs calculation are also discussed. It is shown that for the wide class of the spatially extended systems, the set of quantities included in the system state for SLEs calculation can be reduced using the appropriate feature of the plasma systems.« less
Abnormal early dynamic individual patterns of functional networks in low gamma band for depression recognition.

PubMed

Bi, Kun; Chattun, Mahammad Ridwan; Liu, Xiaoxue; Wang, Qiang; Tian, Shui; Zhang, Siqi; Lu, Qing; Yao, Zhijian

2018-06-13

The functional networks are associated with emotional processing in depression. The mapping of dynamic spatio-temporal brain networks is used to explore individual performance during early negative emotional processing. However, the dysfunctions of functional networks in low gamma band and their discriminative potentialities during early period of emotional face processing remain to be explored. Functional brain networks were constructed from the MEG recordings of 54 depressed patients and 54 controls in low gamma band (30-48 Hz). Dynamic connectivity regression (DCR) algorithm analyzed the individual change points of time series in response to emotional stimuli and constructed individualized spatio-temporal patterns. The nodal characteristics of patterns were calculated and fed into support vector machine (SVM). Performance of the classification algorithm in low gamma band was validated by dynamic topological characteristics of individual patterns in comparison to alpha and beta band. The best discrimination accuracy of individual spatio-temporal patterns was 91.01% in low gamma band. Individual temporal patterns had better results compared to group-averaged temporal patterns in all bands. The most important discriminative networks included affective network (AN) and fronto-parietal network (FPN) in low gamma band. The sample size is relatively small. High gamma band was not considered. The abnormal dynamic functional networks in low gamma band during early emotion processing enabled depression recognition. The individual information processing is crucial in the discovery of abnormal spatio-temporal patterns in depression during early negative emotional processing. Individual spatio-temporal patterns may reflect the real dynamic function of subjects while group-averaged data may neglect some individual information. Copyright © 2018. Published by Elsevier B.V.
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.
Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation technique

NASA Astrophysics Data System (ADS)

Hoffmann, Sebastian; Shutler, Jamie D.; Lobbes, Marc; Burgeth, Bernhard; Meyer-Bäse, Anke

2013-12-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) represents an established method for the detection and diagnosis of breast lesions. While mass-like enhancing lesions can be easily categorized according to the Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon, a majority of diagnostically challenging lesions, the so called non-mass-like enhancing lesions, remain both qualitatively as well as quantitatively difficult to analyze. Thus, the evaluation of kinetic and/or morphological characteristics of non-masses represents a challenging task for an automated analysis and is of crucial importance for advancing current computer-aided diagnosis (CAD) systems. Compared to the well-characterized mass-enhancing lesions, non-masses have no well-defined and blurred tumor borders and a kinetic behavior that is not easily generalizable and thus discriminative for malignant and benign non-masses. To overcome these difficulties and pave the way for novel CAD systems for non-masses, we will evaluate several kinetic and morphological descriptors separately and a novel technique, the Zernike velocity moments, to capture the joint spatio-temporal behavior of these lesions, and additionally consider the impact of non-rigid motion compensation on a correct diagnosis.
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.
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.
Determination of stress glut moments of total degree 2 from teleseismic surface wave amplitude spectra

NASA Astrophysics Data System (ADS)

Bukchin, B. G.

1995-08-01

A special case of the seismic source, where the stress glut tensor can be expressed as a product of a uniform moment tensor and a scalar function of spatial coordinates and time, is considered. For such a source, a technique of determining stress glut moments of total degree 2 from surface wave amplitude spectra is described. The results of application of this technique for the estimation of spatio-temporal characteristics of the Georgian earthquake, 29.04.91 are presented.
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.
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.
Spatio-temporal analysis of sub-hourly rainfall over Mumbai, India: Is statistical forecasting futile?

NASA Astrophysics Data System (ADS)

Singh, Jitendra; Sekharan, Sheeba; Karmakar, Subhankar; Ghosh, Subimal; Zope, P. E.; Eldho, T. I.

2017-04-01

Mumbai, the commercial and financial capital of India, experiences incessant annual rain episodes, mainly attributable to erratic rainfall pattern during monsoons and urban heat-island effect due to escalating urbanization, leading to increasing vulnerability to frequent flooding. After the infamous episode of 2005 Mumbai torrential rains when only two rain gauging stations existed, the governing civic body, the Municipal Corporation of Greater Mumbai (MCGM) came forward with an initiative to install 26 automatic weather stations (AWS) in June 2006 (MCGM 2007), which later increased to 60 AWS. A comprehensive statistical analysis to understand the spatio-temporal pattern of rainfall over Mumbai or any other coastal city in India has never been attempted earlier. In the current study, a thorough analysis of available rainfall data for 2006-2014 from these stations was performed; the 2013-2014 sub-hourly data from 26 AWS was found useful for further analyses due to their consistency and continuity. Correlogram cloud indicated no pattern of significant correlation when we considered the closest to the farthest gauging station from the base station; this impression was also supported by the semivariogram plots. Gini index values, a statistical measure of temporal non-uniformity, were found above 0.8 in visible majority showing an increasing trend in most gauging stations; this sufficiently led us to conclude that inconsistency in daily rainfall was gradually increasing with progress in monsoon. Interestingly, night rainfall was lesser compared to daytime rainfall. The pattern-less high spatio-temporal variation observed in Mumbai rainfall data signifies the futility of independently applying advanced statistical techniques, and thus calls for simultaneous inclusion of physics-centred models such as different meso-scale numerical weather prediction systems, particularly the Weather Research and Forecasting (WRF) model.
Temporal coding in a silicon network of integrate-and-fire neurons.

PubMed

Liu, Shih-Chii; Douglas, Rodney

2004-09-01

Spatio-temporal processing of spike trains by neuronal networks depends on a variety of mechanisms distributed across synapses, dendrites, and somata. In natural systems, the spike trains and the processing mechanisms cohere though their common physical instantiation. This coherence is lost when the natural system is encoded for simulation on a general purpose computer. By contrast, analog VLSI circuits are, like neurons, inherently related by their real-time physics, and so, could provide a useful substrate for exploring neuronlike event-based processing. Here, we describe a hybrid analog-digital VLSI chip comprising a set of integrate-and-fire neurons and short-term dynamical synapses that can be configured into simple network architectures with some properties of neocortical neuronal circuits. We show that, despite considerable fabrication variance in the properties of individual neurons, the chip offers a viable substrate for exploring real-time spike-based processing in networks of neurons.
Computation Methods for NASA Data-streams for Agricultural Efficiency Applications

NASA Astrophysics Data System (ADS)

Shrestha, B.; O'Hara, C. G.; Mali, P.

2007-12-01

Temporal Map Algebra (TMA) is a novel technique for analyzing time-series of satellite imageries using simple algebraic operators that treats time-series imageries as a three-dimensional dataset, where two dimensions encode planimetric position on earth surface and the third dimension encodes time. Spatio-temporal analytical processing methods such as TMA that utilize moderate spatial resolution satellite imagery having high temporal resolution to create multi-temporal composites are data intensive as well as computationally intensive. TMA analysis for multi-temporal composites provides dramatically enhanced usefulness that will yield previously unavailable capabilities to user communities, if deployment is coupled with significant High Performance Computing (HPC) capabilities; and interfaces are designed to deliver the full potential for these new technological developments. In this research, cross-platform data fusion and adaptive filtering using TMA was employed to create highly useful daily datasets and cloud-free high-temporal resolution vegetation index (VI) composites with enhanced information content for vegetation and bio-productivity monitoring, surveillance, and modeling. Fusion of Normalized Difference Vegetation Index (NDVI) data created from Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) surface-reflectance data (MOD09) enables the creation of daily composites which are of immense value to a broad spectrum of global and national applications. Additionally these products are highly desired by many natural resources agencies like USDA/FAS/PECAD. Utilizing data streams collected by similar sensors on different platforms that transit the same areas at slightly different times of the day offers the opportunity to develop fused data products that have enhanced cloud-free and reduced noise characteristics. Establishing a Fusion Quality Confidence Code (FQCC) provides a metadata product that quantifies the method of fusion for a given pixel and enables a relative quality and confidence factor to be established for a given daily pixel value. When coupled with metadata that quantify the source sensor, day and time of acquisition, and the fusion method of each pixel to create the daily product; a wealth of information is available to assist in deriving new data and information products. These newly developed abilities to create highly useful daily data sets imply that temporal composites for a geographic area of interest may be created for user-defined temporal intervals that emphasize a user designated day of interest. At GeoResources Institute, Mississippi State University, solutions have been developed to create custom composites and cross-platform satellite data fusion using TMA which are useful for National Aeronautics and Space Administration (NASA) Rapid Prototyping Capability (RPC) and Integrated System Solutions (ISS) experiments for agricultural applications.
Increased biomagnetic activity in the ventral pathway in mild cognitive impairment.

PubMed

Maestú, F; Campo, P; Del Río, D; Moratti, S; Gil-Gregorio, P; Fernández, A; Capilla, A; Ortiz, T

2008-06-01

Mild cognitive impairment (MCI) patients represent an intermediary state between healthy aging and dementia. MCI activation profiles, recorded during a memory task, have been studied either through high spatial resolution or high temporal resolution techniques. However, little is known about the benefit of combining both dimensions. Here, we investigate, by means of magnetoencephalography (MEG), whether spatio-temporal profiles of neuromagnetic activity could differentiate between MCI and age-matched elderly participants. Taking the advantage of the high temporal resolution and good spatial resolution of MEG, neuromagnetic activity from 15 elderly MCI patients and 20 age-matched controls was recorded during the performance of a modified version of the Sternberg paradigm. Behavioral performance was similar in both groups. A between group analysis revealed that MCI patients showed bilateral higher activity in the ventral pathway, in both the target and the non-target stimuli. A within-group analysis of the target stimuli, indicates a lack of asymmetry through all late latency windows in both groups. MCI patients showed a compensatory mechanism represented by an increased bilateral activity of the ventral pathway in order to achieve a behavioral performance similar to the control group. This spatio-temporal pattern of activity could be another tool to differentiate between healthy aging and MCI patients.
Unveiling TRPV1 Spatio-Temporal Organization in Live Cell Membranes

PubMed Central

Storti, Barbara; Di Rienzo, Carmine; Cardarelli, Francesco; Bizzarri, Ranieri; Beltram, Fabio

2015-01-01

Transient Receptor Potential Vanilloid 1 (TRPV1) is a non-selective cation channel that integrates several stimuli into nociception and neurogenic inflammation. Here we investigated the subtle TRPV1 interplay with candidate membrane partners in live cells by a combination of spatio-temporal fluctuation techniques and fluorescence resonance energy transfer (FRET) imaging. We show that TRPV1 is split into three populations with fairly different molecular properties: one binding to caveolin-1 and confined into caveolar structures, one actively guided by microtubules through selective binding, and one which diffuses freely and is not directly implicated in regulating receptor functionality. The emergence of caveolin-1 as a new interactor of TRPV1 evokes caveolar endocytosis as the main desensitization pathway of TRPV1 receptor, while microtubule binding agrees with previous data suggesting the receptor stabilization in functional form by these cytoskeletal components. Our results shed light on the hitherto unknown relationships between spatial organization and TRPV1 function in live-cell membranes. PMID:25764349
Towards a theoretical determination of the geographical probability distribution of meteoroid impacts on Earth

NASA Astrophysics Data System (ADS)

Zuluaga, Jorge I.; Sucerquia, Mario

2018-06-01

Tunguska and Chelyabinsk impact events occurred inside a geographical area of only 3.4 per cent of the Earth's surface. Although two events hardly constitute a statistically significant demonstration of a geographical pattern of impacts, their spatial coincidence is at least tantalizing. To understand if this concurrence reflects an underlying geographical and/or temporal pattern, we must aim at predicting the spatio-temporal distribution of meteoroid impacts on Earth. For this purpose we designed, implemented, and tested a novel numerical technique, the `Gravitational Ray Tracing' (GRT) designed to compute the relative impact probability (RIP) on the surface of any planet. GRT is inspired by the so-called ray-casting techniques used to render realistic images of complex 3D scenes. In this paper we describe the method and the results of testing it at the time of large impact events. Our findings suggest a non-trivial pattern of impact probabilities at any given time on the Earth. Locations at 60-90° from the apex are more prone to impacts, especially at midnight. Counterintuitively, sites close to apex direction have the lowest RIP, while in the antapex RIP are slightly larger than average. We present here preliminary maps of RIP at the time of Tunguska and Chelyabinsk events and found no evidence of a spatial or temporal pattern, suggesting that their coincidence was fortuitous. We apply the GRT method to compute theoretical RIP at the location and time of 394 large fireballs. Although the predicted spatio-temporal impact distribution matches marginally the observed events, we successfully predict their impact speed distribution.
Detection of contaminated pixels based on the short-term continuity of NDVI and correction using spatio-temporal continuity

NASA Astrophysics Data System (ADS)

Cho, A.-Ra; Suh, Myoung-Seok

2013-08-01

The present study developed and assessed a correction technique (CSaTC: Correction based on Spatial and Temporal Continuity) for the detection and correction of contaminated Normalized Difference Vegetation Index (NDVI) time series data. Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data from 1982 to 2006 with a 15-day period and an 8-km spatial resolution was used. CSaTC utilizes short-term continuity of vegetation to detect contaminated pixels, and then, corrects the detected pixels using the spatio-temporal continuity of vegetation. CSaTC was applied to the NDVI data over the East Asian region, which exhibits diverse seasonal and interannual variations in vegetation activities. The correction skill of CSaTC was compared to two previously applied methods, IDR (iterative Interpolation for Data Reconstruction) and Park et al. (2011) using GIMMS NDVI data. CSaTC reasonably resolved the overcorrection and spreading phenomenon caused by excessive correction of Park et al. (2011). The validation using the simulated NDVI time series data showed that CSaTC shows a systematically better correction skill in bias and RMSE irrespective of phenology types of vegetation and noise levels. In general, CSaTC showed a good recovery of the contaminated data appearing over the short-term period on a level similar to that obtained using the IDR technique. In addition, it captured the multi-peak of NDVI, and the germination and defoliating patterns more accurately than that by IDR, which overly compensates for seasons with a high temporal variation and where NDVI data exhibit multi-peaks.
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...
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.
Optical control and study of biological processes at the single-cell level in a live organism

NASA Astrophysics Data System (ADS)

Feng, Zhiping; Zhang, Weiting; Xu, Jianmin; Gauron, Carole; Ducos, Bertrand; Vriz, Sophie; Volovitch, Michel; Jullien, Ludovic; Weiss, Shimon; Bensimon, David

2013-07-01

Living organisms are made of cells that are capable of responding to external signals by modifying their internal state and subsequently their external environment. Revealing and understanding the spatio-temporal dynamics of these complex interaction networks is the subject of a field known as systems biology. To investigate these interactions (a necessary step before understanding or modelling them) one needs to develop means to control or interfere spatially and temporally with these processes and to monitor their response on a fast timescale (< minute) and with single-cell resolution. In 2012, an EMBO workshop on ‘single-cell physiology’ (organized by some of us) was held in Paris to discuss those issues in the light of recent developments that allow for precise spatio-temporal perturbations and observations. This review will be largely based on the investigations reported there. We will first present a non-exhaustive list of examples of cellular interactions and developmental pathways that could benefit from these new approaches. We will review some of the novel tools that have been developed for the observation of cellular activity and then discuss the recent breakthroughs in optical super-resolution microscopy that allow for optical observations beyond the diffraction limit. We will review the various means to photo-control the activity of biomolecules, which allow for local perturbations of physiological processes. We will end up this review with a report on the current status of optogenetics: the use of photo-sensitive DNA-encoded proteins as sensitive reporters and efficient actuators to perturb and monitor physiological processes.
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.
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.
On Patterns in Affective Media

NASA Astrophysics Data System (ADS)

ADAMATZKY, ANDREW

In computational experiments with cellular automaton models of affective solutions, where chemical species represent happiness, anger, fear, confusion and sadness, we study phenomena of space time dynamic of emotions. We demonstrate feasibility of the affective solution paradigm in example of emotional abuse therapy. Results outlined in the present paper offer unconventional but promising technique to design, analyze and interpret spatio-temporal dynamic of mass moods in crowds.

Mapping of heavy metal pollution in river water at daily time-scale using spatio-temporal fusion of MODIS-aqua and Landsat satellite imageries.

PubMed

Swain, Ratnakar; Sahoo, Bhabagrahi

2017-05-01

For river water quality monitoring at 30m × 1-day spatio-temporal scales, a spatial and temporal adaptive reflectance fusion model (STARFM) is developed for estimating turbidity (T u ), total suspended solid (TSS), and six heavy metals (HV) of iron, zinc, copper, chromium, lead and cadmium, by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat (L s ) spectral bands. A combination of regression analysis and genetic algorithm (GA) techniques are applied to develop spectral relationships between T u -L s , TSS-T u , and each HV-TSS. The STARFM algorithm and all the developed relationship models are evaluated satisfactorily by various performance evaluation measures to develop heavy metal pollution index-based vulnerability maps at 1-km resolution in the Brahmani River in eastern India. The Monte-Carlo simulation based analysis of the developed formulations reveals that the uncertainty in estimating Zn and Cd is the minimum (1.04%) and the maximum (5.05%), respectively. Hence, the remote sensing based approach developed herein can effectively be used in many world rivers for real-time monitoring of heavy metal pollution. Copyright © 2017 Elsevier Ltd. All rights reserved.
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.
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

PubMed Central

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

2016-01-01

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

PubMed

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

2016-06-10

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.
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
Visualizing spatio-temporal war casualty data in Google Earth - A case study of Map the Fallen (Invited)

NASA Astrophysics Data System (ADS)

Askay, S.

2009-12-01

Published on Memorial Day 2009, Map the Fallen is a Google Earth visualization of the 5500+ US and international soldiers that have died in Iraq and Afghanistan since 2001. In addition to providing photos, stories and links for each solider, the time-animated map visually connects hometowns to places of death. This novel way of representing casualty data brings the geographic reach and magnitude of the issue into focus together with the very personal nature of individual stories. Innovative visualizations techniques were used that illustrate the spatio-temporal nature of this information and to show the global reach and interconnectivity of this issue. Several of advanced KML techniques employed to create this engaging and performance-conscious map will be discussed during this session. These include: 1) the use of HTML iframes and javascript to minimize the KML size, and extensive cross-linking throughout content; 2) the creation of a time-animated, on-screen casualty counter; 3) the use of parabolic arcs to connect each hometown to place of death; 4) the use of concentric spirals to represent chronological data; and 5) numerous performance optimizations to ensure the 23K placemarks, 2500 screen overlays and nearly 250k line vertices performed well in Google Earth. This session will include a demonstration of the map, conceptual discussions of the techniques used, and some in-depth technical explanation of the KML code.
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.
A radial map of multi-whisker correlation selectivity in the rat barrel cortex

PubMed Central

Estebanez, Luc; Bertherat, Julien; Shulz, Daniel E.; Bourdieu, Laurent; Léger, Jean- François

2016-01-01

In the barrel cortex, several features of single-whisker stimuli are organized in functional maps. The barrel cortex also encodes spatio-temporal correlation patterns of multi-whisker inputs, but so far the cortical mapping of neurons tuned to such input statistics is unknown. Here we report that layer 2/3 of the rat barrel cortex contains an additional functional map based on neuronal tuning to correlated versus uncorrelated multi-whisker stimuli: neuron responses to uncorrelated multi-whisker stimulation are strongest above barrel centres, whereas neuron responses to correlated and anti-correlated multi-whisker stimulation peak above the barrel–septal borders, forming rings of multi-whisker synchrony-preferring cells. PMID:27869114
A radial map of multi-whisker correlation selectivity in the rat barrel cortex.

