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PRIN: a predicted rice interactome network  

PubMed Central

Background Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa. Results To better understand the interactions of proteins in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization. Conclusions PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology. PRIN is available online at



The Predicted Arabidopsis Interactome Resource and Network Topology-Based Systems Biology Analyses[W][OA  

PubMed Central

Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the Predicted Arabidopsis Interactome Resource (PAIR; ) presents 149,900 potential molecular interactions, which are expected to cover ~24% of the entire interactome with ~40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks.

Lin, Mingzhi; Zhou, Xi; Shen, Xueling; Mao, Chuanzao; Chen, Xin



Prediction and comparison of Salmonella-human and Salmonella-Arabidopsis interactomes.  


Salmonellosis caused by Salmonella bacteria is a food-borne disease and a worldwide health threat causing millions of infections and thousands of deaths every year. This pathogen infects an unusually broad range of host organisms including human and plants. A better understanding of the mechanisms of communication between Salmonella and its hosts requires identifying the interactions between Salmonella and host proteins. Protein-protein interactions (PPIs) are the fundamental building blocks of communication. Here, we utilize the prediction platform BIANA to obtain the putative Salmonella-human and Salmonella-Arabidopsis interactomes based on sequence and domain similarity to known PPIs. A gold standard list of Salmonella-host PPIs served to validate the quality of the human model. 24,726 and 10,926 PPIs comprising interactions between 38 and 33 Salmonella effectors and virulence factors with 9,740 human and 4,676 Arabidopsis proteins, respectively, were predicted. Putative hub proteins could be identified, and parallels between the two interactomes were discovered. This approach can provide insight into possible biological functions of so far uncharacterized proteins. The predicted interactions are available via a web interface which allows filtering of the database according to parameters provided by the user to narrow down the list of suspected interactions. The interactions are available via a web interface at PMID:22589098

Schleker, Sylvia; Garcia-Garcia, Javier; Klein-Seetharaman, Judith; Oliva, Baldo



Prediction of spatiotemporal time series based on reconstructed local states  


Spatiotemporal time series are analyzed and predicted using reconstructed local states. As numerical examples the evolution of a Kuramoto-Sivashinsky equation and a coupled map lattice are predicted from previously sampled data. PMID:11017653

Parlitz; Merkwirth



Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef  

PubMed Central

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk.

Sarmady, Mahdi; Dampier, William; Tozeren, Aydin



Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection.  


Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at PMID:22848443

Zhang, Minlu; Su, Shengchang; Bhatnagar, Raj K; Hassett, Daniel J; Lu, Long J



Seizure prediction using EEG spatiotemporal correlation structure.  


A seizure prediction algorithm is proposed that combines novel multivariate EEG features with patient-specific machine learning. The algorithm computes the eigenspectra of space-delay correlation and covariance matrices from 15-s blocks of EEG data at multiple delay scales. The principal components of these features are used to classify the patient's preictal or interictal state. This is done using a support vector machine (SVM), whose outputs are averaged using a running 15-minute window to obtain a final prediction score. The algorithm was tested on 19 of 21 patients in the Freiburg EEG data set who had three or more seizures, predicting 71 of 83 seizures, with 15 false predictions and 13.8 h in seizure warning during 448.3 h of interictal data. The proposed algorithm scales with the number of available EEG signals by discovering the variations in correlation structure among any given set of signals that correlate with seizure risk. PMID:23041171

Williamson, James R; Bliss, Daniel W; Browne, David W; Narayanan, Jaishree T



Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations  

NASA Astrophysics Data System (ADS)

We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.

Marre, O.; El Boustani, S.; Frégnac, Y.; Destexhe, A.



Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations  

SciTech Connect

We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.

Marre, O.; El Boustani, S.; Fregnac, Y.; Destexhe, A. [Unite de Neurosciences Integratives et Computationnelles (UNIC), UPR CNRS 2191, Gif-sur-Yvette (France)



Network-based function prediction and interactomics: the case for metabolic enzymes.  


As sequencing technologies increase in power, determining the functions of unknown proteins encoded by the DNA sequences so produced becomes a major challenge. Functional annotation is commonly done on the basis of amino-acid sequence similarity alone. Long after sequence similarity becomes undetectable by pair-wise comparison, profile-based identification of homologs can often succeed due to the conservation of position-specific patterns, important for a protein's three dimensional folding and function. Nevertheless, prediction of protein function from homology-driven approaches is not without problems. Homologous proteins might evolve different functions and the power of homology detection has already started to reach its maximum. Computational methods for inferring protein function, which exploit the context of a protein in cellular networks, have come to be built on top of homology-based approaches. These network-based functional inference techniques provide both a first hand hint into a proteins' functional role and offer complementary insights to traditional methods for understanding the function of uncharacterized proteins. Most recent network-based approaches aim to integrate diverse kinds of functional interactions to boost both coverage and confidence level. These techniques not only promise to solve the moonlighting aspect of proteins by annotating proteins with multiple functions, but also increase our understanding on the interplay between different functional classes in a cell. In this article we review the state of the art in network-based function prediction and describe some of the underlying difficulties and successes. Given the volume of high-throughput data that is being reported the time is ripe to employ these network-based approaches, which can be used to unravel the functions of the uncharacterized proteins accumulating in the genomic databases. PMID:20654726

Janga, S C; Díaz-Mejía, J Javier; Moreno-Hagelsieb, G



Virtual Interactomics of Proteins from Biochemical Standpoint  

PubMed Central

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.

Kubrycht, Jaroslav; Sigler, Karel; Soucek, Pavel



Expanding the mitochondrial interactome  

PubMed Central

The integration of information on different aspects of the composition and function of mitochondria is defining a more comprehensive mitochondrial interactome and elucidating its role in a multitude of cellular processes and human disease.

Shutt, Timothy E; Shadel, Gerald S



Ozone Concentration Prediction via Spatiotemporal Autoregressive Model With Exogenous Variables  

NASA Astrophysics Data System (ADS)

Forecast of environmental variables are nowadays of main concern for public health or agricultural management. In this context a large literature is devoted to spatio-temporal modelling of these variables using different statistical approaches. However, most of studies ignored the potential contribution of local (e.g. meteorological and/or geographical) covariables as well as the dynamical characteristics of observations. In this study, we present a spatiotemporal short term forecasting model for ozone concentration based on regularly observed covariables in predefined geographical sites. Our driving system simply combines a multidimensional second order autoregressive structured process with a linear regression model over influent exogenous factors and reads as follows: ?2 ?q j Z (t) = A (Î&,cedil;D )Ã- [ ?iZ(t- i)]+ B (Î&,cedil;D )Ã- [ ?jX (t)]+ É?(t) i=1 j=1 Z(t)=(Z1(t),â?¦,Zn(t)) represents the vector of ozone concentration at time t of the n geographical sites, whereas Xj(t)=(X1j(t),â?¦,Xnj(t)) denotes the jth exogenous variable observed over these sites. The nxn matrix functions A and B account for the spatial relationships between sites through the inter site distance matrix D and a vector parameter Î&.cedil; Multidimensional white noise É? is assumed to be Gaussian and spatially correlated but temporally independent. A covariance structure of Z that takes account of noise spatial dependences is deduced under a stationary hypothesis and then included in the likelihood function. Statistical model and estimation procedure: Contrarily to the widely used choice of a {0,1}-valued neighbour matrix A, we put forward two more natural choices of exponential or power decay. Moreover, the model revealed enough stable to readily accommodate the crude observations without the usual tedious and somewhat arbitrarily variable transformations. Data set and preliminary analysis: In our case, ozone variable represents here the daily maximum ozone concentration recorded in n=42 stations during the year 2005 within a south region in France, covering an area of approximately 10565 km2. Meteorological covariates are the daily maxima of temperature, wind speed, daily maxima of humidity and atmospheric pressure. Actually, the meteorological factors are not recorded in ozone monitoring sites and thus preliminary interpolation techniques were used and compared subsequently (Gaussian conditional simulation, ordinary kriging or kriging with external drift). Concluding remarks: From the statistical point of view, both simulation study and data analysis showed a fairly robust behaviour of estimation procedures. In both cases, the analysis of residuals proved a significant improvement of error prediction within this framework. From the environmental point of view, the ability of accounting for pertinent local and dynamical meteorological covariates clearly provided a useful tool in prediction methods. Bib [1]: Pfeifer.P.E; Deutsh.S.J. (1980) "A Three-Stage Iterative Procedure for Space-Time Modelling." Technometrics 22: 35-47. Bib [2]: Raffaella Giacomini and Cliff W.J.Granger 2002 - 07 "Aggregation of Space-Time Process" Departement of Economics, University of California, San Diego.

Kamoun, W.; Senoussi, R.



Interactome networks and human disease.  


Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease. PMID:21414488

Vidal, Marc; Cusick, Michael E; Barabási, Albert-László



AtPID: Arabidopsis thaliana protein interactome database - an integrative platform for plant systems biology  

Microsoft Academic Search

Arabidopsis thaliana Protein Interactome Database (AtPID) is an object database that integrates data from several bioinformatics prediction meth- ods and manually collected information from the literature. It contains data relevant to protein- protein interaction, protein subcellular location, ortholog maps, domain attributes and gene regula- tion. The predicted protein interaction data were obtained from ortholog interactome, microarray profiles, GO annotation, and

Jian Cui; Peng Li; Guang Li; Feng Xu; Chen Zhao; Yuhua Li; Zhongnan Yang; Guang Wang; Qingbo Yu; Yi-xue Li; Tieliu Shi



Plant Protein-Protein Interaction Network and Interactome  

PubMed Central

Protein-protein interaction network represents an important aspect of systems biology. The understanding of the plant protein-protein interaction network and interactome will provide crucial insights into the regulation of plant developmental, physiological, and pathological processes. In this review, we will first define the concept of plant interactome and the protein-protein interaction network. The significance of the plant interactome study will be discussed. We will then compare the pros and cons for different strategies for interactome mapping including yeast two-hybrid system (Y2H), affinity purification mass spectrometry (AP-MS), bimolecular fluorescence complementation (BiFC), and in silico prediction. The application of these platforms on specific plant biology questions will be further discussed. The recent advancements revealed the great potential for plant protein-protein interaction network and interactome to elucidate molecular mechanisms for signal transduction, stress responses, cell cycle control, pattern formation, and others. Mapping the plant interactome in model species will provide important guideline for the future study of plant biology.

Zhang, Yixiang; Gao, Peng; Yuan, Joshua S



NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems  

Microsoft Academic Search

\\u000a Accurate and fine-grained prediction of future user location and geographical profile has interesting and promising applications\\u000a including targeted content service, advertisement dissemination for mobile users, and recreational social networking tools\\u000a for smart-phones. Existing techniques based on linear and probabilistic models are not able to provide accurate prediction\\u000a of the location patterns from a spatio-temporal perspective, especially for long-term estimation. More

Salvatore Scellato; Mirco Musolesi; Cecilia Mascolo; Vito Latora; Andrew T. Campbell


From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.  


Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial. PMID:23563395

Cannistraci, Carlo Vittorio; Alanis-Lobato, Gregorio; Ravasi, Timothy



A spatio-temporal model to predict visual fixation: description and assessment  

Microsoft Academic Search

To what extent can a computational model of the bottom-up visual attention predict what an observer is looking at? What is the contribution of the low-level visual fea- tures in the attention deployment? To answer these questions, a new spatio-temporal computational model is proposed. This model incorporates several visual features; therefore, a fusion algorithm is required to combine the dierent

Olivier Le Meur; Patrick Le Callet; Dominique Barba


A video coding scheme based on joint spatiotemporal and adaptive prediction.  


We propose a video coding scheme that departs from traditional Motion Estimation/DCT frameworks and instead uses Karhunen-Loeve Transform (KLT)/Joint Spatiotemporal Prediction framework. In particular, a novel approach that performs joint spatial and temporal prediction simultaneously is introduced. It bypasses the complex H.26x interframe techniques and it is less computationally intensive. Because of the advantage of the effective joint prediction and the image-dependent color space transformation (KLT), the proposed approach is demonstrated experimentally to consistently lead to improved video quality, and in many cases to better compression rates and improved computational speed. PMID:19342337

Jiang, Wenfei; Latecki, Longin Jan; Liu, Wenyu; Liang, Hui; Gorman, Ken



A data mining based approach to predict spatiotemporal changes in satellite images  

NASA Astrophysics Data System (ADS)

The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited. This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.

Boulila, W.; Farah, I. R.; Ettabaa, K. Saheb; Solaiman, B.; Ghézala, H. Ben



Viruses and Interactomes in Translation*  

PubMed Central

A decade of high-throughput screenings for intraviral and virus-host protein-protein interactions led to the accumulation of data and to the development of theories on laws governing interactome organization for many viruses. We present here a computational analysis of intraviral protein networks (EBV, FLUAV, HCV, HSV-1, KSHV, SARS-CoV, VACV, and VZV) and virus-host protein networks (DENV, EBV, FLUAV, HCV, and VACV) from up-to-date interaction data, using various mathematical approaches. If intraviral networks seem to behave similarly, they are clearly different from the human interactome. Viral proteins target highly central human proteins, which are precisely the Achilles' heel of the human interactome. The intrinsic structural disorder is a distinctive feature of viral hubs in virus-host interactomes. Overlaps between virus-host data sets identify a core of human proteins involved in the cellular response to viral infection and in the viral capacity to hijack the cell machinery for viral replication. Host proteins that are strongly targeted by a virus seem to be particularly attractive for other viruses. Such protein-protein interaction networks and their analysis represent a powerful resource from a therapeutic perspective.

Meyniel-Schicklin, Laurene; de Chassey, Benoit; Andre, Patrice; Lotteau, Vincent



Towards Establishment of a Rice Stress Response Interactome  

PubMed Central

Rice (Oryza sativa) is a staple food for more than half the world and a model for studies of monocotyledonous species, which include cereal crops and candidate bioenergy grasses. A major limitation of crop production is imposed by a suite of abiotic and biotic stresses resulting in 30%–60% yield losses globally each year. To elucidate stress response signaling networks, we constructed an interactome of 100 proteins by yeast two-hybrid (Y2H) assays around key regulators of the rice biotic and abiotic stress responses. We validated the interactome using protein–protein interaction (PPI) assays, co-expression of transcripts, and phenotypic analyses. Using this interactome-guided prediction and phenotype validation, we identified ten novel regulators of stress tolerance, including two from protein classes not previously known to function in stress responses. Several lines of evidence support cross-talk between biotic and abiotic stress responses. The combination of focused interactome and systems analyses described here represents significant progress toward elucidating the molecular basis of traits of agronomic importance.

Seo, Young-Su; Chern, Mawsheng; Bartley, Laura E.; Han, Muho; Jung, Ki-Hong; Lee, Insuk; Walia, Harkamal; Richter, Todd; Xu, Xia; Cao, Peijian; Bai, Wei; Ramanan, Rajeshwari; Amonpant, Fawn; Arul, Loganathan; Canlas, Patrick E.; Ruan, Randy; Park, Chang-Jin; Chen, Xuewei; Hwang, Sohyun; Jeon, Jong-Seong; Ronald, Pamela C.



A Multi-Scale Modeling Framework for Individualized, Spatiotemporal Prediction of Drug Effects and Toxicological Risk  

PubMed Central

In this study, we focus on a novel multi-scale modeling approach for spatiotemporal prediction of the distribution of substances and resulting hepatotoxicity by combining cellular models, a 2D liver model, and whole body model. As a case study, we focused on predicting human hepatotoxicity upon treatment with acetaminophen based on in vitro toxicity data and potential inter-individual variability in gene expression and enzyme activities. By aggregating mechanistic, genome-based in silico cells to a novel 2D liver model and eventually to a whole body model, we predicted pharmacokinetic properties, metabolism, and the onset of hepatotoxicity in an in silico patient. Depending on the concentration of acetaminophen in the liver and the accumulation of toxic metabolites, cell integrity in the liver as a function of space and time as well as changes in the elimination rate of substances were estimated. We show that the variations in elimination rates also influence the distribution of acetaminophen and its metabolites in the whole body. Our results are in agreement with experimental results. What is more, the integrated model also predicted variations in drug toxicity depending on alterations of metabolic enzyme activities. Variations in enzyme activity, in turn, reflect genetic characteristics or diseases of individuals. In conclusion, this framework presents an important basis for efficiently integrating inter-individual variability data into models, paving the way for personalized or stratified predictions of drug toxicity and efficacy.

Diaz Ochoa, Juan G.; Bucher, Joachim; Pery, Alexandre R. R.; Zaldivar Comenges, Jose M.; Niklas, Jens; Mauch, Klaus



Predicted spatio-temporal dynamics of radiocesium deposited onto forests following the Fukushima nuclear accident.  


The majority of the area contaminated by the Fukushima Dai-ichi nuclear power plant accident is covered by forest. To facilitate effective countermeasure strategies to mitigate forest contamination, we simulated the spatio-temporal dynamics of radiocesium deposited into Japanese forest ecosystems in 2011 using a model that was developed after the Chernobyl accident in 1986. The simulation revealed that the radiocesium inventories in tree and soil surface organic layer components drop rapidly during the first two years after the fallout. Over a period of one to two years, the radiocesium is predicted to move from the tree and surface organic soil to the mineral soil, which eventually becomes the largest radiocesium reservoir within forest ecosystems. Although the uncertainty of our simulations should be considered, the results provide a basis for understanding and anticipating the future dynamics of radiocesium in Japanese forests following the Fukushima accident. PMID:23995073

Hashimoto, Shoji; Matsuura, Toshiya; Nanko, Kazuki; Linkov, Igor; Shaw, George; Kaneko, Shinji



Neurobiological Mechanisms Behind the Spatiotemporal Illusions of Awareness Used for Advocating Prediction or Postdiction  

PubMed Central

The fact that it takes time for the brain to process information from the changing environment underlies many experimental phenomena of awareness of spatiotemporal events, including a number of astonishing illusions. These phenomena have been explained from the predictive and postdictive theoretical perspectives. Here I describe the most extensively studied phenomena in order to see how well the two perspectives can explain them. Next, the neurobiological perceptual retouch mechanism of producing stimulation awareness is characterized and its work in causing the listed illusions is described. A perspective on how brain mechanisms of conscious perception produce the phenomena supportive of the postdictive view is presented in this article. At the same time, some of the phenomena cannot be explained by the traditional postdictive account, but can be interpreted from the perceptual retouch theory perspective.

Bachmann, Talis



Predicted spatio-temporal dynamics of radiocesium deposited onto forests following the Fukushima nuclear accident  

PubMed Central

The majority of the area contaminated by the Fukushima Dai-ichi nuclear power plant accident is covered by forest. To facilitate effective countermeasure strategies to mitigate forest contamination, we simulated the spatio-temporal dynamics of radiocesium deposited into Japanese forest ecosystems in 2011 using a model that was developed after the Chernobyl accident in 1986. The simulation revealed that the radiocesium inventories in tree and soil surface organic layer components drop rapidly during the first two years after the fallout. Over a period of one to two years, the radiocesium is predicted to move from the tree and surface organic soil to the mineral soil, which eventually becomes the largest radiocesium reservoir within forest ecosystems. Although the uncertainty of our simulations should be considered, the results provide a basis for understanding and anticipating the future dynamics of radiocesium in Japanese forests following the Fukushima accident.

Hashimoto, Shoji; Matsuura, Toshiya; Nanko, Kazuki; Linkov, Igor; Shaw, George; Kaneko, Shinji



An instantaneous spatiotemporal model to predict a bicyclist's Black Carbon exposure based on mobile noise measurements  

NASA Astrophysics Data System (ADS)

Several studies have shown that a significant amount of daily air pollution exposure, in particular Black Carbon (BC), is inhaled during trips. Assessing this contribution to exposure remains difficult because on the one hand local air pollution maps lack spatio-temporal resolution, at the other hand direct measurement of particulate matter concentration remains expensive. This paper proposes to use in-traffic noise measurements in combination with geographical and meteorological information for predicting BC exposure during commuting trips. Mobile noise measurements are cheaper and easier to perform than mobile air pollution measurements and can easily be used in participatory sensing campaigns. The uniqueness of the proposed model lies in the choice of noise indicators that goes beyond the traditional overall A-weighted noise level used in previous work. Noise and BC exposures are both related to the traffic intensity but also to traffic speed and traffic dynamics. Inspired by theoretical knowledge on the emission of noise and BC, the low frequency engine related noise and the difference between high frequency and low frequency noise that indicates the traffic speed, are introduced in the model. In addition, it is shown that splitting BC in a local and a background component significantly improves the model. The coefficients of the proposed model are extracted from 200 commuter bicycle trips. The predicted average exposure over a single trip correlates with measurements with a Pearson coefficient of 0.78 using only four parameters: the low frequency noise level, wind speed, the difference between high and low frequency noise and a street canyon index expressing local air pollution dispersion properties.

Dekoninck, Luc; Botteldooren, Dick; Int Panis, Luc



Spatio-temporal variability in Ebro river basin (NE Spain): Global SST as potential source of predictability on decadal time scales  

NASA Astrophysics Data System (ADS)

Spatio-temporal variability and predictability in Ebro river basin is investigated. Basque-Cantabrian, Pyrenees and Southern Mediterranean regions are differentiated. At decadal time scales SST anomalies are a significant source of predictability for the streamflow. At interannual time scales ARMA modelling provides potential skill in forecasting. Basin-specific hydroclimatic predictions are provided for the Ebro River.

Gámiz-Fortis, S. R.; Hidalgo-Muñoz, J. M.; Argüeso, D.; Esteban-Parra, M. J.; Castro-Díez, Y.



A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer  

PubMed Central

Background Systematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases. Methods We integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets. Results We successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method. Conclusions Our method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.



Theoretical predictions of spatiotemporal receptive fields of fly LMCs, and experimental validation  

Microsoft Academic Search

1.A theory is presented that utilizes the structure of natural images, and how they change in time, to produce spatiotemporal filters that maximize information flow through a noisy channel of limited dynamic range. For low signal-to-noise ratios (SNRs) the filter has low-pass, and for high SNRs band-pass characteristics, both in space and time.2.Theoretical impulse responses are compared to measurements in

J. H. van Hateren



Mapping the functional yeast ABC transporter interactome.  


ATP-binding cassette (ABC) transporters are a ubiquitous class of integral membrane proteins of immense clinical interest because of their strong association with human disease and pharmacology. To improve our understanding of these proteins, we used membrane yeast two-hybrid technology to map the protein interactome of all of the nonmitochondrial ABC transporters in the model organism Saccharomyces cerevisiae and combined this data with previously reported yeast ABC transporter interactions in the BioGRID database to generate a comprehensive, integrated 'interactome'. We show that ABC transporters physically associate with proteins involved in an unexpectedly diverse range of functions. We specifically examine the importance of the physical interactions of ABC transporters in both the regulation of one another and in the modulation of proteins involved in zinc homeostasis. The interaction network presented here will be a powerful resource for increasing our fundamental understanding of the cellular role and regulation of ABC transporters. PMID:23831759

Snider, Jamie; Hanif, Asad; Lee, Mid Eum; Jin, Ke; Yu, Analyn R; Graham, Chris; Chuk, Matthew; Damjanovic, Dunja; Wierzbicka, Marta; Tang, Priscilla; Balderes, Dina; Wong, Victoria; Jessulat, Matthew; Darowski, Katelyn D; San Luis, Bryan-Joseph; Shevelev, Igor; Sturley, Stephen L; Boone, Charles; Greenblatt, Jack F; Zhang, Zhaolei; Paumi, Christian M; Babu, Mohan; Park, Hay-Oak; Michaelis, Susan; Stagljar, Igor



Inferring modules from human protein interactome classes  

PubMed Central

Background The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features. Results We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated) data sources, which we call interactome classes. Due to class diversity, we deal with variable dependencies of data features arising from structural specificities and biases, but also from possible overlaps. Since highly connected regions of the human interactome may point to potential protein complexes, we have focused on the concept of modularity, and elucidated the detection power of module extraction algorithms by independent validations based on GO, MIPS and KEGG. From the combination of protein interactions with gene expressions, a confidence scoring scheme has been proposed before proceeding via GO with further classification in permanent and transient modules. Conclusions Disaggregated interactomes are shown to be informative for inferring modularity, thus contributing to perform an effective integrative analysis. Validation of the extracted modules by multiple annotation allows for the assessment of confidence measures assigned to the modules in a protein pathway context. Notably, the proposed multilayer confidence scheme can be used for network calibration by enabling a transition from unweighted to weighted interactomes based on biological evidence.



Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale  

NASA Astrophysics Data System (ADS)

Whilst exposure to air pollution is linked to a wide range of adverse health outcomes, assessing levels of this exposure has remained a challenge. This study reports a modeling approach for the estimation of weekly levels of ambient black smoke (BS) at residential postcodes across Northeast England (2055 km 2) over a 12 year period (1985-1996). A two-stage modeling strategy was developed using monitoring data on BS together with a range of covariates including data on traffic, population density, industrial activity, land cover (remote sensing), and meteorology. The first stage separates the temporal trend in BS for the region as a whole from within-region spatial variation and the second stage is a linear model which predicts BS levels at all locations in the region using spatially referenced covariate data as predictors and the regional predicted temporal trend as an offset. Traffic and land cover predictors were included in the final model, which predicted 70% of the spatio-temporal variation in BS across the study region over the study period. This modeling approach appears to provide a robust way of estimating exposure to BS at an inter-urban scale.

Dadvand, Payam; Rushton, Stephen; Diggle, Peter J.; Goffe, Louis; Rankin, Judith; Pless-Mulloli, Tanja



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

PubMed Central

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

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



Toward the dynamic interactome: it's about time  

PubMed Central

Dynamic molecular interactions play a central role in regulating the functioning of cells and organisms. The availability of experimentally determined large-scale cellular networks, along with other high-throughput experimental data sets that provide snapshots of biological systems at different times and conditions, is increasingly helpful in elucidating interaction dynamics. Here we review the beginnings of a new subfield within computational biology, one focused on the global inference and analysis of the dynamic interactome. This burgeoning research area, which entails a shift from static to dynamic network analysis, promises to be a major step forward in our ability to model and reason about cellular function and behavior.

Singh, Mona; Slonim, Donna K.



Motion-based competitive spatio-temporal technique with multi-frames references for efficient H.264\\/AVC motion information prediction  

Microsoft Academic Search

Motion estimation and compensation is an essential part of several video coding standards. Moreover motion information takes significant part of compressed bit stream, especially in low bit rate situation. This paper proposes an efficient technique which merges motion-based competitive spatio-temporal technique and multi-frames references scheme for efficient motion information prediction. Simulation results show an improved coding efficiency, compared to conventional

Kostas E. Psannis



Predicting the spatio-temporal distribution of Culicoides imicola in Sardinia using a discrete-time population model  

PubMed Central

Background Culicoides imicola KIEFFER, 1913 (Diptera: Ceratopogonidae) is the principal vector of Bluetongue disease in the Mediterranean basin, Africa and Asia. Previous studies have identified a range of eco-climatic variables associated with the distribution of C. imicola, and these relationships have been used to predict the large-scale distribution of the vector. However, these studies are not temporally-explicit and can not be used to predict the seasonality in C. imicola abundances. Between 2001 and 2006, longitudinal entomological surveillance was carried out throughout Italy, and provided a comprehensive spatio-temporal dataset of C. imicola catches in Onderstepoort-type black-light traps, in particular in Sardinia where the species is considered endemic. Methods We built a dynamic model that allows describing the effect of eco-climatic indicators on the monthly abundances of C. imicola in Sardinia. Model precision and accuracy were evaluated according to the influence of process and observation errors. Results A first-order autoregressive cofactor, a digital elevation model and MODIS Land Surface Temperature (LST)/or temperatures acquired from weather stations explained ~77% of the variability encountered in the samplings carried out in 9 sites during 6?years. Incorporating Normalized Difference Vegetation Index (NDVI) or rainfall did not increase the model's predictive capacity. On average, dynamics simulations showed good accuracy (predicted vs. observed r corr?=?0.9). Although the model did not always reproduce the absolute levels of monthly abundances peaks, it succeeded in reproducing the seasonality in population level and allowed identifying the periods of low abundances and with no apparent activity. On that basis, we mapped C. imicola monthly distribution over the entire Sardinian region. Conclusions This study demonstrated prospects for modelling data arising from Culicoides longitudinal entomological surveillance. The framework explicitly incorporates the influence of eco-climatic factors on population growth rates and accounts for observation and process errors. Upon validation, such a model could be used to predict monthly population abundances on the basis of environmental conditions, and hence can potentially reduce the amount of entomological surveillance.



Remote flow sensing of complex systems: steps towards spatio-temporal prediction of flow patterns  

NASA Astrophysics Data System (ADS)

Prediction of the spatial and temporal phenomena of wind flow patterns through urban areas is investigated. Typically sparse measurements are used in wind forecasting models for updating and prediction via a method called variational data assimilation. To improve upon this method, an experimental investigation combining various measurement tools (Hot Wire Anemometry, Stereoscopic Particle Image Velocimetry SPIV), static pressure measurements and Laser Doppler Velocimetry(LDV)) is carried out to study the airflow around wall mounted obstacles in a turbulent boundary layer. The method of Proper Orthogonal Decomposition (POD) is used to decompose the flow field into a finite set of POD coefficients which vary only in time associated with a corresponding set of POD basis functions which vary only in space. Direct measurement models utilizing the measurements from SPIV and LDV, along with indirect measurement models using sparse measurements from microphones are investigated and may ultimately be combined with state-space models to obtain more robust dynamical models.

Monnier, Bruno; Mokhasi, Paritosh; Rempfer, Dietmar; Wark, Candace



Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions  

Microsoft Academic Search

Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM downscaling techniques focus on preserving only observed temporal variability on point by point basis,

S. Hwang; W. D. Graham; J. Geurink; A. Adams; C. J. Martinez



How Perfect Can Protein Interactomes Be?  

NSDL National Science Digital Library

Any engineered device should certainly not contain nonfunctional components, for this would be a waste of energy and money. In contrast, evolutionary theory tells us that biological systems need not be optimized and may very well accumulate nonfunctional elements. Mutational and demographic processes contribute to the cluttering of eukaryotic genomes and transcriptional networks with “junk” DNA and spurious DNA binding sites. Here, we question whether such a notion should be applied to protein interactomes—that is, whether these protein interactomes are expected to contain a fraction of nonselected, nonfunctional protein-protein interactions (PPIs), which we term “noisy.” We propose a simple relationship between the fraction of noisy interactions expected in a given organism and three parameters: (i) the number of mutations needed to create and destroy interactions, (ii) the size of the proteome, and (iii) the fitness cost of noisy interactions. All three parameters suggest that noisy PPIs are expected to exist. Their existence could help to explain why PPIs determined from large-scale studies often lack functional relationships between interacting proteins, why PPIs are poorly conserved across organisms, and why the PPI space appears to be immensely large. Finally, we propose experimental strategies to estimate the fraction of evolutionary noise in PPI networks.

Emmanuel D. Levy (Quebec;Universit頤e Montr顬 REV); Christian R. Landry (Quebec;Universit頤e Montr顬 REV); Stephen W. Michnick (Quebec;Universit頤e Montr顬 REV)



Organization of Physical Interactomes as Uncovered by Network Schemas  

Microsoft Academic Search

Large-scale protein-protein interaction networks provide new opportunities for understanding cellular organization and functioning. We introduce network schemas to elucidate shared mechanisms within interactomes. Network schemas specify descriptions of proteins and the topology of interactions among them. We develop algorithms for systematically uncovering recurring, over-represented schemas in physical interaction networks. We apply our methods to the S. cerevisiae interactome, focusing on

Eric Banks; Elena Nabieva; Bernard Chazelle; Mona Singh



Comparative protein interactomics of neuroglobin and myoglobin  

PubMed Central

Neuroglobin is a hypoxia-inducible O2-binding protein with neuroprotective effects in cell and animal models of stroke and Alzheimer’s disease. The mechanism underlying neuroglobin’s cytoprotective action is unknown, although several possibilities have been proposed, including antioxidative and antiapoptotic effects. We used affinity purification-mass spectrometry methods to identify neuroglobin-interacting proteins in normoxic and hypoxic murine neuronal (HN33) cell lysates, and to compare these interactions with those of a structurally and functionally related protein, myoglobin. We report that the protein interactomes of neuroglobin and myoglobin overlap substantially and are modified by hypoxia. In addition, neuroglobin-interacting proteins include partners consistent with both antioxidative and antiapoptotic functions, as well as with a relationship to several neurodegenerative diseases.

Haines, Bryan A.; Davis, Darcy A.; Zykovich, Artem; Peng, Botao; Rao, Rammohan; Mooney, Sean D.; Jin, Kunlin; Greenberg, David A.



Comparative interactomics with Funcoup 2.0.  


FunCoup ( is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup achieves high coverage by orthology-based integration of data sources from different model organisms and from different platforms. We here present release 2.0 in which the data sources have been updated and the methodology has been refined. It contains a new data type Genetic Interaction, and three new species: chicken, dog and zebra fish. As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks. The number of high-confidence network links has increased dramatically. For instance, the human network has more than eight times as many links above confidence 0.5 as the previous release. FunCoup provides facilities for analysing the conservation of subnetworks in multiple species. We here explain how to do comparative interactomics on the FunCoup website. PMID:22110034

Alexeyenko, Andrey; Schmitt, Thomas; Tjärnberg, Andreas; Guala, Dmitri; Frings, Oliver; Sonnhammer, Erik L L



Hierarchical modularity and the evolution of genetic interactomes across species  

PubMed Central

To date, cross-species comparisons of genetic interactomes have been restricted to small or functionally related gene sets, limiting our ability to infer evolutionary trends. To facilitate a more comprehensive analysis, we constructed a genome-scale epistasis map (E-MAP) for the fission yeast Schizosaccharomyces pombe, providing phenotypic signatures for ~60% of the non-essential genome. Using these signatures, we generated a catalogue of 297 functional modules, and assigned function to 144 previously uncharacterised genes, including mRNA splicing and DNA damage checkpoint factors. Comparison with an integrated genetic interactome from the budding yeast Saccharomyces cerevisiae revealed a hierarchical model for the evolution of genetic interactions, with conservation highest within protein complexes, lower within biological processes, and lowest between distinct biological processes. Despite the large evolutionary distance and extensive rewiring of individual interactions, both networks retain conserved features and display similar levels of functional cross-talk between biological processes, suggesting general design principles of genetic interactomes.

Ryan, Colm J.; Roguev, Assen; Patrick, Kristin; Xu, Jiewei; Jahari, Harlizawati; Tong, Zongtian; Beltrao, Pedro; Shales, Michael; Qu, Hong; Collins, Sean R.; Kliegman, Joseph I.; Jiang, Lingli; Kuo, Dwight; Tosti, Elena; Kim, Hyun-Soo; Edelmann, Winfried; Keogh, Michael-Christopher; Greene, Derek; Tang, Chao; Cunningham, Padraig; Shokat, Kevan M.; Cagney, Gerard; Svensson, J. Peter; Guthrie, Christine; Espenshade, Peter J.; Ideker, Trey; Krogan, Nevan J.



Evidence for network evolution in an Arabidopsis interactome map.  


Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. We describe a proteome-wide binary protein-protein interaction map for the interactome network of the plant Arabidopsis thaliana containing about 6200 highly reliable interactions between about 2700 proteins. A global organization of plant biological processes emerges from community analyses of the resulting network, together with large numbers of novel hypothetical functional links between proteins and pathways. We observe a dynamic rewiring of interactions following gene duplication events, providing evidence for a model of evolution acting upon interactome networks. This and future plant interactome maps should facilitate systems approaches to better understand plant biology and improve crops. PMID:21798944



Evidence for Network Evolution in an Arabidopsis Interactome Map  

PubMed Central

Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. We describe a proteome-wide binary protein-protein interaction map for the interactome network of the plant Arabidopsis thaliana containing ~6,200 highly reliable interactions between ~2,700 proteins. A global organization of plant biological processes emerges from community analyses of the resulting network, together with large numbers of novel hypothetical functional links between proteins and pathways. We observe a dynamic rewiring of interactions following gene duplication events, providing evidence for a model of evolution acting upon interactome networks. This and future plant interactome maps should facilitate systems approaches to better understand plant biology and improve crops.



Dynamic Zebrafish Interactome Reveals Transcriptional Mechanisms of Dioxin Toxicity  

PubMed Central

Background In order to generate hypotheses regarding the mechanisms by which 2,3,7,8-tetrachlorodibenzo-p-dioxin (dioxin) causes toxicity, we analyzed global gene expression changes in developing zebrafish embryos exposed to this potent toxicant in the context of a dynamic gene network. For this purpose, we also computationally inferred a zebrafish (Danio rerio) interactome based on orthologs and interaction data from other eukaryotes. Methodology/Principal Findings Using novel computational tools to analyze this interactome, we distinguished between dioxin-dependent and dioxin-independent interactions between proteins, and tracked the temporal propagation of dioxin-dependent transcriptional changes from a few genes that were altered initially, to large groups of biologically coherent genes at later times. The most notable processes altered at later developmental stages were calcium and iron metabolism, embryonic morphogenesis including neuronal and retinal development, a variety of mitochondria-related functions, and generalized stress response (not including induction of antioxidant genes). Within the interactome, many of these responses were connected to cytochrome P4501A (cyp1a) as well as other genes that were dioxin-regulated one day after exposure. This suggests that cyp1a may play a key role initiating the toxic dysregulation of those processes, rather than serving simply as a passive marker of dioxin exposure, as suggested by earlier research. Conclusions/Significance Thus, a powerful microarray experiment coupled with a flexible interactome and multi-pronged interactome tools (which are now made publicly available for microarray analysis and related work) suggest the hypothesis that dioxin, best known in fish as a potent cardioteratogen, has many other targets. Many of these types of toxicity have been observed in mammalian species and are potentially caused by alterations to cyp1a.

Alexeyenko, Andrey; Wassenberg, Deena M.; Lobenhofer, Edward K.; Yen, Jerry; Linney, Elwood; Sonnhammer, Erik L. L.; Meyer, Joel N.



Arabidopsis G-protein interactome reveals connections to cell wall carbohydrates and morphogenesis.  


The heterotrimeric G-protein complex is minimally composed of G?, G?, and G? subunits. In the classic scenario, the G-protein complex is the nexus in signaling from the plasma membrane, where the heterotrimeric G-protein associates with heptahelical G-protein-coupled receptors (GPCRs), to cytoplasmic target proteins called effectors. Although a number of effectors are known in metazoans and fungi, none of these are predicted to exist in their canonical forms in plants. To identify ab initio plant G-protein effectors and scaffold proteins, we screened a set of proteins from the G-protein complex using two-hybrid complementation in yeast. After deep and exhaustive interrogation, we detected 544 interactions between 434 proteins, of which 68 highly interconnected proteins form the core G-protein interactome. Within this core, over half of the interactions comprising two-thirds of the nodes were retested and validated as genuine in planta. Co-expression analysis in combination with phenotyping of loss-of-function mutations in a set of core interactome genes revealed a novel role for G-proteins in regulating cell wall modification. PMID:21952135

Klopffleisch, Karsten; Phan, Nguyen; Augustin, Kelsey; Bayne, Robert S; Booker, Katherine S; Botella, Jose R; Carpita, Nicholas C; Carr, Tyrell; Chen, Jin-Gui; Cooke, Thomas Ryan; Frick-Cheng, Arwen; Friedman, Erin J; Fulk, Brandon; Hahn, Michael G; Jiang, Kun; Jorda, Lucia; Kruppe, Lydia; Liu, Chenggang; Lorek, Justine; McCann, Maureen C; Molina, Antonio; Moriyama, Etsuko N; Mukhtar, M Shahid; Mudgil, Yashwanti; Pattathil, Sivakumar; Schwarz, John; Seta, Steven; Tan, Matthew; Temp, Ulrike; Trusov, Yuri; Urano, Daisuke; Welter, Bastian; Yang, Jing; Panstruga, Ralph; Uhrig, Joachim F; Jones, Alan M



Arabidopsis G-protein interactome reveals connections to cell wall carbohydrates and morphogenesis  

SciTech Connect

The heterotrimeric G-protein complex is minimally composed of G{alpha}, G{beta}, and G{gamma} subunits. In the classic scenario, the G-protein complex is the nexus in signaling from the plasma membrane, where the heterotrimeric G-protein associates with heptahelical G-protein-coupled receptors (GPCRs), to cytoplasmic target proteins called effectors. Although a number of effectors are known in metazoans and fungi, none of these are predicted to exist in their canonical forms in plants. To identify ab initio plant G-protein effectors and scaffold proteins, we screened a set of proteins from the G-protein complex using two-hybrid complementation in yeast. After deep and exhaustive interrogation, we detected 544 interactions between 434 proteins, of which 68 highly interconnected proteins form the core G-protein interactome. Within this core, over half of the interactions comprising two-thirds of the nodes were retested and validated as genuine in planta. Co-expression analysis in combination with phenotyping of loss-of-function mutations in a set of core interactome genes revealed a novel role for G-proteins in regulating cell wall modification.

Klopffleisch, Karsten [University of Cologne; Phan, Nguyen [University of North Carolina, Chapel Hill; Chen, Jay [ORNL; Panstruga, Ralph [Max-Planck Institute for Plant Breeding Research; Uhrig, Joachim [University of Cologne; Jones, Alan M [University of North Carolina, Chapel Hill



High-Quality Binary Protein Interaction Map of the Yeast Interactome Network  

Microsoft Academic Search

Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed

Haiyuan Yu; Pascal Braun; Muhammed A. Yildirim; Irma Lemmens; Kavitha Venkatesan; Julie Sahalie; Tomoko Hirozane-Kishikawa; Fana Gebreab; Na Li; Nicolas Simonis; Tong Hao; Jean-François Rual; Amélie Dricot; Alexei Vazquez; Ryan R. Murray; Christophe Simon; Leah Tardivo; Stanley Tam; Nenad Svrzikapa; Changyu Fan; Anne-Sophie de Smet; Adriana Motyl; Michael E. Hudson; Xiaofeng Xin; Michael E. Cusick; Troy Moore; Charlie Boone; Michael Snyder; Frederick P. Roth; Albert-László Barabási; Jan Tavernier; David E. Hill; Marc Vidal



A Bayesian Spatio-Temporal Approach to Forecast Future Climate - Blending Regional Climate Model Predictions for the American Southwest  

NASA Astrophysics Data System (ADS)

We consider the problem of combining multiple climate models to forecast future regional climate. Our method is based on blending different members of an ensemble of regional climate model (RCM) simulations while accounting for the discrepancies between these simulations and observational records under current climate conditions.. To this end, we develop Bayesian spatio-temporal models that assess the discrepancies between climate model simulations and observational records over a 30 year period in the recent past. Those discrepancies are then propagated into the future to obtain blended forecasts of 21st century climate. The models allow for location-dependent spatial heterogeneities, providing local comparisons between the different simulations. We focus on regional climate model simulations performed in the context of the North American Regional Climate Change Assessment Program (NARCCAP). We consider, in particular, simulations from RegCM3 using three different forcings: NCEP, GFDL and CGCM3. We use simulations for two time periods: current climate conditions, covering 1971 to 2000, and future climate conditions under the SRES A2 emissions scenario, covering 2041 to 2070. We investigate yearly maximum and mean summer temperature as well as yearly minimum and mean winter temperature for a domain in the South West of the United States. The results indicated that the RCM simulations generally underestimate the temperature increase for most of the domain compared to the blended forecasts from our model. These differences, however, strongly depend on the specific location within the domain. These findings further denote the importance of using a blending approach that allows for spatial heterogeneity.

Salazar, E.; Sanso, B.; Finley, A.; Hammerling, D.; Steinsland, I.; Wang, X.; Delamater, P.




PubMed Central

It has been shown that the invasive trypomastigote forms of Trypanosoma cruzi use and modulate components of the extracellular matrix (ECM) during the initial process of infection. Infective trypomastigotes up-regulate the expression of laminin ?-1 (LAMC1) and thrombospondin (THBS1) to facilitate the recruitment of trypomastigotes to enhance cellular infection. Silencing the expression of LAMC1 and THBS1 by stable RNAi dramatically reduces trypanosome infection. T. cruzi gp83, a ligand that mediates the attachment of trypanosomes to cells to initiate infection, up-regulates LAMC1 expression to enhance cellular infection. Infective trypomastigotes interact with LAMC1 through galectin-3 (LGALS3), a human lectin, to enhance cellular infection. Silencing the expression of LGALS3 also reduces cellular infection. Some trypanosome surface molecules also interact with the ECM to facilitate infection. Despite the role of the ECM in T. cruzi infection, almost nothing is known about the ECM interactome networks operating in the process of T. cruzi infection. In this mini review, we critically analyze and discuss the regulation of the ECM by T. cruzi and its gp83 ligand, and present the first elucidation of the human ECM interactome network, regulated by T. cruzi and its gp83 ligand, to facilitate cellular infection. The elucidation of the human ECM interactome regulated by T. cruzi is critically important to the understanding of the molecular pathogenesis of T. cruzi infection and developing novel approaches of intervention in Chagas’ disease.

Cardenas, Tatiana C.; Johnson, Candice A.; Pratap, Siddharth; Nde, Pius N.; Furtak, Vyacheslav; Kleshchenko, Yuliya Y.; Lima, Maria F.; Villalta, Fernando



STPMiner: A Highperformance Spatiotemporal Pattern Mining Toolbox  

SciTech Connect

The volume of spatiotemporal data being generated from scientific simulations and observations from sensors is growing at an astronomical rate. This data explosion is going to pose three challenges to the existing data mining infrastructure: algorithmic, computational, and I/O. Over the years we have implemented several spatiotemporal data mining algorithms including: outliers/anomalies, colocation patterns, change patterns, clustering, classification, and prediction algorithms. In this paper we briefly discuss the core spatiotemporal pattern mining algorithms along with some of the computational and I/O challenges associated with the big data.

Vatsavai, Raju [ORNL



Summer Rains and Dry Seasons in the Upper Blue Nile Basin: The Predictability of Half a Century of Past and Future Spatiotemporal Patterns  

PubMed Central

During the last 100 years the Ethiopian upper Blue Nile Basin (BNB) has undergone major changes in land use, and is now potentially facing changes in climate. Rainfall over BNB supplies over two-thirds of the water to the Nile and supports a large local population living mainly on subsistence agriculture. Regional food security is sensitive to both the amount and timing of rain and is already an important political challenge that will be further complicated if scenarios of climate change are realized. In this study a simple spatial model of the timing and duration of summer rains (Kiremt) and dry season (Bega), and annual rain over the upper BNB was established from observed data between 1952 and 2004. The model was used to explore potential impacts of climate change on these rains, using a down-scaled ECHAM5/MP1-OM scenario between 2050 and 2100. Over the observed period the amount, onset and duration of Kiremt rains and rain-free Bega days have exhibited a consistent spatial pattern. The spatially averaged annual rainfall was 1490 mm of which 93% was Kiremt rain. The average Kiremt rain and number of rainy days was higher in the southwest (322 days) and decreased towards the north (136 days). Under the 2050–2100 scenario, the annual mean rainfall is predicted to increase by 6% and maintain the same spatial pattern as in the past. A larger change in annual rainfall is expected in the southwest (ca. +130 mm) with a gradually smaller change towards the north (ca. +70 mm). Results highlight the need to account for the characteristic spatiotemporal zonation when planning water management and climate adaptation within the upper BNB. The presented simple spatial resolved models of the presence of Kiremt and annual total rainfall could be used as a baseline for such long-term planning.

Mellander, Per-Erik; Gebrehiwot, Solomon G.; Gardenas, Annemieke I.; Bewket, Woldeamlak; Bishop, Kevin



A Map of the Interactome Network of the Metazoan C. elegans  

Microsoft Academic Search

To initiate studies on how protein-protein interaction (or ``interactome'') networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together

Siming Li; Christopher M. Armstrong; Nicolas Bertin; Hui Ge; Stuart Milstein; Mike Boxem; Pierre-Olivier Vidalain; Jing-Dong J. Han; Alban Chesneau; Tong Hao; Debra S. Goldberg; Ning Li; Monica Martinez; Jean-François Rual; Philippe Lamesch; Lai Xu; Muneesh Tewari; Sharyl L. Wong; Lan V. Zhang; Gabriel F. Berriz; Laurent Jacotot; Philippe Vaglio; Jérôme Reboul; Tomoko Hirozane-Kishikawa; Qianru Li; Harrison W. Gabel; Ahmed Elewa; Bridget Baumgartner; Debra J. Rose; Haiyuan Yu; Stephanie Bosak; Reynaldo Sequerra; Andrew Fraser; Susan E. Mango; William M. Saxton; Susan Strome; Sander van den Heuvel; Fabio Piano; Jean Vandenhaute; Claude Sardet; Mark Gerstein; Lynn Doucette-Stamm; Kristin C. Gunsalus; J. Wade Harper; Michael E. Cusick; Frederick P. Roth; David E. Hill; Marc Vidal



Global topological features of cancer proteins in the human interactome  

PubMed Central

Motivation The study of interactomes, or networks of protein-protein interactions, is increasingly providing valuable information on biological systems. Here we report a study of cancer proteins in an extensive human protein-protein interaction network constructed by computational methods. Results We show that human proteins translated from known cancer genes exhibit a network topology that is different from that of proteins not documented as being mutated in cancer. In particular, cancer proteins show an increase in the number of proteins they interact with. They also appear to participate in central hubs rather than peripheral ones, mirroring their greater centrality and participation in networks that form the backbone of the proteome. Moreover, we show that cancer proteins contain a high ratio of highly promiscuous structural domains, i.e., domains with a high propensity for mediating protein interactions. These observations indicate an underlying evolutionary distinction between the two groups of proteins, reflecting the central roles of proteins, whose mutations lead to cancer.



CTCF-Mediated Functional Chromatin Interactome in Pluripotent Cells  

PubMed Central

Mammalian genomes are viewed as functional organizations that orchestrate spatial and temporal gene regulation. CTCF, the most characterized insulator-binding protein, has been implicated as a key genome organizer. Yet, little is known about CTCF-associated higher order chromatin structures at a global scale. Here, we applied Chromatin Interaction Analysis by Paired-End-Tag sequencing to elucidate the CTCF-chromatin interactome in pluripotent cells. From this analysis, 1,480 cis and 336 trans interacting loci were identified with high reproducibility and precision. Associating these chromatin interaction loci with their underlying epigenetic states, promoter activities, enhancer binding and nuclear lamina occupancy, we uncovered five distinct chromatin domains that suggest potential new models of CTCF function in chromatin organization and transcriptional control. Specifically, CTCF interactions demarcate chromatin-nuclear membrane attachments and influence proper gene expression through extensive crosstalk between promoters and regulatory elements. This highly complex nuclear organization offers insights towards the unifying principles governing genome plasticity and function.

Handoko, Lusy; Xu, Han; Li, Guoliang; Ngan, Chew Yee; Chew, Elaine; Schnapp, Marie; Lee, Charlie Wah Heng; Ye, Chaopeng; Ping, Joanne Lim Hui; Mulawadi, Fabianus; Wong, Eleanor; Sheng, Jianpeng; Zhang, Yubo; Poh, Thompson; Chan, Chee Seng; Kunarso, Galih; Shahab, Atif; Bourque, Guillaume; Cacheux-Rataboul, Valere; Sung, Wing-Kin; Ruan, Yijun; Wei, Chia-Lin



Syndecan and integrin interactomes: large complexes in small spaces  

PubMed Central

The syndecan family of transmembrane proteoglycans cooperate with integrins to regulate both early and late events in adhesion formation. The heparan sulphate chains substituted on to the syndecan ectodomains are capable of engaging ligands over great distance, while the protein core spans the plasma membrane and initiates cytoplasmic signals through a short cytoplasmic tail. These properties create a spatial paradox. The volume of the heparan sulphate chains greatly exceeds that of the integrins with which it cooperates, while the short cytodomain must bind to multiple cytoplasmic factors, despite being long enough to bind only one or two. In this review we consider the structural rearrangements that a cell undertakes to overcome spatial restrictions and compare the interactomes of syndecans and integrins to gain insight into the composition of adhesions and how they are regulated over time.

Roper, James A; Williamson, Rosalind C; Bass, Mark D



An in vivo map of the yeast protein interactome.  


Protein interactions regulate the systems-level behavior of cells; thus, deciphering the structure and dynamics of protein interaction networks in their cellular context is a central goal in biology. We have performed a genome-wide in vivo screen for protein-protein interactions in Saccharomyces cerevisiae by means of a protein-fragment complementation assay (PCA). We identified 2770 interactions among 1124 endogenously expressed proteins. Comparison with previous studies confirmed known interactions, but most were not known, revealing a previously unexplored subspace of the yeast protein interactome. The PCA detected structural and topological relationships between proteins, providing an 8-nanometer-resolution map of dynamically interacting complexes in vivo and extended networks that provide insights into fundamental cellular processes, including cell polarization and autophagy, pathways that are evolutionarily conserved and central to both development and human health. PMID:18467557

Tarassov, Kirill; Messier, Vincent; Landry, Christian R; Radinovic, Stevo; Serna Molina, Mercedes M; Shames, Igor; Malitskaya, Yelena; Vogel, Jackie; Bussey, Howard; Michnick, Stephen W



Chemical-protein interactome and its application in off-target identification.  


Drugs exert their therapeutic and adverse effects by interacting with molecular targets. Although designed to interact with specific targets in a desirable manner, drug molecules often bind to unexpected proteins (off-targets). By activating or inhibiting off-targets and the associated biological processes and pathways, the resulting chemical-protein interactions can influence drug reaction directly or indirectly. Exploring the relationship between drug and off-targets and the downstream drug reaction can help understand the polypharmacology of the drug, hence significantly advance the drug repositioning pipeline and the application of personalized medicine in understanding and preventing adverse drug reaction. This review summarizes works on predicting off-targets via chemical-protein interactome (CPI), an interaction strength matrix of drugs across multiple human proteins aiming at exploring the unexpected drug-protein interactions, with a variety of computational strategies, including docking, chemical structure comparison and text-mining etc. Effective recall on previous knowledge, de novo prediction and subsequent experimental validation conferred us strong confidence in these methods. Such studies present prospect of large scale in silico methodologies for off-target discovery with low cost and high efficiency. PMID:21369884

Yang, Lun; Wang, Ke-Jian; Wang, Li-Shan; Jegga, Anil G; Qin, Sheng-Ying; He, Guang; Chen, Jian; Xiao, Yue; He, Lin



Profiling the Human Protein-DNA Interactome Reveals MAPK1 as a Transcriptional Repressor of Interferon Signalling  

PubMed Central

SUMMARY Protein-DNA interactions (PDIs) mediate a broad range of functions essential for cellular differentiation, function, and survival. However, it is still a daunting task to comprehensively identify and profile sequence-specific PDIs in complex genomes. Here, we have used a combined bioinformatics and protein microarray-based strategy to systematically characterize the human protein-DNA interactome. We identified 17,718 PDIs between 460 DNA motifs predicted to regulate transcription and 4,191 human proteins of various functional classes. Among them, we recovered many known PDIs for transcription factors (TFs). We also identified a large number of new PDIs for known TFs, as well as for previously uncharacterized TFs. Remarkably, we found that over three hundred proteins not previously annotated as TFs also showed sequence-specific PDIs, including RNA binding proteins, mitochondrial proteins, and protein kinases. One of such unconventional DNA-binding proteins, MAPK1, acts as a transcriptional repressor for interferon gamma-induced genes.

Hu, Shaohui; Xie, Zhi; Onishi, Akishi; Yu, Xueping; Jiang, Lizhi; Lin, Jimmy; Rho, Hee-sool; Woodard, Crystal; Wang, Hong; Jeong, Jun-Seop; Long, Shunyou; He, Xiaofei; Wade, Herschel; Blackshaw, Seth; Qian, Jiang; Zhu, Heng



Spatiotemporal averaging of in-stream solute removal dynamics  

Microsoft Academic Search

The k-h dependence arises as an emergent pattern of a mechanistic modelSpatiotemporal averaging does not alter the k-h relationship in wet domainsThe pdf of k could be adequately predicted using analytical approaches

Nandita B. Basu; P. Suresh C. Rao; Sally E. Thompson; Natalia V. Loukinova; Simon D. Donner; Sheng Ye; Murugesu Sivapalan



Global tumor protein p53/p63 interactome  

PubMed Central

Cisplatin chemoresistance is a clinical problem that leads to treatment failure in various human epithelial cancers. Members of tumor protein (TP) p53 family play various critical roles in the multiple molecular mechanisms underlying the chemoresistance of tumor cells. However, the in-depth mechanisms of the cellular response to cisplatin-induced cell death are still under thorough investigation. We previously showed that squamous cell carcinoma (SCC) cells exposed to cisplatin display an ATM-dependent phosphorylation of ?Np63?, leading to a specific function of the phosphorylated (p)-?Np63? transcription factor in cisplatin-sensitive tumor cells. We further found that SCC cells expressing non-p-?Np63?-S385G became cisplatin-resistant. Using quantitative mass-spectrometry of protein complexes labeled with isobaric tags, we showed that TP53 and ?Np63? are involved in numerous protein-protein interactions, which are likely to be implicated in the response of tumor cells to cisplatin exposure. We found that p-?Np63? binds to the splicing complex, leading to repression of mRNA splicing and activation of ACIN1-mediated cell death pathway. In contrast to p-?Np63?, non-p-?Np63? fails to bind the critical members of the splicing complex, thereby leading to activation of RNA splicing and reduction of cell death pathway. Overall, our studies provide an integrated proteomic platform in making a case for the role of the p53/p63 interactome in cisplatin chemoresistance.

Huang, Yiping; Jeong, Jun Seop; Okamura, Jun; Sook-Kim, Myoung; Zhu, Heng; Guerrero-Preston, Rafael; Ratovitski, Edward A.



Examining the Interactome of Huperzine A by Magnetic Biopanning  

PubMed Central

Huperzine A is a bioactive compound derived from traditional Chinese medicine plant Qian Ceng Ta (Huperzia serrata), and was found to have multiple neuroprotective effects. In addition to being a potent acetylcholinesterase inhibitor, it was thought to act through other mechanisms such as antioxidation, antiapoptosis, etc. However, the molecular targets involved with these mechanisms were not identified. In this study, we attempted to exam the interactome of Huperzine A using a cDNA phage display library and also mammalian brain tissue extracts. The drugs were chemically linked on the surface of magnetic particles and the interactive phages or proteins were collected and analyzed. Among the various cDNA expressing phages selected, one was identified to encode the mitochondria NADH dehydrogenase subunit 1. Specific bindings between the drug and the target phages and target proteins were confirmed. Another enriched phage clone was identified as mitochondria ATP synthase, which was also panned out from the proteome of mouse brain tissue lysate. These data indicated the possible involvement of mitochondrial respiratory chain matrix enzymes in Huperzine A's pharmacological effects. Such involvement had been suggested by previous studies based on enzyme activity changes. Our data supported the new mechanism. Overall we demonstrated the feasibility of using magnetic biopanning as a simple and viable method for investigating the complex molecular mechanisms of bioactive molecules.

Guo, Wei; Liu, Shupeng; Peng, Jinliang; Wei, Xiaohui; Sun, Ye; Qiu, Yangsheng; Gao, Guangwei; Wang, Peng; Xu, Yuhong



Examining the interactome of huperzine A by magnetic biopanning.  


Huperzine A is a bioactive compound derived from traditional Chinese medicine plant Qian Ceng Ta (Huperzia serrata), and was found to have multiple neuroprotective effects. In addition to being a potent acetylcholinesterase inhibitor, it was thought to act through other mechanisms such as antioxidation, antiapoptosis, etc. However, the molecular targets involved with these mechanisms were not identified. In this study, we attempted to exam the interactome of Huperzine A using a cDNA phage display library and also mammalian brain tissue extracts. The drugs were chemically linked on the surface of magnetic particles and the interactive phages or proteins were collected and analyzed. Among the various cDNA expressing phages selected, one was identified to encode the mitochondria NADH dehydrogenase subunit 1. Specific bindings between the drug and the target phages and target proteins were confirmed. Another enriched phage clone was identified as mitochondria ATP synthase, which was also panned out from the proteome of mouse brain tissue lysate. These data indicated the possible involvement of mitochondrial respiratory chain matrix enzymes in Huperzine A's pharmacological effects. Such involvement had been suggested by previous studies based on enzyme activity changes. Our data supported the new mechanism. Overall we demonstrated the feasibility of using magnetic biopanning as a simple and viable method for investigating the complex molecular mechanisms of bioactive molecules. PMID:22615909

Guo, Wei; Liu, Shupeng; Peng, Jinliang; Wei, Xiaohui; Sun, Ye; Qiu, Yangsheng; Gao, Guangwei; Wang, Peng; Xu, Yuhong



CTCF-mediated functional chromatin interactome in pluripotent cells.  


Mammalian genomes are viewed as functional organizations that orchestrate spatial and temporal gene regulation. CTCF, the most characterized insulator-binding protein, has been implicated as a key genome organizer. However, little is known about CTCF-associated higher-order chromatin structures at a global scale. Here we applied chromatin interaction analysis by paired-end tag (ChIA-PET) sequencing to elucidate the CTCF-chromatin interactome in pluripotent cells. From this analysis, we identified 1,480 cis- and 336 trans-interacting loci with high reproducibility and precision. Associating these chromatin interaction loci with their underlying epigenetic states, promoter activities, enhancer binding and nuclear lamina occupancy, we uncovered five distinct chromatin domains that suggest potential new models of CTCF function in chromatin organization and transcriptional control. Specifically, CTCF interactions demarcate chromatin-nuclear membrane attachments and influence proper gene expression through extensive cross-talk between promoters and regulatory elements. This highly complex nuclear organization offers insights toward the unifying principles that govern genome plasticity and function. PMID:21685913

Handoko, Lusy; Xu, Han; Li, Guoliang; Ngan, Chew Yee; Chew, Elaine; Schnapp, Marie; Lee, Charlie Wah Heng; Ye, Chaopeng; Ping, Joanne Lim Hui; Mulawadi, Fabianus; Wong, Eleanor; Sheng, Jianpeng; Zhang, Yubo; Poh, Thompson; Chan, Chee Seng; Kunarso, Galih; Shahab, Atif; Bourque, Guillaume; Cacheux-Rataboul, Valere; Sung, Wing-Kin; Ruan, Yijun; Wei, Chia-Lin



Interactome mapping suggests new mechanistic details underlying Alzheimer's disease  

PubMed Central

Recent advances toward the characterization of Alzheimer's disease (AD) have permitted the identification of a dozen of genetic risk factors, although many more remain undiscovered. In parallel, works in the field of network biology have shown a strong link between protein connectivity and disease. In this manuscript, we demonstrate that AD-related genes are indeed highly interconnected and, based on this observation, we set up an interaction discovery strategy to unveil novel AD causative and susceptibility genes. In total, we report 200 high-confidence protein–protein interactions between eight confirmed AD-related genes and 66 candidates. Of these, 31 are located in chromosomal regions containing susceptibility loci related to the etiology of late-onset AD, and 17 show dysregulated expression patterns in AD patients, which makes them very good candidates for further functional studies. Interestingly, we also identified four novel direct interactions among well-characterized AD causative/susceptibility genes (i.e., APP, A2M, APOE, PSEN1, and PSEN2), which support the suggested link between plaque formation and inflammatory processes and provide insights into the intracellular regulation of APP cleavage. Finally, we contextualize the discovered relationships, integrating them with all the interaction data reported in the literature, building the most complete interactome associated to AD. This general view facilitates the analyses of global properties of the network, such as its functional modularity, and triggers many hypotheses on the molecular mechanisms implicated in AD. For instance, our analyses suggest a putative role for PDCD4 as a neuronal death regulator and ECSIT as a molecular link between oxidative stress, inflammation, and mitochondrial dysfunction in AD.