PubMed

Estebanez, Luc; Bertherat, Julien; Shulz, Daniel E; Bourdieu, Laurent; Léger, Jean-François

2016-11-21

In the barrel cortex, several features of single-whisker stimuli are organized in functional maps. The barrel cortex also encodes spatio-temporal correlation patterns of multi-whisker inputs, but so far the cortical mapping of neurons tuned to such input statistics is unknown. Here we report that layer 2/3 of the rat barrel cortex contains an additional functional map based on neuronal tuning to correlated versus uncorrelated multi-whisker stimuli: neuron responses to uncorrelated multi-whisker stimulation are strongest above barrel centres, whereas neuron responses to correlated and anti-correlated multi-whisker stimulation peak above the barrel-septal borders, forming rings of multi-whisker synchrony-preferring cells.
Challenges for modelling spatio-temporal variations of malaria risk in Malawi

NASA Astrophysics Data System (ADS)

Lowe, R.; Chirombo, J.; Tompkins, A. M.

2012-04-01

Malaria is the leading cause of morbidity and mortality in Malawi with more than 6 million episodes reported each year. Malaria poses a huge economic burden to Malawi in terms of the direct cost of treating malaria patients and also indirect costs resulting from workdays lost in agriculture and industry and absenteeism from school. Malawi implements malaria control activities within the Roll Back Malaria framework, with the objective to provide those most at risk (i.e. children under five years, pregnant woman and individuals with suppressed immune systems) access to personal and community protective measures. However, at present there is no mechanism by which to target the most 'at risk' populations ahead of an impending epidemic. Malaria transmission is influenced by variations in meteorological conditions, which impact the biology of the mosquito and the availability of breeding sites, but also socio-economic conditions such as levels of urbanisation, poverty and education, which influence human vulnerability and vector habitat. The many potential drivers of malaria, both extrinsic, such as climate, and intrinsic, such as population immunity are often difficult to disentangle. This presents a challenge for modelling of malaria risk in space and time. Using an age-stratified spatio-temporal dataset of malaria cases at the district level from July 2004 - June 2011, we use a spatio-temporal modelling framework to model variations in malaria risk in Malawi. Climatic and topographic variations are accounted for using an interpolation method to relate gridded products to administrative districts. District level data is tested in the model to account for confounding factors, including the proportion of the population living in urban areas; residing in traditional housing; with no toilet facilities; who do not attend school, etc, the number of health facilities per population and yearly estimates of insecticide-treated mosquito net distribution. In order to account for the unobserved confounding factors that influence malaria, which are not accounted for using measured covariates, a negative binomial generalised linear mixed model (GLMM) is adopted, which includes structured and unstructured spatial and temporal random effects. The parameters in this spatio-temporal Bayesian hierarchical model are estimated using Markov Chain Monte Carlo (MCMC). This allows posterior predictive distributions for disease risk to be derived for each spatial location and time period. A novel visualisation technique is then used to display seasonal probabilistic forecasts of malaria risk, derived from the developed model using pre-defined risk category thresholds, on a map. This technique allows decision makers to identify areas where the model predicts with certainty a particular malaria risk category (high, medium or low); in order to effectively target limited resources to those districts most at risk for a given season.
Trends in spatio-temporal dynamics of visceral leishmaniasis cases in a highly-endemic focus of Bihar, India: an investigation based on GIS tools.

PubMed

Mandal, Rakesh; Kesari, Shreekant; Kumar, Vijay; Das, Pradeep

2018-04-02

Visceral leishmaniasis (VL) in Bihar State (India) continues to be endemic, despite the existence of effective treatment and a vector control program to control disease morbidity. A clear understanding of spatio-temporal distribution of VL may improve surveillance and control implementation. This study explored the trends in spatio-temporal dynamics of VL endemicity at a meso-scale level in Vaishali District, based on geographical information systems (GIS) tools and spatial statistical analysis. A GIS database was used to integrate the VL case data from the study area between 2009 and 2014. All cases were spatially linked at a meso-scale level. Geospatial techniques, such as GIS-layer overlaying and mapping, were employed to visualize and detect the spatio-temporal patterns of a VL endemic outbreak across the district. The spatial statistic Moran's I Index (Moran's I) was used to simultaneously evaluate spatial-correlation between endemic villages and the spatial distribution patterns based on both the village location and the case incidence rate (CIR). Descriptive statistics such as mean, standard error, confidence intervals and percentages were used to summarize the VL case data. There were 624 endemic villages with 2719 (average 906 cases/year) VL cases during 2012-2014. The Moran's I revealed a cluster pattern (P < 0.05) of CIR distribution at the meso-scale level. On average, 68 villages were newly-endemic each year. Of which 93.1% of villages' endemicity were found to have occurred on the peripheries of the previous year endemic villages. The mean CIR of the endemic villages that were peripheral to the following year newly-endemic villages, compared to all endemic villages of the same year, was higher (P < 0.05). The results show that the VL endemicity of new villages tends to occur on the periphery of villages endemic in the previous year. High-CIR plays a major role in the spatial dispersion of the VL cases between non-endemic and endemic villages. This information can help achieve VL elimination throughout the Indian subcontinent by improving vector control design and implementation in highly-endemic district.
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.
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.
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
Panoramic 3D Reconstruction by Fusing Color Intensity and Laser Range Data

NASA Astrophysics Data System (ADS)

Jiang, Wei; Lu, Jian

Technology for capturing panoramic (360 degrees) three-dimensional information in a real environment have many applications in fields: virtual and complex reality, security, robot navigation, and so forth. In this study, we examine an acquisition device constructed of a regular CCD camera and a 2D laser range scanner, along with a technique for panoramic 3D reconstruction using a data fusion algorithm based on an energy minimization framework. The acquisition device can capture two types of data of a panoramic scene without occlusion between two sensors: a dense spatio-temporal volume from a camera and distance information from a laser scanner. We resample the dense spatio-temporal volume for generating a dense multi-perspective panorama that has equal spatial resolution to that of the original images acquired using a regular camera, and also estimate a dense panoramic depth-map corresponding to the generated reference panorama by extracting trajectories from the dense spatio-temporal volume with a selecting camera. Moreover, for determining distance information robustly, we propose a data fusion algorithm that is embedded into an energy minimization framework that incorporates active depth measurements using a 2D laser range scanner and passive geometry reconstruction from an image sequence obtained using the CCD camera. Thereby, measurement precision and robustness can be improved beyond those available by conventional methods using either passive geometry reconstruction (stereo vision) or a laser range scanner. Experimental results using both synthetic and actual images show that our approach can produce high-quality panoramas and perform accurate 3D reconstruction in a panoramic environment.
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
Quantifying the Spatial and Seasonal Hydrodynamics of Subsurface Soil Salinity and Selenium Mobilization in the Pariette Watershed, Uintah Basin, UT

NASA Astrophysics Data System (ADS)

Amakor, X. N.; Jacobson, A. R.; Cardon, G. E.; Grossl, P. R.

2011-12-01

A recent water quality report recognized concentrations of salts and selenium above total maximum daily loads (TMDLs) in the Pariette Wetlands located in the Uintah Basin, Utah. Since the wetlands are located in the Pacific Migratory Flyway and frequented by numerous water fowl, the elevated levels of total dissolved solids and Se are of concern. To determine whether it possible to manage the mobilization of salts and associated contaminants through the watershed soils into the Pariette Wetlands, knowledge of the spatio-temporal dynamics and distribution of these contaminants is required. Thus, the objective of this study is to characterize the spatio-temporal mobilization of salts and total selenium in the Pariette Draw watershed. Intensive soil information is being collected along the streams feeding the wetlands from fields representing the dominant land-uses in the watershed (irrigated agricultural fields, fallow salt-crusted fields, oil and natural gas extraction fields) using both the noninvasive electromagnetic induction (EMI) sensing technique (EM38DD) and the invasive time-domain reflectometry (TDR). At each site, ground truth samples were collected from optimally determined points generated using the ESAP-RSSD program based on the bulk soil electrical conductivity survey information. Stable soil properties affecting the measurement of salinity (e.g., clay content, organic matter content, cation exchange capacity, bulk density) were also characterized at these points. Parameters affected by fluctuations in soil moisture content (e.g., pH, electrical conductivity of saturation paste extract (ECe), dissolved organic carbon (DOC), and total selenium in the dissolved saturation extract) are being measured repeatedly over a minimum of 1 year. Based on regression models of collocated EMI, TDR and ECe measurements, the dense survey data are transformed into ECe. Geostatistical kriging methods are applied to the transformed ECe and volumetric water content to reveal the complex spatio-temporal patterns of salinity, water content, and total selenium (based on the association between ECe and total Se) across portions of the watershed. Temporal changes are being compared using the paired t-test. Here we present the spatio-temporal correlations among the properties and over the sampling times for the 2011 summer and fall seasons with an initial evaluation of the underlying processes contributing to the elevated contaminant loads at the wetlands. Additional measurements will be made in 2012 to capture the effects of early spring snowmelt and runoff.

Transtibial Prosthetic Socket Shape in a Developing Country: A study to compare initial outcomes in Pressure Cast hydrostatic and Patella Tendon Bearing designs.

PubMed

Laing, Sheridan; Lythgo, Noel; Lavranos, Jim; Lee, Peter Vee Sin

2017-10-01

This study compared the physical function and comfort level of patients with unilateral transtibial amputation after being fitted with a hand-cast Patella Tendon Bearing (PTB) socket and a pressure-cast (PCAST) hydrocast socket. The latter technique aims to reduce the skill dependency currently required for socket manufacture and fit. The study was conducted at the Vietnamese Training Centre for Orthopaedic Technologies and involved seventeen Vietnamese participants with unilateral transtibial amputation, all of whom were long term users of prosthetics. All participants were fitted with two sockets manufactured using both hand-cast and PCAST techniques with International Committee of the Red Cross components. Walking tests (timed up and go test and six-minute-walk-test), spatio-temporal gait analyses and subjective comfort assessments were completed after a short acclimatisation period with each socket. The participant-preferred socket was also noted. No significant differences were found for the measures of mobility, functional capacity, spatio-temporal gait parameters, gait symmetry, perceived comfort or participant socket preference. The results show the initial patient outcomes are similar when participants are fitted with a hand-cast PTB socket and a PCAST hydrocast sockets. Future work should confirm these findings in a longer trial. Copyright © 2017 Elsevier B.V. All rights reserved.
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
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
Imaging the NADH:NAD+ Homeostasis for Understanding the Metabolic Response of Mycobacterium to Physiologically Relevant Stresses

PubMed Central

Bhat, Shabir A.; Iqbal, Iram K.; Kumar, Ashwani

2016-01-01

The NADH:NAD+ ratio is the primary indicator of the metabolic state of bacteria. NAD(H) homeostasis is critical for Mycobacterium tuberculosis (Mtb) survival and is thus considered an important drug target, but the spatio-temporal measurements of NAD(H) remain a challenge. Genetically encoded fluorescent biosensors of the NADH:NAD+ ratios were recently described, paving the way for investigations of the metabolic state of pathogens during infection. Here we have adapted the genetically encoded biosensor Peredox for measurement of the metabolic state of Mtb in vitro and during infection of macrophage cells. Using Peredox, here we show that inhibition of the electron transport chain, disruption of the membrane potential and proton gradient, exposure to reactive oxygen species and treatment with antimycobacterial drugs led to the accumulation of NADH in mycobacterial cells. We have further demonstrated that Mtb residing in macrophages displays higher NADH:NAD+ ratios, that may indicate a metabolic stress faced by the intracellular Mtb. We also demonstrate that the Mtb residing in macrophages display a metabolic heterogeneity, which may perhaps explain the tolerance displayed by intracellular Mtb. Next we studied the effect of immunological modulation by interferon gamma on metabolism of intracellular Mtb, since macrophage activation is known to restrict mycobacterial growth. We observed that activation of resting macrophages with interferon-gamma results in higher NADH:NAD+ levels in resident Mtb cells. We have further demonstrated that exposure of Isoniazid, Bedaquiline, Rifampicin, and O-floxacin results in higher NADH:NAD+ ratios in the Mtb residing in macrophages. However, intracellular Mtb displays lower NADH:NAD+ ratio upon exposure to clofazimine. In summary, we have generated reporter strains capable of measuring the metabolic state of Mtb cells in vitro and in vivo with spatio-temporal resolution. We believe that this tool will facilitate further studies on mycobacterial physiology and will create new avenues of research for anti-tuberculosis drug discovery. PMID:27878107
Imaging the NADH:NAD+ Homeostasis for Understanding the Metabolic Response of Mycobacterium to Physiologically Relevant Stresses.

PubMed

Bhat, Shabir A; Iqbal, Iram K; Kumar, Ashwani

2016-01-01

The NADH:NAD + ratio is the primary indicator of the metabolic state of bacteria. NAD(H) homeostasis is critical for Mycobacterium tuberculosis (Mtb) survival and is thus considered an important drug target, but the spatio-temporal measurements of NAD(H) remain a challenge. Genetically encoded fluorescent biosensors of the NADH:NAD + ratios were recently described, paving the way for investigations of the metabolic state of pathogens during infection. Here we have adapted the genetically encoded biosensor Peredox for measurement of the metabolic state of Mtb in vitro and during infection of macrophage cells. Using Peredox, here we show that inhibition of the electron transport chain, disruption of the membrane potential and proton gradient, exposure to reactive oxygen species and treatment with antimycobacterial drugs led to the accumulation of NADH in mycobacterial cells. We have further demonstrated that Mtb residing in macrophages displays higher NADH:NAD + ratios, that may indicate a metabolic stress faced by the intracellular Mtb. We also demonstrate that the Mtb residing in macrophages display a metabolic heterogeneity, which may perhaps explain the tolerance displayed by intracellular Mtb. Next we studied the effect of immunological modulation by interferon gamma on metabolism of intracellular Mtb, since macrophage activation is known to restrict mycobacterial growth. We observed that activation of resting macrophages with interferon-gamma results in higher NADH:NAD + levels in resident Mtb cells. We have further demonstrated that exposure of Isoniazid, Bedaquiline, Rifampicin, and O-floxacin results in higher NADH:NAD + ratios in the Mtb residing in macrophages. However, intracellular Mtb displays lower NADH:NAD + ratio upon exposure to clofazimine. In summary, we have generated reporter strains capable of measuring the metabolic state of Mtb cells in vitro and in vivo with spatio-temporal resolution. We believe that this tool will facilitate further studies on mycobacterial physiology and will create new avenues of research for anti-tuberculosis drug discovery.
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.
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.
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).
Spatio-temporal clustering and density estimation of lightning data for the tracking of convective events

NASA Astrophysics Data System (ADS)

Strauss, Cesar; Rosa, Marcelo Barbio; Stephany, Stephan

2013-12-01

Convective cells are cloud formations whose growth, maturation and dissipation are of great interest among meteorologists since they are associated with severe storms with large precipitation structures. Some works suggest a strong correlation between lightning occurrence and convective cells. The current work proposes a new approach to analyze the correlation between precipitation and lightning, and to identify electrically active cells. Such cells may be employed for tracking convective events in the absence of weather radar coverage. This approach employs a new spatio-temporal clustering technique based on a temporal sliding-window and a standard kernel density estimation to process lightning data. Clustering allows the identification of the cells from lightning data and density estimation bounds the contours of the cells. The proposed approach was evaluated for two convective events in Southeast Brazil. Image segmentation of radar data was performed to identify convective precipitation structures using the Steiner criteria. These structures were then compared and correlated to the electrically active cells in particular instants of time for both events. It was observed that most precipitation structures have associated cells, by comparing the ground tracks of their centroids. In addition, for one particular cell of each event, its temporal evolution was compared to that of the associated precipitation structure. Results show that the proposed approach may improve the use of lightning data for tracking convective events in countries that lack weather radar coverage.
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.
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.
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

Spatio-temporal models of mental processes from fMRI.

PubMed

Janoos, Firdaus; Machiraju, Raghu; Singh, Shantanu; Morocz, Istvan Ákos

2011-07-15

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Conventional analysis methods focus on the spatial dimension of the data discarding the information about brain function contained in the temporal dimension. This paper presents a fully spatio-temporal multivariate analysis method using a state-space model (SSM) for brain function that yields not only spatial maps of activity but also its temporal structure along with spatially varying estimates of the hemodynamic response. Efficient algorithms for estimating the parameters along with quantitative validations are given. A novel low-dimensional feature-space for representing the data, based on a formal definition of functional similarity, is derived. Quantitative validation of the model and the estimation algorithms is provided with a simulation study. Using a real fMRI study for mental arithmetic, the ability of this neurophysiologically inspired model to represent the spatio-temporal information corresponding to mental processes is demonstrated. Moreover, by comparing the models across multiple subjects, natural patterns in mental processes organized according to different mental abilities are revealed. Copyright © 2011 Elsevier Inc. All rights reserved.
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.
Spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013.

PubMed

Wang, Lizhe; Chen, Lajiao

2016-07-05

Spatio-temporal data on human population and its driving factors is critical to understanding and responding to population problems. Unfortunately, such spatio-temporal data on a large scale and over the long term are often difficult to obtain. Here, we present a dataset on Chinese population distribution and its driving factors over a remarkably long period, from 1949 to 2013. Driving factors of population distribution were selected according to the push-pull migration laws, which were summarized into four categories: natural environment, natural resources, economic factors and social factors. Natural environment and natural resources indicators were calculated using Geographic Information System (GIS) and Remote Sensing (RS) techniques, whereas economic and social factors from 1949 to 2013 were collected from the China Statistical Yearbook and China Compendium of Statistics from 1949 to 2008. All of the data were quality controlled and unified into an identical dataset with the same spatial scope and time period. The dataset is expected to be useful for understanding how population responds to and impacts environmental change.
Dynamic imaging of protein-protein interactions by MP-FLIM

NASA Astrophysics Data System (ADS)

Ameer-Beg, Simon M.; Peter, Marion; Keppler, Melanie D.; Prag, Soren; Barber, Paul R.; Ng, Tony C.; Vojnovic, Borivoj

2005-03-01

The spatio-temporal localization of molecular interactions within cells in situ is of great importance in elucidating the key mechanisms in regulation of fundamental process within the cell. Measurements of such near-field localization of protein complexes may be achieved by the detection of fluorescence (or Forster) resonance energy transfer (FRET) between protein-conjugated fluorophores. We demonstrate the applicability of time-correlated single photon counting multiphoton microscopy to the spatio-temporal localization of protein-protein interactions in live and fixed cell populations. Intramolecular interactions between protein hetero-dimers are investigated using green fluorescent protein variants. We present an improved monomeric form of the red fluorescent protein, mRFP1, as the acceptor in biological fluorescence resonance energy transfer (FRET) experiments using the enhanced green fluorescent protein as donor. We find particular advantage in using this fluorophore pair for quantitative measurements of FRET. The technique was exploited to demonstrate a novel receptor-kinase interaction between the chemokine receptor (CXCR4) and protein kinase C (PKC) α in carcinoma cells for both live and fixed cell experiments.
Tropospheric Ozone Lidar Network (TOLNet) Observations of Processes Controlling Spatio-Temporal Tropospheric-Ozone Distributions

NASA Astrophysics Data System (ADS)

Newchurch, M.; Johnson, M. S.; Leblanc, T.; Langford, A. O.; Senff, C. J.; Kuang, S.; Strawbridge, K. B.; McGee, T. J.; Berkoff, T.; Chen, G.