Soler-Lopez, Montserrat; Zanzoni, Andreas; Lluis, Ricart; Stelzl, Ulrich; Aloy, Patrick



Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets  

Microsoft Academic Search

We present the first analysis of the human proteome with regard to interactions between proteins. We also compare the human interactome with the available interaction datasets from yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans) and fly (Drosophila melanogaster). Of >70,000 binary interactions, only 42 were common to human, worm and fly, and only 16 were common to all four datasets. An

T K B Gandhi; Jun Zhong; Suresh Mathivanan; L Karthick; K N Chandrika; S Sujatha Mohan; Salil Sharma; Stefan Pinkert; Shilpa Nagaraju; Balamurugan Periaswamy; Goparani Mishra; Kannabiran Nandakumar; Beiyi Shen; Nandan Deshpande; Rashmi Nayak; Malabika Sarker; Jef D Boeke; Giovanni Parmigiani; Jörg Schultz; Joel S Bader; Akhilesh Pandey



High-quality binary protein interaction map of the yeast interactome network.  


Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a "second-generation" high-quality, high-throughput Y2H data set covering approximately 20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy. PMID:18719252

Yu, Haiyuan; Braun, Pascal; Yildirim, Muhammed A; Lemmens, Irma; Venkatesan, Kavitha; Sahalie, Julie; Hirozane-Kishikawa, Tomoko; Gebreab, Fana; Li, Na; Simonis, Nicolas; Hao, Tong; Rual, Jean-François; Dricot, Amélie; Vazquez, Alexei; Murray, Ryan R; Simon, Christophe; Tardivo, Leah; Tam, Stanley; Svrzikapa, Nenad; Fan, Changyu; de Smet, Anne-Sophie; Motyl, Adriana; Hudson, Michael E; Park, Juyong; Xin, Xiaofeng; Cusick, Michael E; Moore, Troy; Boone, Charlie; Snyder, Michael; Roth, Frederick P; Barabási, Albert-László; Tavernier, Jan; Hill, David E; Vidal, Marc



Protein interactions: mapping interactome networks to support drug target discovery and selection.  


Proteins are biomolecular structures that build the microscopic working machinery of any living system. Proteins within the cells and biological systems do not act alone, but rather team up into macromolecular structures enclosing intricate physicochemical dynamic connections to undertake biological functions. A critical step towards unraveling the complex molecular relationships in living systems is the mapping of protein-to-protein physical "interactions". The complete map of protein interactions that can occur in a living organism is called the "interactome". Achieving an adequate atlas of all the protein interactions within a living system should allow to build its interaction network and to identity the "central nodes" that can be critical for the function, the homeostasis, and the movement of such system. Focusing on human studies, the data about the human interactome are most relevant for current biomedical research, because it is clear that the location of the proteins in the interactome network will allow to evaluate their centrality and to redefine the potential value of each protein as a drug target. This chapter presents our current knowledge on the human protein-protein interactome and explains how such knowledge can help us to select adequate targets for drugs. PMID:22821600

De Las Rivas, Javier; Prieto, Carlos



Uncovering new signaling proteins and potential drug targets through the interactome analysis of Mycobacterium tuberculosis  

Microsoft Academic Search

BACKGROUND: Analysis of the pathogen interactome is a powerful approach for dissecting potential signal transduction and virulence pathways. It also offers opportunities for exploring new drug targets. RESULTS: In this study, a protein-protein interaction (PPI) network of Mycobacterium tuberculosis H37Rv was constructed using a homogenous protein mapping method, which has shown molecular chaperones, ribosomal proteins and ABC transporters to be

Tao Cui; Lei Zhang; Xizhou Wang; Zheng-Guo He



Improved Microarray-Based Decision Support with Graph Encoded Interactome Data  

Microsoft Academic Search

In the past, microarray studies have been criticized due to noise and the limited overlap between gene signatures. Prior biological knowledge should therefore be incorporated as side information in models based on gene expression data to improve the accuracy of diagnosis and prognosis in cancer. As prior knowledge, we investigated interaction and pathway information from the human interactome on different

Anneleen Daemen; Marco Signoretto; Olivier Gevaert; Johan A. K. Suykens; Bart de Moor; I. King Jordan



Parabolic Resonance: A Route to Hamiltonian Spatiotemporal Chaos  

SciTech Connect

We show that initial data near an unperturbed stable plane wave can evolve into a regime of spatiotemporal chaos in the slightly forced conservative periodic one-dimensional nonlinear Schroedinger equation. Statistical measures are employed to demonstrate that this spatiotemporal chaos is intermittent: there are windows in time for which the solution gains spatial coherence. The parameters and initial profiles that lead to such intermittency are predicted by utilizing a novel geometrical description of the integrable unforced equation.

Shlizerman, Eli; Rom-Kedar, Vered [Faculty of Mathematics and Computer Science, Weizmann Institute of Science, Post Office Box 26, Rehovot 76100 (Israel)



A "Candidate-Interactome" Aggregate Analysis of Genome-Wide Association Data in Multiple Sclerosis  

PubMed Central

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a “candidate interactome” (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms.

Policano, Claudia; Annibali, Viviana; Coarelli, Giulia; Ricigliano, Vito A. G.; Vittori, Danila; Fornasiero, Arianna; Buscarinu, Maria Chiara; Romano, Silvia; Salvetti, Marco; Ristori, Giovanni



Spatiotemporal multipartite entanglement  

NASA Astrophysics Data System (ADS)

In this Rapid Communication, we propose, following the spirit of quantum imaging, to generalize the theory of multipartite entanglement for the continuous-variable Gaussian states by considering, instead of the global covariance matrix, the local correlation matrix at two different spatiotemporal points (??,t) and (??',t'), with ?? being the transverse coordinate. Our approach makes it possible to introduce the characteristic spatial length and the characteristic time of the multipartite entanglement, which in general depend on the number of “parties” in the system. As an example, we consider tripartite entanglement in spontaneous parametric down-conversion with a spatially structured pump. We investigate spatiotemporal properties of such entanglement and calculate its characteristic spatial length and time.

Kolobov, Mikhail I.; Patera, Giuseppe



Three-Wave Interactions and Spatiotemporal Chaos  

NASA Astrophysics Data System (ADS)

Three-wave interactions form the basis of our understanding of many pattern-forming systems because they encapsulate the most basic nonlinear interactions. In problems with two comparable length scales, it is possible for two waves of the shorter wavelength to interact with one wave of the longer, as well as for two waves of the longer wavelength to interact with one wave of the shorter. Consideration of both types of three-wave interactions can generically explain the presence of complex patterns and spatiotemporal chaos. Two length scales arise naturally in the Faraday wave experiment, and our results enable some previously unexplained experimental observations of spatiotemporal chaos to be interpreted in a new light. Our predictions are illustrated with numerical simulations of a model partial differential equation.

Rucklidge, A. M.; Silber, M.; Skeldon, A. C.



A human phenome-interactome network of protein complexes implicated in genetic disorders  

Microsoft Academic Search

We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking

Kasper Lage; E Olof Karlberg; Zenia M Størling; Páll Í Ólason; Anders G Pedersen; Olga Rigina; Anders M Hinsby; Zeynep Tümer; Flemming Pociot; Niels Tommerup; Yves Moreau; Søren Brunak



Hubba: hub objects analyzer - a framework of interactome hubs identification for network biology  

Microsoft Academic Search

One major task in the post-genome era is to recon- struct proteomic and genomic interacting networks using high-throughput experiment data. To identify essential nodes\\/hubs in these interactomes is a way to decipher the critical keys inside biochemical pathways or complex networks. These essential nodes\\/hubs may serve as potential drug-targets for developing novel therapy of human diseases, such as cancer or

Chung-yen Lin; Chia-hao Chin; Hsin-hung Wu; Shu-hwa Chen; Chin-wen Ho; Ming-tat Ko



[Protocols of proteins interactomics: molecular fishing on optical chips and magnetic nanoparticles].  


Now it is absolutely clear, that the majority of proteins in living systems function due to interaction with each other in stable or dynamic proteins complexes. Therefore necessity of deeper studies of proteins functions causes expansion of protein-protein interaction research. In the present review the brief description and comparative estimation of experimental methods and protocols of protein interactomics, based on technology of molecular fishing on an optical chips and paramagnetic nanoparticles is given. PMID:23789344

Ivanov, A S; Ershov, P V; Poverennaya, E V; Lisitsa, A V; Archakov, A I


Use of biotinylated ubiquitin for analysis of rat brain mitochondrial proteome and interactome.  


Applicability of in vitro biotinylated ubiquitin for evaluation of endogenous ubiquitin conjugation and analysis of ubiquitin-associated protein-protein interactions has been investigated. Incubation of rat brain mitochondria with biotinylated ubiquitin followed by affinity chromatography on avidin-agarose, intensive washing, tryptic digestion of proteins bound to the affinity sorbent and their mass spectrometry analysis resulted in reliable identification of 50 proteins belonging to mitochondrial and extramitochondrial compartments. Since all these proteins were bound to avidin-agarose only after preincubation of the mitochondrial fraction with biotinylated ubiquitin, they could therefore be referred to as specifically bound proteins. A search for specific ubiquitination signature masses revealed several extramitochondrial and intramitochondrial ubiquitinated proteins representing about 20% of total number of proteins bound to avidin-agarose. The interactome analysis suggests that the identified non-ubiquitinated proteins obviously form tight complexes either with ubiquitinated proteins or with their partners and/or mitochondrial membrane components. Results of the present study demonstrate that the use of biotinylated ubiquitin may be considered as the method of choice for in vitro evaluation of endogenous ubiquitin-conjugating machinery in particular subcellular organelles and changes in ubiquitin/organelle associated interactomes. This may be useful for evaluation of changes in interactomes induced by protein ubiquitination under norm and various brain pathologies. PMID:23109873

Buneeva, Olga A; Medvedeva, Marina V; Kopylov, Arthur T; Zgoda, Victor G; Medvedev, Alexei E



Spatiotemporal impulse response and cortical magnification.  


According to a model of the spatiotemporal weighting function (Manahilov, V. Spatiotemporal visual response at suprathreshold stimuli. Vision Research, 1995, 35, 227-237; and Triphasic temporal impulse responses and Mach bands in time. Vision Research, 38, 447-458) the waveform of the temporal-impulse response and the cortical spread of the spatial-impulse response should not depend on the retinal site of stimulation. To verify these model predictions, the spatiotemporal responses to brief near-threshold lines presented in the fovea and the near retinal periphery were studied. The effect of an inducing stimulus on the threshold for pattern detection of a test stimulus was measured, assuming that the pattern-detection threshold was determined by the test peak response. The spatial spread of the line response expressed in visual-field units was increased with eccentricity. The temporal-impulse responses to foveal and peripheral stimuli were similar. The model of the weighting function was used to evaluate the relative magnification factor for the retinal location tested. The calculated cortical spatial-impulse responses did not depend on the stimulation site. The data obtained are in line with the cortical magnification theory of peripheral vision. PMID:9666971

Manahilov, V; Atanassova, S



Indeterminacy of spatiotemporal cardiac alternans  

NASA Astrophysics Data System (ADS)

Cardiac alternans, a beat-to-beat alternation in action potential duration (at the cellular level) or in electrocardiogram morphology (at the whole heart level), is a marker of ventricular fibrillation, a fatal heart rhythm that kills hundreds of thousands of people in the United States each year. Investigating cardiac alternans may lead to a better understanding of the mechanisms of cardiac arrhythmias and eventually better algorithms for the prediction and prevention of such dreadful diseases. In paced cardiac tissue, alternans develops under increasingly shorter pacing period. Existing experimental and theoretical studies adopt the assumption that alternans in homogeneous cardiac tissue is exclusively determined by the pacing period. In contrast, we find that, when calcium-driven alternans develops in cardiac fibers, it may take different spatiotemporal patterns depending on the pacing history. Because there coexist multiple alternans solutions for a given pacing period, the alternans pattern on a fiber becomes unpredictable. Using numerical simulation and theoretical analysis, we show that the coexistence of multiple alternans patterns is induced by the interaction between electrotonic coupling and an instability in calcium cycling.

Zhao, Xiaopeng



Spatio-temporal modulated polarimetry  

NASA Astrophysics Data System (ADS)

Recently, a polarimetric data reduction technique has been developed that in the presence of a time varying signals and noise free measurement process can achieve an error free reconstruction provided that the signal was band limited. Error free reconstruction for such a signal is not possible using conventional data reduction methods. The new approach provides insight for processing arbitrary modulation schemes in space, time, and wavelength. Theory predicts that a polarimeter that employs a spatio-temporal modulation scheme may be able to use the high temporal resolution of a spatially modulated device combined with the high spatial resolution of a temporally modulated system to attain greater combined resolution capabilities than either modulation on scheme can produce alone. A polarimeter that contains both spatial and temporal modulation can be constructed (for example) by placing a rotating retarder in front of a micropolarizer array (microgrid). This study develops theory and analysis for the rotating retarder microgrid polarimeter to show how the available bandwidth for each channel is affected by additional dimensions of modulation and demonstrates a working polarimeter with a simulation of Stokes parameters that are band limited in both space and time with a noisy measurement process.

Lacasse, Charles F.; Ririe, Tyson; Chipman, Russell A.; Tyo, J. Scott



Spatial and Spatiotemporal Data Mining: Recent Advances  

SciTech Connect

Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns. In this chapter we explore the emerging field of spatial data mining, focusing on four major topics: prediction and classification, outlier detection, co-location mining, and clustering. Spatiotemporal data mining is also briefly discussed.

Shekhar, Shashi [University of Minnesota; Vatsavai, Raju [ORNL; Celik, Mete [University of Minnesota



Efficient Indexing of Spatiotemporal Objects  

Microsoft Academic Search

Spatiotemporal objects, i.e., objects which change their p osition and\\/or extent over time appear in many applications. In this paper we examine the problem of indexing large volumes of such data. Important in this environment is how the spatiotemporal objects move and\\/or change. We consider a rather general case where object movements\\/changes are defined by combinations of polynomial functions. We

Marios Hadjieleftheriou; George Kollios; Vassilis J. Tsotras; Dimitrios Gunopulos



Spatiotemporal boundaries of linear vection  

Microsoft Academic Search

Thresholds for the perception of linear vection were measured. These thresholds allowed us to define the spatiotemporal contrast\\u000a surface sensitivity and the spatiotemporal domain of the perception of rectilinear vection (a visually induced self-motion\\u000a in a straight line). Moreover, a Weber’s law was found, such that a mean relative differential threshold in angular velocity\\u000a of about 41% is necessary to

Xavier M. Sauvan; Claude Bonnet



Spatiotemporal exploratory models for broad-scale survey data.  


The distributions of animal populations change and evolve through time. Migratory species exploit different habitats at different times of the year. Biotic and abiotic features that determine where a species lives vary due to natural and anthropogenic factors. This spatiotemporal variation needs to be accounted for in any modeling of species' distributions. In this paper we introduce a semiparametric model that provides a flexible framework for analyzing dynamic patterns of species occurrence and abundance from broad-scale survey data. The spatiotemporal exploratory model (STEM) adds essential spatiotemporal structure to existing techniques for developing species distribution models through a simple parametric structure without requiring a detailed understanding of the underlying dynamic processes. STEMs use a multi-scale strategy to differentiate between local and global-scale spatiotemporal structure. A user-specified species distribution model accounts for spatial and temporal patterning at the local level. These local patterns are then allowed to "scale up" via ensemble averaging to larger scales. This makes STEMs especially well suited for exploring distributional dynamics arising from a variety of processes. Using data from eBird, an online citizen science bird-monitoring project, we demonstrate that monthly changes in distribution of a migratory species, the Tree Swallow (Tachycineta bicolor), can be more accurately described with a STEM than a conventional bagged decision tree model in which spatiotemporal structure has not been imposed. We also demonstrate that there is no loss of model predictive power when a STEM is used to describe a spatiotemporal distribution with very little spatiotemporal variation; the distribution of a nonmigratory species, the Northern Cardinal (Cardinalis cardinalis). PMID:21265447

Fink, Daniel; Hochachka, Wesley M; Zuckerberg, Benjamin; Winkler, David W; Shaby, Ben; Munson, M Arthur; Hooker, Giles; Riedewald, Mirek; Sheldon, Daniel; Kelling, Steve



Resolving the structure of interactomes with hierarchical agglomerative clustering  

PubMed Central

Background Graphs provide a natural framework for visualizing and analyzing networks of many types, including biological networks. Network clustering is a valuable approach for summarizing the structure in large networks, for predicting unobserved interactions, and for predicting functional annotations. Many current clustering algorithms suffer from a common set of limitations: poor resolution of top-level clusters; over-splitting of bottom-level clusters; requirements to pre-define the number of clusters prior to analysis; and an inability to jointly cluster over multiple interaction types. Results A new algorithm, Hierarchical Agglomerative Clustering (HAC), is developed for fast clustering of heterogeneous interaction networks. This algorithm uses maximum likelihood to drive the inference of a hierarchical stochastic block model for network structure. Bayesian model selection provides a principled method for collapsing the fine-structure within the smallest groups, and for identifying the top-level groups within a network. Model scores are additive over independent interaction types, providing a direct route for simultaneous analysis of multiple interaction types. In addition to inferring network structure, this algorithm generates link predictions that with cross-validation provide a quantitative assessment of performance for real-world examples. Conclusions When applied to genome-scale data sets representing several organisms and interaction types, HAC provides the overall best performance in link prediction when compared with other clustering methods and with model-free graph diffusion kernels. Investigation of performance on genome-scale yeast protein interactions reveals roughly 100 top-level clusters, with a long-tailed distribution of cluster sizes. These are in turn partitioned into 1000 fine-level clusters containing 5 proteins on average, again with a long-tailed size distribution. Top-level clusters correspond to broad biological processes, whereas fine-level clusters correspond to discrete complexes. Surprisingly, link prediction based on joint clustering of physical and genetic interactions performs worse than predictions based on individual data sets, suggesting a lack of synergy in current high-throughput data.



Investigation of PARP-1, PARP-2, and PARG interactomes by affinity-purification mass spectrometry  

PubMed Central

Background Poly(ADP-ribose) polymerases (PARPs) catalyze the formation of poly(ADP-ribose) (pADPr), a post-translational modification involved in several important biological processes, namely surveillance of genome integrity, cell cycle progression, initiation of the DNA damage response, apoptosis, and regulation of transcription. Poly(ADP-ribose) glycohydrolase (PARG), on the other hand, catabolizes pADPr and thereby accounts for the transient nature of poly(ADP-ribosyl)ation. Our investigation of the interactomes of PARP-1, PARP-2, and PARG by affinity-purification mass spectrometry (AP-MS) aimed, on the one hand, to confirm current knowledge on these interactomes and, on the other hand, to discover new protein partners which could offer insights into PARPs and PARG functions. Results PARP-1, PARP-2, and PARG were immunoprecipitated from human cells, and pulled-down proteins were separated by gel electrophoresis prior to in-gel trypsin digestion. Peptides were identified by tandem mass spectrometry. Our AP-MS experiments resulted in the identifications of 179 interactions, 139 of which are novel interactions. Gene Ontology analysis of the identified protein interactors points to five biological processes in which PARP-1, PARP-2 and PARG may be involved: RNA metabolism for PARP-1, PARP-2 and PARG; DNA repair and apoptosis for PARP-1 and PARP-2; and glycolysis and cell cycle for PARP-1. Conclusions This study reveals several novel protein partners for PARP-1, PARP-2 and PARG. It provides a global view of the interactomes of these proteins as well as a roadmap to establish the systems biology of poly(ADP-ribose) metabolism.



A Y2H-seq approach defines the human protein methyltransferase interactome.  


To accelerate high-density interactome mapping, we developed a yeast two-hybrid interaction screening approach involving short-read second-generation sequencing (Y2H-seq) with improved sensitivity and a quantitative scoring readout allowing rapid interaction validation. We applied Y2H-seq to investigate enzymes involved in protein methylation, a largely unexplored post-translational modification. The reported network of 523 interactions involving 22 methyltransferases or demethylases is comprehensively annotated and validated through coimmunoprecipitation experiments and defines previously undiscovered cellular roles of nonhistone protein methylation. PMID:23455924

Weimann, Mareike; Grossmann, Arndt; Woodsmith, Jonathan; Özkan, Ziya; Birth, Petra; Meierhofer, David; Benlasfer, Nouhad; Valovka, Taras; Timmermann, Bernd; Wanker, Erich E; Sauer, Sascha; Stelzl, Ulrich



Spatiotemporal dynamics of HIV infection  

NASA Astrophysics Data System (ADS)

Mathematical models of the dynamics of infection with the human immunodeficiency virus (HIV) have contributed to tremendous advances over the past 20 years. This thesis extends this previous work by exploring the importance of spatial heterogeneity in HIV infection both in vitro and in vivo in patients treated with highly-active antiretroviral therapy. Viral infections propagate locally in space, yet HIV infection has been widely regarded as equilibrated over the entire body of an infected patient. This dissertation constructs and explores a cellular automata model of viral spread at the cellular level. Coupling the automata to a blood compartment represented by a differential equation leads to a whole-body model of HIV infection that explicitly includes spatial effects at both the cellular and tissue levels. These models are tested by comparison with experimental data. A central prediction of the spatial model is that, due to competition between Brownian motion and viral lability, HIV infectivity increases with target cell density. This production is verified in a series of in vitro experiments in cell culture. The predicted independence of inhibitory concentrations of antiretoviral agents is verified for nevirapine, but azidothymidine inhibits HIV replication less efficiently in more dense cultures. These in vitro results suggest that systems allowing cell concentrations closer to tissue densities would better reflect virus replication kinetics, although standard measures of relative drug susceptibility may accurately reflect in vivo conditions. The coupled spatial model of in vivo dynamics is compared with novel mathematical analysis of experiments in HIV-infected patients. These analyses indicate that HIV DNA provides a useful marker of the size of long-lived cellular reservoirs of HIV. Levels of HIV DNA in peripheral blood are predictive of the average rate of residual virus production after years of treatment, regardless of whether patients initiate therapy during the primary or chronic stages of HIV infection. The nonlinear clearance of HIV DNA therefore predicts lifelong virus production, even in treated patients. Collectively, these results demonstrate that important features of the spatiotemporal dynamics of HIV infection both in vitro and in vivo are best explained with explicit spatial models.

Strain, Matthew Carl


Characterization of carbonic anhydrase IX interactome reveals proteins assisting its nuclear localization in hypoxic cells.  


Carbonic anhydrase IX (CA IX) is a transmembrane protein affecting pH regulation, cell migration/invasion, and survival in hypoxic tumors. Although the pathways related to CA IX have begun to emerge, molecular partners mediating its functions remain largely unknown. Here we characterize the CA IX interactome in hypoxic HEK-293 cells. Most of the identified CA IX-binding partners contain the HEAT/ARM repeat domain and belong to the nuclear transport machinery. We show that the interaction with two of these proteins, namely XPO1 exportin and TNPO1 importin, occurs via the C-terminal region of CA IX and increases with protein phosphorylation. We also demonstrate that nuclear CA IX is enriched in hypoxic cells and is present in renal cell carcinoma tissues. These data place CA IX among the cell-surface signal transducers undergoing nuclear translocation. Accordingly, CA IX interactome involves also CAND1, which participates in both gene transcription and assembly of SCF ubiquitin ligase complexes. It is noteworthy that down-regulation of CAND1 leads to decreased CA IX protein levels apparently via affecting its stability. Our findings provide the first evidence that CA IX interacts with proteins involved in nuclear/cytoplasmic transport, gene transcription, and protein stability, and suggest the existence of nuclear CA IX protein subpopulations with a potential intracellular function, distinct from the crucial CA IX role at the cell surface. PMID:23181366

Buanne, Pasquale; Renzone, Giovanni; Monteleone, Francesca; Vitale, Monica; Monti, Simona Maria; Sandomenico, AnnaMaria; Garbi, Corrado; Montanaro, Donatella; Accardo, Marina; Troncone, Giancarlo; Zatovicova, Miriam; Csaderova, Lucia; Supuran, Claudiu T; Pastorekova, Silvia; Scaloni, Andrea; De Simone, Giuseppina; Zambrano, Nicola



Aging defined by a chronologic-replicative protein network in Saccharomyces cerevisiae: an interactome analysis.  


Aging is a multifactorial condition that results in the loss of an organism's fitness over time. Different theories have been formulated to explain the mechanisms of aging, but a synthesis of these theories has not been possible until now. In addition, the increase in molecular data gathered by proteomics projects utilizing different organisms has permitted a better picture of proteins that function in aging. In this sense, the yeast Saccharomyces cerevisiae is a biological model for aging, and it shows two distinct aging states: a replicative state termed the replicative lifespan (RLS) and a quiescent state known as the chronological lifespan (CLS). Interestingly, both RLS and CLS appear to share common groups of proteins, but a combined model of both aging mechanisms has not been defined. Thus, by applying systems biology tools that allow mining of the yeast proteins associated with aging, it was possible to obtain an interactome network in which both RLS and CLS are represented. In addition, four subgraphs comprising ubiquitin-dependent proteasome/regulation of cell growth, nucleic acid metabolism, carbohydrate metabolism/RNA metabolism, and carbohydrate-organic acid-amino acid/DNA metabolism were found within the interactome, defining a new model of aging for yeast termed the chronologic-replicative protein network (CRPN). PMID:19433103

Barea, Fernanda; Bonatto, Diego



Interactomes, manufacturomes and relational biology: analogies between systems biology and manufacturing systems  

PubMed Central

Background We review and extend the work of Rosen and Casti who discuss category theory with regards to systems biology and manufacturing systems, respectively. Results We describe anticipatory systems, or long-range feed-forward chemical reaction chains, and compare them to open-loop manufacturing processes. We then close the loop by discussing metabolism-repair systems and describe the rationality of the self-referential equation f = f (f). This relationship is derived from some boundary conditions that, in molecular systems biology, can be stated as the cardinality of the following molecular sets must be about equal: metabolome, genome, proteome. We show that this conjecture is not likely correct so the problem of self-referential mappings for describing the boundary between living and nonliving systems remains an open question. We calculate a lower and upper bound for the number of edges in the molecular interaction network (the interactome) for two cellular organisms and for two manufacturomes for CMOS integrated circuit manufacturing. Conclusions We show that the relevant mapping relations may not be Abelian, and that these problems cannot yet be resolved because the interactomes and manufacturomes are incomplete.



Controlling spatiotemporal chaos in coupled nonlinear oscillators  

Microsoft Academic Search

A method for controlling spatiotemporal chaos in coupled ordinary differential equations is presented. It is based on two ideas: stabilization of unstable periodic patterns embedded in spatiotemporal chaos, and perturbation of dynamical variables only at regular time intervals.

Ljupco. Kocarev; Ulrich Parlitz; Toni Stojanovski; Predrag Janjic



Physical and in silico approaches identify DNA-PK in a Tax DNA-damage response interactome  

PubMed Central

Background We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process. Results We first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of these four proteins were assembled from available human protein interaction databases. Through an analysis of betweenness and closeness centrality measures, and numbers of interactions, we ranked proteins in the first neighborhood. When this rank list was compared to the list of physical Tax-binding proteins, DNA-PK was the highest ranked protein common to both lists. An overlapping clustering of the Tax-specific second-neighborhood protein network showed DNA-PK to be one of three bridge proteins that link multiple clusters in the DNA damage response network. Conclusion The interaction of Tax with DNA-PK represents an important biological paradigm as suggested via consensus findings in vivo and in silico. We present this methodology as an approach to discovery and as a means of validating components of a consensus Tax interactome.

Ramadan, Emad; Ward, Michael; Guo, Xin; Durkin, Sarah S; Sawyer, Adam; Vilela, Marcelo; Osgood, Christopher; Pothen, Alex; Semmes, Oliver J



Shared Molecular and Functional Frameworks among Five Complex Human Disorders: A Comparative Study on Interactomes Linked to Susceptibility Genes  

PubMed Central

Background Genome-wide association studies (gwas) are invaluable in revealing the common variants predisposing to complex human diseases. Yet, until now, the large volumes of data generated from such analyses have not been explored extensively enough to identify the molecular and functional framework hosting the susceptibility genes. Methodology/Principal Findings We investigated the relationships among five neurodegenerative and/or autoimmune complex human diseases (Parkinson's disease-Park, Alzheimer's disease-Alz, multiple sclerosis-MS, rheumatoid arthritis-RA and Type 1 diabetes-T1D) by characterising the interactomes linked to their gwas-genes. An initial study on the MS interactome indicated that several genes predisposing to the other autoimmune or neurodegenerative disorders may come into contact with it, suggesting that susceptibility to distinct diseases may converge towards common molecular and biological networks. In order to test this hypothesis, we performed pathway enrichment analyses on each disease interactome independently. Several issues related to immune function and growth factor signalling pathways appeared in all autoimmune diseases, and, surprisingly, in Alzheimer's disease. Furthermore, the paired analyses of disease interactomes revealed significant molecular and functional relatedness among autoimmune diseases, and, unexpectedly, between T1D and Alz. Conclusions/Significance The systems biology approach highlighted several known pathogenic processes, indicating that changes in these functions might be driven or sustained by the framework linked to genetic susceptibility. Moreover, the comparative analyses among the five genetic interactomes revealed unexpected genetic relationships, which await further biological validation. Overall, this study outlines the potential of systems biology to uncover links between genetics and pathogenesis of complex human disorders.

Menon, Ramesh; Farina, Cinthia



Spatiotemporal interpolation methods in GIS  

NASA Astrophysics Data System (ADS)

In this dissertation, spatiotemporal interpolation of geographic data is considered. Two methods are discussed which are the reduction and the extension methods. The reduction method treats time as an independent dimension, whereas the extension method treats time equivalent to a spatial dimension. Both 2-D and 3-D shape functions are adopted, usually used in finite element methods, for the spatiotemporal interpolation of 2-D spatial & 1-D temporal and 3-D spatial & 1-D temporal data sets. Domains are divided into a finite number of sub-domains (such as triangles and tetrahedra) in which local shape functions are assumed. New 4-D shape functions that can be applied for each 4-D Delaunay Tessellation element are developed using the extension method for 3-D spatial & 1-D temporal problems. The visualization of shape function interpolation results is also explained and illustrated. Using an actual real estate data set with house prices, we compare these methods with other spatiotemporal interpolation methods based on inverse distance weighting and kriging. We compare these methods with respect to interpolation accuracy, error-proneness to time aggregation, invariance to scaling on the coordinate axes, and the type of constraints used in the representation of the interpolated data. Our experimental results show that the extension method based on shape functions is the most accurate and the overall best spatiotemporal interpolation method. Constraint databases provide a general approach to express and solve constraint problems. We show that spatiotemporal interpolation data can be represented in constraint databases efficiently and accurately. The advantage of constraint databases is that many queries that could not be done in traditional GIS systems can now be easily expressed and evaluated in constraint database systems. Finally, the constraint database system MLPQ (Management of Linear Programming Queries) is used to animate and query some spatiotemporal data examples. A translation algorithm between ArcGIS shape files and MLPQ input data files is also discussed.