2017-12-01

The Tropospheric Ozone Lidar Network, TOLNet, has matured into a credible scientific group of six ozone lidars that are capable of accurate, high-spatio-temporal-resolution measurement of tropospheric ozone structures and morphology These lidars have demonstrated their 10% accuracy in several intercomparison campaigns and have participated in several scientific investigations both in small and large instrumentation groups. They have investigated many scientific phenomena including stratosphere-to-troposphere exchange, boundary-layer development, the interaction between the boundary layer and the free troposphere, Front-range-ozone morphology, urban outflow, land/sea interactions, et al. These processes determine the ozone distribution affecting large portions of the population. The TOLNet group is now making significant contributions to the innovation of ozone lidar instrumentation and retrieval techniques. The campaigns proposed over the next few years build on demonstrated capability to address more difficult scientific issues, especially the ozone production potential and distribution from wildfires and prescribed burns. Through scientific cooperation with other ground-based profiling instrumentation, TOLNet is also contributing to the validation of the new measurement capabilities of TEMPO.
Spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013

NASA Astrophysics Data System (ADS)

Wang, Lizhe; Chen, Lajiao

2016-07-01

Spatio-temporal data on human population and its driving factors is critical to understanding and responding to population problems. Unfortunately, such spatio-temporal data on a large scale and over the long term are often difficult to obtain. Here, we present a dataset on Chinese population distribution and its driving factors over a remarkably long period, from 1949 to 2013. Driving factors of population distribution were selected according to the push-pull migration laws, which were summarized into four categories: natural environment, natural resources, economic factors and social factors. Natural environment and natural resources indicators were calculated using Geographic Information System (GIS) and Remote Sensing (RS) techniques, whereas economic and social factors from 1949 to 2013 were collected from the China Statistical Yearbook and China Compendium of Statistics from 1949 to 2008. All of the data were quality controlled and unified into an identical dataset with the same spatial scope and time period. The dataset is expected to be useful for understanding how population responds to and impacts environmental change.
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.
Spatio-temporal visualization of air-sea CO2 flux and carbon budget using volume rendering

NASA Astrophysics Data System (ADS)

Du, Zhenhong; Fang, Lei; Bai, Yan; Zhang, Feng; Liu, Renyi

2015-04-01

This paper presents a novel visualization method to show the spatio-temporal dynamics of carbon sinks and sources, and carbon fluxes in the ocean carbon cycle. The air-sea carbon budget and its process of accumulation are demonstrated in the spatial dimension, while the distribution pattern and variation of CO2 flux are expressed by color changes. In this way, we unite spatial and temporal characteristics of satellite data through visualization. A GPU-based direct volume rendering technique using half-angle slicing is adopted to dynamically visualize the released or absorbed CO2 gas with shadow effects. A data model is designed to generate four-dimensional (4D) data from satellite-derived air-sea CO2 flux products, and an out-of-core scheduling strategy is also proposed for on-the-fly rendering of time series of satellite data. The presented 4D visualization method is implemented on graphics cards with vertex, geometry and fragment shaders. It provides a visually realistic simulation and user interaction for real-time rendering. This approach has been integrated into the Information System of Ocean Satellite Monitoring for Air-sea CO2 Flux (IssCO2) for the research and assessment of air-sea CO2 flux in the China Seas.
Nonlinear spatio-temporal filtering of dynamic PET data using a four-dimensional Gaussian filter and expectation-maximization deconvolution

NASA Astrophysics Data System (ADS)

Floberg, J. M.; Holden, J. E.

2013-02-01

We introduce a method for denoising dynamic PET data, spatio-temporal expectation-maximization (STEM) filtering, that combines four-dimensional Gaussian filtering with EM deconvolution. The initial Gaussian filter suppresses noise at a broad range of spatial and temporal frequencies and EM deconvolution quickly restores the frequencies most important to the signal. We aim to demonstrate that STEM filtering can improve variance in both individual time frames and in parametric images without introducing significant bias. We evaluate STEM filtering with a dynamic phantom study, and with simulated and human dynamic PET studies of a tracer with reversible binding behaviour, [C-11]raclopride, and a tracer with irreversible binding behaviour, [F-18]FDOPA. STEM filtering is compared to a number of established three and four-dimensional denoising methods. STEM filtering provides substantial improvements in variance in both individual time frames and in parametric images generated with a number of kinetic analysis techniques while introducing little bias. STEM filtering does bias early frames, but this does not affect quantitative parameter estimates. STEM filtering is shown to be superior to the other simple denoising methods studied. STEM filtering is a simple and effective denoising method that could be valuable for a wide range of dynamic PET applications.
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.
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
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.
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.
Simulating Future Changes in Spatio-temporal Precipitation by Identifying and Characterizing Individual Rainstorm Events

NASA Astrophysics Data System (ADS)

Chang, W.; Stein, M.; Wang, J.; Kotamarthi, V. R.; Moyer, E. J.

2015-12-01

A growing body of literature suggests that human-induced climate change may cause significant changes in precipitation patterns, which could in turn influence future flood levels and frequencies and water supply and management practices. Although climate models produce full three-dimensional simulations of precipitation, analyses of model precipitation have focused either on time-averaged distributions or on individual timeseries with no spatial information. We describe here a new approach based on identifying and characterizing individual rainstorms in either data or model output. Our approach enables us to readily characterize important spatio-temporal aspects of rainstorms including initiation location, intensity (mean and patterns), spatial extent, duration, and trajectory. We apply this technique to high-resolution precipitation over the continental U.S. both from radar-based observations (NCEP Stage IV QPE product, 1-hourly, 4 km spatial resolution) and from model runs with dynamical downscaling (WRF regional climate model, 3-hourly, 12 km spatial resolution). In the model studies we investigate the changes in storm characteristics under a business-as-usual warming scenario to 2100 (RCP 8.5). We find that in these model runs, rainstorm intensity increases as expected with rising temperatures (approximately 7%/K, following increased atmospheric moisture content), while total precipitation increases by a lesser amount (3%/K), consistent with other studies. We identify for the first time the necessary compensating mechanism: in these model runs, individual precipitation events become smaller. Other aspects are approximately unchanged in the warmer climate. Because these spatio-temporal changes in rainfall patterns would impact regional hydrology, it is important that they be accurately incorporated into any impacts assessment. For this purpose we have developed a methodology for producing scenarios of future precipitation that combine observational data and model-projected changes. We statistically describe the future changes in rainstorm characteristics suggested by the WRF model and apply those changes to observational data. The resulting high spatial and temporal resolution scenarios have immediate applications for impacts assessment and adaptation studies.

Identification of repeating earthquakes and spatio-temporal variations of fault zone properties around the Parkfield section of the San Andreas fault and the central Calaveras fault

NASA Astrophysics Data System (ADS)

Zhao, P.; Peng, Z.

2008-12-01

We systemically identify repeating earthquakes and investigate spatio-temporal variations of fault zone properties associated with the 2004 Mw6.0 Parkfield earthquake along the Parkfield section of the San Andreas fault, and the 1984 Mw6.2 Morgan Hill earthquake along the central Calaveras fault. The procedure for identifying repeating earthquakes is based on overlapping of the source regions and the waveform similarity, and is briefly described as follows. First, we estimate the source radius of each event based on a circular crack model and a normal stress drop of 3 MPa. Next, we compute inter-hypocentral distance for events listed in the relocated catalog of Thurber et al. (2006) around Parkfield, and Schaff et al. (2002) along the Calaveras fault. Then, we group all events into 'initial' clusters by requiring the separation distance between each event pair to be less than the source radius of larger event, and their magnitude difference to be less than 1. Next, we calculate the correlation coefficients between every event pair within each 'initial' cluster using a 3-s time window around the direct P waves for all available stations. The median value of the correlation coefficients is used as a measure of similarity between each event pair. We drop an event if the median similarity to the rest events in that cluster is less than 0.9. After identifying repeating clusters in both regions, our next step is to apply a sliding window waveform cross-correlation technique (Niu et al., 2003; Peng and Ben-Zion, 2006) to calculate the delay time and decorrelation index for each repeating cluster. By measuring temporal changes in waveforms of repeating clusters at different locations and depth, we hope to obtain a better constraint on spatio-temporal variations of fault zone properties and near-surface layers associated with the occurrence of major earthquakes.
Visual control of flight speed in Drosophila melanogaster.

PubMed

Fry, Steven N; Rohrseitz, Nicola; Straw, Andrew D; Dickinson, Michael H

2009-04-01

Flight control in insects depends on self-induced image motion (optic flow), which the visual system must process to generate appropriate corrective steering maneuvers. Classic experiments in tethered insects applied rigorous system identification techniques for the analysis of turning reactions in the presence of rotating pattern stimuli delivered in open-loop. However, the functional relevance of these measurements for visual free-flight control remains equivocal due to the largely unknown effects of the highly constrained experimental conditions. To perform a systems analysis of the visual flight speed response under free-flight conditions, we implemented a 'one-parameter open-loop' paradigm using 'TrackFly' in a wind tunnel equipped with real-time tracking and virtual reality display technology. Upwind flying flies were stimulated with sine gratings of varying temporal and spatial frequencies, and the resulting speed responses were measured from the resulting flight speed reactions. To control flight speed, the visual system of the fruit fly extracts linear pattern velocity robustly over a broad range of spatio-temporal frequencies. The speed signal is used for a proportional control of flight speed within locomotor limits. The extraction of pattern velocity over a broad spatio-temporal frequency range may require more sophisticated motion processing mechanisms than those identified in flies so far. In Drosophila, the neuromotor pathways underlying flight speed control may be suitably explored by applying advanced genetic techniques, for which our data can serve as a baseline. Finally, the high-level control principles identified in the fly can be meaningfully transferred into a robotic context, such as for the robust and efficient control of autonomous flying micro air vehicles.
Harmonics rejection in pixelated interferograms using spatio-temporal demodulation.

PubMed

Padilla, J M; Servin, M; Estrada, J C

2011-09-26

Pixelated phase-mask interferograms have become an industry standard in spatial phase-shifting interferometry. These pixelated interferograms allow full wavefront encoding using a single interferogram. This allows the study of fast dynamic events in hostile mechanical environments. Recently an error-free demodulation method for ideal pixelated interferograms was proposed. However, non-ideal conditions in interferometry may arise due to non-linear response of the CCD camera, multiple light paths in the interferometer, etc. These conditions generate non-sinusoidal fringes containing harmonics which degrade the phase estimation. Here we show that two-dimensional Fourier demodulation of pixelated interferograms rejects most harmonics except the complex ones at {-3(rd), +5(th), -7(th), +9(th), -11(th),…}. We propose temporal phase-shifting to remove these remaining harmonics. In particular, a 2-step phase-shifting algorithm is used to eliminate the -3(rd) and +5(th) complex harmonics, while a 3-step one is used to remove the -3(rd), +5<(th), -7(th) and +9(th) complex harmonics. © 2011 Optical Society of America
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.
Conformable actively multiplexed high-density surface electrode array for brain interfacing

DOEpatents

Rogers, John; Kim, Dae-Hyeong; Litt, Brian; Viventi, Jonathan

2015-01-13

Provided are methods and devices for interfacing with brain tissue, specifically for monitoring and/or actuation of spatio-temporal electrical waveforms. The device is conformable having a high electrode density and high spatial and temporal resolution. A conformable substrate supports a conformable electronic circuit and a barrier layer. Electrodes are positioned to provide electrical contact with a brain tissue. A controller monitors or actuates the electrodes, thereby interfacing with the brain tissue. In an aspect, methods are provided to monitor or actuate spatio-temporal electrical waveform over large brain surface areas by any of the devices disclosed herein.
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
Implementing Signature Neural Networks with Spiking Neurons

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks. PMID:28066221
Implementing Signature Neural Networks with Spiking Neurons.

PubMed

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.
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.
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.
Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

USGS Publications Warehouse

Szabo, J.K.; Fedriani, E.M.; Segovia-Gonzalez, M. M.; Astheimer, L.B.; Hooper, M.J.

2010-01-01

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. ?? 2010 World Scientific Publishing Company.
On the Characterization of the Spatio-Temporal Profiles of Brain Activity Associated with Face Naming and the Tip-of-the-Tongue State: A Magnetoencephalographic (MEG) Study

ERIC Educational Resources Information Center

Lindin, Monica; Diaz, Fernando; Capilla, Almudena; Ortiz, Tomas; Maestu, Fernando

2010-01-01

The tip-of-the-tongue state (TOT) in face naming is a transient state of difficulty in access to a person's name along with the conviction that the name is known. The aim of the present study was to characterize the spatio-temporal course of brain activation in the successful naming and TOT states, by means of magnetoencephalography, during a…
Mathematical neuroscience: from neurons to circuits to systems.

PubMed

Gutkin, Boris; Pinto, David; Ermentrout, Bard

2003-01-01

Applications of mathematics and computational techniques to our understanding of neuronal systems are provided. Reduction of membrane models to simplified canonical models demonstrates how neuronal spike-time statistics follow from simple properties of neurons. Averaging over space allows one to derive a simple model for the whisker barrel circuit and use this to explain and suggest several experiments. Spatio-temporal pattern formation methods are applied to explain the patterns seen in the early stages of drug-induced visual hallucinations.
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
Photogrammetric determination of spatio-temporal velocity fields at Glaciar San Rafael in the Northern Patagonian Icefield

NASA Astrophysics Data System (ADS)

Maas, H.-G.; Casassa, G.; Schneider, D.; Schwalbe, E.; Wendt, A.

2010-11-01

Glaciar San Rafael in the Northern Patagonian Icefield, with a length of 46 km and an ice area of 722 km2, is the lowest latitude tidewater outlet glacier in the world and one of the fastest and most productive glaciers in southern South America in terms of iceberg flux. In a joint project of the TU Dresden and CECS, spatio-temporal velocity fields in the region of the glacier front were determined in a campaign in austral spring of 2009. Monoscopic terrestrial image sequences were recorded with an intervallometer mode high resolution digital camera over several days. In these image sequences, a large number of glacier surface points were tracked by subpixel accuracy feature tracking techniques. Scaling and georeferencing of the trajectories obtained from image space tracking was performed via a multi-station GPS-supported photogrammetric network. The technique allows for tracking hundreds of glacier surface points at a measurement accuracy in the order of one decimeter and an almost arbitrarily high temporary resolution. The results show velocities of up to 16 m per day. No significant tidal signals could be observed. Our velocities are in agreement with earlier measurements from theodolite and satellite interferometry performed in 1986-1994, suggesting that the current thinning of 3.5 m/y at the front is not due to dynamic thinning but rather by enhanced melting.

Multi-level emulation of complex climate model responses to boundary forcing data

NASA Astrophysics Data System (ADS)

Tran, Giang T.; Oliver, Kevin I. C.; Holden, Philip B.; Edwards, Neil R.; Sóbester, András; Challenor, Peter

2018-04-01

Climate model components involve both high-dimensional input and output fields. It is desirable to efficiently generate spatio-temporal outputs of these models for applications in integrated assessment modelling or to assess the statistical relationship between such sets of inputs and outputs, for example, uncertainty analysis. However, the need for efficiency often compromises the fidelity of output through the use of low complexity models. Here, we develop a technique which combines statistical emulation with a dimensionality reduction technique to emulate a wide range of outputs from an atmospheric general circulation model, PLASIM, as functions of the boundary forcing prescribed by the ocean component of a lower complexity climate model, GENIE-1. Although accurate and detailed spatial information on atmospheric variables such as precipitation and wind speed is well beyond the capability of GENIE-1's energy-moisture balance model of the atmosphere, this study demonstrates that the output of this model is useful in predicting PLASIM's spatio-temporal fields through multi-level emulation. Meaningful information from the fast model, GENIE-1 was extracted by utilising the correlation between variables of the same type in the two models and between variables of different types in PLASIM. We present here the construction and validation of several PLASIM variable emulators and discuss their potential use in developing a hybrid model with statistical components.
A high speed multifocal multiphoton fluorescence lifetime imaging microscope for live-cell FRET imaging

PubMed Central

Poland, Simon P.; Krstajić, Nikola; Monypenny, James; Coelho, Simao; Tyndall, David; Walker, Richard J.; Devauges, Viviane; Richardson, Justin; Dutton, Neale; Barber, Paul; Li, David Day-Uei; Suhling, Klaus; Ng, Tony; Henderson, Robert K.; Ameer-Beg, Simon M.

2015-01-01

We demonstrate diffraction limited multiphoton imaging in a massively parallel, fully addressable time-resolved multi-beam multiphoton microscope capable of producing fluorescence lifetime images with sub-50ps temporal resolution. This imaging platform offers a significant improvement in acquisition speed over single-beam laser scanning FLIM by a factor of 64 without compromising in either the temporal or spatial resolutions of the system. We demonstrate FLIM acquisition at 500 ms with live cells expressing green fluorescent protein. The applicability of the technique to imaging protein-protein interactions in live cells is exemplified by observation of time-dependent FRET between the epidermal growth factor receptor (EGFR) and the adapter protein Grb2 following stimulation with the receptor ligand. Furthermore, ligand-dependent association of HER2-HER3 receptor tyrosine kinases was observed on a similar timescale and involved the internalisation and accumulation or receptor heterodimers within endosomes. These data demonstrate the broad applicability of this novel FLIM technique to the spatio-temporal dynamics of protein-protein interaction. PMID:25780724
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.
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].
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
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
"Disorganized in time": impact of bottom-up and top-down negative emotion generation on memory formation among healthy and traumatized adolescents.

PubMed

Guillery-Girard, Bérengère; Clochon, Patrice; Giffard, Bénédicte; Viard, Armelle; Egler, Pierre-Jean; Baleyte, Jean-Marc; Eustache, Francis; Dayan, Jacques

2013-09-01

"Travelling in time," a central feature of episodic memory is severely affected among individuals with Post Traumatic Stress Disorder (PTSD) with two opposite effects: vivid traumatic memories are unorganized in temporality (bottom-up processes), non-traumatic personal memories tend to lack spatio-temporal details and false recognitions occur more frequently that in the general population (top-down processes). To test the effect of these two types of processes (i.e. bottom-up and top-down) on emotional memory, we conducted two studies in healthy and traumatized adolescents, a period of life in which vulnerability to emotion is particularly high. Using negative and neutral images selected from the international affective picture system (IAPS), stimuli were divided into perceptual images (emotion generated by perceptual details) and conceptual images (emotion generated by the general meaning of the material). Both categories of stimuli were then used, along with neutral pictures, in a memory task with two phases (encoding and recognition). In both populations, we reported a differential effect of the emotional material on encoding and recognition. Negative perceptual scenes induced an attentional capture effect during encoding and enhanced the recollective distinctiveness. Conversely, the encoding of conceptual scenes was similar to neutral ones, but the conceptual relatedness induced false memories at retrieval. However, among individuals with PTSD, two subgroups of patients were identified. The first subgroup processed the scenes faster than controls, except for the perceptual scenes, and obtained similar performances to controls in the recognition task. The second subgroup group desmonstrated an attentional deficit in the encoding task with no benefit from the distinctiveness associated with negative perceptual scenes on memory performances. These findings provide a new perspective on how negative emotional information may have opposite influences on memory in normal and traumatized individuals. It also gives clues to understand how intrusive memories and overgeneralization takes place in PTSD. Copyright © 2013 Elsevier Ltd. All rights reserved.
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
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.
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
Tracking the time course of multi-word noun phrase production with ERPs or on when (and why) cat is faster than the big cat.

PubMed

Bürki, Audrey; Laganaro, Marina

2014-01-01

Words are rarely produced in isolation. Yet, our understanding of multi-word production, and especially its time course, is still rather poor. In this research, we use event-related potentials to examine the production of multi-word noun phrases in the context of overt picture naming. We track the processing costs associated with the production of these noun phrases as compared with the production of bare nouns, from picture onset to articulation. Behavioral results revealed longer naming latencies for French noun phrases with determiners and pre-nominal adjectives (D-A-N, the big cat) than for noun phrases with a determiner (D-N, the cat), or bare nouns (N, cat). The spatio-temporal analysis of the ERPs revealed differences in the duration of stable global electrophysiological patterns as a function of utterance format in two time windows, from ~190 to 300 ms after picture onset, and from ~530 ms after picture onset to 100 ms before articulation. These findings can be accommodated in the following model. During grammatical encoding (here from ~190 to 300 ms), the noun and adjective lemmas are accessed in parallel, followed by the selection of the gender-agreeing determiner. Phonological encoding (after ~530 ms) operates sequentially. As a consequence, the phonological encoding process is longer for longer utterances. In addition, when determiners are repeated across trials, their phonological encoding can be anticipated or primed, resulting in a shortened encoding process.

Collaborative Beamfocusing Radio (COBRA)

NASA Astrophysics Data System (ADS)

Rode, Jeremy P.; Hsu, Mark J.; Smith, David; Husain, Anis

2013-05-01

A Ziva team has recently demonstrated a novel technique called Collaborative Beamfocusing Radios (COBRA) which enables an ad-hoc collection of distributed commercial off-the-shelf software defined radios to coherently align and beamform to a remote radio. COBRA promises to operate even in high multipath and non-line-of-sight environments as well as mobile applications without resorting to computationally expensive closed loop techniques that are currently unable to operate with significant movement. COBRA exploits two key technologies to achieve coherent beamforming. The first is Time Reversal (TR) which compensates for multipath and automatically discovers the optimal spatio-temporal matched filter to enable peak signal gains (up to 20 dB) and diffraction-limited focusing at the intended receiver in NLOS and severe multipath environments. The second is time-aligned buffering which enables TR to synchronize distributed transmitters into a collaborative array. This time alignment algorithm avoids causality violations through the use of reciprocal buffering. Preserving spatio-temporal reciprocity through the TR capture and retransmission process achieves coherent alignment across multiple radios at ~GHz carriers using only standard quartz-oscillators. COBRA has been demonstrated in the lab, aligning two off-the-shelf software defined radios over-the-air to an accuracy of better than 2 degrees of carrier alignment at 450 MHz. The COBRA algorithms are lightweight, with computation in 5 ms on a smartphone class microprocessor. COBRA also has low start-up latency, achieving high accuracy from a cold-start in 30 ms. The COBRA technique opens up a large number of new capabilities in communications, and electronic warfare including selective spatial jamming, geolocation and anti-geolocation.
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.
Fusion of LBP and SWLD using spatio-spectral information for hyperspectral face recognition

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Jiang, Peng; Zhang, Shuai; Xiong, Jinquan

2018-01-01

Hyperspectral imaging, recording intrinsic spectral information of the skin cross different spectral bands, become an important issue for robust face recognition. However, the main challenges for hyperspectral face recognition are high data dimensionality, low signal to noise ratio and inter band misalignment. In this paper, hyperspectral face recognition based on LBP (Local binary pattern) and SWLD (Simplified Weber local descriptor) is proposed to extract discriminative local features from spatio-spectral fusion information. Firstly, the spatio-spectral fusion strategy based on statistical information is used to attain discriminative features of hyperspectral face images. Secondly, LBP is applied to extract the orientation of the fusion face edges. Thirdly, SWLD is proposed to encode the intensity information in hyperspectral images. Finally, we adopt a symmetric Kullback-Leibler distance to compute the encoded face images. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (92.8%) than the state of the art hyperspectral face recognition algorithms.
High-performance Raman memory with spatio-temporal reversal

NASA Astrophysics Data System (ADS)

Vernaz-Gris, Pierre; Tranter, Aaron D.; Everett, Jesse L.; Leung, Anthony C.; Paul, Karun V.; Campbell, Geoff T.; Lam, Ping Koy; Buchler, Ben C.

2018-05-01

A number of techniques exist to use an ensemble of atoms as a quantum memory for light. Many of these propose to use backward retrieval as a way to improve the storage and recall efficiency. We report on a demonstration of an off-resonant Raman memory that uses backward retrieval to achieve an efficiency of $65\\pm6\\%$ at a storage time of one pulse duration. The memory has a characteristic decay time of 60 $\\mu$s, corresponding to a delay-bandwidth product of $160$.
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
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.
Spatio-Temporal Tracking and Phylodynamics of an Urban Dengue 3 Outbreak in São Paulo, Brazil

PubMed Central

Mondini, Adriano; Bronzoni, Roberta Vieira de Moraes; Nunes, Silvia Helena Pereira; Chiaravalloti Neto, Francisco; Massad, Eduardo; Alonso, Wladimir J.; Lázzaro, Eduardo S. M.; Ferraz, Amena Alcântara; de Andrade Zanotto, Paolo Marinho; Nogueira, Maurício Lacerda

2009-01-01

The dengue virus has a single-stranded positive-sense RNA genome of ∼10.700 nucleotides with a single open reading frame that encodes three structural (C, prM, and E) and seven nonstructural (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5) proteins. It possesses four antigenically distinct serotypes (DENV 1–4). Many phylogenetic studies address particularities of the different serotypes using convenience samples that are not conducive to a spatio-temporal analysis in a single urban setting. We describe the pattern of spread of distinct lineages of DENV-3 circulating in São José do Rio Preto, Brazil, during 2006. Blood samples from patients presenting dengue-like symptoms were collected for DENV testing. We performed M-N-PCR using primers based on NS5 for virus detection and identification. The fragments were purified from PCR mixtures and sequenced. The positive dengue cases were geo-coded. To type the sequenced samples, 52 reference sequences were aligned. The dataset generated was used for iterative phylogenetic reconstruction with the maximum likelihood criterion. The best demographic model, the rate of growth, rate of evolutionary change, and Time to Most Recent Common Ancestor (TMRCA) were estimated. The basic reproductive rate during the epidemics was estimated. We obtained sequences from 82 patients among 174 blood samples. We were able to geo-code 46 sequences. The alignment generated a 399-nucleotide-long dataset with 134 taxa. The phylogenetic analysis indicated that all samples were of DENV-3 and related to strains circulating on the isle of Martinique in 2000–2001. Sixty DENV-3 from São José do Rio Preto formed a monophyletic group (lineage 1), closely related to the remaining 22 isolates (lineage 2). We assumed that these lineages appeared before 2006 in different occasions. By transforming the inferred exponential growth rates into the basic reproductive rate, we obtained values for lineage 1 of R0 = 1.53 and values for lineage 2 of R0 = 1.13. Under the exponential model, TMRCA of lineage 1 dated 1 year and lineage 2 dated 3.4 years before the last sampling. The possibility of inferring the spatio-temporal dynamics from genetic data has been generally little explored, and it may shed light on DENV circulation. The use of both geographic and temporally structured phylogenetic data provided a detailed view on the spread of at least two dengue viral strains in a populated urban area. PMID:19478848
Integrating Map Algebra and Statistical Modeling for Spatio- Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain.

PubMed

Evrendilek, Fatih

2007-12-12

This study aims at quantifying spatio-temporal dynamics of monthly mean dailyincident photosynthetically active radiation (PAR) over a vast and complex terrain such asTurkey. The spatial interpolation method of universal kriging, and the combination ofmultiple linear regression (MLR) models and map algebra techniques were implemented togenerate surface maps of PAR with a grid resolution of 500 x 500 m as a function of fivegeographical and 14 climatic variables. Performance of the geostatistical and MLR modelswas compared using mean prediction error (MPE), root-mean-square prediction error(RMSPE), average standard prediction error (ASE), mean standardized prediction error(MSPE), root-mean-square standardized prediction error (RMSSPE), and adjustedcoefficient of determination (R² adj. ). The best-fit MLR- and universal kriging-generatedmodels of monthly mean daily PAR were validated against an independent 37-year observeddataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifingmethod. The spatial variability patterns of monthly mean daily incident PAR were moreaccurately reflected in the surface maps created by the MLR-based models than in thosecreated by the universal kriging method, in particular, for spring (May) and autumn(November). The MLR-based spatial interpolation algorithms of PAR described in thisstudy indicated the significance of the multifactor approach to understanding and mappingspatio-temporal dynamics of PAR for a complex terrain over meso-scales.
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.
A high-power fiber-coupled semiconductor light source with low spatio-temporal coherence

NASA Astrophysics Data System (ADS)

Schittko, Robert; Mazurenko, Anton; Tai, M. Eric; Lukin, Alexander; Rispoli, Matthew; Menke, Tim; Kaufman, Adam M.; Greiner, Markus

2017-04-01

Interference-induced distortions pose a significant challenge to a variety of experimental techniques, ranging from full-field imaging applications in biological research to the creation of optical potentials in quantum gas microscopy. Here, we present a design of a high-power, fiber-coupled semiconductor light source with low spatio-temporal coherence that bears the potential to reduce the impact of such distortions. The device is based on an array of non-lasing semiconductor emitters mounted on a single chip whose optical output is coupled into a multi-mode fiber. By populating a large number of fiber modes, the low spatial coherence of the input light is further reduced due to the differing optical path lengths amongst the modes and the short coherence length of the light. In addition to theoretical calculations showcasing the feasibility of this approach, we present experimental measurements verifying the low degree of spatial coherence achievable with such a source, including a detailed analysis of the speckle contrast at the fiber end. We acknowledge support from the National Science Foundation, the Gordon and Betty Moore Foundation's EPiQS Initiative, an Air Force Office of Scientific Research MURI program and an Army Research Office MURI program.
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.
Anatomically constrained dipole adjustment (ANACONDA) for accurate MEG/EEG focal source localizations

NASA Astrophysics Data System (ADS)

Im, Chang-Hwan; Jung, Hyun-Kyo; Fujimaki, Norio

2005-10-01

This paper proposes an alternative approach to enhance localization accuracy of MEG and EEG focal sources. The proposed approach assumes anatomically constrained spatio-temporal dipoles, initial positions of which are estimated from local peak positions of distributed sources obtained from a pre-execution of distributed source reconstruction. The positions of the dipoles are then adjusted on the cortical surface using a novel updating scheme named cortical surface scanning. The proposed approach has many advantages over the conventional ones: (1) as the cortical surface scanning algorithm uses spatio-temporal dipoles, it is robust with respect to noise; (2) it requires no a priori information on the numbers and initial locations of the activations; (3) as the locations of dipoles are restricted only on a tessellated cortical surface, it is physiologically more plausible than the conventional ECD model. To verify the proposed approach, it was applied to several realistic MEG/EEG simulations and practical experiments. From the several case studies, it is concluded that the anatomically constrained dipole adjustment (ANACONDA) approach will be a very promising technique to enhance accuracy of focal source localization which is essential in many clinical and neurological applications of MEG and EEG.

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.
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
Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE

NASA Technical Reports Server (NTRS)

Kuo, Kwo-Sen; Rilee, Michael Lee

2017-01-01

Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding (STARE) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.
Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE

NASA Astrophysics Data System (ADS)

Kuo, K. S.; Rilee, M. L.

2017-12-01

Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding ( STARE ) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.
Temporal and Spatio-Temporal Dynamic Instabilities: Novel Computational and Experimental approaches

NASA Astrophysics Data System (ADS)

Doedel, Eusebius J.; Panayotaros, Panayotis; Lambruschini, Carlos L. Pando

2016-11-01

This special issue contains a concise account of significant research results presented at the international workshop on Advanced Computational and Experimental Techniques in Nonlinear Dynamics, which was held in Cusco, Peru in August 2015. The meeting gathered leading experts, as well as new researchers, who have contributed to different aspects of Nonlinear Dynamics. Particularly significant was the presence of many active scientists from Latin America. The topics covered in this special issue range from advanced numerical techniques to novel physical experiments, and reflect the present state of the art in several areas of Nonlinear Dynamics. It contains seven review articles, followed by twenty-one regular papers that are organized in five categories, namely (1) Nonlinear Evolution Equations and Applications, (2) Numerical Continuation in Self-sustained Oscillators, (3) Synchronization, Control and Data Analysis, (4) Hamiltonian Systems, and (5) Scaling Properties in Maps.
Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors.

PubMed

Dutton, Neale A W; Gyongy, Istvan; Parmesan, Luca; Henderson, Robert K

2016-07-20

SPAD-based solid state CMOS image sensors utilising analogue integrators have attained deep sub-electron read noise (DSERN) permitting single photon counting (SPC) imaging. A new method is proposed to determine the read noise in DSERN image sensors by evaluating the peak separation and width (PSW) of single photon peaks in a photon counting histogram (PCH). The technique is used to identify and analyse cumulative noise in analogue integrating SPC SPAD-based pixels. The DSERN of our SPAD image sensor is exploited to confirm recent multi-photon threshold quanta image sensor (QIS) theory. Finally, various single and multiple photon spatio-temporal oversampling techniques are reviewed.
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.
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.
Quantifying utricular stimulation during natural behavior

PubMed Central

Rivera, Angela R. V.; Davis, Julian; Grant, Wally; Blob, Richard W.; Peterson, Ellengene; Neiman, Alexander B.; Rowe, Michael

2012-01-01

The use of natural stimuli in neurophysiological studies has led to significant insights into the encoding strategies used by sensory neurons. To investigate these encoding strategies in vestibular receptors and neurons, we have developed a method for calculating the stimuli delivered to a vestibular organ, the utricle, during natural (unrestrained) behaviors, using the turtle as our experimental preparation. High-speed digital video sequences are used to calculate the dynamic gravito-inertial (GI) vector acting on the head during behavior. X-ray computed tomography (CT) scans are used to determine the orientation of the otoconial layer (OL) of the utricle within the head, and the calculated GI vectors are then rotated into the plane of the OL. Thus, the method allows us to quantify the spatio-temporal structure of stimuli to the OL during natural behaviors. In the future, these waveforms can be used as stimuli in neurophysiological experiments to understand how natural signals are encoded by vestibular receptors and neurons. We provide one example of the method which shows that turtle feeding behaviors can stimulate the utricle at frequencies higher than those typically used in vestibular studies. This method can be adapted to other species, to other vestibular end organs, and to other methods of quantifying head movements. PMID:22753360
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.
Glomerular latency coding in artificial olfaction.

PubMed

Yamani, Jaber Al; Boussaid, Farid; Bermak, Amine; Martinez, Dominique

2011-01-01

Sensory perception results from the way sensory information is subsequently transformed in the brain. Olfaction is a typical example in which odor representations undergo considerable changes as they pass from olfactory receptor neurons (ORNs) to second-order neurons. First, many ORNs expressing the same receptor protein yet presenting heterogeneous dose-response properties converge onto individually identifiable glomeruli. Second, onset latency of glomerular activation is believed to play a role in encoding odor quality and quantity in the context of fast information processing. Taking inspiration from the olfactory pathway, we designed a simple yet robust glomerular latency coding scheme for processing gas sensor data. The proposed bio-inspired approach was evaluated using an in-house SnO(2) sensor array. Glomerular convergence was achieved by noting the possible analogy between receptor protein expressed in ORNs and metal catalyst used across the fabricated gas sensor array. Ion implantation was another technique used to account both for sensor heterogeneity and enhanced sensitivity. The response of the gas sensor array was mapped into glomerular latency patterns, whose rank order is concentration-invariant. Gas recognition was achieved by simply looking for a "match" within a library of spatio-temporal spike fingerprints. Because of its simplicity, this approach enables the integration of sensing and processing onto a single-chip.
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.
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.

Patterning of functional human astrocytes onto parylene-C/SiO2 substrates for the study of Ca2+ dynamics in astrocytic networks

NASA Astrophysics Data System (ADS)

Raos, B. J.; Simpson, M. C.; Doyle, C. S.; Murray, A. F.; Graham, E. S.; Unsworth, C. P.

2018-06-01

Objective. Recent literature suggests that astrocytes form organized functional networks and communicate through transient changes in cytosolic Ca2+. Traditional techniques to investigate network activity, such as pharmacological blocking or genetic knockout, are difficult to restrict to individual cells. The objective of this work is to develop cell-patterning techniques to physically manipulate astrocytic interactions to enable the study of Ca2+ in astrocytic networks. Approach. We investigate how an in vitro cell-patterning platform that utilizes geometric patterns of parylene-C on SiO2 can be used to physically isolate single astrocytes and small astrocytic networks. Main results. We report that single astrocytes are effectively isolated on 75 × 75 µm square parylene nodes, whereas multi-cellular astrocytic networks are isolated on larger nodes, with the mean number of astrocytes per cluster increasing as a function of node size. Additionally, we report that astrocytes in small multi-cellular clusters exhibit spatio-temporal clustering of Ca2+ transients. Finally, we report that the frequency and regularity of Ca2+ transients was positively correlated with astrocyte connectivity. Significance. The significance of this work is to demonstrate how patterning hNT astrocytes replicates spatio-temporal clustering of Ca2+ signalling that is observed in vivo but not in dissociated in vitro cultures. We therefore highlight the importance of the structure of astrocytic networks in determining ensemble Ca2+ behaviour.
Knowledge Discovery from Climate Data using Graph-Based Methods

NASA Astrophysics Data System (ADS)

Steinhaeuser, K.

2012-04-01

Climate and Earth sciences have recently experienced a rapid transformation from a historically data-poor to a data-rich environment, thus bringing them into the realm of the Fourth Paradigm of scientific discovery - a term coined by the late Jim Gray (Hey et al. 2009), the other three being theory, experimentation and computer simulation. In particular, climate-related observations from remote sensors on satellites and weather radars, in situ sensors and sensor networks, as well as outputs of climate or Earth system models from large-scale simulations, provide terabytes of spatio-temporal data. These massive and information-rich datasets offer a significant opportunity for advancing climate science and our understanding of the global climate system, yet current analysis techniques are not able to fully realize their potential benefits. We describe a class of computational approaches, specifically from the data mining and machine learning domains, which may be novel to the climate science domain and can assist in the analysis process. Computer scientists have developed spatial and spatio-temporal analysis techniques for a number of years now, and many of them may be applicable and/or adaptable to problems in climate science. We describe a large-scale, NSF-funded project aimed at addressing climate science question using computational analysis methods; team members include computer scientists, statisticians, and climate scientists from various backgrounds. One of the major thrusts is in the development of graph-based methods, and several illustrative examples of recent work in this area will be presented.
Effects of load and maintenance duration on the time course of information encoding and retrieval in working memory: from perceptual analysis to post-categorization processes.