Li, Lixin


Spatiotemporal wavelet analysis for functional MRI  

Microsoft Academic Search

Characterizing the spatiotemporal behavior of the BOLD signal in functional Magnetic Resonance Imaging (fMRI) is a central issue in understanding brain function. While the nature of functional activation clusters is fundamentally heterogeneous, many current analysis approaches use spatially invariant models that can degrade anatomic boundaries and distort the underlying spatiotemporal signal. Furthermore, few analysis approaches use true spatiotemporal continuity in

Chris Long; Emery N. Brown; Dara Manoach; Victor Soloa



Network-based function prediction and interactomics: The case for metabolic enzymes  

Microsoft Academic Search

As sequencing technologies increase in power, determining the functions of unknown proteins encoded by the DNA sequences so produced becomes a major challenge. Functional annotation is commonly done on the basis of amino-acid sequence similarity alone. Long after sequence similarity becomes undetectable by pair-wise comparison, profile-based identification of homologs can often succeed due to the conservation of position-specific patterns, important

S. C. Janga; J. Javier Díaz-Mejía; G. Moreno-Hagelsieb



Historical spatio-temporal aggregation  

Microsoft Academic Search

Spatio-temporal databases store information about the positions of individual objects over time. However, in many applications such as traffic supervision or mobile communication systems, only summarized data, like the number of cars in an area for a specific period, or phone-calls serviced by a cell each day, is required. Although this information can be obtained from operational databases, its computation

Yufei Tao; Dimitris Papadias



Spatiotemporal Wave Patterns: Information Dynamics  

SciTech Connect

Pattern formation has traditionally been studied in non-equilibrium physics from the viewpoint of describing the basic structures and their interactions. While this is still an important area of research, the emphasis in the last few years has shifted towards analysis of specific properties of patterns in various complex media. For example, diverse and unexpected phenomena occur in neuro-like media that are characterized by highly non-trivial local dynamics. We carried out an active research program on analysis of spatio-temporal patterns in various physical systems (convection, oscillating fluid layer, soap film), as well as in neuro-like media, with an emphasis on informational aspects of the dynamics. Nonlinear nonequilibrium media and their discrete analogs have a unique ability to represent, memorize, and process the information contained in spatio-temporal patterns. Recent neurophysiological experiments demonstrated a certain universality of spatio-temporal representation of information by neural ensembles. Information processing is also revealed in the spatio-temporal dynamics of cellular patterns in nonequilibrium media. It is extremely important for many applications to study the informational aspects of these dynamics, including the origins and mechanisms of information generation, propagation and storage. Some of our results are: the discovery of self-organization of periodically oscillatory patterns in chaotic heterogeneous media; the analysis of the propagation of the information along a chaotic media as function of the entropy of the signal; the analysis of wave propagation in discrete non-equilibrium media with autocatalytic properties, which simulates the calcium dynamics in cellular membranes. Based on biological experiments we suggest the mechanism by which the spatial sensory information is transferred into the spatio-temporal code in the neural media. We also found a new mechanism of self-pinning in cellular structures and the related phenomenon of stick-slip front motion.

Mikhail Rabinovich; Lev Tsimring



Proteomic analysis of the SIRT6 interactome: novel links to genome maintenance and cellular stress signaling  

PubMed Central

The chromatin regulatory factor SIRT6 plays pivotal roles in metabolism, tumor suppression, and aging biology. Despite the fundamental roles of SIRT6 in physiology and disease, only a handful of molecular and functional interactions of SIRT6 have been reported. Here, we characterize the SIRT6 interactome and identify 80+ novel SIRT6-interacting proteins. The discovery of these SIRT6-associations considerably expands knowledge of the SIRT6 interaction network, and suggests previously unknown functional interactions of SIRT6 in fundamental cellular processes. These include chromatin remodeling, mitotic chromosome segregation, protein homeostasis, and transcriptional elongation. Extended analysis of the SIRT6 interaction with G3BP1, a master stress response factor, uncovers an unexpected role and mechanism of SIRT6 in regulating stress granule assembly and cellular stress resistance.

Simeoni, Federica; Tasselli, Luisa; Tanaka, Shinji; Villanova, Lidia; Hayashi, Mayumi; Kubota, Kazuishi; Isono, Fujio; Garcia, Benjamin A.; Michishita-Kioi, Eriko; Chua, Katrin F.



Implicit learning of spatiotemporal contingencies in spatial cueing.  


We investigated the role of implicit spatiotemporal learning in the Posner spatial cueing of attention task. During initial training, the proportion of different trial types was altered to produce a complex pattern of spatiotemporal contingencies between cues and targets. For example, in the short invalid and long valid condition, targets reliably appeared either at an uncued location after a short stimulus onset asynchrony (SOA; 100 ms) or at a cued location after a long SOA (350 ms). As revealed by postexperiment questioning, most participants were unaware of these manipulations. Whereas prior studies have examined reaction times during training, the current study examined the long-term effect of training on subsequent testing that removed these contingencies. An initial experiment found training effects only for the long SOAs that typically produce inhibition of return (IOR) effects. For instance, after short invalid and long valid training, there was a benefit at long SOAs rather than an IOR effect. A 2nd experiment ruled out target-cue overlap as an explanation of the difference between learning for long versus short SOAs. Rather than a mix of perfectly predictable spatiotemporal contingencies, Experiment 3 used only short SOA trials during training with a probabilistic spatial contingency. There was a smaller but reliable training effect in subsequent testing. These results demonstrate that implicit learning for specific combinations of location and SOA can affect behavior in spatial cueing paradigms, which is a necessary result if more generally spatial cueing reflects learned spatiotemporal regularities. PMID:23181686

Rieth, Cory A; Huber, David E



Dynamics of Turing Patterns under Spatiotemporal Forcing  

NASA Astrophysics Data System (ADS)

We study, both theoretically and experimentally, the dynamical response of Turing patterns to a spatiotemporal forcing in the form of a traveling-wave modulation of a control parameter. We show that from strictly spatial resonance, it is possible to induce new, generic dynamical behaviors, including temporally modulated traveling waves and localized traveling solitonlike solutions. The latter make contact with the soliton solutions of Coullet [

Phys. Rev. Lett.PRLTAO0031-9007 56, 724 (1986)
] and generalize them. The stability diagram for the different propagating modes in the Lengyel-Epstein model is determined numerically. Direct observations of the predicted solutions in experiments carried out with light modulations in the photosensitive chlorine dioxide-iodine-malonic acid reaction are also reported.

Rüdiger, S.; Míguez, D. G.; Muñuzuri, A. P.; Sagués, F.; Casademunt, J.



Spatiotemporal saliency in dynamic scenes.  


A spatiotemporal saliency algorithm based on a center-surround framework is proposed. The algorithm is inspired by biological mechanisms of motion-based perceptual grouping and extends a discriminant formulation of center-surround saliency previously proposed for static imagery. Under this formulation, the saliency of a location is equated to the power of a predefined set of features to discriminate between the visual stimuli in a center and a surround window, centered at that location. The features are spatiotemporal video patches and are modeled as dynamic textures, to achieve a principled joint characterization of the spatial and temporal components of saliency. The combination of discriminant center-surround saliency with the modeling power of dynamic textures yields a robust, versatile, and fully unsupervised spatiotemporal saliency algorithm, applicable to scenes with highly dynamic backgrounds and moving cameras. The related problem of background subtraction is treated as the complement of saliency detection, by classifying nonsalient (with respect to appearance and motion dynamics) points in the visual field as background. The algorithm is tested for background subtraction on challenging sequences, and shown to substantially outperform various state-of-the-art techniques. Quantitatively, its average error rate is almost half that of the closest competitor. PMID:19926907

Mahadevan, Vijay; Vasconcelos, Nuno



Organization of real estate spatiotemporal data  

Microsoft Academic Search

Spatiotemporal data model(STDM)are gradually becomes hot spots currently. As an application of STDM, the organization of real state spatiotemporal data has been paid more and more attention. The paper analyze the dynamic change of real estate data, puts forward an effective solution by adding time field to be able; and also propose a method which solve the problem that the

Guo Guihai; Jin Lihui



Voronoi Region-Based Spatiotemporal GIS Databases  

Microsoft Academic Search

The use of Voronoi diagrams in GIS-oriented spatiotemporal databases is considered. The Voronoi algorithms generate appropriate meshes for data interpolation. We illustrate the data representation in this database generated from a point-based spatiotemporal relation in the National Agricultural Statistics Service (NASS) database.

Lixin Li; Reinhard Piltner



Synchronizing Spatiotemporal Chaos of Partial Differential Equations  

Microsoft Academic Search

A general approach for synchronizing pairs of unidirectionally coupled partial differential equations (PDEs) with spatiotemporally chaotic dynamics is introduced. We show that for a large class of PDEs, a pair of PDEs can be synchronized by driving the response system only at a finite number of space points. We also discuss the relevance of our results for control of spatiotemporal

Ljupco Kocarev; Zarko Tasev; Ulrich Parlitz



Identification of LMO2 transcriptome and interactome in diffuse large B-cell lymphoma  

PubMed Central

LMO2 regulates gene expression by facilitating the formation of multipartite DNA-binding complexes. In B cells, LMO2 is specifically up-regulated in the germinal center (GC) and is expressed in GC-derived non-Hodgkin lymphomas. LMO2 is one of the most powerful prognostic indicators in diffuse large B-cell (DLBCL) patients. However, its function in GC B cells and DLBCL is currently unknown. In this study, we characterized the LMO2 transcriptome and transcriptional complex in DLBCL cells. LMO2 regulates genes implicated in kinetochore function, chromosome assembly, and mitosis. Overexpression of LMO2 in DLBCL cell lines results in centrosome amplification. In DLBCL, the LMO2 complex contains some of the traditional partners, such as LDB1, E2A, HEB, Lyl1, ETO2, and SP1, but not TAL1 or GATA proteins. Furthermore, we identified novel LMO2 interacting partners: ELK1, nuclear factor of activated T-cells (NFATc1), and lymphoid enhancer-binding factor1 (LEF1) proteins. Reporter assays revealed that LMO2 increases transcriptional activity of NFATc1 and decreases transcriptional activity of LEF1 proteins. Overall, our studies identified a novel LMO2 transcriptome and interactome in DLBCL and provides a platform for future elucidation of LMO2 function in GC B cells and DLBCL pathogenesis.

Cubedo, Elena; Gentles, Andrew J.; Huang, Chuanxin; Natkunam, Yasodha; Bhatt, Shruti; Lu, Xiaoqing; Jiang, Xiaoyu; Romero-Camarero, Isabel; Freud, Aharon; Zhao, Shuchun; Bacchi, Carlos E.; Martinez-Climent, Jose A.; Sanchez-Garcia, Isidro; Melnick, Ari



What model organisms and interactomics can reveal about the genetics of human obesity.  


Genome-wide association studies have identified a number of genes associated with human body weight. While some of these genes are large fields within obesity research, such as MC4R, POMC, FTO and BDNF, the majority do not have a clearly defined functional role explaining why they may affect body weight. Here, we searched biological databases and discovered 33 additional genes associated with human obesity (CADM2, GIPR, GPCR5B, LRP1B, NEGR1, NRXN3, SH2B1, FANCL, GNPDA2, HMGCR, MAP2K5, NUDT3, PRKD1, QPCTL, TNNI3K, MTCH2, DNAJC27, SLC39A8, MTIF3, RPL27A, SEC16B, ETV5, HMGA1, TFAP2B, TUB, ZNF608, FAIM2, KCTD15, LINGO2, POC5, PTBP2, TMEM18, TMEM160). We find that the majority have orthologues in distant species, such as D. melanogaster and C. elegans, suggesting that they are important for the biology of most bilateral species. Intriguingly, signalling cascade genes and transcription factors are enriched among these obesity genes, and several of the genes show properties that could be useful for potential drug discovery. In this review, we demonstrate how information from several distant model species, interactomics and signalling pathway analysis represents an important way to better understand the functional diversity of the surprisingly high number of molecules that seem to be important for human obesity. PMID:22618246

Williams, Michael J; Almén, Markus S; Fredriksson, Robert; Schiöth, Helgi B



Genome-wide bimolecular fluorescence complementation analysis of SUMO interactome in yeast  

PubMed Central

The definition of protein–protein interactions (PPIs) in the natural cellular context is essential for properly understanding various biological processes. So far, however, most large-scale PPI analyses have not been performed in the natural cellular context. Here, we describe the construction of a Saccharomyces cerevisiae fusion library in which each endogenous gene is C-terminally tagged with the N-terminal fragment of Venus (VN) for a genome-wide bimolecular fluorescence complementation assay, a powerful technique for identifying PPIs in living cells. We illustrate the utility of the VN fusion library by systematically analyzing the interactome of the small ubiquitin-related modifier (SUMO) and provide previously unavailable information on the subcellular localization, types, and protease dependence of SUMO interactions. Our data set is highly complementary to the existing data sets and represents a useful resource for expanding the understanding of the physiological roles of SUMO. In addition, the VN fusion library provides a useful research tool that makes it feasible to systematically analyze PPIs in the natural cellular context.

Sung, Min-Kyung; Lim, Gyubum; Yi, Dae-Gwan; Chang, Yeon Ji; Yang, Eun Bin; Lee, KiYoung; Huh, Won-Ki



Intrinsic Disorder in PTEN and its Interactome Confers Structural Plasticity and Functional Versatility.  


IDPs, while structurally poor, are functionally rich by virtue of their flexibility and modularity. However, how mutations in IDPs elicit diseases, remain elusive. Herein, we have identified tumor suppressor PTEN as an intrinsically disordered protein (IDP) and elucidated the molecular principles by which its intrinsically disordered region (IDR) at the carboxyl-terminus (C-tail) executes its functions. Post-translational modifications, conserved eukaryotic linear motifs and molecular recognition features present in the C-tail IDR enhance PTEN's protein-protein interactions that are required for its myriad cellular functions. PTEN primary and secondary interactomes are also enriched in IDPs, most being cancer related, revealing that PTEN functions emanate from and are nucleated by the C-tail IDR, which form pliable network-hubs. Together, PTEN higher order functional networks operate via multiple IDP-IDP interactions facilitated by its C-tail IDR. Targeting PTEN IDR and its interaction hubs emerges as a new paradigm for treatment of PTEN related pathologies. PMID:23783762

Malaney, Prerna; Pathak, Ravi Ramesh; Xue, Bin; Uversky, Vladimir N; Davé, Vrushank



Proteomic analysis of the NOS2 interactome in human airway epithelial cells.  


The cytokine-inducible isoform of nitric oxide synthase (NOS2) is constitutively expressed in human respiratory epithelia and is upregulated in inflammatory lung disease. Here, we sought to better define the protein interactions that may be important for NOS2 activity and stability, as well as to identify potential targets of NOS2-derived NO, in the respiratory epithelium. We overexpressed Flag-tagged, catalytically-inactive NOS2 in A549 cells and used mass spectrometry to qualitatively identify NOS2 co-immunoprecipitating proteins. Stable isotope labeling of amino acids in cell culture (SILAC) was used to quantify the coordinate effects of cytokine stimulation on NOS2-protein interactions. Multi-protein networks dominated the NOS2 interactome, and cytokine-inducible interactions with allosteric activators and with the ubiquitin-proteasome system were correlated with cytokine-dependent increases in NO metabolites and in NOS2 ubiquitination. The ubiquitin ligase scaffolding protein, FBXO45, was identified as a novel, direct NOS2 interactor. Similar to the SPRY domain-containing SOCS box (SPSB) proteins, FBXO45 requires Asn27 in the (23)DINNN(27) motif of NOS2 for its interaction. However, FBXO45 is unique from the SPSBs in that it recruits a distinct E3 ligase complex containing MYCBP2 and SKP1. Collectively, these findings demonstrate the general utility of interaction proteomics for defining new aspects of NOS2 physiology. PMID:23438482

Foster, Matthew W; Thompson, J Will; Forrester, Michael T; Sha, Yonggang; McMahon, Timothy J; Bowles, Dawn E; Moseley, M Arthur; Marshall, Harvey E



A Synthetic Coiled-Coil Interactome Provides Heterospecific Modules for Molecular Engineering  

SciTech Connect

The versatile coiled-coil protein motif is widely used to induce and control macromolecular interactions in biology and materials science. Yet the types of interaction patterns that can be constructed using known coiled coils are limited. Here we greatly expand the coiled-coil toolkit by measuring the complete pairwise interactions of 48 synthetic coiled coils and 7 human bZIP coiled coils using peptide microarrays. The resulting 55-member protein 'interactome' includes 27 pairs of interacting peptides that preferentially heteroassociate. The 27 pairs can be used in combinations to assemble sets of 3 to 6 proteins that compose networks of varying topologies. Of special interest are heterospecific peptide pairs that participate in mutually orthogonal interactions. Such pairs provide the opportunity to dimerize two separate molecular systems without undesired crosstalk. Solution and structural characterization of two such sets of orthogonal heterodimers provide details of their interaction geometries. The orthogonal pair, along with the many other network motifs discovered in our screen, provide new capabilities for synthetic biology and other applications.

Reinke, Aaron W.; Grant, Robert A.; Keating, Amy E. (MIT)



Transition to spatiotemporal chaos via stationary branching shocks and holes  

NASA Astrophysics Data System (ADS)

Spatiotemporal chaos in the complex Ginzburg-Landau equation is known to be associated with a rapid increase in the density of defects, which are isolated points at which the solution amplitude is zero and the phase is undefined. Recently there have been significant advances in understanding the details and interactions of defects and other coherent structures, and in the theory of convective and absolute stability. In this paper, the authors exploit both of these advances to update and clarify the onset of spatiotemporal chaos in the particular case of the complex Ginzburg-Landau equation with zero linear dispersion. They show that very slow increases in the coefficient of nonlinear dispersion cause a shock-hole (defect) pair to develop in the midst of a uniform expanse of plane wave. This is followed by a cascade of splittings of holes into shock-hole-shock triplets, culminating in spatiotemporal chaos at a parameter value that matches the change in absolute stability of the plane wave. The authors demonstrate a close correspondence between the splitting events and theoretical predictions, based on the theory of absolute stability. They also use measures based on power spectra and spatial correlations to show that when the plane wave is convectively unstable, chaos is restricted to localised regions, whereas it is extensive when the plane wave is absolutely unstable.

Sherratt, Jonathan A.; Smith, Matthew J.



Energy model for contrast detection: spatiotemporal characteristics of threshold vision.  


A model for contrast detection of spatiotemporal stimuli is proposed which consists of a spatiotemporal linear filter, an energy device and a threshold device. Assuming the existence of independent intrinsic noise, the probability of stimulus detection was approximated by a Weibull function of the response energy. With this assumption, the stimulus energy is a constant at fixed detection probability. This energy model for contrast detection satisfactorily accounted for the elliptical threshold contours of line pairs at stimulus separations within the range 2-30 min and at stimulus onset asynchronies within the range 20-140 ms. The threshold contour at a large stimulus onset asynchrony (300 ms) was in the form of a rounded square. This finding was explained by assuming that the probability of seeing the line pair was determined by the joint probability that at least one stimulus had been detected. With the energy model, the temporal and spatial autocorrelation functions of the response to a flashed line were evaluated. The autocorrelation functions thus determined were used to predict the temporal contrast sensitivity function to a flickering line stimulus and the spatial contrast sensitivity function to flashed gratings, which were in agreement with the experimental data. The data obtained were fitted adequately by an impulse response approximated by a spatiotemporal Gabor-like function. PMID:10434391

Manahilov, V; Simpson, W



A probabilistic approach to spatiotemporal theme pattern mining on weblogs  

Microsoft Academic Search

Mining subtopics from weblogs and analyzing their spatiotemporal patterns have applications in multiple domains. In this paper, we de?ne the novel problem of mining spatiotemporal theme patterns from weblogs and propose a novel probabilistic approach to model the subtopic themes and spatiotemporal theme patterns simultaneously. The proposed model discovers spatiotemporal theme patterns by (1) extracting common themes from weblogs; (2)

Qiaozhu Mei; Chao Liu; Hang Su; Chengxiang Zhai



Rice Mitogen-Activated Protein Kinase Interactome Analysis Using the Yeast Two-Hybrid System1[C][W  

PubMed Central

Mitogen-activated protein kinase (MAPK) cascades support the flow of extracellular signals to intracellular target molecules and ultimately drive a diverse array of physiological functions in cells, tissues, and organisms by interacting with other proteins. Yet, our knowledge of the global physical MAPK interactome in plants remains largely fragmented. Here, we utilized the yeast two-hybrid system and coimmunoprecipitation, pull-down, bimolecular fluorescence complementation, subcellular localization, and kinase assay experiments in the model crop rice (Oryza sativa) to systematically map what is to our knowledge the first plant MAPK-interacting proteins. We identified 80 nonredundant interacting protein pairs (74 nonredundant interactors) for rice MAPKs and elucidated the novel proteome-wide network of MAPK interactors. The established interactome contains four membrane-associated proteins, seven MAP2Ks (for MAPK kinase), four MAPKs, and 59 putative substrates, including 18 transcription factors. Several interactors were also validated by experimental approaches (in vivo and in vitro) and literature survey. Our results highlight the importance of OsMPK1, an ortholog of tobacco (Nicotiana benthamiana) salicyclic acid-induced protein kinase and Arabidopsis (Arabidopsis thaliana) AtMPK6, among the rice MAPKs, as it alone interacts with 41 unique proteins (51.2% of the mapped MAPK interaction network). Additionally, Gene Ontology classification of interacting proteins into 34 functional categories suggested MAPK participation in diverse physiological functions. Together, the results obtained essentially enhance our knowledge of the MAPK-interacting protein network and provide a valuable research resource for developing a nearly complete map of the rice MAPK interactome.

Singh, Raksha; Lee, Mi-Ok; Lee, Jae-Eun; Choi, Jihyun; Park, Ji Hun; Kim, Eun Hye; Yoo, Ran Hee; Cho, Jung-Il; Jeon, Jong-Seong; Rakwal, Randeep; Agrawal, Ganesh Kumar; Moon, Jae Sun; Jwa, Nam-Soo



An integrative in silico approach for discovering candidates for drug-targetable protein-protein interactions in interactome data  

PubMed Central

Background Protein-protein interactions (PPIs) are challenging but attractive targets for small chemical drugs. Whole PPIs, called the 'interactome', have been emerged in several organisms, including human, based on the recent development of high-throughput screening (HTS) technologies. Individual PPIs have been targeted by small drug-like chemicals (SDCs), however, interactome data have not been fully utilized for exploring drug targets due to the lack of comprehensive methodology for utilizing these data. Here we propose an integrative in silico approach for discovering candidates for drug-targetable PPIs in interactome data. Results Our novel in silico screening system comprises three independent assessment procedures: i) detection of protein domains responsible for PPIs, ii) finding SDC-binding pockets on protein surfaces, and iii) evaluating similarities in the assignment of Gene Ontology (GO) terms between specific partner proteins. We discovered six candidates for drug-targetable PPIs by applying our in silico approach to original human PPI data composed of 770 binary interactions produced by our HTS yeast two-hybrid (HTS-Y2H) assays. Among them, we further examined two candidates, RXRA/NRIP1 and CDK2/CDKN1A, with respect to their biological roles, PPI network around each candidate, and tertiary structures of the interacting domains. Conclusion An integrative in silico approach for discovering candidates for drug-targetable PPIs was applied to original human PPIs data. The system excludes false positive interactions and selects reliable PPIs as drug targets. Its effectiveness was demonstrated by the discovery of the six promising candidate target PPIs. Inhibition or stabilization of the two interactions may have potential therapeutic effects against human diseases.

Sugaya, Nobuyoshi; Ikeda, Kazuyoshi; Tashiro, Toshiyuki; Takeda, Shizu; Otomo, Jun; Ishida, Yoshiko; Shiratori, Akiko; Toyoda, Atsushi; Noguchi, Hideki; Takeda, Tadayuki; Kuhara, Satoru; Sakaki, Yoshiyuki; Iwayanagi, Takao



Spatiotemporal Stochastic Forcing In Ensemble Systems  

NASA Astrophysics Data System (ADS)

In 1998, the ECMWF introduced in the operational Ensemble Prediction System (EPS) a new scheme to simulate random model errors due to parameterized phys- ical processes (Buizza et al., 1999). This scheme is based on the notion that this randomness is coherent between the different parameterization modules and has a certain coherence on the space and time scales represented by the model. Following this idea, we have perturbed with a spatiotemporal correlated noise of the Ornstein- Uhlenbeck type both, a diffusively coupled one-dimensional array of Lorenz chaotic cells (Lorenzo and Pérez-Muñuzuri, 1999, 2001), and a simplified atmospheric global circulation model, PUMA (Portable University Model of the Atmosphere) (Frisius et al., 1998). In both cases, forcing increases the spread of the ensemble for a certain value of the correlation time where the predictability also attains a critical value. On the other hand, for increasing correlation length ( fixed) the numerical results suggest a nonmonotonous behavior of the ensemble spread. The influence of noise amplitude, as well as the effect of a multiplicative or additive contribution of the noise is also shown. Finally, the impact of model resolution and ensemble size on the performance of the ensemble forecast has been analyzed numerically. newline [1] Buizza, R., Miller, M. and Palmer, T.N. (1999) Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Q.J.R. Meteorol. Soc. 125, 2887-2908. [2] Frisius, T., Lunkeit, F., Fraedrich, K. and James, I.N. (1998) Storm-track orga- nization and variability in a simplified atmospheric global circulation model. Q.J.R. Meteorol. Soc. 124, 1019-1043. [3] Lorenzo, M.N. and Pérez-Muñuzuri, V. (1999) Colored noise-induced chaotic ar- ray synchronization. Phys. Rev. E 60 2779-2787. [4] Lorenzo, M.N. and Pérez-Muñuzuri, V. (2001) Influence of low intensity noise on assemblies of diffusively coupled chaotic cells. Chaos 11, 371-376.

Lorenzo, M. N.; Montero, P.; Pérez-Muñuzuri, V.


Spatiotemporal control of nanooptical excitations.  


The most general investigation and exploitation of light-induced processes require simultaneous control over spatial and temporal properties of the electromagnetic field on a femtosecond time and nanometer length scale. Based on the combination of polarization pulse shaping and time-resolved two-photon photoemission electron microscopy, we demonstrate such control over nanoscale spatial and ultrafast temporal degrees of freedom of an electromagnetic excitation in the vicinity of a nanostructure. The time-resolved cross-correlation measurement of the local photoemission yield reveals the switching of the nanolocalized optical near-field distribution with a lateral resolution well below the diffraction limit and a temporal resolution on the femtosecond time scale. In addition, successful adaptive spatiotemporal control demonstrates the flexibility of the method. This flexible simultaneous control of temporal and spatial properties of nanophotonic excitations opens new possibilities to tailor and optimize the light-matter interaction in spectroscopic methods as well as in nanophotonic applications. PMID:20212153

Aeschlimann, Martin; Bauer, Michael; Bayer, Daniela; Brixner, Tobias; Cunovic, Stefan; Dimler, Frank; Fischer, Alexander; Pfeiffer, Walter; Rohmer, Martin; Schneider, Christian; Steeb, Felix; Strüber, Christian; Voronine, Dmitri V



3D hybrid wound devices for spatiotemporally controlled release kinetics.  


This paper presents localized and temporal control of release kinetics over 3-dimensional (3D) hybrid wound devices to improve wound-healing process. Imaging study is performed to extract wound bed geometry in 3D. Non-Uniform Rational B-Splines (NURBS) based surface lofting is applied to generate functionally graded regions. Diffusion-based release kinetics model is developed to predict time-based release of loaded modifiers for functionally graded regions. Multi-chamber single nozzle solid freeform dispensing system is used to fabricate wound devices with controlled dispensing concentration. Spatiotemporal control of biological modifiers thus enables a way to achieve target delivery to improve wound healing. PMID:22672934

Ozbolat, Ibrahim T; Koc, Bahattin



Spatio-Temporal Continuity in Geographic Space  

Microsoft Academic Search

The paper is an investigation into different notions of spat ial, temporal, and spatio-temporal continuity. A formal framework is proposed in which a number of different notions of continuity is situated.

Anthony G Cohn; Shyamanta M Hazarika


Survey of Spatio-Temporal Grouping Techniques.  

National Technical Information Service (NTIS)

Spatio-temporal segmentation video sequences attempts to extract backgrounds and independent objects in the dynamic scenes captured in the sequences. It is an essential step of video analysis. It has important applications in video coding, video logging, ...

D. DeMenthon R. Megret



STPMiner: A Highperformance Spatiotemporal Pattern Mining Toolbox.  

National Technical Information Service (NTIS)

The volume of spatiotemporal data being generated from scientific simulations and observations from sensors is growing at an astronomical rate. This data explosion is going to pose three challenges to the existing data mining infrastructure: algorithmic, ...

R. R. Vatsaval



Spatiotemporal Symmetry in Rings of Coupled Biological Oscillators of Physarum Plasmodial Slime Mold  

Microsoft Academic Search

Spatiotemporal patterns in rings of coupled biological oscillators of the plasmodial slime mold, Physarum polycephalum, were investigated by comparing with results analyzed by the symmetric Hopf bifurcation theory based on group theory. In three-, four-, and five-oscillator systems, all types of oscillation modes predicted by the theory were observed including a novel oscillation mode, a half period oscillation, which has

Atsuko Takamatsu; Reiko Tanaka; Hiroyasu Yamada; Toshiyuki Nakagaki; Teruo Fujii; Isao Endo



In Vivo Analysis of Proteomes and Interactomes Using Parallel Affinity Capture (iPAC) Coupled to Mass Spectrometry*  

PubMed Central

Affinity purification coupled to mass spectrometry provides a reliable method for identifying proteins and their binding partners. In this study we have used Drosophila melanogaster proteins triple tagged with Flag, Strep II, and Yellow fluorescent protein in vivo within affinity pull-down experiments and isolated these proteins in their native complexes from embryos. We describe a pipeline for determining interactomes by Parallel Affinity Capture (iPAC) and show its use by identifying partners of several protein baits with a range of sizes and subcellular locations. This purification protocol employs the different tags in parallel and involves detailed comparison of resulting mass spectrometry data sets, ensuring the interaction lists achieved are of high confidence. We show that this approach identifies known interactors of bait proteins as well as novel interaction partners by comparing data achieved with published interaction data sets. The high confidence in vivo protein data sets presented here add new data to the currently incomplete D. melanogaster interactome. Additionally we report contaminant proteins that are persistent with affinity purifications irrespective of the tagged bait.

Rees, Johanna S.; Lowe, Nick; Armean, Irina M.; Roote, John; Johnson, Glynnis; Drummond, Emma; Spriggs, Helen; Ryder, Edward; Russell, Steven; Johnston, Daniel St; Lilley, Kathryn S.



The eIF3 interactome reveals the translasome, a supercomplex linking protein synthesis and degradation machineries  

PubMed Central

Summary eIF3 promotes translation initiation, but relatively little is known about its full range of activities in the cell. Here, we employed affinity purification and highly sensitive LC-MS/MS to decipher the fission yeast eIF3 interactome, which was found to contain 230 proteins. eIF3 assembles into a large supercomplex, the translasome, which contains elongation factors, tRNA-synthetases, 40S and 60S ribosomal proteins, chaperones, and the proteasome. eIF3 also associates with ribosome biogenesis factors and the importins-? Kap123p and Sal3p. Our genetic data indicated that the binding to both importins-? is essential for cell growth, and photobleaching experiments revealed a critical role for Sal3p in the nuclear import of one of the translasome constituents, the proteasome. Our data reveal the breadth of the eIF3 interactome and suggest that factors involved in translation initiation, ribosome biogenesis, translation elongation, quality control, and transport are physically linked to facilitate efficient protein synthesis.

Sha, Zhe; Brill, Laurence M.; Cabrera, Rodrigo; Kleifeld, Oded; Scheliga, Judith S.; Glickman, Michael H.; Chang, Eric C.; Wolf, Dieter A.



Toxoplasmosis and Polygenic Disease Susceptibility Genes: Extensive Toxoplasma gondii Host/Pathogen Interactome Enrichment in Nine Psychiatric or Neurological Disorders.  


Toxoplasma gondii is not only implicated in schizophrenia and related disorders, but also in Alzheimer's or Parkinson's disease, cancer, cardiac myopathies, and autoimmune disorders. During its life cycle, the pathogen interacts with ~3000 host genes or proteins. Susceptibility genes for multiple sclerosis, Alzheimer's disease, schizophrenia, bipolar disorder, depression, childhood obesity, Parkinson's disease, attention deficit hyperactivity disorder (P??from??8.01E - 05??(ADHD)??to??1.22E - 71) (multiple sclerosis), and autism (P = 0.013), but not anorexia or chronic fatigue are highly enriched in the human arm of this interactome and 18 (ADHD) to 33% (MS) of the susceptibility genes relate to it. The signalling pathways involved in the susceptibility gene/interactome overlaps are relatively specific and relevant to each disease suggesting a means whereby susceptibility genes could orient the attentions of a single pathogen towards disruption of the specific pathways that together contribute (positively or negatively) to the endophenotypes of different diseases. Conditional protein knockdown, orchestrated by T. gondii proteins or antibodies binding to those of the host (pathogen derived autoimmunity) and metabolite exchange, may contribute to this disruption. Susceptibility genes may thus be related to the causes and influencers of disease, rather than (and as well as) to the disease itself. PMID:23533776

Carter, C J



Hierarchical network model for the analysis of human spatio-temporal information processing  

NASA Astrophysics Data System (ADS)

The perception of spatio-temporal pattern is a fundamental part of visual cognition. In order to understand more about the principles behind these biological processes, we are analyzing and modeling the presentation of spatio-temporal structures on different levels of abstraction. For the low- level processing of motion information we have argued for the existence of a spatio-temporal memory in early vision. The basic properties of this structure are reflected in a neural network model which is currently developed. Here we discuss major architectural features of this network which is base don Kohonens SOMs. In order to enable the representation, processing and prediction of spatio-temporal pattern on different levels of granularity and abstraction the SOMs are organized in a hierarchical manner. The model has the advantage of a 'self-teaching' learning algorithm and stored temporal information try local feedback in each computational layer. The constraints for the neural modeling and data set for training the neural network are obtained by psychophysical experiments where human subjects' abilities for dealing with spatio-temporal information is investigated.