PubMed

Pinal, Diego; Zurrón, Montserrat; Díaz, Fernando

2014-01-01

information encoding, maintenance, and retrieval; these are supported by brain activity in a network of frontal, parietal and temporal regions. Manipulation of WM load and duration of the maintenance period can modulate this activity. Although such modulations have been widely studied using the event-related potentials (ERP) technique, a precise description of the time course of brain activity during encoding and retrieval is still required. Here, we used this technique and principal component analysis to assess the time course of brain activity during encoding and retrieval in a delayed match to sample task. We also investigated the effects of memory load and duration of the maintenance period on ERP activity. Brain activity was similar during information encoding and retrieval and comprised six temporal factors, which closely matched the latency and scalp distribution of some ERP components: P1, N1, P2, N2, P300, and a slow wave. Changes in memory load modulated task performance and yielded variations in frontal lobe activation. Moreover, the P300 amplitude was smaller in the high than in the low load condition during encoding and retrieval. Conversely, the slow wave amplitude was higher in the high than in the low load condition during encoding, and the same was true for the N2 amplitude during retrieval. Thus, during encoding, memory load appears to modulate the processing resources for context updating and post-categorization processes, and during retrieval it modulates resources for stimulus classification and context updating. Besides, despite the lack of differences in task performance related to duration of the maintenance period, larger N2 amplitude and stronger activation of the left temporal lobe after long than after short maintenance periods were found during information retrieval. Thus, results regarding the duration of maintenance period were complex, and future work is required to test the time-based decay theory predictions.
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.
Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

NASA Astrophysics Data System (ADS)

Hengl, Tomislav; Heuvelink, Gerard B. M.; Perčec Tadić, Melita; Pebesma, Edzer J.

2012-01-01

A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval, resampling and filtering of MODIS images—are anticipated.
High resolution mapping of riffle-pool dynamics based on ADCP and close-range remote sensing data

NASA Astrophysics Data System (ADS)

Salmela, Jouni; Kasvi, Elina; Alho, Petteri

2017-04-01

Present development of mobile laser scanning (MLS) and close-range photogrammetry with unmanned aerial vehicle (UAV) enable us to create seamless digital elevation models (DEMs) of the riverine environment. Remote-controlled flow measurement platforms have also improved spatio-temporal resolution of the flow field data. In this study, acoustic Doppler current profiler (ADCP) attached to remote-controlled mini-boat, UAV-based bathymetry and MLS techniques were utilized to create the high-resolution DEMs of the river channel. These high-resolution measurements can be used in many fluvial applications such as computational fluid dynamics, channel change detection, habitat mapping or hydro-electric power plant planning. In this study we aim: 1) to analyze morphological changes of river channel especially riffle and pool formations based on fine-scale DEMs and ADCP measurements, 2) to analyze flow fields and their effect on morphological changes. The interest was mainly focused on reach-scale riffle-pool dynamics within two-year period of 2013 and 2014. The study was performed in sub-arctic meandering Pulmankijoki River located in Northern Finland. The river itself has shallow and clear water and sandy bed sediment. Discharge remains typically below 10 m3s-1 most of the year but during snow melt period in spring the discharge may exceed 70 m3s-1. We compared DEMs and ADCP measurements to understand both magnitude and spatio-temporal change of the river bed. Models were accurate enough to study bed form changes and locations and persistence of riffles and pools. We analyzed their locations with relation to flow during the peak and low discharge. Our demonstrated method has improved significantly spatio-temporal resolution of riverine DEMs compared to other cross-sectional and photogrammetry based models. Together with flow field measurements we gained better understanding of riverbed-water interaction
Analysis of the Mediterranean fruit fly [Ceratitis capitata (Wiedemann)] spatio-temporal distribution in relation to sex and female mating status for precision IPM.

PubMed

Sciarretta, Andrea; Tabilio, Maria Rosaria; Lampazzi, Elena; Ceccaroli, Claudio; Colacci, Marco; Trematerra, Pasquale

2018-01-01

The Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedemann), is a key pest of fruit crops in many tropical, subtropical and mild temperate areas worldwide. The economic importance of this fruit fly is increasing due to its invasion of new geographical areas. Efficient control and eradication efforts require adequate information regarding C. capitata adults in relation to environmental and physiological cues. This would allow effective characterisation of the population spatio-temporal dynamic of the C. capitata population at both the orchard level and the area-wide landscape. The aim of this study was to analyse population patterns of adult medflies caught using two trapping systems in a peach orchard located in central Italy. They were differentiated by adult sex (males or females) and mating status of females (unmated or mated females) to determine the spatio-temporal dynamic and evaluate the effect of cultivar and chemical treatments on trap catches. Female mating status was assessed by spermathecal dissection and a blind test was carried out to evaluate the reliability of the technique. Geostatistical methods, variogram and kriging, were used to produce distributional maps. Results showed a strong correlation between the distribution of males and unmated females, whereas males versus mated females and unmated females versus mated females showed a lower correlation. Both cultivar and chemical treatments had significant effects on trap catches, showing associations with sex and female mating status. Medfly adults showed aggregated distributions in the experimental field, but hot spots locations varied. The spatial pattern of unmated females reflected that of males, whereas mated females were largely distributed around ripening or ripe fruit. The results give relevant insights into pest management. Mated females may be distributed differently to unmated females and the identification of male hot spots through monitoring would allow localisation of virgin female populations. Based on our results, a more precise IPM strategy, coupled with effective sanitation practices, could represent a more effective approach to medfly control.
Adaptation of running pattern to the drop of standard cushioned shoes: A randomised controlled trial with a 6-month follow-up.

PubMed

Malisoux, Laurent; Gette, Paul; Chambon, Nicolas; Urhausen, Axel; Theisen, Daniel

2017-08-01

While several cross-sectional studies have investigated the acute effects of shoe drop on running biomechanics, the long-term consequences are currently unknown. This study aimed to investigate if the drop of standard cushioned shoes induces specific adaptations in running technique over a six-month period in leisure-time runners. Double-blinded randomised controlled trial. The participants (n=59) received a pair of shoes with a heel-to-toe drop of 10mm (D10), 6mm (D6) or 0mm (D0) and were followed-up regarding running training over 6 months or 500km, whichever came first. Spatio-temporal variables and kinematics (foot/ground, ankle and knee joint angles) were investigated while running at preferred speed on a treadmill before and after the follow-up. The participants ran 332±178km in the study shoes between pre- and post-tests. There was no shoe version by time interaction for any of the spatio-temporal variables nor for lower limb angles at initial ground contact. A small but significant shoe drop effect was found for knee abduction at mid-stance (p=0.032), as it decreased for the D0 version (-0.3±3.1 vs. -1.3±2.6°) while it increased for the D6 (0.3±2.7 vs. 1.3±3.1°) and D10 version (-0.2±3.2 vs. 0.5±3.1°). However, none of the pairwise comparisons was significant in the post-hoc analysis. Apart from knee abduction at mid-stance, no specific adaptation in spatio-temporal variables and kinematics was found between the three shoe versions during this 6-month follow-up. Thus, shoe drop of standard cushioned shoes does not seem to influence running biomechanics in the long term. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Classification of Regional Ionospheric Disturbances Based on Support Vector Machines

NASA Astrophysics Data System (ADS)

Begüm Terzi, Merve; Arikan, Feza; Arikan, Orhan; Karatay, Secil

2016-07-01

Ionosphere is an anisotropic, inhomogeneous, time varying and spatio-temporally dispersive medium whose parameters can be estimated almost always by using indirect measurements. Geomagnetic, gravitational, solar or seismic activities cause variations of ionosphere at various spatial and temporal scales. This complex spatio-temporal variability is challenging to be identified due to extensive scales in period, duration, amplitude and frequency of disturbances. Since geomagnetic and solar indices such as Disturbance storm time (Dst), F10.7 solar flux, Sun Spot Number (SSN), Auroral Electrojet (AE), Kp and W-index provide information about variability on a global scale, identification and classification of regional disturbances poses a challenge. The main aim of this study is to classify the regional effects of global geomagnetic storms and classify them according to their risk levels. For this purpose, Total Electron Content (TEC) estimated from GPS receivers, which is one of the major parameters of ionosphere, will be used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. In this work, for the automated classification of the regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. SVM is a supervised learning model used for classification with associated learning algorithm that analyze the data and recognize patterns. In addition to performing linear classification, SVM can efficiently perform nonlinear classification by embedding data into higher dimensional feature spaces. Performance of the developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from the GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing the developed classification technique to the Global Ionospheric Map (GIM) TEC data which is provided by the NASA Jet Propulsion Laboratory (JPL), it will be shown that SVM can be a suitable learning method to detect the anomalies in Total Electron Content (TEC) variations. This study is supported by TUBITAK 114E541 project as a part of the Scientific and Technological Research Projects Funding Program (1001).
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.
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.
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.
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

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.
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.
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.
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
Spatio-temporal optimization of agricultural practices to achieve a sustainable development at basin level; framework of a case study in Colombia

NASA Astrophysics Data System (ADS)

Uribe, Natalia; corzo, Gerald; Solomatine, Dimitri

2016-04-01

The flood events present during the last years in different basins of the Colombian territory have raised questions on the sensitivity of the regions and if this regions have common features. From previous studies it seems important features in the sensitivity of the flood process were: land cover change, precipitation anomalies and these related to impacts of agriculture management and water management deficiencies, among others. A significant government investment in the outreach activities for adopting and promoting the Colombia National Action Plan on Climate Change (NAPCC) is being carried out in different sectors and regions, having as a priority the agriculture sector. However, more information is still needed in the local environment in order to assess were the regions have this sensitivity. Also the continuous change in one region with seasonal agricultural practices have been pointed out as a critical information for optimal sustainable development. This combined spatio-temporal dynamics of crops cycle in relation to climate change (or variations) has an important impact on flooding events at basin areas. This research will develop on the assessment and optimization of the aggregated impact of flood events due to determinate the spatio-temporal dynamic of changes in agricultural management practices. A number of common best agricultural practices have been identified to explore their effect in a spatial hydrological model that will evaluate overall changes. The optimization process consists on the evaluation of best performance in the agricultural production, without having to change crops activities or move to other regions. To achieve this objectives a deep analysis of different models combined with current and future climate scenarios have been planned. An algorithm have been formulated to cover the parametric updates such that the optimal temporal identification will be evaluated in different region on the case study area. Different hydroinformatics techniques for optimization and uncertainty analysis are included in a framework that will solve partially the computational load found in the pre-runs of the case study. The work will focus on the region Fuquene basin in Colombia but this will not limit the scope of this study to have general methodological applications to other areas. Key words Modelling, WFlow_sbm, agriculture practices, climate change, optimization, flooding, spatial and temporal analysis
[Spatio-temporal process and the influencing factors on influenza A (H1N1) pandemic in Changsha].

PubMed

Xiao, Hong; Tian, Huai-yu; Zhao, Jian; Zhang, Xi-xing; Zhu, Pei-juan; Liu, Ru-chun; Chen, Tian-mu

2011-06-01

To analyze the spatio-temporal process on 2009 influenza A (H1N1) pandemic in Changsha and the influencing factors during the diffusion process. Data were from the following 5 sources, influenza A (H1N1) pandemic gathered in 2009, Geographic Information System (GIS) of Changsha, the broad range of theorems and techniques of hot spot analysis, spatio-temporal process analysis and Spearman correlation analysis. Hot spot areas appeared to be more in the economically developed areas, such as cities and townships. The cluster of spatial-temporal distribution of influenza A (H1N1) pandemic was most likely appearing in Liuyang city (RR = 22.70, P < 0.01). The secondary cluster would include districts as Yuelu (RR = 6.49, P < 0.01), Yuhua (RR = 81.63, P < 0.01). Xingsha township appeared as the center in the Changsha county (RR = 2.90, P < 0.01) while townships as Yutangping (RR = 19.31, P < 0.01), Chengjiao (RR = 73.14, P < 0.01) and Longtian appeared as the center in the west of Ningxiang county (RR = 14.43, P < 0.01) and Wushan as the center in the Wangcheng county (RR = 13.84, P < 0.01). As time went on, the epidemic moved towards the eastern and more developed regions. Regarding factor analysis, population, the amount of students, geographic relationship and business activities etc. appeared to be the key elements influencing the transmission of influenza A (H1N1) pandemic. At the beginning of the epidemic, population density served as the main factor (r = 0.477, P < 0.05) but during the initial and fast growing stages, it was replaced by the size of students to serve as the important indicator (r = 0.831, P < 0.01; r = 0.518, P < 0.01). However, during the peak of the epidemics, the business activities played an important role (r = -0.676, P < 0.01). Groups under high risk and districts with high incidence rates were shifting, along with the temporal process of influenza A (H1N1) pandemic, suggesting that the protection measures need to be adjusted, according to the significance of influencing factors at different stages.
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.
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
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...
Spatio-Temporal Assessment of Margalla Hills Forest by Using Landsat Imagery for Year 2000 and 2018

NASA Astrophysics Data System (ADS)

Batool, R.; Javaid, K.

2018-04-01

Environmental imbalance due to human activities has shown serious threat to ecosystem and produced negative impacts. The main goal of this study is to identify, monitor and classify temporal changes of forest cover, build up and open spaces in Margalla Hills National Park, Islamabad. Geographic Information Sciences (GISc) and Remote Sensing (RS) techniques has been used for the assessment of analysis. LANDSAT-7 Enhanced Thematic Mapper (ETM+) and LANDSAT-8 Operational Land Imager (OLI) were utilized for obtaining data of year 2000 and 2018. Temporal changes were evaluated after applying supervised classification and discrimination was analyzed by Per-Pixel based change detection. Results depicts forest cover decrease from 87 % to 74 % whereas build up has increased from 5 % to 7 % over the span. Consequences also justify the presence of open land in study area that has been increased from 2 % to 7 % respectively.
Spatial and temporal variability of microgeographic genetic structure in white-tailed deer

USGS Publications Warehouse

Scribner, Kim T.; Smith, Michael H.; Chesser, Ronald K.

1997-01-01

Techniques are described that define contiguous genetic subpopulations of white-tailed deer (Odocoileus virginianus) based on the spatial dispersion of 4,749 individuals that possessed discrete character values (alleles or genotypes) during each of 6 years (1974-1979). White-tailed deer were not uniformly distributed in space, but exhibited considerable spatial genetic structuring. Significant non-random clusters of individuals were documented during each year based on specific alleles and genotypes at the Sdh locus. Considerable temporal variation was observed in the position and genetic composition of specific clusters, which reflected changes in allele frequency in small geographic areas. The position of clusters did not consistently correspond with traditional management boundaries based on major discontinuities in habitat (swamp versus upland) and hunt compartments that were defined by roads and streams. Spatio-temporal stability of observed genetic contiguous clusters was interpreted relative to method and intensity of harvest, movements, and breeding ecology.
Discovering Communicable Scientific Knowledge from Spatio-Temporal Data

NASA Technical Reports Server (NTRS)

Schwabacher, Mark; Langley, Pat; Norvig, Peter (Technical Monitor)

2001-01-01

This paper describes how we used regression rules to improve upon a result previously published in the Earth science literature. In such a scientific application of machine learning, it is crucially important for the learned models to be understandable and communicable. We recount how we selected a learning algorithm to maximize communicability, and then describe two visualization techniques that we developed to aid in understanding the model by exploiting the spatial nature of the data. We also report how evaluating the learned models across time let us discover an error in the data.
Organic–Inorganic Eu3+/Tb3+ codoped hybrid films for temperature mapping in integrated circuits

PubMed Central

Brites, Carlos D. S.; Lima, Patrícia P.; Silva, Nuno J. O.; Millán, Angel; Amaral, Vitor S.; Palacio, Fernando; Carlos, Luís D.

2013-01-01

The continuous decrease on the geometric size of electronic devices and integrated circuits generates higher local power densities and localized heating problems that cannot be characterized by conventional thermographic techniques. Here, a self-referencing intensity-based molecular thermometer involving a di-ureasil organic-inorganic hybrid thin film co-doped with Eu3+ and Tb3+ tris (β-diketonate) chelates is used to obtain the temperature map of a FR4 printed wiring board with spatio-temporal resolutions of 0.42 μm/4.8 ms. PMID:24790938
High spatial and temporal resolution cell manipulation techniques in microchannels.

PubMed

Novo, Pedro; Dell'Aica, Margherita; Janasek, Dirk; Zahedi, René P

2016-03-21

The advent of microfluidics has enabled thorough control of cell manipulation experiments in so called lab on chips. Lab on chips foster the integration of actuation and detection systems, and require minute sample and reagent amounts. Typically employed microfluidic structures have similar dimensions as cells, enabling precise spatial and temporal control of individual cells and their local environments. Several strategies for high spatio-temporal control of cells in microfluidics have been reported in recent years, namely methods relying on careful design of the microfluidic structures (e.g. pinched flow), by integration of actuators (e.g. electrodes or magnets for dielectro-, acousto- and magneto-phoresis), or integrations thereof. This review presents the recent developments of cell experiments in microfluidics divided into two parts: an introduction to spatial control of cells in microchannels followed by special emphasis in the high temporal control of cell-stimulus reaction and quenching. In the end, the present state of the art is discussed in line with future perspectives and challenges for translating these devices into routine applications.

Spatio-temporal control of gene expression and cancer treatment using magnetic resonance imaging-guided focused ultrasound.

PubMed

Moonen, Chrit T W

2007-06-15

Local temperature elevation may be used for tumor ablation, gene expression, drug activation, and gene and/or drug delivery. High-intensity focused ultrasound (HIFU) is the only clinically viable technology that can be used to achieve a local temperature increase deep inside the human body in a noninvasive way. Magnetic resonance imaging (MRI) guidance of the procedure allows in situ target definition and identification of nearby healthy tissue to be spared. In addition, MRI can be used to provide continuous temperature mapping during HIFU for spatial and temporal control of the heating procedure and prediction of the final lesion based on the received thermal dose. The primary purpose of the development of MRI-guided HIFU was to achieve safe noninvasive tissue ablation. The technique has been tested extensively in preclinical studies and is now accepted in the clinic for ablation of uterine fibroids. MRI-guided HIFU for ablation shows conceptual similarities with radiation therapy. However, thermal damage generally shows threshold-like behavior, with necrosis above the critical thermal dose and full recovery below. MRI-guided HIFU is being clinically evaluated in the cancer field. The technology also shows great promise for a variety of advanced therapeutic methods, such as gene therapy. MR-guided HIFU, together with the use of a temperature-sensitive promoter, provides local, physical, and spatio-temporal control of transgene expression. Specially designed contrast agents, together with the combined use of MRI and ultrasound, may be used for local gene and drug delivery.
Predation on larval Atlantic herring (Clupea harengus) in inshore waters of the Baltic Sea

NASA Astrophysics Data System (ADS)

Kotterba, Paul; Moll, Dorothee; von Nordheim, Lena; Peck, Myron A.; Oesterwind, Daniel; Polte, Patrick

2017-11-01

In fishery science, early life-stage survival and development are regarded as major factors driving the population dynamics of marine fishes. During the last century, the main research focus has been on the spatio-temporal match of larval fish and appropriate food (bottom-up processes). However, these field studies are often criticised for their limited capability to disentangle their results from mortality caused by predation since these top-down mechanisms are rarely studied. We examined the predation on herring (Clupea harengus) larvae in a Baltic inshore lagoon by investigating the spatio-temporal overlap of larval herring and their potential predators such as the dominant threespine stickleback (Gasterosteus aculeatus) in distinct habitats (sublittoral and littoral areas) using a set of different gears and sampling techniques. Despite significant spatial and temporal predator-prey overlap, stomach analyses suggested that very few larvae were consumed by sticklebacks, even if projected to the entire study area and season. Other well-known predators of clupeid larvae such as gelatinous plankton occur later in the year after young herring have migrated out of the system. The observed predation on herring larvae was much less than expected and appears being a minor factor in determining herring reproduction success in our study area, particularly if compared to other causes of mortality such as egg predation. Providing a relatively good shelter from predation might be a key element making transitional waters valuable nursery grounds for the offspring of migrating marine fish species.
Accelerated dynamic EPR imaging using fast acquisition and compressive recovery

NASA Astrophysics Data System (ADS)

Ahmad, Rizwan; Samouilov, Alexandre; Zweier, Jay L.