Schill, Kerstin; Baier, Volker; Roehrbein, Florian; Brauer, Wilfried



Determination of Protein Interactome of Transcription Factor Sox2 in Embryonic Stem Cells Engineered for Inducible Expression of Four Reprogramming Factors*  

PubMed Central

Unbiased proteomic screens provide a powerful tool for defining protein-protein interaction networks. Previous studies employed multidimensional protein identification technology to identify the Sox2-interactome in embryonic stem cells (ESC) undergoing differentiation in response to a small increase in the expression of epitope-tagged Sox2. Thus far the Sox2-interactome in ESC has not been determined. To identify the Sox2-interactome in ESC, we engineered ESC for inducible expression of different combinations of epitope-tagged Sox2 along with Oct4, Klf4, and c-Myc. Epitope-tagged Sox2 was used to circumvent the lack of suitable Sox2 antibodies needed to perform an unbiased proteomic screen of Sox2-associated proteins. Although i-OS-ESC differentiate when both Oct4 and Sox2 are elevated, i-OSKM-ESC do not differentiate even when the levels of the four transcription factors are coordinately elevated ?2–3-fold. Our findings with i-OS-ESC and i-OSKM-ESC provide new insights into the reasons why ESC undergo differentiation when Sox2 and Oct4 are elevated in ESC. Importantly, the use of i-OSKM-ESC enabled us to identify the Sox2-interactome in undifferentiated ESC. Using multidimensional protein identification technology, we identified >70 proteins that associate with Sox2 in ESC. We extended these findings by testing the function of the Sox2-assoicated protein Smarcd1 and demonstrate that knockdown of Smarcd1 disrupts the self-renewal of ESC and induces their differentiation. Together, our work provides the first description of the Sox2-interactome in ESC and indicates that Sox2 along with other master regulators is part of a highly integrated protein-protein interaction landscape in ESC.

Gao, Zhiguang; Cox, Jesse L.; Gilmore, Joshua M.; Ormsbee, Briana D.; Mallanna, Sunil K.; Washburn, Michael P.; Rizzino, Angie



Spatiotemporal analysis of brightness induction  

PubMed Central

Brightness induction refers to a class of visual illusions in which the perceived intensity of a region of space is influenced by the luminance of surrounding regions. These illusions are significant because they provide insight into the neural organization of the visual system. A novel quadrature-phase motion cancelation technique was developed to measure the magnitude of the grating induction brightness illusion across a wide range of spatial frequencies, temporal frequencies and test field heights. Canceling contrast is greatest at low frequencies and declines with increasing frequency in both dimensions, and with increasing test field height. Canceling contrast scales as the product of inducing grating spatial frequency and test field height (the number of inducing grating cycles per test field height). When plotted using a spatial axis which indexes this product, the spatiotemporal induction surfaces for four test field heights can be described as four partially overlapping sections of a single larger surface. These properties of brightness induction are explained in the context of multiscale spatial filtering. The present study is the first to measure the magnitude of grating induction as a function of temporal frequency. Taken in conjunction with several other studies (Blakeslee & McCourt, 2008; Robinson & de Sa, 2008; Magnussen & Glad, 1975) the results of this study illustrate that at least one form of brightness induction is very much faster than that reported by DeValois et al. (1986) and Rossi and Paradiso (1996), and are inconsistent with the proposition that brightness induction results from a slow “filling in” process.

McCourt, Mark E.



Proteome-wide prediction of protein-protein interactions from high-throughput data.  


In this paper, we present a brief review of the existing computational methods for predicting proteome-wide protein-protein interaction networks from high-throughput data. The availability of various types of omics data provides great opportunity and also unprecedented challenge to infer the interactome in cells. Reconstructing the interactome or interaction network is a crucial step for studying the functional relationship among proteins and the involved biological processes. The protein interaction network will provide valuable resources and alternatives to decipher the mechanisms of these functionally interacting elements as well as the running system of cellular operations. In this paper, we describe the main steps of predicting protein-protein interaction networks and categorize the available approaches to couple the physical and functional linkages. The future topics and the analyses beyond prediction are also discussed and concluded. PMID:22729399

Liu, Zhi-Ping; Chen, Luonan



Sustained Emerging Spatio-Temporal Co-occurrence Pattern Mining: A Summary of Results  

Microsoft Academic Search

Sustained emerging spatio-temporal co-occurrence patterns (SECOPs) represent subsets of object-types that are increasingly located together in space and time. Discovering SECOPs is important due to many applications, e.g., predicting emerging infectious diseases, predicting defensive and offensive intent from troop movement patterns, and novel predator-prey interactions. However, mining SECOPs is computationally very expensive because the interest measures are computationally complex, datasets

Mete Celik; Shashi Shekhar; James P. Rogers; James A. Shine



Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia  

PubMed Central

Background It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia. Methods Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia. Results The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models. Conclusions The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.



Mercury Toolset for Spatiotemporal Metadata  

NASA Astrophysics Data System (ADS)

Mercury ( is a set of tools for federated harvesting, searching, and retrieving metadata, particularly spatiotemporal metadata. Version 3.0 of the Mercury toolset provides orders of magnitude improvements in search speed, support for additional metadata formats, integration with Google Maps for spatial queries, facetted type search, support for RSS (Really Simple Syndication) delivery of search results, and enhanced customization to meet the needs of the multiple projects that use Mercury. It provides a single portal to very quickly search for data and information contained in disparate data management systems, each of which may use different metadata formats. Mercury harvests metadata and key data from contributing project servers distributed around the world and builds a centralized index. The search interfaces then allow the users to perform a variety of fielded, spatial, and temporal searches across these metadata sources. This centralized repository of metadata with distributed data sources provides extremely fast search results to the user, while allowing data providers to advertise the availability of their data and maintain complete control and ownership of that data. Mercury periodically (typically daily)harvests metadata sources through a collection of interfaces and re-indexes these metadata to provide extremely rapid search capabilities, even over collections with tens of millions of metadata records. A number of both graphical and application interfaces have been constructed within Mercury, to enable both human users and other computer programs to perform queries. Mercury was also designed to support multiple different projects, so that the particular fields that can be queried and used with search filters are easy to configure for each different project.

Devarakonda, Ranjeet; Palanisamy, Giri; Green, James; Wilson, Bruce; Rhyne, B. Timothy; Lindsley, Chris



Spatiotemporal model for the progression of transgressive dunes  

NASA Astrophysics Data System (ADS)

Transgressive dune fields, which are active sand areas surrounded by vegetation, exist on many coasts. In some regions like in Fraser Island in Australia, small dunes shrink while large ones grow, although both experience the same climatic conditions. We propose a general mathematical model for the spatiotemporal dynamics of vegetation cover on sand dunes and focus on the dynamics of transgressive dunes. Among other possibilities, the model predicts growth parallel to the wind with shrinkage perpendicular to the wind, where, depending on geometry and size, a transgressive dune can initially grow although eventually shrink. The larger is the initial area the slower its stabilization process. The model's predictions are supported by field observations from Fraser Island in Australia.

Yizhaq, Hezi; Ashkenazy, Yosef; Levin, Noam; Tsoar, Haim



Spatiotemporal representation of cardiac vectorcardiogram (VCG) signals  

PubMed Central

Background Vectorcardiogram (VCG) signals monitor both spatial and temporal cardiac electrical activities along three orthogonal planes of the body. However, the absence of spatiotemporal resolution in conventional VCG representations is a major impediment for medical interpretation and clinical usage of VCG. This is especially so because time-domain features of 12-lead ECG, instead of both spatial and temporal characteristics of VCG, are widely used for the automatic assessment of cardiac pathological patterns. Materials and methods We present a novel representation approach that captures critical spatiotemporal heart dynamics by displaying the real time motion of VCG cardiac vectors in a 3D space. Such a dynamic display can also be realized with only one lead ECG signal (e.g., ambulatory ECG) through an alternative lag-reconstructed ECG representation from nonlinear dynamics principles. Furthermore, the trajectories are color coded with additional dynamical properties of space-time VCG signals, e.g., the curvature, speed, octant and phase angles to enhance the information visibility. Results In this investigation, spatiotemporal VCG signal representation is used to characterize various spatiotemporal pathological patterns for healthy control (HC), myocardial infarction (MI), atrial fibrillation (AF) and bundle branch block (BBB). The proposed color coding scheme revealed that the spatial locations of the peak of T waves are in the Octant 6 for the majority (i.e., 74 out of 80) of healthy recordings in the PhysioNet PTB database. In contrast, the peak of T waves from 31.79% (117/368) of MI subjects are found to remain in Octant 6 and the rest (68.21%) spread over all other octants. The spatiotemporal VCG signal representation is shown to capture the same important heart characteristics as the 12-lead ECG plots and more. Conclusions Spatiotemporal VCG signal representation is shown to facilitate the characterization of space-time cardiac pathological patterns and enhance the automatic assessment of cardiovascular diseases.



Transitions between spatiotemporal patterns in cell culture  

NASA Astrophysics Data System (ADS)

Cardiac monolayers undergo transitions between different spatiotemporal states as experimental conditions are varied. Monolayers display periodic target pattern waves, stable spiral waves, spirals that spontaneously initiate and terminate, multiple interacting wavefronts, and quiescence. Transitions between these spatiotemporal states are observed during washout of beta-glycyrrhetinic acid, a pharmacological agent that reduces cell-cell coupling. Simple excitable media models undergo the same transitions as local connectivity is continuously varied. Similar mechanisms may be responsible for transitions between healthy and unhealthy rhythms in whole hearts.

Bub, Gil



Large-scale De Novo Prediction of Physical Protein-Protein Association*  

PubMed Central

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at:

Elefsinioti, Antigoni; Sarac, Omer Sinan; Hegele, Anna; Plake, Conrad; Hubner, Nina C.; Poser, Ina; Sarov, Mihail; Hyman, Anthony; Mann, Matthias; Schroeder, Michael; Stelzl, Ulrich; Beyer, Andreas



A Modified Consumer Inkjet for Spatiotemporal Control of Gene Expression  

PubMed Central

This paper presents a low-cost inkjet dosing system capable of continuous, two-dimensional spatiotemporal regulation of gene expression via delivery of diffusible regulators to a custom-mounted gel culture of E. coli. A consumer-grade, inkjet printer was adapted for chemical printing; E. coli cultures were grown on 750 µm thick agar embedded in micro-wells machined into commercial compact discs. Spatio-temporal regulation of the lac operon was demonstrated via the printing of patterns of lactose and glucose directly into the cultures; X-Gal blue patterns were used for visual feedback. We demonstrate how the bistable nature of the lac operon's feedback, when perturbed by patterning lactose (inducer) and glucose (inhibitor), can lead to coordination of cell expression patterns across a field in ways that mimic motifs seen in developmental biology. Examples of this include sharp boundaries and the generation of traveling waves of mRNA expression. To our knowledge, this is the first demonstration of reaction-diffusion effects in the well-studied lac operon. A finite element reaction-diffusion model of the lac operon is also presented which predicts pattern formation with good fidelity.

Cohen, Daniel J.; Morfino, Roberto C.; Maharbiz, Michel M.



Spatio-temporal statistical models with applications to atmospheric processes  

SciTech Connect

This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model.

Wikle, C.K.



On the Generation of Spatiotemporal Datasets  

Microsoft Academic Search

An efficient benchmarking environment for spatiotemporal access methods should at least include modules for generating synthetic datasets, storing datasets (real datasets included), collecting and running access structures, and visualizing experimental results. Focusing on the dataset repository module, a collection of synthetic data that would simulate a variety of real life scenarios is required. Several algorithms have been implemented in the

Yannis Theodoridis; Jefferson R. O. Silva; Mario A. Nascimento



An Interactive Visual Language for Spatiotemporal Patterns  

Microsoft Academic Search

Patterns exist in many contexts and they can be considered as knowledge sources providing useful information for our decision making. However, sometimes they are not easily identifiable and may not be visible. A visual interface for helping users to recognize patterns is needed. In this paper, we present a computer-based visual language called STVL to visualize spatiotemporal patterns. An event-anchored

K. Priyantha Hewagamage; Masahito Hirakawa



Spatiotemporal Segmentation Based on Region Merging  

Microsoft Academic Search

This paper proposes a technique for spatio-temporal segmentation to identify the objects present in the scene represented in a video sequence. This technique processes two consecutive frames at a time. A region-merging approach is used to identify the objects in the scene. Starting from an oversegmentation of the current frame, the objects are formed by iteratively merging regions together. Regions

Fabrice Moscheni; Sushil Bhattacharjee; Murat Kunt



Synchronizing Spatiotemporal Chaos in Coupled Nonlinear Oscillators  

Microsoft Academic Search

The synchronization of spatiotemporal chaos of two arrays of coupled nonlinear oscillators is achieved by discrete time coupling of individual cells of the arrays. This synchronization method is based on the knowledge of the local dynamics and can be applied to any type of arrays where the synchronization properties of the cells are known. Furthermore, we discuss possible applications of

Ljupco Kocarev; Ulrich Parlitz



Spatiotemporal Relational Probability Trees: An Introduction  

Microsoft Academic Search

We introduce spatiotemporal relational probability trees (SRPTs), probability estimation trees for relational data that can vary in both space and time. The SRPT algo- rithm addresses the exponential increase in search complex- ity through sampling. We validate the SRPT using a sim- ulated data set and we empirically demonstrate the SRPT algorithm on two real-world data sets.

Amy Mcgovern; Nathan C. Hiers; Matthew W. Collier; David J. Gagne II; Rodger A. Brown



Measuring Microcirculation Using Spatiotemporal Image Analysis  

Microsoft Academic Search

. This paper describes a method for recognizing and measuringthe motion of each individual leukocyte in microvessels from a sequenceof images. A spatiotemporal image is generated whose spatial axes areparallel and vertical to vessel region contours. In order to enhance and extractonly leukocyte traces with a tuned velocity range even under noisybackground, we use a combination of a filtering process

Yoshinobu Sato; Jian Chen; Shuzo Yamamoto; Shinichi Tamura; Noboru Harada; Takeshi Shiga; Seiyo Harino; Yusuke Oshima



Spatiotemporal chaos in Easter Island ecology.  


This paper demonstrates that a recently proposed spatiotemporal model for the ecology of Easter Island admits periodic and chaotic attractors, not previously reported. Such behavior may more realistically depict the population dynamics of general ecosystems and illustrates the power of simple models to produce the kind of complex behavior that is ubiquitous in such systems. PMID:22980451

Sprott, J C



Proteomic Profiling of the TRAF3 Interactome Network Reveals a New Role for the ER-to-Golgi Transport Compartments in Innate Immunity  

Microsoft Academic Search

Tumor Necrosis Factor receptor-associated factor-3 (TRAF3) is a central mediator important for inducing type I interferon (IFN) production in response to intracellular double-stranded RNA (dsRNA). Here, we report the identification of Sec16A and p115, two proteins of the ER-to-Golgi vesicular transport system, as novel components of the TRAF3 interactome network. Notably, in non-infected cells, TRAF3 was found associated with markers

Wendy J. van Zuylen; Priscilla Doyon; Jean-François Clément; Kashif Aziz Khan; Lisa M. DAmbrosio; Florence Dô; Myriam St-Amant-Verret; Tasheen Wissanji; Gregory Emery; Anne-Claude Gingras; Sylvain Meloche; Marc J. Servant



Propagation of epileptic spikes reconstructed from spatiotemporal magnetoencephalographic and electroencephalographic source analysis  

PubMed Central

The purpose of this study is to assess the accuracy of spatiotemporal source analysis of magnetoencephalography (MEG) and scalp electroencephalography (EEG) for representing the propagation of frontotemporal spikes in patients with partial epilepsy. This study focuses on frontotemporal spikes, which are typically characterized by a preceding anterior temporal peak followed by an ipsilateral inferior frontal peak. Ten patients with frontotemporal spikes on MEG/EEG were studied. We analyzed the propagation of temporal to frontal epileptic spikes on both MEG and EEG independently by using a cortically-constrained minimum norm estimate (MNE). Spatiotemporal source distribution of each spike was obtained on the cortical surface derived from the patient’s MRI. All patients underwent an extraoperative intracranial EEG (IEEG) recording covering temporal and frontal lobes after presurgical evaluation. We extracted source waveforms of MEG and EEG from the source distribution of interictal spikes at the sites corresponding to the location of intracranial electrodes. The time differences of the ipsilateral temporal and frontal peaks as obtained by MEG, EEG and IEEG were statistically compared in each patient. In all patients, MEG and IEEG showed similar time differences between temporal and frontal peaks. The time differences of EEG spikes were significantly smaller than those of IEEG in nine of ten patients. Spatiotemporal analysis of MEG spikes models the time course of frontotemporal spikes as observed on IEEG more adequately than EEG in our patients. Spatiotemporal source analysis may be useful for planning epilepsy surgery, by predicting the pattern of IEEG spikes.

Tanaka, Naoaki; Hamalainen, Matti S; Ahlfors, Seppo P.; Liu, Hesheng; Madsen, Joseph R.; Bourgeois, Blaise F.; Lee, Jong Woo; Dworetzky, Barbara A.; Belliveau, John W.; Stufflebeam, Steven M.



The effects of density-dependent dispersal on the spatiotemporal dynamics of cyclic populations  

Microsoft Academic Search

Density-dependent dispersal occurs throughout the animal kingdom, and has been shown to occur in some taxa whose populations exhibit multi-year population cycles. However, the importance of density-dependent dispersal for the spatiotemporal dynamics of cyclic populations is unknown. We investigated the potential effects of density-dependent dispersal on the properties of periodic travelling waves predicted by two coupled reaction–diffusion models: a commonly

Matthew J. Smith; Jonathan A. Sherratt; Xavier Lambin



Experimental observation of a skewed X-type spatiotemporal correlation of ultrabroadband twin beams.  


This work presents the experimental observation of the nonfactorable near-field spatiotemporal correlation of ultrabroadband twin beams generated by parametric down-conversion, in an interferometric-type experiment using sum frequency generation, where both the temporal and the spatial degrees of freedom of parametric down-conversion light are controlled with high resolution. The revealed correlation is skewed in space-time in accordance with the X structure predicted by the theory. PMID:23368318

Jedrkiewicz, O; Gatti, A; Brambilla, E; Di Trapani, P



Arresting soliton collapse in two-dimensional nonlinear Schrödinger systems via spatiotemporal modulation of the external potential  

NASA Astrophysics Data System (ADS)

We predict stable, collapse-free solitonslike structures in two-dimensional nonlinear Schrödinger systems in subdiffractive regimes, accomplished by a spatiotemporal modulation of the external potential. We investigate the scaling laws, the stability, and the dynamical properties of these subdiffractive solitons.

Staliunas, Kestutis; Herrero, Ramon; de Valcárcel, Germán J.



Finding equilibrium statistical mechanics in spatiotemporal chaos  

NASA Astrophysics Data System (ADS)

Ruelle has argued that the extensivity of the complicated dynamics of spatiotemporal chaos is evidence that these systems can be viewed as a gas of weakly-interacting regions of a characteristic size. We have performed large-scale computational studies of spatiotemporal chaos in the 1D complex Ginzburg-Landau equation and have found that histograms of the number of maxima in the amplitude are well-described by an equilibrium Tonks gas (and variants) in the grand canonical ensemble. Furthermore, for small system sizes, the average number of particles in the Tonks gas (with particle sizes and temperatures determined from fits to the CGL histograms) exhibits oscillatory, decaying deviations from extensivity in agreement with the deviations in the fractal dimension found by Fishman and Egolf. This result not only supports Ruelle's picture but also suggests that the coarse-grained behavior of this far-from-equilibrium system might be understood using equilibrium statistical mechanics.

Esty, C. Clark; Ballard, Christopher C.; Kerin, John A.; Egolf, David A.



Spatiotemporal video segmentation based on graphical models.  


This paper proposes a probabilistic framework for spatiotemporal segmentation of video sequences. Motion information, boundary information from intensity segmentation, and spatial connectivity of segmentation are unified in the video segmentation process by means of graphical models. A Bayesian network is presented to model interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notion of the Markov random field is used to encourage the formation of continuous regions. Given consecutive frames, the conditional joint probability density of the three fields is maximized in an iterative way. To effectively utilize boundary information from the intensity segmentation, distance transformation is employed in local objective functions. Experimental results show that the method is robust and generates spatiotemporally coherent segmentation results. Moreover, the proposed video segmentation approach can be viewed as the compromise of previous motion based approaches and region merging approaches. PMID:16028557

Wang, Yang; Loe, Kia-Fock; Tan, Tele; Wu, Jian-Kang



Visual Features Extraction Through Spatiotemporal Slice Analysis  

Microsoft Academic Search

In this paper we propose a novel feature extracting method based on spatiotemporal slice analyzing. To date, video features\\u000a are focused on the character of every single video frame. With our method, the video content is no longer represented with\\u000a every single frame. The temporal variation of visual information is taken as an important feature of video in our method.

Xuefeng Pan; Jintao Li; Shan Ba; Yongdong Zhang; Sheng Tang



Spatiotemporal Autoregressive Models of Neighborhood Effects  

Microsoft Academic Search

Using 70,822 observations on housing prices from 1969 to 1991 from Fairfax County Virginia, this article demonstrates the substantial benefits obtained by modeling the spatial as well as the temporal dependence of the data. Specifically, the spatiotemporal autoregression with twelve variables reduced median absolute error by 37.35% relative to an indicator-based model with twenty-six variables. One-step ahead forecasts also document

Ronald Barry; John M. Clapp; Mauricio Rodriquez



Controlling spatiotemporal chaos in coupled map lattices  

SciTech Connect

A simple method is presented for controlling spatiotemporal chaos in coupled map lattices to a homogeneous state. This method can be applied to many kinds of models such as coupled map lattices (CML), one-way open CML (the open-flow model), and globally coupled map. We offer the stability analysis of the homogeneous state. Simple and sufficient conditions are obtained for controlling the above mentioned models. Our theoretical results agree well with numerical simulations.

Zhu, KaiEn; Chen, Tianlun



Computational complexity of spatio-temporal logics  

Microsoft Academic Search

Recently, a hierarchy of spatio-temporal logics based on the propositional temporal logic PTL and the spatial languages RCC-8, BRCC-8 and S4u has been introduced. Although a number of results on their computational properties were obtained, the most important questions were left open. In this paper, we solve almost all of these problems and provide a clear picture of the balance

David Gabelaia; Roman Kontchakov; Agi Kurucz; Frank Wolter; Michael Zakharyaschev



Azimuthal instability of spinning spatiotemporal solitons  


We find one-parameter families of three-dimensional spatiotemporal bright vortex solitons (doughnuts, or spinning light bullets), in dispersive quadratically nonlinear media. We show that they are subject to a strong instability against azimuthal perturbations, similarly to the previously studied (2+1)-dimensional bright spatial vortex solitons. The instability breaks the spinning soliton into several fragments, each being a stable nonspinning light bullet. PMID:11088714

Mihalache; Mazilu; Crasovan; Malomed; Lederer



A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG.  


Multichannel electroencephalography (EEG) offers a non-invasive tool to explore spatio-temporal dynamics of brain activity. With EEG recordings consisting of multiple trials, traditional signal processing approaches that ignore inter-trial variability in the data may fail to accurately estimate the underlying spatio-temporal brain patterns. Moreover, precise characterization of such inter-trial variability per se can be of high scientific value in establishing the relationship between brain activity and behavior. In this paper, a statistical modeling framework is introduced for learning spatio-temporal decompositions of multiple-trial EEG data recorded under two contrasting experimental conditions. By modeling the variance of source signals as random variables varying across trials, the proposed two-stage hierarchical Bayesian model is able to capture inter-trial amplitude variability in the data in a sparse way where a parsimonious representation of the data can be obtained. A variational Bayesian (VB) algorithm is developed for statistical inference of the hierarchical model. The efficacy of the proposed modeling framework is validated with the analysis of both synthetic and real EEG data. In the simulation study we show that even at low signal-to-noise ratios our approach is able to recover with high precision the underlying spatio-temporal patterns and the dynamics of source amplitude across trials; on two brain-computer interface (BCI) data sets we show that our VB algorithm can extract physiologically meaningful spatio-temporal patterns and make more accurate predictions than other two widely used algorithms: the common spatial patterns (CSP) algorithm and the Infomax algorithm for independent component analysis (ICA). The results demonstrate that our statistical modeling framework can serve as a powerful tool for extracting brain patterns, characterizing trial-to-trial brain dynamics, and decoding brain states by exploiting useful structures in the data. PMID:21420499

Wu, Wei; Chen, Zhe; Gao, Shangkai; Brown, Emery N



Video compression using spatiotemporal regularity flow.  


We propose a new framework in wavelet video coding to improve the compression rate by exploiting the spatiotemporal regularity of the data. A sequence of images creates a spatiotemporal volume. This volume is said to be regular along the directions in which the pixels vary the least, hence the entropy is the lowest. The wavelet decomposition of regularized data results in a fewer number of significant coefficients, thus yielding a higher compression rate. The directions of regularity of an image sequence depend on both its motion content and spatial structure. We propose the representation of these directions by a 3-D vector field, which we refer to as the spatiotemporal regularity flow (SPREF). SPREF uses splines to approximate the directions of regularity. The compactness of the spline representation results in a low storage overhead for SPREF, which is a desired property in compression applications. Once SPREF directions are known, they can be converted into actual paths along which the data is regular. Directional decomposition of the data along these paths can be further improved by using a special class of wavelet basis called the 3-D orthonormal bandelet basis. SPREF -based video compression not only removes the temporal redundancy, but it also compensates for the spatial redundancy. Our experiments on several standard video sequences demonstrate that the proposed method results in higher compression rates as compared to the standard wavelet based compression. PMID:17153954

Alatas, Orkun; Javed, Omar; Shah, Mubarak



Event-based Spatio-temporal Database Design  

Microsoft Academic Search

Both structural and behavioral aspects need to be modeled in some spatio-temporal databases. There existing initial efforts to represent behavioral aspects of the spatio-temporal applications via events gave their priority to some local or partial behaviors rather than an overview of the system's behavior. In this paper, an event-based approach is proposed for modeling the system's behavior of the spatio-temporal



Geostatistical Analysis of Spatio-Temporal Forest Fire Data  

NASA Astrophysics Data System (ADS)

Forest fire is one of the major phenomena causing degradation of environment, landscape, natural ecosystems, human health and economy. One of the main topic in forest fire data studies deals with the detection, analysis and modelling of spatio-temporal patterns of clustering. Spatial patterns of forest fire locations, their sizes and their sequence in time are of great interest for fire prediction and for forest fire management planning and distribution in optimal way necessary resources. Currently, fires can be analyzed and monitored by using different statistical tools, for example, Ripley's k-function, fractals, Allan factor, scan statistics, etc. Some of them are adapted to temporal or spatial data and are either local or global. In the present study the main attention is paid to the application of geostatistical tools - variography and methods for the analysis of monitoring networks (MN) clustering techniques (topological, statistical and fractal measures), in order to detect and to characterize spatio-temporal forest fire patterns. The main studies performed include: a) analysis of forest fires temporal sequences; b) spatial clustering of forest fires; c) geostatistical spatial analysis of burnt areas. Variography was carried out both for temporal and spatial data. Real case study is based on the forest-fire event data from Canton of Ticino (Switzerland) for a period of 1969 to 2008. The results from temporal analysis show the presence of clustering and seasonal periodicities. Comprehensive analysis of the variograms shows an anisotropy in the direction 30° East-North where smooth changes are detected, while on the direction 30° North-West a greater variability was identified. The research was completed with an application of different MN analysis techniques including, analysis of distributions of distances between events, Morisita Index (MI), fractal dimensions (sandbox counting and box counting methods) and functional fractal dimensions, adapted and applied to characterize spatio-temporal events. The results are compared with the reference patterns (no spatial clustering) simulated within the natural validity domains (forests). The research was partly supported by SNSF projects IZAIZO-12777 and 200020-121835.

Vega Orozco, Carmen D.; Kanevski, Mikhail; Tonini, Marj; Conedera, Marc



Proteomic identification of Hsc70 as a mediator of RGS9-2 degradation by in vivo interactome analysis  

PubMed Central

Changes in interactions between signaling proteins underlie many cellular functions. In the mammalian nervous system a member of the Regulator of G protein Signaling family, RGS9-2 (Regulator of G protein Signaling, type 9) is a key regulator of dopamine and opioid signaling pathways that mediate motor control and reward behavior. Dynamic association of RGS9-2 with a neuronal protein R7BP (R7 family Binding Protein) has been found to be critically important for the regulation of the expression level of the complex by proteolytic mechanisms. Changes in RGS9-2 expression are observed in response to a number of signaling events and are thought to contribute to the plasticity of the neurotransmitter action. In this study, we report an identification of molecular chaperone Hsc70 (Heat shock cognate protein 70) as a critical mediator of RGS9-2 expression that is specifically recruited to the intrinsically disordered C-terminal domain of RGS9-2 following its dissociation from R7BP. Hsc70 was identified by a novel application of the quantitative proteomics approach developed to monitor interactome dynamics in mice using a set of controls contributed by knockout strains. We propose this application to be a useful tool for studying the dynamics of protein assemblies in complex models, such as signaling in the mammalian nervous system.

Posokhova, Ekaterina; Uversky, Vladimir; Martemyanov, Kirill A.



Tomographic reconstruction of the pulse-echo spatiotemporal impulse response  

NASA Astrophysics Data System (ADS)

Virtually every area of ultrasonic imaging research requires accurate estimation of the spatiotemporal impulse response of the instrument, and yet accurate measurements are difficult to achieve. The impulse response can also be difficult to predict numerically for a specific device because small unknown perturbations in array properties can generate significant changes in predicted pulse-echo field patterns. A typical measurement for a 1-D array transducer employs a line scatterer oriented perpendicular to the scan plane. Echoes from line scatterers located throughout the field of view constitute estimates of shift-varying line response functions. We propose an inverse-problem approach to the reconstruction of point-spread functions from line-spread functions. A collection of echoes recorded for a range of line-scatterer rotation angles are treated as projections of sound pressure onto the transducer array surface. Although the reconstruction is mathematically equivalent to filtered backprojection, it provides significant advantages with respect to interpolation that confound straightforward implementations. Field II predictions used to model measurements made on commercial systems suggest the reconstruction accuracy is with 0.32% for noiseless echo data. Application of the method to data acquired from a commercial system are evaluated from the perspective of deconvolution.

Nguyen, Nghia Q.; Abbey, Craig K.; Yapp, Rebecca D.; Insana, Michael F.



Spatiotemporal activity of magnetic storms  

NASA Astrophysics Data System (ADS)

We have constructed a data-derived model of the evolution of the spatial structure of the ring current geomagnetic signature during storms. A spatially dependent generalization of the Dessler-Parker-Skopke relation has been derived to explain the spatial structure in the midlatitude magnetic fluctuations (MLMF) as observed by ground magnetometers. Such a relation is used as a basis for constructing solar-wind-driven, data-derived models of the MLMF. The model includes a coupling to the solar wind as the energy driver and also includes a nonlocal coupling as an explanation of the inhomogeneity in the energy density that appears in the ring current during the main phase of a storm. Both linear and nonlinear models for the evolution of the spatial structure of the MLMF are constructed, and the nonlinear spatial model of the ring current produces better predictions than the linear one. This can be taken as an indication that during strong magnetic storms the ring current evolves in a nonlinear fashion. The spatial data used in the generation of the models are rotated to a frame ``fixed'' with the ring current, and presure effects were accounted through a kinematic relation. The techniques developed in this paper are very general and can be used to study other systems that show spatial structure, such as the high-latitude current system.

Valdivia, J. A.; Vassiliadis, D.; Klimas, A.; Sharma, A. S.; Papadopoulos, K.




Microsoft Academic Search

The spatio-temporal data is our cognition to external matter and the spatio-temporal data model is the fundamental basic to manage the spatio-temporal data. The spatio-temporal object is always changing so we need a spatio-temporal data model which can reflect the change information and change reason and roundly and exactly describe the spatio-temporal world. At the same time more and more

XiaoChun Wua; Weihong Cuib; YongQi Huang; XiaoDong Yang


Detection of fatigue cracks in a beam using a spatio-temporal dynamical system identification method  

NASA Astrophysics Data System (ADS)

The identification of fatigue cracks in a beam is investigated in this paper. It is shown that due to the influence of the elastic nonlinearity of fatigue cracks, the homogeneity, along the length of the beam, of the spatio-temporal dynamics of the vibrating beam is destroyed. By using spatio-temporal dynamical system identification techniques, a new approach is developed to detect this nonhomogeneity. The cracked beam is divided into several spatial regions and a coupled map lattice (CML) model is identified and verified in one of the regions using an orthogonal forward regression (OFR) least-squares algorithm. This CML model is then used to predict the dynamical behaviour of the other regions and in this way to detect the nonhomogeneity of the overall system.

Guo, L. Z.; Billings, S. A.



Bayesian spatiotemporal analysis of foot-and-mouth disease data from the Republic of Turkey  

PubMed Central

SUMMARY A flexible hierarchical Bayesian spatiotemporal regression model for foot-and-mouth disease (FMD) was applied to data on the annual number of reported FMD cases in Turkey from 1996 to 2003. The longitudinal component of the model was specified as a latent province-specific stochastic process. This stochastic process can accommodate various types of FMD temporal profiles. The model accounted for differences in FMD occurrence across provinces and for spatial correlation. Province-level covariate information was incorporated into the analysis. Results pointed to a decreasing trend in the number of FMD cases in western Turkey and an increasing trend in eastern Turkey from 1996 to 2003. The model also identified provinces with high and with low propensities for FMD occurrence. The model's use of flexible structures for temporal trend and of generally applicable methods for spatial correlation has broad application to predicting future spatiotemporal distributions of disease in other regions of the world.




A spatio-temporal absorbing state model for disease and syndromic surveillance.  


Reliable surveillance models are an important tool in public health because they aid in mitigating disease outbreaks, identify where and when disease outbreaks occur, and predict future occurrences. Although many statistical models have been devised for surveillance purposes, none are able to simultaneously achieve the important practical goals of good sensitivity and specificity, proper use of covariate information, inclusion of spatio-temporal dynamics, and transparent support to decision-makers. In an effort to achieve these goals, this paper proposes a spatio-temporal conditional autoregressive hidden Markov model with an absorbing state. The model performs well in both a large simulation study and in an application to influenza/pneumonia fatality data. PMID:22388709

Heaton, Matthew J; Banks, David L; Zou, Jian; Karr, Alan F; Datta, Gauri; Lynch, James; Vera, Francisco



Spatiotemporal selective extrapolation for 3-D signals and its applications in video communications.  


In this paper, we derive a spatiotemporal extrapolation method for 3-D discrete signals. Extending a discrete signal beyond a limited number of known samples is commonly referred to as discrete signal extrapolation. Extrapolation problems arise in many applications in video communications. Transmission errors in video communications may cause data losses which are concealed by extrapolating the surrounding video signal into the missing area. The same principle is applied for TV logo removal. Prediction in hybrid video coding is also interpreted as an extrapolation problem. Conventionally, the unknown areas in the video sequence are estimated from either the spatial or temporal surrounding. Our approach considers the spatiotemporal signal including the missing area in a volume and replaces the unknown samples by extrapolating the surrounding signal from spatial, as well as temporal direction. By exploiting spatial and temporal correlations at the same time, it is possible to inherently compensate motion. Deviations in luminance occurring from frame to frame can be compensated, too. PMID:17784607

Meisinger, Katrin; Kaup, André



Spatiotemporal Dynamics in Ecology:. Insights from Physics  

NASA Astrophysics Data System (ADS)

Simulations of invasion in cyclic predator-prey systems show plane waves behind the invasion front. When the selected plane wave is unstable, there is a band of plane waves of constant width, followed by spatiotemporal chaos. We describe a new method for calculating the width of this band, based on the absolute stability of plane waves in moving frames of reference. This calculation shows that the band width can be very sensitive to changes in parameters, and we discuss the ecological implications of this result.

Sherratt, Jonathan A.; Smith, Matthew J.; Rademacher, Jens D. M.



Spatiotemporal dynamics of networks of excitable nodes  

NASA Astrophysics Data System (ADS)

A network of excitable nodes based on the photosensitive Belousov-Zhabotinsky reaction is studied in experiments and simulations. The addressable medium allows both local and nonlocal links between the nodes. The initial spread of excitation across the network as well as the asymptotic oscillatory behavior are described. Synchronization of the spatiotemporal dynamics occurs by entrainment to high-frequency network pacemakers formed by excitation loops. Analysis of the asymptotic behavior reveals that the dynamics of the network is governed by a subnetwork selected during the initial transient period.

Steele, Aaron J.; Tinsley, Mark; Showalter, Kenneth



Spatiotemporal analysis of prior appropriations water calls  

NASA Astrophysics Data System (ADS)

A spatiotemporal model is developed to examine prior appropriations-based water curtailment in Idaho's Snake River Plain Aquifer. Using a 100 year horizon, prior appropriations-based curtailment supplemented with optimized water use reductions is shown to produce a spatial distribution of water use reductions that differs from that produced by regulatory curtailment based strictly on initial water right assignments. Discounted profits over 100 years of crop production are up to 7% higher when allocation is optimized. Total pumping over 100 years is 0.3%, 3%, and 40% higher under 1, 10, and 100 year prior appropriations-based regulatory curtailment, respectively.

Elbakidze, Levan; Shen, Xiaozhe; Taylor, Garth; Mooney, SiâN.



Dynamics and spatiotemporal variability of ice streams  

NASA Astrophysics Data System (ADS)

Ice sheets evolve over a wide range of time scales. They grow by snowfall, spread gravitationally, and diminish through melting or iceberg calving at the ice-sheet margin. The evolution of ice-sheets can be substantially affected by the rate of ice transport from their interior to their margins, and ice streams are the dominant transport mechanism in present ice sheets. Ice streams are bands of fast flowing glacier ice whose flow pattern varies both temporally and spatially. In particular ice-streams can become stagnant, reactivate, and flow in varying paths. In this thesis I investigate the dynamics that leads to ice-stream formation and their spatiotemporal variability. The two major dynamical factors I study are the frictional stress at the base of the ice and the non-Newtonian ice rheology. Both of these components are poorly constrained from observations, and may affect the stability of ice flow: the shear-thinning rheology of ice through shear instability, and the frictional bottom stress through the generation of melt water in the basal porous sediments that can lubricate the motion of the overlying ice. While we do not find a flow instability or ice-stream formation caused by the shear-thinning rheology, we do find that a triple-valued bottom sliding law can lead to ice-stream formation in our model and can account for various observed spatiotemporal characteristics of ice-streams. In particular the flow under such a sliding law can generate both steady and oscillatory ice stream solutions, independently of the shear thinning ice rheology. We then analyze the motion of the ice-stream shear-margins by linking the leading order dynamics of ice-streams to the Landau-Ginzburg reaction-diffusion equation. Next, we study the consequences of the non-Newtonian ice rheology on ice flow under a triple-valued sliding law, and investigate the dependence of the ice-stream shear-margin width on the rheology. Finally, we study the spatiotemporal variability due to the interaction of two ice streams. We demonstrate that a spatially symmetric two-stream pattern can be unstable under an asymmetric perturbation, which results in a flow with asymmetric patterns that are maintained through the competition of the two ice-streams over a shared mass source. The rich spatiotemporal variability is found to mostly be a result of the triple valued sliding law, but non Newtonian effects are found to play a significant role in setting a more realistic shear margin width and allowing for relevant time scales of the variability.

Sayag, Roiy


kltool: A tool to analyze spatiotemporal complexity.  


We announce the availability of a software package, called kltool, that can extract phase space information from complex spatiotemporal data via the Karhunen-Loeve analysis. Data generated by the periodic, quasiperiodic or chaotic evolution of a small number of spatially coherent structures can be processed. A key feature of kltool is that it allows the user to interact easily with the data processing and its graphical display. We illustrate the use of kltool on numerical data from the Kuramoto-Sivashinsky equation and laboratory data from a flame experiment. PMID:12780117

Armbruster, Dieter; Heiland, Randy; Kostelich, Eric J.



Spatiotemporal rogue events in femtosecond filamentation  

SciTech Connect

We present experimental and numerical investigations of optical extreme (rogue) event statistics recorded in the regime of femtosecond pulse filamentation in water. In the spectral domain, the extreme events manifest themselves as either large or small extremes of the spectral intensity, justified by right- or left-tailed statistical distributions, respectively. In the time domain, the observed extreme events are associated with pulse splitting and energy redistribution in space and therefore are exquisitely linked to three-dimensional, spatiotemporal dynamics and formation of the X waves.

Majus, D.; Jukna, V.; Valiulis, G.; Dubietis, A. [Department of Quantum Electronics, Vilnius University, Sauletekio Avenue 9, Building 3, LT-10222 Vilnius (Lithuania); Faccio, D. [School of Engineering and Physical Sciences, SUPA, Heriot-Watt University, Edinburgh EH14 4AS (United Kingdom)



A study of the spatiotemporal health impacts of ozone exposure  

Microsoft Academic Search

Exposure analysis and mapping of spatiotemporal pollutants in relation to their health effects are important challenges facing environmental health scientists and integrated assessment modellers. In this work, a methodological framework is discussed to study the impact of spatiotemporal ozone (O3) exposure distributions on the health of human populations. The framework, however, is very general and can be used to study




Using GeoRSS to syndicate the spatiotemporal information  

Microsoft Academic Search

This paper describes a number of ways to encode spatiotemporal information in RSS feeds. As RSS becomes more and more prevalent as a way to publish and share information, it becomes increasingly important that location and time is described in an interoperable manner so that applications can request, aggregate, share and map spatiotemporally tagged feeds. This paper describes the GeoRSS

Bo Zhao; Manchun Li; Zhixin Jiang



Quantication of spatiotemporal phenomena by means of cellular automata techniques  

Microsoft Academic Search

Quantication methods for spatiotemporal patterns are introduced, which are based on nearest- neighbor considerations inspired by cellular automata as well as by more complex spatiotemporal dynamics. In particular, spatial and temporal structures, which can be found in aggregation and clustering phenomena, are quantied by introducing the concept of cellular automata (CA) mea- sures for homogeneity and for the amount of

R. Neb


Quantification of spatiotemporal phenomena by means of cellular automata techniques  

Microsoft Academic Search

Quantification methods for spatiotemporal patterns are introduced, which are based on nearest-neighbor considerations inspired by cellular automata as well as by more complex spatiotemporal dynamics. In particular, spatial and temporal structures, which can be found in aggregation and clustering phenomena, are quantified by introducing the concept of cellular automata (CA) measures for homogeneity and for the amount of fluctuations contributing

M.-Th. Hütt; R. Neff



A Probabilistic Approach to Spatiotemporal Theme Pattern Mining on Weblogs  

Microsoft Academic Search

Mining subtopics from weblogs and analyzing their spatiotem- poral patterns have applications in multiple domains. In this paper, we deflne the novel problem of mining spatiotemporal theme patterns from weblogs and propose a novel probabilis- tic approach to model the subtopic themes and spatiotem- poral theme patterns simultaneously. The proposed model discovers spatiotemporal theme patterns by (1) extracting common themes

Qiaozhu Mei; Chao Liu; Hang Su; ChengXiang Zhai


Workload induced spatio-temporal distortions and safety of flight  

SciTech Connect

A theoretical analysis of the relationship between cognitive complexity and the perception of time and distance is presented and experimentally verified. Complex tasks produce high rates of mental representation which affect the subjective sense of duration and, through the subjective time scale, the percept of distance derived from dynamic visual cues (i.e., visual cues requiring rate integration). The analysis of the interrelationship of subjective time and subjective distance yields the prediction that, as a function of cognitive complexity, distance estimates derived from dynamic visual cues will be longer than the actual distance whereas estimates based on perceived temporal duration will be shorter than the actual distance. This prediction was confirmed in an experiment in which subjects (both pilots and non-pilots) estimated distances using either temporal cues or dynamic visual cues. The distance estimation task was also combined with secondary loading tasks in order to vary the overall task complexity. The results indicated that distance estimates based on temporal cues were underestimated while estimates based on visual cues were overestimated. This spatio-temporal distortion effect increased with increases in overall task complexity. 30 refs., 6 figs., 1 tab.

Barrett, C.L.; Weisgerber, S.A. (Los Alamos National Lab., NM (USA); Naval Weapons Center, China Lake, CA (USA))



The Hsp90 co-chaperone p23 of Toxoplasma gondii: Identification, functional analysis and dynamic interactome determination  

PubMed Central

Toxoplasma gondii is among the most successful parasites, with nearly half of the human population chronically infected. Recently a link between the T. gondii Hsp90 chaperone machinery and parasite development was observed. Here, the T. gondii Hsp90 co-chaperones p23 and Hip were identified mining the Toxoplasma- database ( Their identity was confirmed by domain structure and blast analysis. Additionally, analysis of the secondary structure and studies on the chaperone function of the purified protein verified the p23 identity. Studies of co-immunoprecipitation (co-IP) identified two different types of complexes, one comprising at least Hip-Hsp70-Hsp90 and another containing at least p23-Hsp90. Indirect immunofluorescence assays showed that Hip is localized in the cytoplasm in tachyzoites and as well in bradyzoites. For p23 in contrast, a solely cytoplasmic localization was only observed in the tachyzoite stage whereas nuclear and cytosolic distribution and colocalization with Hsp90 was observed in bradyzoites. These results indicate that the T. gondii Hsp90-heterocomplex cycle is similar to the one proposed for higher eukaryotes, further highlighting the implication of the Hsp90/p23 in parasite development. Furthermore, co-IP experiments of tachyzoite/bradyzoite lysates with anti-p23 antiserum and identification of the complexed proteins together with the use of the curated interaction data available from different source (orthologs and Plasmodium databases) allowed us to construct an interaction network (interactome) covering the dynamics of the Hsp90 chaperone machinery.

Echeverria, Pablo C.; Figueras, Maria J.; Vogler, Malvina; Kriehuber, Thomas; de Miguel, Natalia; Deng, Bin; Dalmasso, Maria C.; Matthews, Dwight E.; Matrajt, Mariana; Haslbeck, Martin; Buchner, Johannes; Angel, Sergio O.



Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling  

PubMed Central

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting ?-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.

Huang, Shao-shan Carol; Clarke, David C.; Gosline, Sara J. C.; Labadorf, Adam; Chouinard, Candace R.; Gordon, William; Lauffenburger, Douglas A.; Fraenkel, Ernest



Integrating environmental isoscapes for spatiotemporal assignment  

NASA Astrophysics Data System (ADS)

Numerous case studies in the ecological and forensic fields have illustrated the potential utility of light stable isotopes as tracers of the geographic origin of biological materials. However, a number of critical challenges continue to limit the application of these tools, among them (1) limitations to our knowledge of isotopic values expected for materials formed at particular locations and times, (2) uncertainty in our understanding of the interplay between temporal and spatial variation in the isotope 'signature' transferred to isotopic materials, and (3) lack of robust, widely used models for quantitative statistical assessment of spatiotemporal origin and associated uncertainty. In order to acknowledge and address each of these limitations, we present new models and analysis of spatiotemporal variation in the stable isotope ratios of hydrogen, oxygen, and strontium in the environment, representing three isotope systems with strong and complementary potential for provenancing applications. We demonstrate a statistical framework for the integration of these isoscapes in assignment problems and describe how this toolkit has been made broadly accessible through the IsoMAP web-GIS portal.

Bowen, G.; Bataille, C.; Kennedy, C.; Zhang, T.; West, J.



Spatio-temporal growth of disturbances in a boundary layer and energy based receptivity analysis  

NASA Astrophysics Data System (ADS)

In fluid dynamical systems, it is not known a priori whether disturbances grow either in space or in time or as spatio-temporal structures. However, for boundary layers, it is customary to treat it as a spatial problem and some limited comparison between prediction and laboratory experiments exist. In the present work, the receptivity problem of a zero pressure gradient boundary layer excited by a localized harmonic source is investigated under the general spatio-temporal framework, using the Bromwich contour integral method. While this approach has been shown to be equivalent to the spatial study, for unstable systems excited by a single frequency source [T. K. Sengupta, M. Ballav, and S. Nijhawan, Phys. Fluids 6, 1213 (1994)], here we additionally show, how the boundary layer behaves when it is excited (i) at a single frequency that corresponds to a stable condition (given by spatial normal-mode analysis) and (ii) by wideband frequencies, that shows the possibility of flow transition due to a spatio-temporally growing forerunner or wave front. An energy based receptivity analysis tool is also developed as an alternative to traditional instability theory. Using this, we reinterpret the concept of critical layer that was originally postulated to explain the mathematical singularity of inviscid disturbance field in traditional instability theory of normal modes.

Sengupta, T. K.; Rao, A. Kameswara; Venkatasubbaiah, K.



A Metascalable Computing Framework for Large Spatiotemporal-Scale Atomistic Simulations  

SciTech Connect

A metascalable (or 'design once, scale on new architectures') parallel computing framework has been developed for large spatiotemporal-scale atomistic simulations of materials based on spatiotemporal data locality principles, which is expected to scale on emerging multipetaflops architectures. The framework consists of: (1) an embedded divide-and-conquer (EDC) algorithmic framework based on spatial locality to design linear-scaling algorithms for high complexity problems; (2) a space-time-ensemble parallel (STEP) approach based on temporal locality to predict long-time dynamics, while introducing multiple parallelization axes; and (3) a tunable hierarchical cellular decomposition (HCD) parallelization framework to map these O(N) algorithms onto a multicore cluster based on hybrid implementation combining message passing and critical section-free multithreading. The EDC-STEP-HCD framework exposes maximal concurrency and data locality, thereby achieving: (1) inter-node parallel efficiency well over 0.95 for 218 billion-atom molecular-dynamics and 1.68 trillion electronic-degrees-of-freedom quantum-mechanical simulations on 212,992 IBM BlueGene/L processors (superscalability); (2) high intra-node, multithreading parallel efficiency (nanoscalability); and (3) nearly perfect time/ensemble parallel efficiency (eon-scalability). The spatiotemporal scale covered by MD simulation on a sustained petaflops computer per day (i.e. petaflops {center_dot} day of computing) is estimated as NT = 2.14 (e.g. N = 2.14 million atoms for T = 1 microseconds).

Nomura, K; Seymour, R; Wang, W; Kalia, R; Nakano, A; Vashishta, P; Shimojo, F; Yang, L H



Interactome analyses of mature ?-secretase complexes reveal distinct molecular environments of presenilin (PS) paralogs and preferential binding of signal peptide peptidase to PS2.  


?-Secretase plays a pivotal role in the production of neurotoxic amyloid ?-peptides (A?) in Alzheimer disease (AD) and consists of a heterotetrameric core complex that includes the aspartyl intramembrane protease presenilin (PS). The human genome codes for two presenilin paralogs. To understand the causes for distinct phenotypes of PS paralog-deficient mice and elucidate whether PS mutations associated with early-onset AD affect the molecular environment of mature ?-secretase complexes, quantitative interactome comparisons were undertaken. Brains of mice engineered to express wild-type or mutant PS1, or HEK293 cells stably expressing PS paralogs with N-terminal tandem-affinity purification tags served as biological source materials. The analyses revealed novel interactions of the ?-secretase core complex with a molecular machinery that targets and fuses synaptic vesicles to cellular membranes and with the H(+)-transporting lysosomal ATPase macrocomplex but uncovered no differences in the interactomes of wild-type and mutant PS1. The catenin/cadherin network was almost exclusively found associated with PS1. Another intramembrane protease, signal peptide peptidase, predominantly co-purified with PS2-containing ?-secretase complexes and was observed to influence A? production. PMID:23589300

Jeon, Amy Hye Won; Böhm, Christopher; Chen, Fusheng; Huo, Hairu; Ruan, Xueying; Ren, Carl He; Ho, Keith; Qamar, Seema; Mathews, Paul M; Fraser, Paul E; Mount, Howard T J; St George-Hyslop, Peter; Schmitt-Ulms, Gerold



Research on spatio-temporal ontology based on description logic  

NASA Astrophysics Data System (ADS)

DL, short for Description Logic, is aimed at getting a balance between describing ability and reasoning complexity. Users can adopt DL to write clear and formalized concept description for domain model, which makes ontology description possess well-defined syntax and semantics and helps to resolve the problem of spatio-temporal reasoning based on ontology. This paper studies on basic theory of DL and relationship between DL and OWL at first. By analyzing spatio-temporal concepts and relationship of spatio-temporal GIS, the purpose of this paper is adopting ontology language based on DL to express spatio-temporal ontology, and employing suitable ontology-building tool to build spatio-temporal ontology. With regard to existing spatio-temporal ontology based on first-order predicate logic, we need to transform it into spatio-temporal ontology based on DL so as to make the best of existing research fruits. This paper also makes a research on translating relationships between DL and first-order predicate logic.

Huang, Yongqi; Ding, Zhimin; Zhao, Zhui; Ouyang, Fucheng



Attention Modulates Spatio-temporal Grouping  

PubMed Central

Dynamic stimuli are ubiquitous in natural viewing conditions implying that grouping operations need to operate, not only in space, but also jointly in space and time. Moreover, in natural viewing, attention plays an important role in controlling how resources are allocated. We investigated how attention interacts with spatiotemporal perceptual grouping by using a bistable stimulus, called the Ternus-Pikler display. Ternus-Pikler displays can give rise to two different motion percepts, called Element Motion (EM) and Group Motion (GM), the former dominating at short Inter-Stimulus Intervals (ISIs) and the latter at long ISIs. Our results indicate that GM grouping requires more attentional resources than EM grouping. Different theoretical accounts of perceptual grouping and attention are discussed and evaluated in the light of the current results.

Ayd?n, Murat; Herzog, Michael H.; Ogmen, Haluk



Dimensions Associated with Defects in Spatiotemporal Chaos  

NASA Astrophysics Data System (ADS)

In recent work,(David A. Egolf. Building blocks of spatiotemporal chaos: the dimension of defects in the 2D complex Ginzburg-Landau equation. In preparation. 1996.) we defined a finite-time Lyapunov dimension D^T based on the evolution of Lyapunov vectors over short time intervals of length T. For the complex Ginzburg-Landau equation in two spatial dimensions, we showed that on average D^T is linearly related to the mean number of defects in the system during the same time interval. The average dimension per defect is about 2 over a wide range of parameter values. We review this work and present new calculations comparing values of D^T to the average number of spirals present in the spiral defect chaos state in a generalized Swift-Hohenberg model of convection. This work is supported by NSF-ASC-9503963 and the Cornell Theory Center.

Egolf, David A.; Bodenschatz, Eberhard



Interactome-wide analysis identifies end-binding protein 1 as a crucial component for the speck-like particle formation of activated absence in melanoma 2 (AIM2) inflammasomes.  


Inflammasomes are cytoplasmic receptors that can recognize intracellular pathogens or danger signals and are critical for interleukin 1? production. Although several key components of inflammasome activation have been identified, there has not been a systematic analysis of the protein components found in the stimulated complex. In this study, we used the isobaric tags for relative and absolute quantification approach to systemically analyze the interactomes of the NLRP3, AIM2, and RIG-I inflammasomes in nasopharyngeal carcinoma cells treated with specific stimuli of these interactomes (H2O2, poly (dA:dT), and EBV noncoding RNA, respectively). We identified a number of proteins that appeared to be involved in the interactomes and also could be precipitated with anti-apoptosis-associated speck-like protein containing caspase activation and recruitment domain antibodies after stimulation. Among them, end binding protein 1 was an interacting component in all three interactomes. Silencing of end binding protein 1 expression by small interfering RNA inhibited the activation of the three inflammasomes, as indicated by reduced levels of interleukin 1? secretion. We confirmed that end binding protein 1 directly interacted with AIM2 and ASC in vitro and in vivo. Most importantly, fluorescence confocal microscopy showed that end binding protein 1 was required for formation of the speck-like particles that represent activation of the AIM2 inflammasome. In nasopharyngeal carcinoma tissues, immunohistochemical staining showed that end binding protein 1 expression was elevated and significantly correlated with AIM2 and ASC expression in nasopharyngeal carcinoma tumor cells. In sum, we profiled the interactome components of three inflammasomes and show for the first time that end binding protein 1 is crucial for the speck-like particle formation that represents activated inflammasomes. PMID:22869553

Wang, Li-Jie; Hsu, Chia-Wei; Chen, Chiu-Chin; Liang, Ying; Chen, Lih-Chyang; Ojcius, David M; Tsang, Ngan-Ming; Hsueh, Chuen; Wu, Chih-Ching; Chang, Yu-Sun



Spatiotemporal modeling and analysis of transient gene delivery.  


A quantitative and mechanistic understanding of intracellular transport processes in eukaryotic cells during transient transfection is an important prerequisite for the systematic and specific optimization of transient gene expression procedures for pharmaceutic and industrial protein production. There is evidence that intracellular transport processes during gene delivery and their regulation may have significant influence on the transfection efficiency. This contribution describes a compartmented, spatiotemporally resolved and stochastic modeling approach that describes intracellular transport processes responsible for gene delivery during transient transfection. It enables a detailed prediction and analysis and identification of potential bottlenecks. This model is currently being adapted to a model cell line, HEK293s. The simulated results are compared with experimental quantitative polymerase chain reaction (qPCR) data and confocal imaging data obtained with transfected and stained HEK293 cells. Global parameter estimation is performed to qPCR data based on two different novel plasmid constructs in order to identify candidates for plasmid-specific transport parameter variations. The influence of the specific property of HEK293 cells to grow in clusters is investigated and the impact of active microtubule transport depending on cell morphology and clustering is examined. A general sensitivity analysis allows for the identification of the sensitive parameters. PMID:21538332

Jandt, Uwe; Shao, Shi; Wirth, Manfred; Zeng, An-Ping



Spatiotemporal topological kriging of runoff time series  

NASA Astrophysics Data System (ADS)

This paper proposes a geostatistical method for estimating runoff time series in ungauged catchments. The method conceptualizes catchments as space-time filters and exploits the space-time correlations of runoff along the stream network topology. We hence term the method topological kriging or top kriging. It accounts for hydrodynamic and geomorphologic dispersion as well as routing and estimates runoff as a weighted average of the observed runoff in neighboring catchments. Top kriging is tested by cross validation on 10 years of hourly runoff data from 376 catchments in Austria and separately for a subset of these data, the Innviertel region. The median Nash-Sutcliffe efficiency of hourly runoff in the Innviertel region is 0.87 but decreases to 0.75 for the entire data set. For a subset of 208 catchments, the median efficiency of daily runoff estimated by top kriging is 0.87 as compared to 0.67 for estimates of a deterministic runoff model that uses regionalized model parameters. The much better performance of top kriging is because it avoids rainfall data errors and avoids the parameter identifiability issues of traditional runoff models. The analyses indicate that the kriging variance can be used for identifying catchments with potentially poor estimates. The Innviertel region is used to examine the kriging weights for nested and nonnested catchments and to compare various variants of top kriging. The spatial kriging variant generally performs better than the more complex spatiotemporal kriging and spatiotemporal cokriging variants. It is argued that top kriging may be preferable to deterministic runoff models for estimating runoff time series in ungauged catchments, provided stream gauge density is high and there is no need to account for causal rainfall-runoff processes. Potential applications include the estimation of flow duration curves in a region and near-real time mapping of runoff.

SkøIen, Jon Olav; BlöSchl, Günter



targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis  

PubMed Central

Background Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. Results We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. Conclusion The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

Raman, Karthik; Yeturu, Kalidas; Chandra, Nagasuma



The use of spatio-temporal correlation to forecast critical transitions  

NASA Astrophysics Data System (ADS)

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.

Karssenberg, Derek; Bierkens, Marc F. P.



Spatiotemporal symmetry in rings of coupled biological oscillators of Physarum plasmodial slime mold.  


Spatiotemporal patterns in rings of coupled biological oscillators of the plasmodial slime mold, Physarum polycephalum, were investigated by comparing with results analyzed by the symmetric Hopf bifurcation theory based on group theory. In three-, four-, and five-oscillator systems, all types of oscillation modes predicted by the theory were observed including a novel oscillation mode, a half period oscillation, which has not been reported anywhere in practical systems. Our results support the effectiveness of the symmetric Hopf bifurcation theory in practical systems. PMID:11497921

Takamatsu, A; Tanaka, R; Yamada, H; Nakagaki, T; Fujii, T; Endo, I



Spatio-Temporal Masking: Hyperacuity and Local Adaptation.  

National Technical Information Service (NTIS)

Our development of an ideal-observer framework and a test-pedestal methodology for modeling vision without the numerous assumptions of previous models has provided a comprehensive understanding of the spatio-temporal characteristics of human vision. The m...

S. A. Klein



Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data  

NASA Astrophysics Data System (ADS)

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 PM2.5 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.

Sampson, Paul D.; Szpiro, Adam A.; Sheppard, Lianne; Lindström, Johan; Kaufman, Joel D.



Query optimization for spatio-temporal data stream management systems  

Microsoft Academic Search

ABSTRACT Location-detection devices are used ubiquitously in moving objects due to the everyday decreasing cost and simplified technology. Usually, these devices will send the moving ob- jects’ location information to a spatio-temporal data stream management,system that will be then responsible for an- swering spatio-temporal queries related to these moving ob- jects. Most of the existing work focused on the continu-

Hicham G. Elmongui



Incremental Learning of Spatio-temporal Patterns with Model Selection  

Microsoft Academic Search

This paper proposes a biologically inspired incremental learning method for spatio-temporal patterns based on our recently\\u000a reported “Incremental learning through sleep (ILS)” method. This method alternately repeats two learning phases: awake and\\u000a sleep. During the awake phase, the system learns new spatio-temporal patterns by rote, whereas in the sleep phase, it rehearses\\u000a the recorded new memories interleaved with old memories.

Koichiro Yamauchi; Masayoshi Sato



Different routes from a matter wavepacket to spatiotemporal chaos.  