2016-12-01

Electron paramagnetic resonance (EPR) allows quantitative imaging of tissue redox status, which provides important information about ischemic syndromes, cancer and other pathologies. For continuous wave EPR imaging, however, poor signal-to-noise ratio and low acquisition efficiency limit its ability to image dynamic processes in vivo including tissue redox, where conditions can change rapidly. Here, we present a data acquisition and processing framework that couples fast acquisition with compressive sensing-inspired image recovery to enable EPR-based redox imaging with high spatial and temporal resolutions. The fast acquisition (FA) allows collecting more, albeit noisier, projections in a given scan time. The composite regularization based processing method, called spatio-temporal adaptive recovery (STAR), not only exploits sparsity in multiple representations of the spatio-temporal image but also adaptively adjusts the regularization strength for each representation based on its inherent level of the sparsity. As a result, STAR adjusts to the disparity in the level of sparsity across multiple representations, without introducing any tuning parameter. Our simulation and phantom imaging studies indicate that a combination of fast acquisition and STAR (FASTAR) enables high-fidelity recovery of volumetric image series, with each volumetric image employing less than 10 s of scan. In addition to image fidelity, the time constants derived from FASTAR also match closely to the ground truth even when a small number of projections are used for recovery. This development will enhance the capability of EPR to study fast dynamic processes that cannot be investigated using existing EPR imaging techniques.
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.
Extracellular matrix remodeling and matrix metalloproteinases (ajMMP-2 like and ajMMP-16 like) characterization during intestine regeneration of sea cucumber Apostichopus japonicus.

PubMed

Miao, Ting; Wan, Zixuan; Sun, Lina; Li, Xiaoni; Xing, Lili; Bai, Yucen; Wang, Fang; Yang, Hongsheng

2017-10-01

Remodeling of extracellular matrix (ECM) regulated by matrix metalloproteinases (MMPs) is essential for tissue regeneration. In the present study, we used immunohistochemistry (IHC) techniques against ECM components to reveal changes of ECM during intestine regeneration of Apostichopus japonicus. The expression of collagen I and laminin reduced apparently from the eviscerated intestine, while fibronectin exhibited continuous expression in all regeneration stages observed. Meanwhile, we cloned two MMP genes from A. japonicus by RACE PCR. The full-length cDNA of ajMMP-2 like is 2733bp and contains a predicted open reading frame (ORF) of 1716bp encoding 572 amino acids. The full-length cDNA of ajMMP-16 like is 2705bp and contains an ORF of 1452bp encoding 484 amino acids. The predicted protein sequences of each MMP contain two conserved domains, ZnMc_MMP and HX. Homology and phylogenetic analysis revealed that ajMMP-2 like and ajMMP-16 like share high sequence similarity with MMP-2 and MMP-16 from Strongylocentrotus purpuratus, respectively. Then we investigated spatio-temporal expression of ajMMP-2 like and ajMMP-16 like during different regeneration stages by qRT-PCR and IHC. The expression pattern of them showed a roughly opposite trend from that of ECM components. According to our results, a fibronectin-dominate temporary matrix is created in intestine regeneration, and it might provide structural integrity for matrix and promote cell movement. We also hypothesize that ajMMP-2 like and ajMMP-16 like could accelerate cell migration and regulate interaction between ECM components and growth factors. This work provides new evidence of ECM and MMPs involvement in sea cucumber regeneration. Copyright © 2017 Elsevier Inc. All rights reserved.
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.
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.
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.
Three-dimensional displacement measurement by fringe projection and speckle photography

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

Barrientos, B.; Garcia-Marquez, J.; Cerca, M.

2008-04-15

3D displacement fields are measured by the combination of two optical methods, fringe projection and speckle photography. The use of only one camera recording the necessary information implies that no calibration procedures are necessary as is the case in techniques based on stereoscopy. The out-of-plane displacement is measured by fringe projection whereas speckle photography yields the 2-D in-plane component. To show the feasibility of the technique, we analyze a detailed morphological spatio-temporal evolution of a model of the Earth's crust while subjected to compression forces. The results show that the combination of fringe projection and speckle photography is well suitedmore » for this type of studies.« less
Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors

PubMed Central

Dutton, Neale A. W.; Gyongy, Istvan; Parmesan, Luca; Henderson, Robert K.

2016-01-01

SPAD-based solid state CMOS image sensors utilising analogue integrators have attained deep sub-electron read noise (DSERN) permitting single photon counting (SPC) imaging. A new method is proposed to determine the read noise in DSERN image sensors by evaluating the peak separation and width (PSW) of single photon peaks in a photon counting histogram (PCH). The technique is used to identify and analyse cumulative noise in analogue integrating SPC SPAD-based pixels. The DSERN of our SPAD image sensor is exploited to confirm recent multi-photon threshold quanta image sensor (QIS) theory. Finally, various single and multiple photon spatio-temporal oversampling techniques are reviewed. PMID:27447643
An ultra-sparse code underliesthe generation of neural sequences in a songbird

NASA Astrophysics Data System (ADS)

Hahnloser, Richard H. R.; Kozhevnikov, Alexay A.; Fee, Michale S.

2002-09-01

Sequences of motor activity are encoded in many vertebrate brains by complex spatio-temporal patterns of neural activity; however, the neural circuit mechanisms underlying the generation of these pre-motor patterns are poorly understood. In songbirds, one prominent site of pre-motor activity is the forebrain robust nucleus of the archistriatum (RA), which generates stereotyped sequences of spike bursts during song and recapitulates these sequences during sleep. We show that the stereotyped sequences in RA are driven from nucleus HVC (high vocal centre), the principal pre-motor input to RA. Recordings of identified HVC neurons in sleeping and singing birds show that individual HVC neurons projecting onto RA neurons produce bursts sparsely, at a single, precise time during the RA sequence. These HVC neurons burst sequentially with respect to one another. We suggest that at each time in the RA sequence, the ensemble of active RA neurons is driven by a subpopulation of RA-projecting HVC neurons that is active only at that time. As a population, these HVC neurons may form an explicit representation of time in the sequence. Such a sparse representation, a temporal analogue of the `grandmother cell' concept for object recognition, eliminates the problem of temporal interference during sequence generation and learning attributed to more distributed representations.
From Maximum Entropy Models to Non-Stationarity and Irreversibility

NASA Astrophysics Data System (ADS)

Cofre, Rodrigo; Cessac, Bruno; Maldonado, Cesar

The maximum entropy distribution can be obtained from a variational principle. This is important as a matter of principle and for the purpose of finding approximate solutions. One can exploit this fact to obtain relevant information about the underlying stochastic process. We report here in recent progress in three aspects to this approach.1- Biological systems are expected to show some degree of irreversibility in time. Based on the transfer matrix technique to find the spatio-temporal maximum entropy distribution, we build a framework to quantify the degree of irreversibility of any maximum entropy distribution.2- The maximum entropy solution is characterized by a functional called Gibbs free energy (solution of the variational principle). The Legendre transformation of this functional is the rate function, which controls the speed of convergence of empirical averages to their ergodic mean. We show how the correct description of this functional is determinant for a more rigorous characterization of first and higher order phase transitions.3- We assess the impact of a weak time-dependent external stimulus on the collective statistics of spiking neuronal networks. We show how to evaluate this impact on any higher order spatio-temporal correlation. RC supported by ERC advanced Grant ``Bridges'', BC: KEOPS ANR-CONICYT, Renvision and CM: CONICYT-FONDECYT No. 3140572.
Spatio-Temporal Dynamics of Impulse Responses to Figure Motion in Optic Flow Neurons

PubMed Central

Lee, Yu-Jen; Jönsson, H. Olof; Nordström, Karin

2015-01-01

White noise techniques have been used widely to investigate sensory systems in both vertebrates and invertebrates. White noise stimuli are powerful in their ability to rapidly generate data that help the experimenter decipher the spatio-temporal dynamics of neural and behavioral responses. One type of white noise stimuli, maximal length shift register sequences (m-sequences), have recently become particularly popular for extracting response kernels in insect motion vision. We here use such m-sequences to extract the impulse responses to figure motion in hoverfly lobula plate tangential cells (LPTCs). Figure motion is behaviorally important and many visually guided animals orient towards salient features in the surround. We show that LPTCs respond robustly to figure motion in the receptive field. The impulse response is scaled down in amplitude when the figure size is reduced, but its time course remains unaltered. However, a low contrast stimulus generates a slower response with a significantly longer time-to-peak and half-width. Impulse responses in females have a slower time-to-peak than males, but are otherwise similar. Finally we show that the shapes of the impulse response to a figure and a widefield stimulus are very similar, suggesting that the figure response could be coded by the same input as the widefield response. PMID:25955416
A nonlinear circuit architecture for magnetoencephalographic signal analysis.

PubMed

Bucolo, M; Fortuna, L; Frasca, M; La Rosa, M; Virzì, M C; Shannahoff-Khalsa, D

2004-01-01

The objective of this paper was to face the complex spatio-temporal dynamics shown by Magnetoencephalography (MEG) data by applying a nonlinear distributed approach for the Blind Sources Separation. The effort was to characterize and differ-entiate the phases of a yogic respiratory exercise used in the treatment of obsessive compulsive disorders. The patient performed a precise respiratory protocol, at one breath per minute for 31 minutes, with 10 minutes resting phase before and after. The two steps of classical Independent Component Approach have been performed by using a Cellular Neural Network with two sets of templates. The choice of the couple of suitable templates has been carried out using genetic algorithm optimization techniques. Performing BSS with a nonlinear distributed approach, the outputs of the CNN have been compared to the ICA ones. In all the protocol phases, the main components founded with CNN have similar trends compared with that ones obtained with ICA. Moreover, using this distributed approach, a spatial location has been associated to each component. To underline the spatio-temporal and the nonlinearly of the neural process a distributed nonlinear architecture has been proposed. This strategy has been designed in order to overcome the hypothesis of linear combination among the sources signals, that is characteristic of the ICA approach, taking advantage of the spatial information.
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.
Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.

PubMed

Lawson, A B; Carroll, R; Faes, C; Kirby, R S; Aregay, M; Watjou, K

2017-12-01

It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this paper, we propose a flexible class of multivariate spatio-temporal mixture models to fill this role. Further, these models offer flexibility with the potential for model selection as well as the ability to accommodate lifestyle, socio-economic, and physical environmental variables with spatial, temporal, or both structures. Here, we explore the capability of this approach via a large scale simulation study and examine a motivating data example involving three cancers in South Carolina. The results which are focused on four model variants suggest that all models possess the ability to recover simulation ground truth and display improved model fit over two baseline Knorr-Held spatio-temporal interaction model variants in a real data application.
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.
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 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.
Uncertainty visualisation in the Model Web

NASA Astrophysics Data System (ADS)

Gerharz, L. E.; Autermann, C.; Hopmann, H.; Stasch, C.; Pebesma, E.

2012-04-01

Visualisation of geospatial data as maps is a common way to communicate spatially distributed information. If temporal and furthermore uncertainty information are included in the data, efficient visualisation methods are required. For uncertain spatial and spatio-temporal data, numerous visualisation methods have been developed and proposed, but only few tools for visualisation of data in a standardised way exist. Furthermore, usually they are realised as thick clients, and lack functionality of handling data coming from web services as it is envisaged in the Model Web. We present an interactive web tool for visualisation of uncertain spatio-temporal data developed in the UncertWeb project. The client is based on the OpenLayers JavaScript library. OpenLayers provides standard map windows and navigation tools, i.e. pan, zoom in/out, to allow interactive control for the user. Further interactive methods are implemented using jStat, a JavaScript library for statistics plots developed in UncertWeb, and flot. To integrate the uncertainty information into existing standards for geospatial data, the Uncertainty Markup Language (UncertML) was applied in combination with OGC Observations&Measurements 2.0 and JavaScript Object Notation (JSON) encodings for vector and NetCDF for raster data. The client offers methods to visualise uncertain vector and raster data with temporal information. Uncertainty information considered for the tool are probabilistic and quantified attribute uncertainties which can be provided as realisations or samples, full probability distributions functions and statistics. Visualisation is supported for uncertain continuous and categorical data. In the client, the visualisation is realised using a combination of different methods. Based on previously conducted usability studies, a differentiation between expert (in statistics or mapping) and non-expert users has been indicated as useful. Therefore, two different modes are realised together in the tool: (i) adjacent maps showing data and uncertainty separately, and (ii) multidimensional mapping providing different visualisation methods in combination to explore the spatial, temporal and uncertainty distribution of the data. Adjacent maps allow a simpler visualisation by separating value and uncertainty maps for non-experts and a first overview. The multidimensional approach allows a more complex exploration of the data for experts by browsing through the different dimensions. It offers the visualisation of maps, statistic plots and time series in different windows and sliders to interactively move through time, space and uncertainty (thresholds).
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.
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.
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.
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.
Techniques for spatio-temporal analysis of vegetation fires in the topical belt of Africa

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

Brivio, P.A.; Ober, G.; Koffi, B.

1995-12-31

Biomass burning of forests and savannas is a phenomenon of continental or even global proportions, capable of causing large scale environmental changes. Satellite space observations, in particular from NOAA-AVHRR GAC data, are the only source of information allowing one to document burning patterns at regional and continental scale and over long periods of time. This paper presents some techniques, such as clustering and rose-diagram, useful in the spatial-temporal analysis of satellite derived fires maps to characterize the evolution of spatial patterns of vegetation fires at regional scale. An automatic clustering approach is presented which enables one to describe and parameterizemore » spatial distribution of fire patterns at different scales. The problem of geographical distribution of vegetation fires with respect to some location of interest, point or line, is also considered and presented. In particular rose-diagrams are used to relate fires patterns to some reference point, as experimental sites of tropospheric chemistry measurements. Different temporal data-sets in the tropical belt of Africa, covering both Northern and Southern Hemisphere dry seasons, using these techniques were analyzed and showed very promising results when compared with data from rain chemistry studies at different sampling sites in the equatorial forest.« less
Spatio-Temporal Progression of Cortical Activity Related to Continuous Overt and Covert Speech Production in a Reading Task.

PubMed

Brumberg, Jonathan S; Krusienski, Dean J; Chakrabarti, Shreya; Gunduz, Aysegul; Brunner, Peter; Ritaccio, Anthony L; Schalk, Gerwin

2016-01-01

How the human brain plans, executes, and monitors continuous and fluent speech has remained largely elusive. For example, previous research has defined the cortical locations most important for different aspects of speech function, but has not yet yielded a definition of the temporal progression of involvement of those locations as speech progresses either overtly or covertly. In this paper, we uncovered the spatio-temporal evolution of neuronal population-level activity related to continuous overt speech, and identified those locations that shared activity characteristics across overt and covert speech. Specifically, we asked subjects to repeat continuous sentences aloud or silently while we recorded electrical signals directly from the surface of the brain (electrocorticography (ECoG)). We then determined the relationship between cortical activity and speech output across different areas of cortex and at sub-second timescales. The results highlight a spatio-temporal progression of cortical involvement in the continuous speech process that initiates utterances in frontal-motor areas and ends with the monitoring of auditory feedback in superior temporal gyrus. Direct comparison of cortical activity related to overt versus covert conditions revealed a common network of brain regions involved in speech that may implement orthographic and phonological processing. Our results provide one of the first characterizations of the spatiotemporal electrophysiological representations of the continuous speech process, and also highlight the common neural substrate of overt and covert speech. These results thereby contribute to a refined understanding of speech functions in the human brain.
Spatio-Temporal Progression of Cortical Activity Related to Continuous Overt and Covert Speech Production in a Reading Task

PubMed Central

Brumberg, Jonathan S.; Krusienski, Dean J.; Chakrabarti, Shreya; Gunduz, Aysegul; Brunner, Peter; Ritaccio, Anthony L.; Schalk, Gerwin

2016-01-01

How the human brain plans, executes, and monitors continuous and fluent speech has remained largely elusive. For example, previous research has defined the cortical locations most important for different aspects of speech function, but has not yet yielded a definition of the temporal progression of involvement of those locations as speech progresses either overtly or covertly. In this paper, we uncovered the spatio-temporal evolution of neuronal population-level activity related to continuous overt speech, and identified those locations that shared activity characteristics across overt and covert speech. Specifically, we asked subjects to repeat continuous sentences aloud or silently while we recorded electrical signals directly from the surface of the brain (electrocorticography (ECoG)). We then determined the relationship between cortical activity and speech output across different areas of cortex and at sub-second timescales. The results highlight a spatio-temporal progression of cortical involvement in the continuous speech process that initiates utterances in frontal-motor areas and ends with the monitoring of auditory feedback in superior temporal gyrus. Direct comparison of cortical activity related to overt versus covert conditions revealed a common network of brain regions involved in speech that may implement orthographic and phonological processing. Our results provide one of the first characterizations of the spatiotemporal electrophysiological representations of the continuous speech process, and also highlight the common neural substrate of overt and covert speech. These results thereby contribute to a refined understanding of speech functions in the human brain. PMID:27875590
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.
Genetically Encoded Calcium Indicators For Studying Long-Term Calcium Dynamics During Apoptosis

PubMed Central

Garcia, M. Iveth; Chen, Jessica J.; Boehning, Darren

2017-01-01

Intracellular calcium release is essential for regulating almost all cellular functions. Specific spatio-temporal patterns of cytosolic calcium elevations are critical determinants of cell fate in response to pro-apoptotic cellular stressors. As the apoptotic program can take hours or days, measurement of long-term calcium dynamics are essential for understanding the mechanistic role of calcium in apoptotic cell death. Due to the technical limitations of using calcium-sensitive dyes to measure cytosolic calcium little is known about long-term calcium dynamics in living cells after treatment with apoptosis-inducing drugs. Genetically encoded calcium indicators could potentially overcome some of the limitations of calcium-sensitive dyes. Here, we compared the performance of the genetically encoded calcium indicators GCaMP6s and GCaMP6f with the ratiometric dye Fura-2. GCaMP6s performed as well or better than Fura-2 in detecting agonist-induced calcium transients. We then examined the utility of GCaMP6s for continuously measuring apoptotic calcium release over the course of ten hours after treatment with staurosporine. We found that GCaMP6s was suitable for measuring apoptotic calcium release over long time courses and revealed significant heterogeneity in calcium release dynamics in individual cells challenged with staurosporine. Our results suggest GCaMP6s is an excellent indicator for monitoring long-term changes cytosolic calcium during apoptosis. PMID:28073595
REV, A BRET-Based Sensor of ERK Activity

PubMed Central

Xu, Chanjuan; Peter, Marion; Bouquier, Nathalie; Ollendorff, Vincent; Villamil, Ignacio; Liu, Jianfeng; Fagni, Laurent; Perroy, Julie

2013-01-01

Networks of signaling molecules are activated in response to environmental changes. How are these signaling networks dynamically integrated in space and time to process particular information? To tackle this issue, biosensors of single signaling pathways have been engineered. Bioluminescence resonance energy transfer (BRET)-based biosensors have proven to be particularly efficient in that matter due to the high sensitivity of this technology to monitor protein–protein interactions or conformational changes in living cells. Extracellular signal-regulated kinases (ERK) are ubiquitously expressed and involved in many diverse cellular functions that might be encoded by the strength and spatio-temporal pattern of ERK activation. We developed a BRET-based sensor of ERK activity, called Rluc8-ERKsubstrate-Venus (REV). As expected, BRET changes of REV were correlated with ERK phosphorylation, which is required for its kinase activity. In neurons, the nature of the stimuli determines the strength, the location, or the moment of ERK activation, thus highlighting how acute modulation of ERK may encode the nature of initial stimulus to specify the consequences of this activation. This study provides evidence for suitability of REV as a new biosensor to address biological questions. PMID:23908646
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.
Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity

NASA Astrophysics Data System (ADS)

Mizusaki, Beatriz E. P.; Agnes, Everton J.; Erichsen, Rubem; Brunnet, Leonardo G.

2017-08-01

The plastic character of brain synapses is considered to be one of the foundations for the formation of memories. There are numerous kinds of such phenomenon currently described in the literature, but their role in the development of information pathways in neural networks with recurrent architectures is still not completely clear. In this paper we study the role of an activity-based process, called pre-synaptic dependent homeostatic scaling, in the organization of networks that yield precise-timed spiking patterns. It encodes spatio-temporal information in the synaptic weights as it associates a learned input with a specific response. We introduce a correlation measure to evaluate the precision of the spiking patterns and explore the effects of different inhibitory interactions and learning parameters. We find that large learning periods are important in order to improve the network learning capacity and discuss this ability in the presence of distinct inhibitory currents.
Criteria for robustness of heteroclinic cycles in neural microcircuits

PubMed Central

2011-01-01

We introduce a test for robustness of heteroclinic cycles that appear in neural microcircuits modeled as coupled dynamical cells. Robust heteroclinic cycles (RHCs) can appear as robust attractors in Lotka-Volterra-type winnerless competition (WLC) models as well as in more general coupled and/or symmetric systems. It has been previously suggested that RHCs may be relevant to a range of neural activities, from encoding and binding to spatio-temporal sequence generation. The robustness or otherwise of such cycles depends both on the coupling structure and the internal structure of the neurons. We verify that robust heteroclinic cycles can appear in systems of three identical cells, but only if we require perturbations to preserve some invariant subspaces for the individual cells. On the other hand, heteroclinic attractors can appear robustly in systems of four or more identical cells for some symmetric coupling patterns, without restriction on the internal dynamics of the cells. PMID:22656192

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.
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
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.
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.
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.
Time series change detection: Algorithms for land cover change

NASA Astrophysics Data System (ADS)

Boriah, Shyam

The climate and earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, climate and ecosystem related observations from remote sensors on satellites, as well as outputs of climate or earth system models from large-scale computational platforms, provide terabytes of temporal, spatial and spatio-temporal data. These massive and information-rich datasets offer huge potential for advancing the science of land cover change, climate change and anthropogenic impacts. One important area where remote sensing data can play a key role is in the study of land cover change. Specifically, the conversion of natural land cover into humandominated cover types continues to be a change of global proportions with many unknown environmental consequences. In addition, being able to assess the carbon risk of changes in forest cover is of critical importance for both economic and scientific reasons. In fact, changes in forests account for as much as 20% of the greenhouse gas emissions in the atmosphere, an amount second only to fossil fuel emissions. Thus, there is a need in the earth science domain to systematically study land cover change in order to understand its impact on local climate, radiation balance, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Land cover conversions include tree harvests in forested regions, urbanization, and agricultural intensification in former woodland and natural grassland areas. These types of conversions also have significant public policy implications due to issues such as water supply management and atmospheric CO2 output. In spite of the importance of this problem and the considerable advances made over the last few years in high-resolution satellite data, data mining, and online mapping tools and services, end users still lack practical tools to help them manage and transform this data into actionable knowledge of changes in forest ecosystems that can be used for decision making and policy planning purposes. In particular, previous change detection studies have primarily relied on examining differences between two or more satellite images acquired on different dates. Thus, a technological solution that detects global land cover change using high temporal resolution time series data will represent a paradigm-shift in the field of land cover change studies. To realize these ambitious goals, a number of computational challenges in spatio-temporal data mining need to be addressed. Specifically, analysis and discovery approaches need to be cognizant of climate and ecosystem data characteristics such as seasonality, non-stationarity/inter-region variability, multi-scale nature, spatio-temporal autocorrelation, high-dimensionality and massive data size. This dissertation, a step in that direction, translates earth science challenges to computer science problems, and provides computational solutions to address these problems. In particular, three key technical capabilities are developed: (1) Algorithms for time series change detection that are effective and can scale up to handle the large size of earth science data; (2) Change detection algorithms that can handle large numbers of missing and noisy values present in satellite data sets; and (3) Spatio-temporal analysis techniques to identify the scale and scope of disturbance events.
Automated selection of brain regions for real-time fMRI brain-computer interfaces

NASA Astrophysics Data System (ADS)

Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

2017-02-01

Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.
Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout.

PubMed

Das, Anup; Pradhapan, Paruthi; Groenendaal, Willemijn; Adiraju, Prathyusha; Rajan, Raj Thilak; Catthoor, Francky; Schaafsma, Siebren; Krichmar, Jeffrey L; Dutt, Nikil; Van Hoof, Chris

2018-03-01

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices. Copyright © 2018 Elsevier Ltd. All rights reserved.
Standardized shrinking LORETA-FOCUSS (SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction.

PubMed

Liu, Hesheng; Schimpf, Paul H; Dong, Guoya; Gao, Xiaorong; Yang, Fusheng; Gao, Shangkai

2005-10-01

This paper presents a new algorithm called Standardized Shrinking LORETA-FOCUSS (SSLOFO) for solving the electroencephalogram (EEG) inverse problem. Multiple techniques are combined in a single procedure to robustly reconstruct the underlying source distribution with high spatial resolution. This algorithm uses a recursive process which takes the smooth estimate of sLORETA as initialization and then employs the re-weighted minimum norm introduced by FOCUSS. An important technique called standardization is involved in the recursive process to enhance the localization ability. The algorithm is further improved by automatically adjusting the source space according to the estimate of the previous step, and by the inclusion of temporal information. Simulation studies are carried out on both spherical and realistic head models. The algorithm achieves very good localization ability on noise-free data. It is capable of recovering complex source configurations with arbitrary shapes and can produce high quality images of extended source distributions. We also characterized the performance with noisy data in a realistic head model. An important feature of this algorithm is that the temporal waveforms are clearly reconstructed, even for closely spaced sources. This provides a convenient way to estimate neural dynamics directly from the cortical sources.

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.
Accelerated dynamic EPR imaging using fast acquisition and compressive recovery.

PubMed

Ahmad, Rizwan; Samouilov, Alexandre; Zweier, Jay L

2016-12-01

Electron paramagnetic resonance (EPR) allows quantitative imaging of tissue redox status, which provides important information about ischemic syndromes, cancer and other pathologies. For continuous wave EPR imaging, however, poor signal-to-noise ratio and low acquisition efficiency limit its ability to image dynamic processes in vivo including tissue redox, where conditions can change rapidly. Here, we present a data acquisition and processing framework that couples fast acquisition with compressive sensing-inspired image recovery to enable EPR-based redox imaging with high spatial and temporal resolutions. The fast acquisition (FA) allows collecting more, albeit noisier, projections in a given scan time. The composite regularization based processing method, called spatio-temporal adaptive recovery (STAR), not only exploits sparsity in multiple representations of the spatio-temporal image but also adaptively adjusts the regularization strength for each representation based on its inherent level of the sparsity. As a result, STAR adjusts to the disparity in the level of sparsity across multiple representations, without introducing any tuning parameter. Our simulation and phantom imaging studies indicate that a combination of fast acquisition and STAR (FASTAR) enables high-fidelity recovery of volumetric image series, with each volumetric image employing less than 10 s of scan. In addition to image fidelity, the time constants derived from FASTAR also match closely to the ground truth even when a small number of projections are used for recovery. This development will enhance the capability of EPR to study fast dynamic processes that cannot be investigated using existing EPR imaging techniques. Copyright © 2016 Elsevier Inc. All rights reserved.
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.
Progressive Dictionary Learning with Hierarchical Predictive Structure for Scalable Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Xiong, Hongkai; Jiang, Xiaoqian; Zou, Junni; Taubman, David

2017-04-12

Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers (ELs) with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a close-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the stateof- the-art scalable extension of H.264/AVC and latest HEVC, standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest SHVC and HEVC simulcast over extensive test sequences with various resolutions.
A Multi-Encoding Approach for LTL Symbolic Satisfiability Checking

NASA Technical Reports Server (NTRS)

Rozier, Kristin Y.; Vardi, Moshe Y.

2011-01-01

Formal behavioral specifications written early in the system-design process and communicated across all design phases have been shown to increase the efficiency, consistency, and quality of the system under development. To prevent introducing design or verification errors, it is crucial to test specifications for satisfiability. Our focus here is on specifications expressed in linear temporal logic (LTL). We introduce a novel encoding of symbolic transition-based Buchi automata and a novel, "sloppy," transition encoding, both of which result in improved scalability. We also define novel BDD variable orders based on tree decomposition of formula parse trees. We describe and extensively test a new multi-encoding approach utilizing these novel encoding techniques to create 30 encoding variations. We show that our novel encodings translate to significant, sometimes exponential, improvement over the current standard encoding for symbolic LTL satisfiability checking.
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
Analysis of the Mediterranean fruit fly [Ceratitis capitata (Wiedemann)] spatio-temporal distribution in relation to sex and female mating status for precision IPM

PubMed Central

Tabilio, Maria Rosaria; Lampazzi, Elena; Ceccaroli, Claudio; Colacci, Marco; Trematerra, Pasquale

2018-01-01

The Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedemann), is a key pest of fruit crops in many tropical, subtropical and mild temperate areas worldwide. The economic importance of this fruit fly is increasing due to its invasion of new geographical areas. Efficient control and eradication efforts require adequate information regarding C. capitata adults in relation to environmental and physiological cues. This would allow effective characterisation of the population spatio-temporal dynamic of the C. capitata population at both the orchard level and the area-wide landscape. The aim of this study was to analyse population patterns of adult medflies caught using two trapping systems in a peach orchard located in central Italy. They were differentiated by adult sex (males or females) and mating status of females (unmated or mated females) to determine the spatio-temporal dynamic and evaluate the effect of cultivar and chemical treatments on trap catches. Female mating status was assessed by spermathecal dissection and a blind test was carried out to evaluate the reliability of the technique. Geostatistical methods, variogram and kriging, were used to produce distributional maps. Results showed a strong correlation between the distribution of males and unmated females, whereas males versus mated females and unmated females versus mated females showed a lower correlation. Both cultivar and chemical treatments had significant effects on trap catches, showing associations with sex and female mating status. Medfly adults showed aggregated distributions in the experimental field, but hot spots locations varied. The spatial pattern of unmated females reflected that of males, whereas mated females were largely distributed around ripening or ripe fruit. The results give relevant insights into pest management. Mated females may be distributed differently to unmated females and the identification of male hot spots through monitoring would allow localisation of virgin female populations. Based on our results, a more precise IPM strategy, coupled with effective sanitation practices, could represent a more effective approach to medfly control. PMID:29617420
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.
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.
Transthoracic Ultrafast Doppler Imaging of Human Left Ventricular Hemodynamic Function

PubMed Central

Osmanski, Bruno-Félix; Maresca, David; Messas, Emmanuel; Tanter, Mickael; Pernot, Mathieu

2016-01-01

Heart diseases can affect intraventricular blood flow patterns. Real-time imaging of blood flow patterns is challenging because it requires both a high frame rate and a large field of view. To date, standard Doppler techniques can only perform blood flow estimation with high temporal resolution within small regions of interest. In this work, we used ultrafast imaging to map in 2D human left ventricular blood flow patterns during the whole cardiac cycle. Cylindrical waves were transmitted at 4800 Hz with a transthoracic phased array probe to achieve ultrafast Doppler imaging of the left ventricle. The high spatio-temporal sampling of ultrafast imaging permits to rely on a much more effective wall filtering and to increase sensitivity when mapping blood flow patterns during the pre-ejection, ejection, early diastole, diastasis and late diastole phases of the heart cycle. The superior sensitivity and temporal resolution of ultrafast Doppler imaging makes it a promising tool for the noninvasive study of intraventricular hemodynamic function. PMID:25073134
Remote sensing captures varying temporal patterns of vegetation between human-altered and natural landscapes.

PubMed

Leong, Misha; Roderick, George K

2015-01-01

Global change has led to shifts in phenology, potentially disrupting species interactions such as plant-pollinator relationships. Advances in remote sensing techniques allow one to detect vegetation phenological diversity between different land use types, but it is not clear how this translates to other communities in the ecosystem. Here, we investigated the phenological diversity of the vegetation across a human-altered landscape including urban, agricultural, and natural land use types. We found that the patterns of change in the vegetation indices (EVI and NDVI) of human-altered landscapes are out of synchronization with the phenology in neighboring natural California grassland habitat. Comparing these findings to a spatio-temporal pollinator distribution dataset, EVI and NDVI were significant predictors of total bee abundance, a relationship that improved with time lags. This evidence supports the importance of differences in temporal dynamics between land use types. These findings also highlight the potential to utilize remote sensing data to make predictions for components of biodiversity that have tight vegetation associations, such as pollinators.
Spatio temporal analysis of microbial habitats in soil-root interfaces

NASA Astrophysics Data System (ADS)

Eickhorst, Thilo; Schmidt, Hannes

2017-04-01

Microbial habitats in soils are formed by the arrangement and availability of inorganic and organic compounds. They can be characterized by physico-chemical parameters and the resulting colonization by microorganisms. Areas being preferably colonized are known as microbial hot spots which can be found in (bio)pores within the aggregatusphere or in the rhizosphere. The latter is directly influenced by plants i.e. the growth and activity of plant roots which has an influence on physico-chemical dynamics in the rhizosphere and can even shape plants' root microbiome. As microbial communities play an important role in nutrient cycling their response in soil-root interfaces is of great importance. Especially in complex systems such as paddy soils used for the cultivation of wetland rice the analysis of spatio-temporal aspects is important to get knowledge about their influence on the microbial dynamics in the respective habitats. But also other spatial variations on larger scales up to landscape scale may have an impact on the soil microorganisms in their habitats. This PICO presentation will introduce a set of techniques which are useful to analyze both the physico-chemical characteristics of microbial habitats and the microbial colonization and dynamics in soil-root interfaces. Examples will be given on various studies from rice cultivation in different paddy soils up to an European transect representing rhizosphere soils of selected plant species.

Low-energy Control of Electrical Turbulence in the Heart

PubMed Central

Luther, Stefan; Fenton, Flavio H.; Kornreich, Bruce G.; Squires, Amgad; Bittihn, Philip; Hornung, Daniel; Zabel, Markus; Flanders, James; Gladuli, Andrea; Campoy, Luis; Cherry, Elizabeth M.; Luther, Gisa; Hasenfuss, Gerd; Krinsky, Valentin I.; Pumir, Alain; Gilmour, Robert F.; Bodenschatz, Eberhard

2011-01-01

Controlling the complex spatio-temporal dynamics underlying life-threatening cardiac arrhythmias such as fibrillation is extremely difficult due to the nonlinear interaction of excitation waves within a heterogeneous anatomical substrate1–4. Lacking a better strategy, strong, globally resetting electrical shocks remain the only reliable treatment for cardiac fibrillation5–7. Here, we establish the relation between the response of the tissue to an electric field and the spatial distribution of heterogeneities of the scale-free coronary vascular structure. We show that in response to a pulsed electric field E, these heterogeneities serve as nucleation sites for the generation of intramural electrical waves with a source density ρ(E), and a characteristic time τ for tissue depolarization that obeys a power law τ∝Eα. These intramural wave sources permit targeting of electrical turbulence near the cores of the vortices of electrical activity that drive complex fibrillatory dynamics. We show in vitro that simultaneous and direct access to multiple vortex cores results in rapid synchronization of cardiac tissue and therefore efficient termination of fibrillation. Using this novel control strategy, we demonstrate, for the first time, low-energy termination of fibrillation in vivo. Our results give new insights into the mechanisms and dynamics underlying the control of spatio-temporal chaos in heterogeneous excitable media and at the same time provide new research perspectives towards alternative, life-saving low-energy defibrillation techniques. PMID:21753855
Climate impacts on environmental risks evaluated from space: a conceptual approach to the case of Rift Valley Fever in Senegal.

PubMed

Tourre, Yves M; Lacaux, Jean-Pierre; Vignolles, Cécile; Lafaye, Murielle

2009-11-11

Climate and environment vary across many spatio-temporal scales, including the concept of climate change, which impact on ecosystems, vector-borne diseases and public health worldwide. To develop a conceptual approach by mapping climatic and environmental conditions from space and studying their linkages with Rift Valley Fever (RVF) epidemics in Senegal. Ponds in which mosquitoes could thrive were identified from remote sensing using high-resolution SPOT-5 satellite images. Additional data on pond dynamics and rainfall events (obtained from the Tropical Rainfall Measuring Mission) were combined with hydrological in-situ data. Localisation of vulnerable hosts such as penned cattle (from QuickBird satellite) were also used. Dynamic spatio-temporal distribution of Aedes vexans density (one of the main RVF vectors) is based on the total rainfall amount and ponds' dynamics. While Zones Potentially Occupied by Mosquitoes are mapped, detailed risk areas, i.e. zones where hazards and vulnerability occur, are expressed in percentages of areas where cattle are potentially exposed to mosquitoes' bites. This new conceptual approach, using precise remote-sensing techniques, simply relies upon rainfall distribution also evaluated from space. It is meant to contribute to the implementation of operational early warning systems for RVF based on both natural and anthropogenic climatic and environmental changes. In a climate change context, this approach could also be applied to other vector-borne diseases and places worldwide.
Effects of Telecoupling on Global Vegetation Dynamics

NASA Astrophysics Data System (ADS)

Viña, A.; Liu, J.