We investigate the dynamics of a quasi-one-dimensional Bose-Einstein condensate confined in a double-well potential with spatiotemporally modulated interaction. A variety of phenomena is identified in different frequency regimes, including the self-compression, splitting, breathing-like, and near-fidelity of the matter wavepacket, which are associated with different routes for the onset of spatiotemporal chaos. The results also reveal that chaos can retain space-inversion symmetry of the system. PMID:23020448

Rong, Shiguang; Hai, Wenhua; Xie, Qiongtao; Zhong, Honghua



The Relationship among GIS-Oriented Spatiotemporal Databases  

Microsoft Academic Search

We overview three major types of GIS-oriented spatiotemporal databases: (1) point-based, (2) region-based, and (3) constraint-based. We analyze the relationship among these spatiotemporal databases and show how they can be translated into each other. We illustrate the translations with an example from National Agricultural Statistics Service (NASS) databases. Finally, we also discuss the advantages and disadvantages of using the various

Lixin Li; Peter Z. Revesz



Multiversion Linear Quadtree for Spatio-Temporal Data  

Microsoft Academic Search

Research in spatio-temporal databases has largely focused on extensions of access methods for the proper handling of time changing spatial information. In this paper, we present the Multiversion Linear Quadtree (MVLQ), a spatio-temporal access method based on Multiver- sion B-trees (MVBT) (2), embedding ideas from Linear Region Quadtrees (4). More specically, instead of storing independent numerical data ha- ving a

Theodoros Tzouramanis; Michael Vassilakopoulos; Yannis Manolopoulos



Discovering partial spatio-temporal co-occurrence patterns  

Microsoft Academic Search

Spatio-temporal co-occurrence patterns represent subsets of object-types that are often located together in space and time. The aim of the discovery of partial spatio-temporal co- occurrence patterns (PACOPs) is to find co-occurrences of the object-types that are partially present in the database. Discovering PACOPs is an important problem with many applications such as discovering interactions between animals and identifying tactics

Mete Celtic



Genes involved in TGF?1-driven epithelial-mesenchymal transition of renal epithelial cells are topologically related in the human interactome map  

PubMed Central

Background Understanding how mesenchymal cells arise from epithelial cells could have a strong impact in unveiling mechanisms of epithelial cell plasticity underlying kidney regeneration and repair. In primary human tubular epithelial cells (HUTEC) under different TGF?1 concentrations we had observed epithelial-to-mesenchymal transition (EMT) but not epithelial-myofibroblast transdifferentiation. We hypothesized that the process triggered by TGF?1 could be a dedifferentiation event. The purpose of this study is to comprehensively delineate genetic programs associated with TGF?1-driven EMT in our in vitro model using gene expression profile on large-scale oligonucleotide microarrays. Results In HUTEC under TGF?1 stimulus, 977 genes were found differentially expressed. Thirty genes were identified whose expression depended directly on TGF?1 concentration. By mapping the differentially expressed genes in the Human Interactome Map using Cytoscape software, we identified a single scale-free network consisting of 2630 interacting proteins and containing 449 differentially expressed proteins. We identified 27 hub proteins in the interactome with more than 29 edges incident on them and encoded by differentially expressed genes. The Gene Ontology analysis showed an excess of up-regulated proteins involved in biological processes, such as "morphogenesis", "cell fate determination" and "regulation of development", and the most up-regulated genes belonged to these categories. In addition, 267 genes were mapped to the KEGG pathways and 14 pathways with more than nine differentially expressed genes were identified. In our model, Smad signaling was not the TGF?1 action effector; instead, the engagement of RAS/MAPK signaling pathway seems mainly to regulate genes involved in the cell cycle and proliferation/apoptosis. Conclusion Our present findings support the hypothesis that context-dependent EMT generated in our model by TGF?1 might be the outcome of a dedifferentiation. In fact: 1) the principal biological categories involved in the process concern morphogenesis and development; 2) the most up-regulated genes belong to these categories; and, finally, 3) some intracellular pathways are involved, whose engagement during kidney development and nephrogenesis is well known. These long-term effects of TGF?1 in HUTEC involve genes that are highly interconnected, thereby generating a scale-free network that we named the "TGF?1 interactome", whose hubs represent proteins that may have a crucial role for HUTEC in response to TGF?1.

Campanaro, Stefano; Picelli, Simone; Torregrossa, Rossella; Colluto, Laura; Ceol, Monica; Del Prete, Dorella; D'Angelo, Angela; Valle, Giorgio; Anglani, Franca



Parallel indexing technique for spatio-temporal data  

NASA Astrophysics Data System (ADS)

The requirements for efficient access and management of massive multi-dimensional spatio-temporal data in geographical information system and its applications are well recognized and researched. The most popular spatio-temporal access method is the R-Tree and its variants. However, it is difficult to use them for parallel access to multi-dimensional spatio-temporal data because R-Trees, and variants thereof, are in hierarchical structures which have severe overlapping problems in high dimensional space. We extended a two-dimensional interval space representation of intervals to a multi-dimensional parallel space, and present a set of formulae to transform spatio-temporal queries into parallel interval set operations. This transformation reduces problems of multi-dimensional object relationships to simpler two-dimensional spatial intersection problems. Experimental results show that the new parallel approach presented in this paper has superior range query performance than R\\midast-trees for handling multi-dimensional spatio-temporal data and multi-dimensional interval data. When the number of CPU cores is larger than that of the space dimensions, the insertion performance of this new approach is also superior to R\\midast-trees. The proposed approach provides a potential parallel indexing solution for fast data retrieval of massive four-dimensional or higher dimensional spatio-temporal data.

He, Zhenwen; Kraak, Menno-Jan; Huisman, Otto; Ma, Xiaogang; Xiao, Jing



Transient dynamics and rhythm coordination of inferior olive spatio-temporal patterns.  


The inferior olive (IO) is a neural network belonging to the olivo-cerebellar system whose neurons are coupled with electrical synapses and display subthreshold oscillations and spiking activity. The IO is frequently proposed as the generator of timing signals to the cerebellum. Electrophysiological and imaging recordings show that the IO network generates complex spatio-temporal patterns. The generation and modulation of coherent spiking activity in the IO is one key issue in cerebellar research. In this work, we build a large scale IO network model of electrically coupled conductance-based neurons to study the emerging spatio-temporal patterns of its transient neuronal activity. Our modeling reproduces and helps to understand important phenomena observed in IO in vitro and in vivo experiments, and draws new predictions regarding the computational properties of this network and the associated cerebellar circuits. The main factors studied governing the collective dynamics of the IO network were: the degree of electrical coupling, the extent of the electrotonic connections, the presence of stimuli or regions with different excitability levels and the modulatory effect of an inhibitory loop (IL). The spatio-temporal patterns were analyzed using a discrete wavelet transform to provide a quantitative characterization. Our results show that the electrotonic coupling produces quasi-synchronized subthreshold oscillations over a wide dynamical range. The synchronized oscillatory activity plays the role of a timer for a coordinated representation of spiking rhythms with different frequencies. The encoding and coexistence of several coordinated rhythms is related to the different clusterization and coherence of transient spatio-temporal patterns in the network, where the spiking activity is commensurate with the quasi-synchronized subthreshold oscillations. In the presence of stimuli, different rhythms are encoded in the spiking activity of the IO neurons that nevertheless remains constrained to a commensurate value of the subthreshold frequency. The stimuli induced spatio-temporal patterns can reverberate for long periods, which contributes to the computational properties of the IO. We also show that the presence of regions with different excitability levels creates sinks and sources of coordinated activity which shape the propagation of spike wave fronts. These results can be generalized beyond IO studies, as the control of wave pattern propagation is a highly relevant problem in the context of normal and pathological states in neural systems (e.g., related to tremor, migraine, epilepsy) where the study of the modulation of activity sinks and sources can have a potential large impact. PMID:24046731

Latorre, Roberto; Aguirre, Carlos; Rabinovich, Mikhail I; Varona, Pablo



Transient dynamics and rhythm coordination of inferior olive spatio-temporal patterns  

PubMed Central

The inferior olive (IO) is a neural network belonging to the olivo-cerebellar system whose neurons are coupled with electrical synapses and display subthreshold oscillations and spiking activity. The IO is frequently proposed as the generator of timing signals to the cerebellum. Electrophysiological and imaging recordings show that the IO network generates complex spatio-temporal patterns. The generation and modulation of coherent spiking activity in the IO is one key issue in cerebellar research. In this work, we build a large scale IO network model of electrically coupled conductance-based neurons to study the emerging spatio-temporal patterns of its transient neuronal activity. Our modeling reproduces and helps to understand important phenomena observed in IO in vitro and in vivo experiments, and draws new predictions regarding the computational properties of this network and the associated cerebellar circuits. The main factors studied governing the collective dynamics of the IO network were: the degree of electrical coupling, the extent of the electrotonic connections, the presence of stimuli or regions with different excitability levels and the modulatory effect of an inhibitory loop (IL). The spatio-temporal patterns were analyzed using a discrete wavelet transform to provide a quantitative characterization. Our results show that the electrotonic coupling produces quasi-synchronized subthreshold oscillations over a wide dynamical range. The synchronized oscillatory activity plays the role of a timer for a coordinated representation of spiking rhythms with different frequencies. The encoding and coexistence of several coordinated rhythms is related to the different clusterization and coherence of transient spatio-temporal patterns in the network, where the spiking activity is commensurate with the quasi-synchronized subthreshold oscillations. In the presence of stimuli, different rhythms are encoded in the spiking activity of the IO neurons that nevertheless remains constrained to a commensurate value of the subthreshold frequency. The stimuli induced spatio-temporal patterns can reverberate for long periods, which contributes to the computational properties of the IO. We also show that the presence of regions with different excitability levels creates sinks and sources of coordinated activity which shape the propagation of spike wave fronts. These results can be generalized beyond IO studies, as the control of wave pattern propagation is a highly relevant problem in the context of normal and pathological states in neural systems (e.g., related to tremor, migraine, epilepsy) where the study of the modulation of activity sinks and sources can have a potential large impact.

Latorre, Roberto; Aguirre, Carlos; Rabinovich, Mikhail I.; Varona, Pablo



Regionalised spatiotemporal rainfall and temperature models for flood studies in the Basque Country, Spain  

NASA Astrophysics Data System (ADS)

A spatiotemporal point process model of rainfall is fitted to data taken from three homogeneous regions in the Basque Country, Spain. The model is the superposition of two spatiotemporal Neyman-Scott processes, in which rain cells are modelled as discs with radii that follow exponential distributions. In addition, the model includes a parameter for the radius of storm discs, so that rain only occurs when both a cell and a storm disc overlap a point. The model is fitted to data for each month, taken from each of the three homogeneous regions, using a modified method of moments procedure that ensures a smooth seasonal variation in the parameter estimates. Daily temperature data from 23 sites are used to fit a stochastic temperature model. A principal component analysis of the maximum daily temperatures across the sites indicates that 92% of the variance is explained by the first component, implying that this component can be used to account for spatial variation. A harmonic equation with autoregressive error terms is fitted to the first principal component. The temperature model is obtained by regressing the maximum daily temperature on the first principal component, an indicator variable for the region, and altitude. This, together with scaling and a regression model of temperature range, enables hourly temperatures to be predicted. Rainfall is included as an explanatory variable but has only a marginal influence when predicting temperatures. A distributed model (TETIS; Francés et al., 2007) is calibrated for a selected catchment. Five hundred years of data are simulated using the rainfall and temperature models and used as input to the calibrated TETIS model to obtain simulated discharges to compare with observed discharges. Kolmogorov-Smirnov tests indicate that there is no significant difference in the distributions of observed and simulated maximum flows at the same sites, thus supporting the use of the spatiotemporal models for the intended application.

Cowpertwait, P.; Ocio, D.; Collazos, G.; de Cos, O.; Stocker, C.



Spatiotemporal Temperature Distribution and Cancer Cell Death in Response to Extracellular Hyperthermia Induced by Gold Nanorods  

PubMed Central

Plasmonic nanoparticles have shown promise in hyperthermic cancer therapy, both in vitro and in vivo. Previous reports have described hyperthermic ablation using targeted and non-targeted nanoparticles internalized by cancer cells, but most reports do not describe a theoretical analysis for determining optimal parameters. The focus of the current research was first to evaluate the spatiotemporal temperature distribution and cell death induced by extracellular hyperthermia in which gold nanorods (GNRs) were maintained in the dispersion outside human prostate cancer cells. The nanorod dispersion was irradiated with near infrared (NIR) laser and the spatiotemporal distribution of temperature was determined experimentally. This information was employed to develop and validate theoretical models of spatiotemporal temperature profiles for gold nanorod dispersions undergoing laser irradiation, and the impact of the resulting heat generation on the viability of human prostate cancer cells. A cell injury/death model was then coupled to the heat transfer model to predict spatial and temporal variations in cell death and injury. The model predictions agreed well with experimental measurements of both, temperature and cell death profiles. Finally, the model was extended to examine the impact of selective binding of gold nanorods to cancer cells compared to non-malignant cells, coupled with a small change in cell injury activation energy. The impact of these relatively minor changes results in a dramatic change in the overall cell death rate. Taken together, extracellular hyperthermia using gold nanorods is a promising strategy and tailoring the cellular binding efficacy of nanorods can result in varying therapeutic efficacies using this approach.

Huang, Huang-Chiao; Rege, Kaushal; Heys, Jeffrey J.



Contextualized trajectory parsing with spatiotemporal graph.  


This work investigates how to automatically parse object trajectories in surveillance videos, which aims at jointly solving three subproblems: 1) spatial segmentation, 2) temporal tracking, and 3) object categorization. We present a novel representation spatiotemporal graph (ST-Graph) in which: 1) Graph nodes express the motion primitives, each representing a short sequence of small-size patches over consecutive images, and 2) every two neighbor nodes are linked with either a positive edge or a negative edge to describe their collaborative or exclusive relationship of belonging to the same object trajectory. Phrasing the trajectory parsing as a graph multicoloring problem, we propose a unified probabilistic formulation to integrate various types of context knowledge as informative priors. An efficient composite cluster sampling algorithm is employed in search of the optimal solution by exploiting both the collaborative and the exclusive relationships between nodes. The proposed framework is evaluated over challenging videos from public datasets, and results show that it can achieve state-of-the-art tracking accuracy. PMID:24136437

Liu, Xiaobai; Lin, Liang; Jin, Hai



Nonlinear Schrödinger equation with spatiotemporal perturbations.  


We investigate the dynamics of solitons of the cubic nonlinear Schrödinger equation (NLSE) with the following perturbations: nonparametric spatiotemporal driving of the form f(x,t)=a exp[iK(t)x], damping, and a linear term which serves to stabilize the driven soliton. Using the time evolution of norm, momentum and energy, or, alternatively, a Lagrangian approach, we develop a collective-coordinate-theory which yields a set of ordinary differential equations (ODEs) for our four collective coordinates. These ODEs are solved analytically and numerically for the case of a constant, spatially periodic force f(x). The soliton position exhibits oscillations around a mean trajectory with constant velocity. This means that the soliton performs, on the average, a unidirectional motion although the spatial average of the force vanishes. The amplitude of the oscillations is much smaller than the period of f(x). In order to find out for which regions the above solutions are stable, we calculate the time evolution of the soliton momentum P(t) and the soliton velocity V(t): This is a parameter representation of a curve P(V) which is visited by the soliton while time evolves. Our conjecture is that the soliton becomes unstable, if this curve has a branch with negative slope. This conjecture is fully confirmed by our simulations for the perturbed NLSE. Moreover, this curve also yields a good estimate for the soliton lifetime: the soliton lives longer, the shorter the branch with negative slope is. PMID:20365492

Mertens, Franz G; Quintero, Niurka R; Bishop, A R



Nonlinear Schrödinger equation with spatiotemporal perturbations  

NASA Astrophysics Data System (ADS)

We investigate the dynamics of solitons of the cubic nonlinear Schrödinger equation (NLSE) with the following perturbations: nonparametric spatiotemporal driving of the form f(x,t)=aexp[iK(t)x] , damping, and a linear term which serves to stabilize the driven soliton. Using the time evolution of norm, momentum and energy, or, alternatively, a Lagrangian approach, we develop a collective-coordinate-theory which yields a set of ordinary differential equations (ODEs) for our four collective coordinates. These ODEs are solved analytically and numerically for the case of a constant, spatially periodic force f(x) . The soliton position exhibits oscillations around a mean trajectory with constant velocity. This means that the soliton performs, on the average, a unidirectional motion although the spatial average of the force vanishes. The amplitude of the oscillations is much smaller than the period of f(x) . In order to find out for which regions the above solutions are stable, we calculate the time evolution of the soliton momentum P(t) and the soliton velocity V(t) : This is a parameter representation of a curve P(V) which is visited by the soliton while time evolves. Our conjecture is that the soliton becomes unstable, if this curve has a branch with negative slope. This conjecture is fully confirmed by our simulations for the perturbed NLSE. Moreover, this curve also yields a good estimate for the soliton lifetime: the soliton lives longer, the shorter the branch with negative slope is.

Mertens, Franz G.; Quintero, Niurka R.; Bishop, A. R.



Spatio-temporal registration of multiple trajectories.  


A growing number of medical datasets now contain both a spatial and a temporal dimension. Trajectories, from tools or body features, are thus becoming increasingly important for their analysis. In this paper, we are interested in recovering the spatial and temporal differences between trajectories coming from different datasets. In particular, we address the case of surgical gestures, where trajectories contain both spatial transformations and speed differences in the execution. We first define the spatio-temporal registration problem between multiple trajectories. We then propose an optimization method to jointly recover both the rigid spatial motions and the non-linear time warpings. The optimization generates also a generic trajectory template, in which spatial and temporal differences have been factored out. This approach can be potentially used to register and compare gestures side-by-side for training sessions, to build gesture trajectory models for automation by a robot, or to register the trajectories of natural or artificial markers which follow similar motions. We demonstrate its usefulness with synthetic and real experiments. In particular, we register and analyze complex surgical gestures performed by tele-manipulation using the da Vinci robot. PMID:22003611

Padoy, Nicolas; Hager, Gregory D



Spatiotemporal molecular analysis of cyanobacteria blooms reveals microcystis - aphanizomenon interactions.  


Spatial and temporal variability in cyanobacterial community composition (CCC) within and between eutrophic lakes is not well-described using culture independent molecular methods. We analyzed CCC across twelve locations in four eutrophic lakes and within-lake locations in the Yahara Watershed, WI, on a weekly basis, for 5 months. Taxa were discriminated by length of MspI-digested cpcB/A intergenic spacer gene sequences and identified by comparison to a PCR-based clone library. CCC across all stations was spatially segregated by depth of sampling locations (ANOSIM R = 0.23, p < 0.001). Accordingly, CCC was correlated with thermal stratification, nitrate and soluble reactive phosphorus (SRP, R = 0.2-0.3). Spatial variability in CCC and temporal trends in taxa abundances were rarely correlative between sampling locations in the same lake indicating significant within lake spatiotemporal heterogeneity. Across all stations, a total of 37 bloom events were observed based on distinct increases in phycocyanin. Out of 97 taxa, a single Microcystis, and two different Aphanizomenon taxa were the dominant cyanobacteria detected during bloom events. The Microcystis and Aphanizomenon taxa rarely bloomed together and were significantly anti-correlated with each other at 9 of 12 stations with Pearson R values of -0.6 to -0.9 (p < 0.001). Of all environmental variables measured, nutrients, especially nitrate were significantly greater during periods of Aphanizomenon dominance while the nitrate+nitrite:SRP ratio was lower. This study shows significant spatial variability in CCC within and between lakes structured by depth of the sampling location. Furthermore, our study reveals specific genotypes involved in bloom formation. More in-depth characterization of these genotypes should lead to a better understanding of factors promoting bloom events in these lakes and more reliable bloom prediction models. PMID:24086400

Miller, Todd R; Beversdorf, Lucas; Chaston, Sheena D; McMahon, Katherine D



Spatio-temporal coupling of EEG signals in epilepsy  

NASA Astrophysics Data System (ADS)

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

Senger, Vanessa; Müller, Jens; Tetzlaff, Ronald



Ultrashort relativistic electron bunches and spatio-temporal radiation biology  

NASA Astrophysics Data System (ADS)

The intensive developments of terawatt Ti:Sa lasers permit to extend laser-plasma interactions into the relativistic regime, providing very-short electron or proton bunches. Experimental researches developed at the interface of laser physics and radiation biology, using the combination of sub-picosecond electron beams in the energy range 2-15 MeV with femtosecond near-IR optical pulses might conjecture the real-time investigation of penetrating radiation effects. A perfect synchronization between the particle beam (pump) and optical beam at 820 nm (probe) allows subpicosecond time resolution. This emerging domain involves high-energy radiation femtochemistry (HERF) for which the early spatial energy deposition is decisive for the prediction of cellular and tissular radiation damages. With vacuum-focused intensities of 2.7 x 1019 W cm-2 and a high energy electron total charge of 2.5 nC, radiation events have been investigated in the temporal range 10-13 - 10-10s. The early radiation effects of secondary electron on biomolecular sensors may be investigated inside sub-micrometric ionisation, considering the radial direction of Gaussian electron bunches. It is shown that short range electron-biosensor interactions lower than 10 A take place in nascent track structures triggered by penetrating radiation bunches. The very high dose delivery 1013 Gy s-1 performed with laser plasma accelerator may challenge our understanding of nanodosimetry on the time scale of molecular target motions. High-quality ultrashort penetrating radiation beams open promising opportunities for the development of spatio-temporal radiation biology, a crucial domain of cancer therapy, and would favor novating applications in nanomedicine such as highly-selective shortrange pro-drug activation.

Gauduel, Y. A.; Faure, J.; Malka, V.



Deformation localization in orogens: Spatiotemporal expression and thermodynamic constraint  

NASA Astrophysics Data System (ADS)

Orogens are spatiotemporal expressions of instabilities in materials under load, constrained by thermodynamics, and preserved in the cold outer shell of the planet. Their pressure-temperature-time histories are consistent with the predictions of differential grade-2 (DG-2) materials in pure shear. We place the statistically invariant shear localization mechanism of these materials in a coherent thermodynamic context using an analysis of strained elastic materials. This prototype system exhibits non-classical thermodynamic symmetry-breaking, where the potentials are all functions of a single variable and the distinction between heat and work fades from view. Consequently, internal energy must be described by a monotonically decreasing function of the entropy in order for heat capacity and absolute temperature to be positive. The entropy itself exhibits an inverse dependence on length. These constraints are satisfied by the overall shape and slope of the distributed deformation threshold ?D for DG-2 materials, and its noted 1/length correlation with naturally observed folds as a function of thermomechanical competence ?/?. We predict that temperature in this non-linear elastic material will vary in proportion to the slope of ?D, being high at low competence, and low at high competence. Similar constraints apply to a self-gravitating body, where the energy function varies inversely with radius. Assigning zero pressure at the surface of the body, we also predict that pressure, the tensor trace of its stress-energy density, will vary inversely with radius. Thus, the body force of gravity will be expressed in this elastic self-gravitating system through the interplay of elastic and thermal lengths. Deformation localization in DG-2 materials arises due to the dynamic rescaling of lengths in response to a spike in the intrinsic energy ?I at ?/? = ½. While the intrinsic ?I and localization ?L thresholds are monotonically decreasing for ?/? > ½, they exhibit positive slopes at lower competence, signaling a return to classical thermodynamics and Joule heating in this transitional domain. Numerous structural and tectonic observations can be correlated using this remarkably simple model, beginning with the thickness and mechanical character of the brittle crust and oceanic lithosphere. In effect, this model projects the kinematic theory of plate tectonics into four-dimensional spacetime.

Patton, R. L.; Watkinson, A. John



BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference.  


Protein-protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at PMID:22689642

Garcia-Garcia, Javier; Schleker, Sylvia; Klein-Seetharaman, Judith; Oliva, Baldo



Fluorescence advantages with microscopic spatiotemporal control  

NASA Astrophysics Data System (ADS)

We present a clever design concept of using femtosecond laser pulses in microscopy by selective excitation or de-excitation of one fluorophore over the other overlapping one. Using either a simple pair of femtosecond pulses with variable delay or using a train of laser pulses at 20-50 Giga-Hertz excitation, we show controlled fluorescence excitation or suppression of one of the fluorophores with respect to the other through wave-packet interference, an effect that prevails even after the fluorophore coherence timescale. Such an approach can be used both under the single-photon excitation as well as in the multi-photon excitation conditions resulting in effective higher spatial resolution. Such high spatial resolution advantage with broadband-pulsed excitation is of immense benefit to multi-photon microscopy and can also be an effective detection scheme for trapped nanoparticles with near-infrared light. Such sub-diffraction limit trapping of nanoparticles is challenging and a two-photon fluorescence diagnostics allows a direct observation of a single nanoparticle in a femtosecond high-repetition rate laser trap, which promises new directions to spectroscopy at the single molecule level in solution. The gigantic peak power of femtosecond laser pulses at high repetition rate, even at low average powers, provide huge instantaneous gradient force that most effectively result in a stable optical trap for spatial control at sub-diffraction limit. Such studies have also enabled us to explore simultaneous control of internal and external degrees of freedom that require coupling of various control parameters to result in spatiotemporal control, which promises to be a versatile tool for the microscopic world.

Goswami, Debabrata; Roy, Debjit; De, Arijit K.



Sequence matching using spatiotemporal wavelet decomposition  

NASA Astrophysics Data System (ADS)

Indexing and retrieval of image sequences are fundamental steps in video editing and film analysis. Correlation-based matching methods are known to be very expensive when used with large amounts of data. As the size of sequence database grows, traditional retrieval methods fail. Exhaustive search quickly breaks down as an efficient strategy for sequence databases. Moreover, traditional indexing with labels has a lot of drawbacks since it requires a human intervention. New advanced correlation filters are being proposed so as to decrease the computational load of the task. A new method for retrieval of images sequences in large database based on a spatio-temporal wavelet decomposition is proposed here. It will be shown how the use of the multiresolution approach can lead to good results in terms of computationally efficiency and robustness to noise. We will assume that the query sequence may not be contained in the database for different reasons: the presence of a noise signal on the query, or different digitation process, or the query is only similar to sequences in the database. As a consequence we are providing have developed a new efficient retrieval strategy that analyses the database in order to extract the most similar sequences to a given query. The wavelet transform has been chose as the framework to implement the multiresolution formalism, because of its good compression capabilities, especially for embedded schemes. And the good features it provides for signal analysis. This paper describes the principles of a multiresolution sequence matching strategy and outlines its performance through a series of experimental simulations.

Corghi, A.; Leonardi, Riccardo



Assessment of soil organic carbon distribution in Europe scale by spatio-temporal data and geostatistics  

NASA Astrophysics Data System (ADS)

Accuracy in assessing the distribution of soil organic carbon (SOC) is an important issue because SOC is an important soil component that plays key roles in the functions of both natural ecosystems and agricultural systems. The SOC content varies from place to place and it is strongly related with climate variables (temperature and rainfall), terrain features, soil texture, parent material, vegetation, land-use types, and human management (management and degradation) at different spatial scales. Geostatistical techniques allow for the prediction of soil properties using soil information and environmental covariates. In this study, assessment of SOC distribution has been predicted using combination of LUCAS soil samples with local soil data and ten spatio-temporal predictors (slope, aspect, elevation, CTI, CORINE land-cover classification, parent material, texture, WRB soil classification, average temperature and precipitation) with Regression-Kriging method in Europe scale. Significant correlation between the covariates and the organic carbon dependent variable was found.

Aksoy, Ece; Panagos, Panos; Montanarella, Luca



Experimental study of spatiotemporally localized surface gravity water waves.  


We present experimental results on the study of spatiotemporally localized surface wave events on deep water that can be modeled using the Peregrine breather solution of the nonlinear Schrödinger equation. These are often considered as prototypes of oceanic rogue waves that can focus wave energy into a single wave packet. For small steepness values of the carrier gravity waves the Peregrine breathers are relatively wide, thus providing an excellent agreement between the theory and experimental results. For larger steepnesses the focusing leads to temporally and spatially shorter events. Nevertheless, agreement between measurements and the Peregrine breather theory remains reasonably good, with discrepancies of modulation gradients and spatiotemporal symmetries being tolerable. Lifetimes and travel distances of the spatiotemporally localized wave events determined from the experiment are in good agreement with the theory. PMID:23005529

Chabchoub, A; Akhmediev, N; Hoffmann, N P



REVIEW ARTICLE: Spatio-temporal couplings in ultrashort laser pulses  

NASA Astrophysics Data System (ADS)

The electric field of an ultrashort laser pulse often fails to separate into a product of purely temporal and purely spatial factors. These so-called spatio-temporal couplings constitute a broad range of physical effects, which often become important in applications. In this review, we compile some recent experimental and theoretical work on the understanding, avoidance and applications of these effects. We first present a discussion of the characteristics of pulses containing spatio-temporal couplings, including their sources, a mathematical description and the interdependence of different couplings. We then review different experimental methods for their characterization. Finally, we describe different applications of spatio-temporal couplings and suggest further schemes for their exploitation and avoidance.

Akturk, Selcuk; Gu, Xun; Bowlan, Pamela; Trebino, Rick



Autophagy-related gene 12 (ATG12) is a novel determinant of primary resistance to HER2-targeted therapies: Utility of transcriptome analysis of the autophagy interactome to guide breast cancer treatment  

PubMed Central

The autophagic process, which can facilitate breast cancer resistance to endocrine, cytotoxic, and molecularly targeted agents, is mainly regulated at the post-translational level. Although recent studies have suggested a possible transcriptome regulation of the autophagic genes, little is known about either the analysis tools that can be applied or the functional importance of putative candidate genes emerging from autophagy-dedicated transcriptome studies. In this context, we evaluated whether the constitutive activation of the autophagy machinery, as revealed by a transcriptome analysis using an autophagy-focused polymerase chain reaction (PCR) array, might allow for the identification of novel autophagy-specific biomarkers for intrinsic (primary) resistance to HER2-targeted therapies. Quantitative real-time PCR (qRT-PCR)-based profiling of 84 genes involved in autophagy revealed that, when compared to trastuzumab-sensitive SKBR3 cells, the positive regulator of autophagic vesicle formation ATG12 (autophagy-related gene 12) was the most differentially up-regulated gene in JIMT1 cells, a model of intrinsic cross-resistance to trastuzumab and other HER1/2-targeting drugs. An analysis of the transcriptional status of ATG12 in > 50 breast cancer cell lines suggested that the ATG12 transcript is commonly upregulated in trastuzumab-unresponsive HER2-overexpressing breast cancer cells. A lentiviral-delivered small hairpin RNA stable knockdown of the ATG12 gene fully suppressed the refractoriness of JIMT1 cells to trastuzumab, erlotinib, gefitinib, and lapatinib in vitro. ATG12 silencing significantly reduced JIMT1 tumor growth induced by subcutaneous injection in nude mice. Remarkably, the outgrowth of trastuzumab-unresponsive tumors was prevented completely when trastuzumab treatment was administered in an ATG12-silenced genetic background. We demonstrate for the first time the usefulness of low-density, autophagy-dedicated qRT-PCR-based platforms for monitoring primary resistance to HER2-targeted therapies by transcriptionally screening the autophagy interactome. The degree of predictive accuracy warrants further investigation in the clinical situation.

Corominas-Faja, Bruna; Urruticoechea, Ander; Martin-Castillo, Begona; Menendez, Javier A.



Heteroassociations of spatio-temporal sequences with the bidirectional associative memory.  


Autoassociations of spatio-temporal sequences have been discussed by a number of authors. We propose a mechanism for storing and retrieving pairs of spatio-temporal sequences with the network architecture of the standard bidirectional associative memory (BAM), thereby achieving hetero-associations of spatio-temporal sequences. PMID:18249876

Wang, L



Heteroassociations of spatio-temporal sequences with the bidirectional associative memory  

Microsoft Academic Search

Autoassociations of spatio-temporal sequences have been discussed by a number of authors. We propose a mechanism for storing and retrieving pairs of spatio-temporal sequences with the network architecture of the standard bidirectional associative memory (BAM), thereby achieving heteroassociations of spatio-temporal sequences.

Lipo Wang



Artificial neural network does better spatiotemporal compressive sampling  

NASA Astrophysics Data System (ADS)

Spatiotemporal sparseness is generated naturally by human visual system based on artificial neural network modeling of associative memory. Sparseness means nothing more and nothing less than the compressive sensing achieves merely the information concentration. To concentrate the information, one uses the spatial correlation or spatial FFT or DWT or the best of all adaptive wavelet transform (cf. NUS, Shen Shawei). However, higher dimensional spatiotemporal information concentration, the mathematics can not do as flexible as a living human sensory system. The reason is obviously for survival reasons. The rest of the story is given in the paper.

Lee, Soo-Young; Hsu, Charles; Szu, Harold



Time reversal and the spatio-temporal matched filter  

SciTech Connect

It is known that focusing of an acoustic field by a time-reversal mirror (TRM) is equivalent to a spatio-temporal matched filter under conditions where the Green's function of the field satisfies reciprocity and is time invariant, i.e. the Green's function is independent of the choice of time origin. In this letter, it is shown that both reciprocity and time invariance can be replaced by a more general constraint on the Green's function that allows a TRM to implement the spatio-temporal matched filter even when conditions are time varying.