2016-12-01

With the ever increasing trend in telecoupling processes, such as international trade, all countries around the world are becoming more interdependent. However, the effects of this growing interdependence on vegetation (e.g., shifts in the geographic extent and distribution) remain unknown even though vegetation dynamics are crucially important for food production, carbon sequestration, provision of other ecosystem services, and biodiversity conservation. In this study we evaluate the effects of international trade on the spatio-temporal trajectories of vegetation at national and global scales, using vegetation index imagery collected over more than three decades by the Advanced Very High Resolution Radiometer (AVHRR) satellite sensor series together with concurrent national and international data on international trade (and its associated movement of people, goods, services and information). The spatio-temporal trajectories of vegetation are obtained using the scale of fluctuation technique, which is based on the decomposition of the AVHRR image time series to obtain information on its spatial dependence structure over time. Similar to the correlation length, the scale of fluctuation corresponds to the range over which fluctuations in the vegetation index are spatially correlated. Results indicate that global vegetation has changed drastically over the last three decades. These changes are not uniform across space, with hotspots in active trading countries. This study not only has direct implications for understanding global vegetation dynamics, but also sheds important insights on the complexity of human-nature interactions across telecoupled systems.
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 variÂ ables across both spatio units and time periods....
T2 shuffling: Sharp, multicontrast, volumetric fast spin-echo imaging.

PubMed

Tamir, Jonathan I; Uecker, Martin; Chen, Weitian; Lai, Peng; Alley, Marcus T; Vasanawala, Shreyas S; Lustig, Michael

2017-01-01

A new acquisition and reconstruction method called T 2 Shuffling is presented for volumetric fast spin-echo (three-dimensional [3D] FSE) imaging. T 2 Shuffling reduces blurring and recovers many images at multiple T 2 contrasts from a single acquisition at clinically feasible scan times (6-7 min). The parallel imaging forward model is modified to account for temporal signal relaxation during the echo train. Scan efficiency is improved by acquiring data during the transient signal decay and by increasing echo train lengths without loss in signal-to-noise ratio (SNR). By (1) randomly shuffling the phase encode view ordering, (2) constraining the temporal signal evolution to a low-dimensional subspace, and (3) promoting spatio-temporal correlations through locally low rank regularization, a time series of virtual echo time images is recovered from a single scan. A convex formulation is presented that is robust to partial voluming and radiofrequency field inhomogeneity. Retrospective undersampling and in vivo scans confirm the increase in sharpness afforded by T 2 Shuffling. Multiple image contrasts are recovered and used to highlight pathology in pediatric patients. A proof-of-principle method is integrated into a clinical musculoskeletal imaging workflow. The proposed T 2 Shuffling method improves the diagnostic utility of 3D FSE by reducing blurring and producing multiple image contrasts from a single scan. Magn Reson Med 77:180-195, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Live-cell Imaging with Genetically Encoded Protein Kinase Activity Reporters.

PubMed

Maryu, Gembu; Miura, Haruko; Uda, Youichi; Komatsubara, Akira T; Matsuda, Michiyuki; Aoki, Kazuhiro

2018-04-25

Protein kinases play pivotal roles in intracellular signal transduction, and dysregulation of kinases leads to pathological results such as malignant tumors. Kinase activity has hitherto been measured by biochemical methods such as in vitro phosphorylation assay and western blotting. However, these methods are less useful to explore spatial and temporal changes in kinase activity and its cell-to-cell variation. Recent advances in fluorescent proteins and live-cell imaging techniques enable us to visualize kinase activity in living cells with high spatial and temporal resolutions. Several genetically encoded kinase activity reporters, which are based on the modes of action of kinase activation and phosphorylation, are currently available. These reporters are classified into single-fluorophore kinase activity reporters and Förster (or fluorescence) resonance energy transfer (FRET)-based kinase activity reporters. Here, we introduce the principles of genetically encoded kinase activity reporters, and discuss the advantages and disadvantages of these reporters.Key words: kinase, FRET, phosphorylation, KTR.
Causal relations among events and states in dynamic geographical phenomena

NASA Astrophysics Data System (ADS)

Huang, Zhaoqiang; Feng, Xuezhi; Xuan, Wenling; Chen, Xiuwan

2007-06-01

There is only a static state of the real world to be recorded in conventional geographical information systems. However, there is not only static information but also dynamic information in geographical phenomena. So that how to record the dynamic information and reveal the relations among dynamic information is an important issue in a spatio-temporal information system. From an ontological perspective, we can initially divide the spatio-temporal entities in the world into continuants and occurrents. Continuant entities endure through some extended (although possibly very short) interval of time (e.g., houses, roads, cities, and real-estate). Occurrent entities happen and are then gone (e.g., a house repair job, road construction project, urban expansion, real-estate transition). From an information system perspective, continuants and occurrents that have a unique identity in the system are referred to as objects and events, respectively. And the change is represented implicitly by static snapshots in current spatial temporal information systems. In the previous models, the objects can be considered as the fundamental components of the system, and the change is modeled by considering time-varying attributes of these objects. In the spatio-temporal database, the temporal information that is either interval or instant is involved and the underlying data structures and indexes for temporal are considerable investigated. However, there is the absence of explicit ways of considering events, which affect the attributes of objects or the state. So the research issue of this paper focuses on how to model events in conceptual models of dynamic geographical phenomena and how to represent the causal relations among events and the objects or states. Firstly, the paper reviews the conceptual modeling in a temporal GIS by researchers. Secondly, this paper discusses the spatio-temporal entities: objects and events. Thirdly, this paper investigates the causal relations amongst events and states. The qualitative spatiotemporal change is an important issue in the dynamic geographic-scale phenomena. In real estate transition, the events and states are needed to be represented explicitly. In our modeling the evolution of a dynamic system, it can not avoid fetching in the view of causality. The object's transition is represented by the state of object. Event causes the state of objects changing and causes other events happen. Events connect with objects closely. The basic causal relations are the state-event and event-state relationships. Lastly, the paper concludes with the overview about the causal relations amongst events and states. And this future work is pointed.
Improvements on GPS Location Cluster Analysis for the Prediction of Large Carnivore Feeding Activities: Ground-Truth Detection Probability and Inclusion of Activity Sensor Measures

PubMed Central

Blecha, Kevin A.; Alldredge, Mat W.

2015-01-01

Animal space use studies using GPS collar technology are increasingly incorporating behavior based analysis of spatio-temporal data in order to expand inferences of resource use. GPS location cluster analysis is one such technique applied to large carnivores to identify the timing and location of feeding events. For logistical and financial reasons, researchers often implement predictive models for identifying these events. We present two separate improvements for predictive models that future practitioners can implement. Thus far, feeding prediction models have incorporated a small range of covariates, usually limited to spatio-temporal characteristics of the GPS data. Using GPS collared cougar (Puma concolor) we include activity sensor data as an additional covariate to increase prediction performance of feeding presence/absence. Integral to the predictive modeling of feeding events is a ground-truthing component, in which GPS location clusters are visited by human observers to confirm the presence or absence of feeding remains. Failing to account for sources of ground-truthing false-absences can bias the number of predicted feeding events to be low. Thus we account for some ground-truthing error sources directly in the model with covariates and when applying model predictions. Accounting for these errors resulted in a 10% increase in the number of clusters predicted to be feeding events. Using a double-observer design, we show that the ground-truthing false-absence rate is relatively low (4%) using a search delay of 2–60 days. Overall, we provide two separate improvements to the GPS cluster analysis techniques that can be expanded upon and implemented in future studies interested in identifying feeding behaviors of large carnivores. PMID:26398546
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.
A design philosophy for multi-layer neural networks with applications to robot control

NASA Technical Reports Server (NTRS)

Vadiee, Nader; Jamshidi, MO

1989-01-01

A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.
Spatio Temporal Detection and Virtual Mapping of Landslide Using High-Resolution Airborne Laser Altimetry (lidar) in Densely Vegetated Areas of Tropics

NASA Astrophysics Data System (ADS)

Bibi, T.; Azahari Razak, K.; Rahman, A. Abdul; Latif, A.

2017-10-01

Landslides are an inescapable natural disaster, resulting in massive social, environmental and economic impacts all over the world. The tropical, mountainous landscape in generally all over Malaysia especially in eastern peninsula (Borneo) is highly susceptible to landslides because of heavy rainfall and tectonic disturbances. The purpose of the Landslide hazard mapping is to identify the hazardous regions for the execution of mitigation plans which can reduce the loss of life and property from future landslide incidences. Currently, the Malaysian research bodies e.g. academic institutions and government agencies are trying to develop a landslide hazard and risk database for susceptible areas to backing the prevention, mitigation, and evacuation plan. However, there is a lack of devotion towards landslide inventory mapping as an elementary input of landslide susceptibility, hazard and risk mapping. The developing techniques based on remote sensing technologies (satellite, terrestrial and airborne) are promising techniques to accelerate the production of landslide maps, shrinking the time and resources essential for their compilation and orderly updates. The aim of the study is to provide a better perception regarding the use of virtual mapping of landslides with the help of LiDAR technology. The focus of the study is spatio temporal detection and virtual mapping of landslide inventory via visualization and interpretation of very high-resolution data (VHR) in forested terrain of Mesilau river, Kundasang. However, to cope with the challenges of virtual inventory mapping on in forested terrain high resolution LiDAR derivatives are used. This study specifies that the airborne LiDAR technology can be an effective tool for mapping landslide inventories in a complex climatic and geological conditions, and a quick way of mapping regional hazards in the tropics.
[Wet deposition of atmospheric nitrogen in Jiulong River Watershed].

PubMed

Chen, Neng-Wang; Hong, Hua-Sheng; Zhang, Luo-Ping

2008-01-01

Spatio-temporal distributions and sources of atmospheric nitrogen (N) in precipitation were examined for Jiulong River Watershed (JRW), an agricultural-dominated watershed located in southeastern China with a drainage area of 1.47 x 10(4) km2. During 2004-2005, 847 rain samples were collected in seventeen sites and analyzed for ammonium N, nitrate N and dissolved total N (DTN) followed by filtration through 0.45 microm nucleopore membranes. Atmospheric N deposition flux was calculated using GIS interpolation technique (Universal Kriging method for precipitation, Inverse distance weighted technique for N) based on measured N value and precipitation data from eight weather stations located in the JRW. ArcView GIS 3.2 was used for surface analysis, interpolation and statistical work. It was found that mean DTN concentration in all sites ranged between 2.20 +/- 1.69 and 3.26 +/- 1.37 mg x L(-1). Ammonium, nitrate and dissolved organic N formed 39%, 25% and 36% of DTN, respectively. N concentration decreased with precipitation intensity as a result of dilution, and showed a significant difference between dry season and wet season. The low isotope value of nitrate delta 15N ranging between -7.48 per thousand and -0.27 per thousand (mean: -3.61 per thousand) indicated that the increasing agricultural and soil emissions together with fossil combustions contributed to atmospheric nitrate sources. The annual wet deposition of atmospheric N flux amounted to 9.9 kg x hm(-2), which accounts for 66% of total atmospheric N deposition flux (14.9 kg x hm(-2)). About 91% of wet atmospheric deposition occurred in spring and summer. The spatio-temporal variation of atmospheric N deposition indicated that intensive precipitation, higher ammonia volatilization from fertilizer application in the growing season, and livestock productions together provided the larger N source.
Velocity landscape correlation resolves multiple flowing protein populations from fluorescence image time series.

PubMed

Pandžić, Elvis; Abu-Arish, Asmahan; Whan, Renee M; Hanrahan, John W; Wiseman, Paul W

2018-02-16

Molecular, vesicular and organellar flows are of fundamental importance for the delivery of nutrients and essential components used in cellular functions such as motility and division. With recent advances in fluorescence/super-resolution microscopy modalities we can resolve the movements of these objects at higher spatio-temporal resolutions and with better sensitivity. Previously, spatio-temporal image correlation spectroscopy has been applied to map molecular flows by correlation analysis of fluorescence fluctuations in image series. However, an underlying assumption of this approach is that the sampled time windows contain one dominant flowing component. Although this was true for most of the cases analyzed earlier, in some situations two or more different flowing populations can be present in the same spatio-temporal window. We introduce an approach, termed velocity landscape correlation (VLC), which detects and extracts multiple flow components present in a sampled image region via an extension of the correlation analysis of fluorescence intensity fluctuations. First we demonstrate theoretically how this approach works, test the performance of the method with a range of computer simulated image series with varying flow dynamics. Finally we apply VLC to study variable fluxing of STIM1 proteins on microtubules connected to the plasma membrane of Cystic Fibrosis Bronchial Epithelial (CFBE) cells. Copyright © 2018 Elsevier Inc. All rights reserved.
Holocene forest dynamics in central and western Mediterranean: periodicity, spatio-temporal patterns and climate influence.

PubMed

Di Rita, Federico; Fletcher, William J; Aranbarri, Josu; Margaritelli, Giulia; Lirer, Fabrizio; Magri, Donatella

2018-06-12

It is well-known that the Holocene exhibits a millennial-scale climate variability. However, its periodicity, spatio-temporal patterns and underlying processes are not fully deciphered yet. Here we focus on the central and western Mediterranean. We show that recurrent forest declines from the Gulf of Gaeta (central Tyrrhenian Sea) reveal a 1860-yr periodicity, consistent with a ca. 1800-yr climate fluctuation induced by large-scale changes in climate modes, linked to solar activity and/or AMOC intensity. We show that recurrent forest declines and dry events are also recorded in several pollen and palaeohydrological proxy-records in the south-central Mediterranean. We found coeval events also in several palaeohydrological records from the south-western Mediterranean, which however show generally wet climate conditions, indicating a spatio-temporal hydrological pattern opposite to the south-central Mediterranean and suggesting that different expressions of climate modes occurred in the two regions at the same time. We propose that these opposite hydroclimate regimes point to a complex interplay of the prevailing or predominant phases of NAO-like circulation, East Atlantic pattern, and extension and location of the North African anticyclone. At a larger geographical scale, displacements of the ITCZ, modulated by solar activity and/or AMOC intensity, may have also indirectly influenced the observed pattern.
Bio-inspired nano-sensor-enhanced CNN visual computer.

PubMed

Porod, Wolfgang; Werblin, Frank; Chua, Leon O; Roska, Tamas; Rodriguez-Vazquez, Angel; Roska, Botond; Fay, Patrick; Bernstein, Gary H; Huang, Yih-Fang; Csurgay, Arpad I

2004-05-01

Nanotechnology opens new ways to utilize recent discoveries in biological image processing by translating the underlying functional concepts into the design of CNN (cellular neural/nonlinear network)-based systems incorporating nanoelectronic devices. There is a natural intersection joining studies of retinal processing, spatio-temporal nonlinear dynamics embodied in CNN, and the possibility of miniaturizing the technology through nanotechnology. This intersection serves as the springboard for our multidisciplinary project. Biological feature and motion detectors map directly into the spatio-temporal dynamics of CNN for target recognition, image stabilization, and tracking. The neural interactions underlying color processing will drive the development of nanoscale multispectral sensor arrays for image fusion. Implementing such nanoscale sensors on a CNN platform will allow the implementation of device feedback control, a hallmark of biological sensory systems. These biologically inspired CNN subroutines are incorporated into the new world of analog-and-logic algorithms and software, containing also many other active-wave computing mechanisms, including nature-inspired (physics and chemistry) as well as PDE-based sophisticated spatio-temporal algorithms. Our goal is to design and develop several miniature prototype devices for target detection, navigation, tracking, and robotics. This paper presents an example illustrating the synergies emerging from the convergence of nanotechnology, biotechnology, and information and cognitive science.
The Central Italy Seismic Sequence (2016): Spatial Patterns and Dynamic Fingerprints

NASA Astrophysics Data System (ADS)

Suteanu, Cristian; Liucci, Luisa; Melelli, Laura

2018-01-01

The paper investigates spatio-temporal aspects of the seismic sequence that started in Central Italy (Amatrice, Lazio region) in August 2016, causing hundreds of fatalities and producing major damage to settlements. On one hand, scaling properties of the landscape topography are identified and related to geomorphological processes, supporting the identification of preferential spatial directions in tectonic activity and confirming the role of the past tectonic periods and ongoing processes with respect to the driving of the geomorphological evolution of the area. On the other hand, relations between the spatio-temporal evolution of the sequence and the seismogenic fault systems are studied. The dynamic fingerprints of seismicity are established with the help of events thread analysis (ETA), which characterizes anisotropy in spatio-temporal earthquake patterns. ETA confirms the fact that the direction of the seismogenic normal fault-oriented (N)NW-(S)SE is characterized by persistent seismic activity. More importantly, it also highlights the role of the pre-existing compressive structures, Neogenic thrust and transpressive regional fronts, with a trend-oriented (N)NE-(S)SW, in the stress transfer. Both the fractal features of the topographic surface and the dynamic fingerprint of the recent seismic sequence point to the hypothesis of an active interaction between the Quaternary fault systems and the pre-existing compressional structures.
What calls for service tell us about suicide: A 7-year spatio-temporal analysis of neighborhood correlates of suicide-related calls.

PubMed

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

2018-04-30

Previous research has shown that neighborhood-level variables such as social deprivation, social fragmentation or rurality are related to suicide risk, but most of these studies have been conducted in the U.S. or northern European countries. The aim of this study was to analyze the spatio-temporal distribution of suicide in a southern European city (Valencia, Spain), and determine whether this distribution was related to a set of neighborhood-level characteristics. We used suicide-related calls for service as an indicator of suicide cases (n = 6,537), and analyzed the relationship of the outcome variable with several neighborhood-level variables: economic status, education level, population density, residential instability, one-person households, immigrant concentration, and population aging. A Bayesian autoregressive model was used to study the spatio-temporal distribution at the census block group level for a 7-year period (2010-2016). Results showed that neighborhoods with lower levels of education and population density, and higher levels of residential instability, one-person households, and an aging population had higher levels of suicide-related calls for service. Immigrant concentration and economic status did not make a relevant contribution to the model. These results could help to develop better-targeted community-level suicide prevention strategies.
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 acquired immunodeficiency syndrome in the State of Rio de Janeiro, Brazil: a spatio-temporal analysis of cases reported in the period 2001-2010.

PubMed

Alves, André T J; Nobre, Flávio F

2014-05-01

Despite increased funding for research on the human immunodeficiency virus (HIV) and the acquired immunodeficiency syndrome (AIDS), neither vaccine nor cure is yet in sight. Surveillance and prevention are essential for disease intervention, and it is recognised that spatio-temporal analysis of AIDS cases can assist the decision-making process for control of the disease. This study investigated the dynamic, spatial distribution of notified AIDS cases in the State of Rio de Janeiro, Brazil, between 2001 and 2010, based on the annual incidence in each municipality. Sequential choropleth maps were developed and used to analyse the incidence distribution and Moran's I spatial autocorrelation statistics was applied for characterisation of the spatio-temporal distribution pattern. A significant, positive spatial autocorrelation of AIDS incidence was observed indicating that municipalities with high incidence are likely to be close to other municipalities with similarly high incidence and, conversely, municipalities with low incidence are likely to be surrounded by municipalities with low incidence. Two clusters were identified; one hotspot related to the State Capital and the other with low to intermediate AIDS incidence comprising municipalities in the north-eastern region of the State of Rio de Janeiro.
Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

NASA Astrophysics Data System (ADS)

Charfi, Imen; Miteran, Johel; Dubois, Julien; Atri, Mohamed; Tourki, Rached

2013-10-01

We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user's trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.

Some links on this page may take you to non-federal websites. Their policies may differ from this site.