Lehman, S K; Poggio, A J; Kallman, J S; Meyer, A W; Candy, J V



Missile-tracking algorithm using target-adapted spatiotemporal wavelets  

NASA Astrophysics Data System (ADS)

This paper presents new results on the tracking of ballistic missiles warheads using spatio-temporal wavelets. Here we focus our attention on handling more general classes of motion, such as acceleration. To accomplish this task the spatio-temporal wavelet transform is adapted to the motion parameters on a frame-by-frame basis. Three different energy densities, associated with velocity, location, and size, have been defined to determine motion parameters. We pointed out that maximizing these energy densities is equivalent to a minimum squared error estimation. Tracking results on synthetically generated image sequences demonstrate the capabilities of the proposed algorithm.

Mujica, Fernando; Leduc, Jean-Pierre; Smith, Mark J.; Murenzi, Romain



Spatiotemporal dynamics and transport reduction in helical magnetic configuration  

SciTech Connect

Effects of multihelicity confinement magnetic fields on turbulent transport and zonal flows are investigated by means of spatiotemporal analysis of gyrokinetic Vlasov simulation results for the ion temperature gradient turbulence, where the standard and the inward-shifted configurations of the Large Helical Device are considered. The analysis of simulation results demonstrates that fluctuations of electrostatic potential for zonal flows exhibit spatiotemporal chaos in both configurations. However, the intensity of chaos found is considerably decreased in the inward-shifted configuration consistent with improved confinement. Enhanced zonal flow generation in the inward shifted case is accompanied by transport reduction which may be a direct consequence of chaos suppression.

Rajkovic, Milan [Institute of Nuclear Sciences Vinca, Belgrade 11001 (Serbia); National Institute for Fusion Science, Toki 509-5292 (Japan); Watanabe, Tomo-Hiko; Skoric, Milos [National Institute for Fusion Science, Toki 509-5292 (Japan)



Chaotic saddles at the onset of intermittent spatiotemporal chaos  

NASA Astrophysics Data System (ADS)

In a recent study [Rempel and Chian, Phys. Rev. Lett. 98, 014101 (2007)], it has been shown that nonattracting chaotic sets (chaotic saddles) are responsible for intermittency in the regularized long-wave equation that undergoes a transition to spatiotemporal chaos (STC) via quasiperiodicity and temporal chaos. In the present paper, it is demonstrated that a similar mechanism is present in the damped Kuramoto-Sivashinsky equation. Prior to the onset of STC, a spatiotemporally chaotic saddle coexists with a spatially regular attractor. After the transition to STC, the chaotic saddle merges with the attractor, generating intermittent bursts of STC that dominate the post-transition dynamics.

Rempel, Erico L.; Chian, Abraham C.-L.; Miranda, Rodrigo A.



Chaotic saddles at the onset of intermittent spatiotemporal chaos.  


In a recent study [Rempel and Chian, Phys. Rev. Lett. 98, 014101 (2007)], it has been shown that nonattracting chaotic sets (chaotic saddles) are responsible for intermittency in the regularized long-wave equation that undergoes a transition to spatiotemporal chaos (STC) via quasiperiodicity and temporal chaos. In the present paper, it is demonstrated that a similar mechanism is present in the damped Kuramoto-Sivashinsky equation. Prior to the onset of STC, a spatiotemporally chaotic saddle coexists with a spatially regular attractor. After the transition to STC, the chaotic saddle merges with the attractor, generating intermittent bursts of STC that dominate the post-transition dynamics. PMID:18233749

Rempel, Erico L; Chian, Abraham C-L; Miranda, Rodrigo A



Spatiotemporal characterization of few-cycle laser pulses.  


In this paper we apply a broadband fiber optic coupler interferometer to the measurement of few-cycle laser pulses. Sub-8-fs pulses delivered by an ultrafast oscillator were characterized spatiotemporally using STARFISH, which is based on spatially resolved spectral interferometry. The reference pulse was measured with the d-scan technique. The pulses were focused by an off-axis parabolic mirror and were characterized at different transverse planes along the focusing region. The evolution of the retrieved pulses is analyzed, exhibiting small variations in the temporal (and spectral) amplitude and phase during propagation. Finally, the peak irradiance evolution is estimated from the integration of the spatiotemporal intensity. PMID:23038338

Alonso, Benjamín; Miranda, Miguel; Sola, Íñigo J; Crespo, Helder



Male reproductive strategy explains spatiotemporal segregation in brown bears  

PubMed Central

1. Spatiotemporal segregation is often explained by the risk for offspring predation or by differences in physiology, predation risk vulnerability or competitive abilities related to size dimorphism. 2. Most large carnivores are size dimorphic and offspring predation is often intraspecific and related to nonparental infanticide (NPI). NPI can be a foraging strategy, a strategy to reduce competition, or a male reproductive strategy. Spatiotemporal segregation is widespread among large carnivores, but its nature remains poorly understood. 3. We evaluated three hypotheses to explain spatiotemporal segregation in the brown bear, a size-dimorphic large carnivore in which NPI is common; the ‘NPI – foraging/competition hypothesis', i.e. NPI as a foraging strategy or a strategy to reduce competition, the ‘NPI – sexual selection hypothesis’, i.e. infanticide as a male reproductive strategy and the ‘body size hypothesis’, i.e. body-size-related differences in physiology, predation risk vulnerability or competitive ability causes spatiotemporal segregation. To test these hypotheses, we quantified spatiotemporal segregation among adult males, lone adult females and females with cubs-of-the-year, based on GPS-relocation data (2006–2010) and resource selection functions in a Scandinavian population. 4. We found that spatiotemporal segregation was strongest between females with cubs-of-the-year and adult males during the mating season. During the mating season, females with cubs-of-the-year selected their resources, in contrast to adult males, in less rugged landscapes in relative close proximity to certain human-related variables, and in more open habitat types. After the mating season, females with cubs-of-the-year markedly shifted their resource selection towards a pattern more similar to that of their conspecifics. No strong spatiotemporal segregation was apparent between females with cubs-of-the-year and conspecifics during the mating and the postmating season. 5. The ‘NPI – sexual selection hypothesis’ best explained spatiotemporal segregation in our study system. We suggest that females with cubs-of-the-year alter their resource selection to avoid infanticidal males. In species exhibiting NPI as a male reproductive strategy, female avoidance of infanticidal males is probably more common than observed or reported, and may come with a fitness cost if females trade safety for optimal resources.

Steyaert, Sam MJG; Kindberg, Jonas; Swenson, Jon E; Zedrosser, Andreas



Spatio-temporal evolution of biogeochemical processes at a landfill site  

NASA Astrophysics Data System (ADS)

Predictions of fate and transport of contaminants are strongly dependent on spatio-temporal variability of soil hydraulic and geochemical properties. This study focuses on time-series signatures of hydrological and geochemical properties at different locations within the Norman landfill site. Norman Landfill is a closed municipal landfill site with prevalent organic contamination. Monthly data at the site include specific conductance, ?18O, ?2H, dissolved organic carbon (DOC) and anions (chloride, sulfate, nitrate) from 1998-2006. Column scale data on chemical concentrations, redox gradients, and flow parameters are also available on daily and hydrological event (infiltration, drainage, etc.) scales. Since high-resolution datasets of contaminant concentrations are usually unavailable, Wavelet and Fourier analyses were used to infer the dominance of different biogeochemical processes at different spatio-temporal scales and to extract linkages between transport and reaction processes. Results indicate that time variability controls the progression of reactions affecting biodegradation of contaminants. Wavelet analysis suggests that iron-sulfide reduction reactions had high seasonal variability at the site, while fermentation processes dominated at the annual time scale. Findings also suggest the dominance of small spatial features such as layered interfaces and clay lenses in driving biogeochemical reactions at both column and landfill scales. A conceptual model that caters to increased understanding and remediating structurally heterogeneous variably-saturated media is developed from the study.

Arora, B.; Mohanty, B. P.; McGuire, J. T.



Disclosing the spatiotemporal structure of parametric down-conversion entanglement through frequency up-conversion  

NASA Astrophysics Data System (ADS)

In this work we propose and analyze a scheme where the full spatiotemporal correlation of twin photons or beams generated by parametric down-conversion is detected by using its inverse process, i.e., sum frequency generation. Our main result is that, by imposing independently a temporal delay ?t and a transverse spatial shift ?x between two twin components of PDC light, the up-converted light intensity provides information on the correlation of the PDC light in the full spatiotemporal domain and should enable the reconstruction of the peculiar X-shaped structure of the correlation predicted previously [A. Gatti Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.102.223601 102, 223601 (2009); L. Caspani Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.81.033808 81, 033808 (2010); E. Brambilla Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.82.013835 82, 013835 (2010)]. Through both a semianalytical and a numerical modeling of the proposed optical system, we analyze the feasibility of the experiment and identify the best conditions to implement it. In particular, the tolerance of the phase-sensitive measurement against the presence of dispersive elements, imperfect imaging conditions, and possible misalignments of the two crystals is evaluated.

Brambilla, E.; Jedrkiewicz, O.; Lugiato, L. A.; Gatti, A.



Spatiotemporal SNP analysis reveals pronounced biocomplexity at the northern range margin of Atlantic cod Gadus morhua.  


Accurate prediction of species distribution shifts in the face of climate change requires a sound understanding of population diversity and local adaptations. Previous modeling has suggested that global warming will lead to increased abundance of Atlantic cod (Gadus morhua) in the ocean around Greenland, but the dynamics of earlier abundance fluctuations are not well understood. We applied a retrospective spatiotemporal population genomics approach to examine the temporal stability of cod population structure in this region and to search for signatures of divergent selection over a 78-year period spanning major demographic changes. Analyzing >900 gene-associated single nucleotide polymorphisms in 847 individuals, we identified four genetically distinct groups that exhibited varying spatial distributions with considerable overlap and mixture. The genetic composition had remained stable over decades at some spawning grounds, whereas complete population replacement was evident at others. Observations of elevated differentiation in certain genomic regions are consistent with adaptive divergence between the groups, indicating that they may respond differently to environmental variation. Significantly increased temporal changes at a subset of loci also suggest that adaptation may be ongoing. These findings illustrate the power of spatiotemporal population genomics for revealing biocomplexity in both space and time and for informing future fisheries management and conservation efforts. PMID:23789034

Therkildsen, Nina Overgaard; Hemmer-Hansen, Jakob; Hedeholm, Rasmus Berg; Wisz, Mary S; Pampoulie, Christophe; Meldrup, Dorte; Bonanomi, Sara; Retzel, Anja; Olsen, Steffen Malskær; Nielsen, Einar Eg



Monitoring and validating spatio-temporal dynamics of biogeochemical properties in Mersin Bay (Turkey) using Landsat ETM+.  


The objective of this study was to devise and validate simple models for estimating spatio-temporal dynamics of seven optically (in)active biogeochemical properties in Mersin Bay using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data and GIS. Spatio-temporal dynamics of Secchi depth (S (depth)), dissolved oxygen (DO), nitrite nitrogen (NO(2)-N), nitrate nitrogen (NO?-N), silicate (SiO?), 5-day biological oxygen demand (BOD5), and chlorophyll-a (Chl-a) were estimated using best-fit multiple linear regression (MLR) models as a function of Landsat 7 ETM+ and ground data in 2007 and 2008, latitude, longitude, and day of year. Validation of the MLR models against Landsat and ground data in 2005 led to r values ranging from 0.39 for NO?-N (P?=?0.008) to 0.79 for S (depth) (P?predicting spatio-temporal dynamics of optically (in)active water quality characteristics in Mersin Bay. PMID:21181257

Karakaya, Nusret; Evrendilek, Fatih



Spatio-temporal evaluation matrices for geospatial data  

NASA Astrophysics Data System (ADS)

The global geospatial community is investing substantial effort in providing tools for geospatial data-quality information analysis and systematizing the criteria for geospatial data quality. The importance of these activities is increasing, especially in the last decade, which has witnessed an enormous expansion of geospatial data use in general and especially among mass users. Although geospatial data producers are striving to define and present data-quality standards to users and users increasingly need to assess the fitness for use of the data, the success of these activities is still far from what is expected or required. As a consequence, neglect or misunderstanding of data quality among users results in misuse or risks. This paper presents an aid in spatio-temporal quality evaluation through the use of spatio-temporal evaluation matrices (STEM) and the index of spatio-temporal anticipations (INSTANT) matrices. With the help of these two simple tools, geospatial data producers can systematically categorize and visualize the granularity of their spatio-temporal data, and users can present their requirements in the same way using business intelligence principles and a Web 2.0 approach. The basic principles and some examples are presented in the paper, and potential further applied research activities are briefly described.

Triglav, Joc; Petrovi?, Dušan; Stopar, Bojan



Modeling spatiotemporal noise covariance for MEG\\/EEG source analysis  

Microsoft Academic Search

We propose a new model for approximating spatiotemporal noise covariance for use in MEG\\/EEG source analysis. Our model is an extension of an existing model [1,2] that uses a single Kronecker product of a pair of matrices - temporal and spatial covariance; we employ a series of Kronecker products in order to construct a better approximation of the full covariance.

S. M. Plis; J. S. George; S. C. Jun; J. Pare-Blagoev; D. M. Ranken; D. M. Schmidt; C. C. Wood



Nonequilibrium pattern formation and spatiotemporal chaos in fluid convection  

SciTech Connect

The final report for grant number DE-FG03-98ER14891 summarizes the application of the unique simulation capabilities developed under DOE support to investigations of important issues in pattern formation and spatiotemporal chaos in Rayleigh-Benard convection, particularly emphasizing quantitative contact with the active experimental programs.

Michael Cross



A Case Study: Ordinal Responses With Spatio-Temporal Dependencies  

Microsoft Academic Search

Data structures with spatial and temporal dependencies are not uncom- mon in environmental and agronomic flelds. We consider the modeling and estima- tion problem for these type of structures, in particular we consider proportional odds models with spatio-temporal covariables with estimation via maximum pseudlikeli- hood. We end by presenting a testing problem on treatment efiects on data from a fleld

Rogelio Ramos-Quiroga; Graciela Gonzalez-Far


Visual Experience Modulates Spatio-Temporal Dynamics of Circuit Activation  

PubMed Central

Persistent reduction in sensory drive in early development results in multiple plastic changes of different cortical synapses. How these experience-dependent modifications affect the spatio-temporal dynamics of signal propagation in neocortical circuits is poorly understood. Here we demonstrate that brief visual deprivation significantly affects the propagation of electrical signals in the primary visual cortex. The spatio-temporal spread of circuit activation upon direct stimulation of its input layer (Layer 4) is reduced, as is the activation of L2/3 – the main recipient of the output from L4. Our data suggest that the decrease in spatio-temporal activation of L2/3 depends on reduced L4 output, and is not intrinsically generated within L2/3. The data shown here suggest that changes in the synaptic components of the visual cortical circuit result not only in alteration of local integration of excitatory and inhibitory inputs, but also in a significant decrease in overall circuit activation. Furthermore, our data indicate a differential effect of visual deprivation on L4 and L2/3, suggesting that while feedforward activation of L2/3 is reduced, its activation by long range, within layer inputs is unaltered. Thus, brief visual deprivation induces experience-dependent circuit re-organization by modulating not only circuit excitability, but also the spatio-temporal patterns of cortical activation within and between layers.

Wang, Lang; Fontanini, Alfredo; Maffei, Arianna



Spatiotemporal Coupling of the Tongue in Amyotrophic Lateral Sclerosis  

ERIC Educational Resources Information Center

|Purpose: The primary aim of the investigation was to identify deficits in spatiotemporal coupling between tongue regions in amyotrophic lateral sclerosis (ALS). The relations between disease-related changes in tongue movement patterns and speech intelligibility were also determined. Methods: The authors recorded word productions from 11…

Kuruvilla, Mili S.; Green, Jordan R.; Yunusova, Yana; Hanford, Kathy



Spatiotemporal characterization of Sclerotinia crown rot epidemics in pyrethrum  

Technology Transfer Automated Retrieval System (TEKTRAN)

Sclerotinia crown rot, caused by Sclerotinia minor and S. sclerotiorum is a disease of pyrethrum in Australia that may cause substantial decline in plant density. The spatiotemporal characteristics of the disease were quantified in 14 fields spread across three growing seasons. Fitting the binary ...


Exploratory spatio-temporal data mining and visualization  

Microsoft Academic Search

Spatio-temporal data sets are often very large and difficult to analyze and display. Since they are fundamental for decision support in many application contexts, recently a lot of interest has arisen toward data-mining techniques to filter out relevant subsets of very large data repositories as well as visualization tools to effectively display the results. In this paper we propose a

P. Compieta; Sergio Di Martino; Michela Bertolotto; Filomena Ferrucci; M. Tahar Kechadi



An objective video quality metric based on spatiotemporal distortion  

Microsoft Academic Search

This paper proposes an objective video quality metric based on an analysis of spatial and temporal distortions. Spatial quality features extracted from the spatiotemporal region of reference and distorted videos are used to express the spatial distortion. Temporal distortion, caused by frame freezing resulting from a packet loss, is derived from the spatial distortion before and after the frozen frames.

Junyong You; Miska M. Hannuksela; Moncef Gabbouj



Representing Spatiotemporal Processes to Support Knowledge Discovery in GIS databases  

Microsoft Academic Search

This article consists of two objectives: (1) to outline a GIS framework that can represent and compute information about spatiotemporal behaviors of processes; and (2) to use this representation in support of automatic GIS query processing to allow integration of GIS and Knowledge Discovery in Databases (KDD) technologies. KDD technology has emerged as an empowering tool in the development of

May Yuan



Finding Spatio-Temporal Patterns in Large Sensor Datasets  

ERIC Educational Resources Information Center

|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…

McGuire, Michael Patrick



Neuronal spatiotemporal pattern discrimination: The dynamical evolution of seizures  

Microsoft Academic Search

We developed a modern numerical approach to the multivariate linear discrimination of Fisher from 1936 based upon singular value decomposition that is sufficiently stable to permit widespread application to spatiotemporal neuronal patterns. We demonstrate this approach on an old problem in neuroscience—whether seizures have distinct dynamical states as they evolve with time. A practical result was the first demonstration that

Steven J. Schiff; Tim Sauer; Rohit Kumar; Steven L. Weinstein



Supporting user-defined granularities in a spatiotemporal conceptual model  

USGS Publications Warehouse

Granularities are integral to spatial and temporal data. A large number of applications require storage of facts along with their temporal and spatial context, which needs to be expressed in terms of appropriate granularities. For many real-world applications, a single granularity in the database is insufficient. In order to support any type of spatial or temporal reasoning, the semantics related to granularities needs to be embedded in the database. Specifying granularities related to facts is an important part of conceptual database design because under-specifying the granularity can restrict an application, affect the relative ordering of events and impact the topological relationships. Closely related to granularities is indeterminacy, i.e., an occurrence time or location associated with a fact that is not known exactly. In this paper, we present an ontology for spatial granularities that is a natural analog of temporal granularities. We propose an upward-compatible, annotation-based spatiotemporal conceptual model that can comprehensively capture the semantics related to spatial and temporal granularities, and indeterminacy without requiring new spatiotemporal constructs. We specify the formal semantics of this spatiotemporal conceptual model via translation to a conventional conceptual model. To underscore the practical focus of our approach, we describe an on-going case study. We apply our approach to a hydrogeologic application at the United States Geologic Survey and demonstrate that our proposed granularity-based spatiotemporal conceptual model is straightforward to use and is comprehensive.

Khatri, V.; Ram, S.; Snodgrass, R. T.; O'Brien, G. M.



Spatiotemporal Spike Encoding of a Continuous External Signal  

Microsoft Academic Search

Interspike intervals of spikes emitted from an integrator neuron model of sensory neurons can encode input information represented as a continuous signal from a deterministic system. If a real brain uses spike timing as a means of information processing, other neurons receiving spatiotemporal spikes from such sensory neurons must also be capable of treating information included in deterministic interspike intervals.

Naoki Masuda; Kazuyuki Aihara



Requirements, definitions, and notations for spatiotemporal application environments  

Microsoft Academic Search

Modeling spatiotemporal applications is a complex task, involving intric ate issues, such as the representation of objects' position in time, and spatial attributes that change val ues depending on specific locations in time periods. Due to this complexity, the ana lysis of users' requirements -as the first phase of an application development methodology -is often neglected, focusing, mainly, on physical

Dieter Pfoser; Nectaria Tryfona



Dynamic Processes Shape Spatiotemporal Properties of Retinal Waves  

Microsoft Academic Search

In the developing mammalian retina, spontaneous waves of action potentials are present in the ganglion cell layer weeks before vision. These waves are known to be generated by a synaptically connected network of amacrine cells and retinal ganglion cells, and exhibit complex spatiotemporal patterns, characterized by shifting domains of coactivation. Here, we present a novel dynamical model consisting of two

Marla B. Feller; Daniel A. Butts; Holly L. Aaron; Daniel S. Rokhsar; Carla J. Shatz



Kernel Averaged Predictors for Spatio-Temporal Regression Models  

PubMed Central

In applications where covariates and responses are observed across space and time, a common goal is to quantify the effect of a change in the covariates on the response while adequately accounting for the spatio-temporal structure of the observations. The most common approach for building such a model is to confine the relationship between a covariate and response variable to a single spatio-temporal location. However, oftentimes the relationship between the response and predictors may extend across space and time. In other words, the response may be affected by levels of predictors in spatio-temporal proximity to the response location. Here, a flexible modeling framework is proposed to capture such spatial and temporal lagged effects between a predictor and a response. Specifically, kernel functions are used to weight a spatio-temporal covariate surface in a regression model for the response. The kernels are assumed to be parametric and non-stationary with the data informing the parameter values of the kernel. The methodology is illustrated on simulated data as well as a physical data set of ozone concentrations to be explained by temperature.

Gelfand, Alan E.



Online identification of nonlinear spatiotemporal systems using kernel learning approach.  


The identification of nonlinear spatiotemporal systems is of significance to engineering practice, since it can always provide useful insight into the underlying nonlinear mechanism and physical characteristics under study. In this paper, nonlinear spatiotemporal system models are transformed into a class of multi-input-multi-output (MIMO) partially linear systems (PLSs), and an effective online identification algorithm is therefore proposed by using a pruning error minimization principle and least square support vector machines. It is shown that many benchmark physical and engineering systems can be transformed into MIMO-PLSs which keep some important physical spatiotemporal relationships and are very helpful in the identification and analysis of the underlying system. Compared with several existing methods, the advantages of the proposed method are that it can make full use of some prior structural information about system physical models, can realize online estimation of the system dynamics, and achieve accurate characterization of some important nonlinear physical characteristics of the system. This would provide an important basis for state estimation, control, optimal analysis, and design of nonlinear distributed parameter systems. The proposed algorithm can also be applied to identification problems of stochastic spatiotemporal dynamical systems. Numeral examples and comparisons are given to demonstrate our results. PMID:21788186

Ning, Hanwen; Jing, Xingjian; Cheng, Li



Spatio-temporal spectral analysis by eigenstructure methods  

Microsoft Academic Search

This paper presents new algorithms for estimating the spatio-temporal spectrum of the signals received by a passive array. The algorithms are based on the eigenstructure of the covariance and spectral density matrices of the received signals. They allow partial correlation between the sources and thus are applicable to certain kinds of multipath problems. Simulation results that illustrate the performance of

M. Wax; Tie-Jun Shan; T. Kailath



Hierarchical spatiotemporal modeling for dynamic video trajectory analysis  

Microsoft Academic Search

Normalcy decision in video security involves highly uncertain phenomena due to its inherited insufficient knowledge, intrinsic ambiguity in human cognition, measurement error, etc. This paper presents a hierarchical spatiotemporal trajectory modeling for dynamic trajectory analysis using sparse trajectory data. The trajectory data is assumed to be given by people detection and tracking methods, which are also challenging issues due to

Byung Hyung Kim; Hak Chul Shin; Phill Kyu Rhee



Indeterminacy and Spatiotemporal Data: Basic Definitions and Case Study  

Microsoft Academic Search

Abstract For some spatiotemporal applications, it can be assumed that the modeled world is precise and bounded, and that also our record of it is precise. While these simplifying assumptions are sufficient inapplications like a land information system, they are unnecessarily crude for many other applications that manage data with spatial and\\/or temporal extents, such as navigational applications. This work

Dieter Pfoser; Nectaria Tryfona; Christian S. Jensen



Statistical physics model for the spatiotemporal evolution of faults  

Microsoft Academic Search

A statistical physics model is used to simulate antiplane shear deformation and rupture of a tectonic plate with heterogeneous material properties. We document the spatiotemporal evolution of the rupture pattern in response to a constant velocity boundary condition. A fundamental feature of this model is that ruptures become strongly correlated in space and time, leading to the development of complex

Patience A. Cowie; Christian Vanneste; Didier Sornette



Spatiotemporal Analysis of Face Profiles: Detection, Segmentation, and Registration  

Microsoft Academic Search

We use a two-image approach to construct a 3D human facial model for multimedia applications. The images used are those of faces at direct frontal and side views. The selection of the side view from a sequence of facial images is automatically done by applying a spatiotemporal approach to face profile analysis. The extracted side profile is then segmented based

Behzad Dariush; Sing Bang Kang; Keith Waters



Fully Bayesian spatio-temporal modeling of FMRI data  

Microsoft Academic Search

We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), incorporating spatio-temporal noise modeling and haemodynamic response function (HRF) modeling. A fully Bayesian approach allows for the uncertainties in the noise and signal modeling to be incorporated together to provide full posterior distributions of the HRF parameters. The noise modeling is achieved via a nonseparable space-time

Mark William Woolrich; Mark Jenkinson; J. Michael Brady; Stephen M. Smith



Querying Mobile Objects in Spatio-Temporal Databases  

Microsoft Academic Search

In dynamic spatio-temporal environments where objects may continuously move in space, maintaining consistent information about the location of objects and processing motion-specic queries is a chal- lenging problem. In this paper, we focus on indexing and query process- ing techniques for mobile objects. Specically, we develop a classication of dierent types of selection queries that arise in mobile environments and

Kriengkrai Porkaew; Iosif Lazaridis; Sharad Mehrotra



Mapping and spatiotemporal analysis tool for hydrological data: Spellmap  

Technology Transfer Automated Retrieval System (TEKTRAN)

Lack of data management and analyses tools is one of the major limitations to effectively evaluate and use large datasets of high-resolution atmospheric, surface, and subsurface observations. High spatial and temporal resolution datasets better represent the spatiotemporal variability of hydrologica...


Spatiotemporal evolution and nonlinear kinetic simulations of stimulated Brillouin scattering  

Microsoft Academic Search

The spatiotemporal evolution of stimulated Brillouin scattering (SBS) in homogeneous plasmas and some aspects of the influence that nonlinear and kinetic effects have on the evolution of SBS were studied. A one-dimensional analytical linear model based on a fluid description of the plasma was developed initially. It was found that the threshold intensity of the absolute instability and the steady-state

Rodolfo E. Giacone



Cubic map algebra functions for spatio-temporal analysis  

USGS Publications Warehouse

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.

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



Asymmetric spatiotemporal chaos induced by a polypoid mass in the excised larynx  

NASA Astrophysics Data System (ADS)

In this paper, asymmetric spatiotemporal chaos induced by a polypoid mass simulating the laryngeal pathology of a vocal polyp is experimentally observed using high-speed imaging in an excised larynx. Spatiotemporal analysis reveals that the normal vocal folds show spatiotemporal correlation and symmetry. Normal vocal fold vibrations are dominated mainly by the first vibratory eigenmode. However, pathological vocal folds with a polypoid mass show broken symmetry and spatiotemporal irregularity. The spatial correlation is decreased. The pathological vocal folds spread vibratory energy across a large number of eigenmodes and induce asymmetric spatiotemporal chaos. High-order eigenmodes show complicated dynamics. Spatiotemporal analysis provides a valuable biomedical application for investigating the spatiotemporal chaotic dynamics of pathological vocal fold systems with a polypoid mass and may represent a valuable clinical tool for the detection of laryngeal mass lesion using high-speed imaging.

Zhang, Yu; Jiang, Jack J.



Interactome Mapping of the Phosphatidylinositol 3-Kinase-Mammalian Target of Rapamycin Pathway Identifies Deformed Epidermal Autoregulatory Factor-1 as a New Glycogen Synthase Kinase-3 Interactor*  

PubMed Central

The phosphatidylinositol 3-kinase-mammalian target of rapamycin (PI3K-mTOR) pathway plays pivotal roles in cell survival, growth, and proliferation downstream of growth factors. Its perturbations are associated with cancer progression, type 2 diabetes, and neurological disorders. To better understand the mechanisms of action and regulation of this pathway, we initiated a large scale yeast two-hybrid screen for 33 components of the PI3K-mTOR pathway. Identification of 67 new interactions was followed by validation by co-affinity purification and exhaustive literature curation of existing information. We provide a nearly complete, functionally annotated interactome of 802 interactions for the PI3K-mTOR pathway. Our screen revealed a predominant place for glycogen synthase kinase-3 (GSK3) A and B and the AMP-activated protein kinase. In particular, we identified the deformed epidermal autoregulatory factor-1 (DEAF1) transcription factor as an interactor and in vitro substrate of GSK3A and GSK3B. Moreover, GSK3 inhibitors increased DEAF1 transcriptional activity on the 5-HT1A serotonin receptor promoter. We propose that DEAF1 may represent a therapeutic target of lithium and other GSK3 inhibitors used in bipolar disease and depression.

Pilot-Storck, Fanny; Chopin, Emilie; Rual, Jean-Francois; Baudot, Anais; Dobrokhotov, Pavel; Robinson-Rechavi, Marc; Brun, Christine; Cusick, Michael E.; Hill, David E.; Schaeffer, Laurent; Vidal, Marc; Goillot, Evelyne



SRC Homology 2 Domain Binding Sites in Insulin, IGF-1 and FGF receptor mediated signaling networks reveal an extensive potential interactome.  


Specific peptide ligand recognition by modular interaction domains is essential for the fidelity of information flow through the signal transduction networks that control cell behavior in response to extrinsic and intrinsic stimuli. Src homology 2 (SH2) domains recognize distinct phosphotyrosine peptide motifs, but the specific sites that are phosphorylated and the complement of available SH2 domains varies considerably in individual cell types. Such differences are the basis for a wide range of available protein interaction microstates from which signaling can evolve in highly divergent ways. This underlying complexity suggests the need to broadly map the signaling potential of systems as a prerequisite for understanding signaling in specific cell types as well as various pathologies that involve signal transduction such as cancer, developmental defects and metabolic disorders. This report describes interactions between SH2 domains and potential binding partners that comprise initial signaling downstream of activated fibroblast growth factor (FGF), insulin (Ins), and insulin-like growth factor-1 (IGF-1) receptors. A panel of 50 SH2 domains screened against a set of 192 phosphotyrosine peptides defines an extensive potential interactome while demonstrating the selectivity of individual SH2 domains. The interactions described confirm virtually all previously reported associations while describing a large set of potential novel interactions that imply additional complexity in the signaling networks initiated from activated receptors. This study of pTyr ligand binding by SH2 domains provides valuable insight into the selectivity that underpins complex signaling networks that are assembled using modular protein interaction domains. PMID:22974441

Liu, Bernard A; Engelmann, Brett W; Jablonowski, Karl; Higginbotham, Katherine; Stergachis, Andrew B; Nash, Piers D



SRC Homology 2 Domain Binding Sites in Insulin, IGF-1 and FGF receptor mediated signaling networks reveal an extensive potential interactome  

PubMed Central

Specific peptide ligand recognition by modular interaction domains is essential for the fidelity of information flow through the signal transduction networks that control cell behavior in response to extrinsic and intrinsic stimuli. Src homology 2 (SH2) domains recognize distinct phosphotyrosine peptide motifs, but the specific sites that are phosphorylated and the complement of available SH2 domains varies considerably in individual cell types. Such differences are the basis for a wide range of available protein interaction microstates from which signaling can evolve in highly divergent ways. This underlying complexity suggests the need to broadly map the signaling potential of systems as a prerequisite for understanding signaling in specific cell types as well as various pathologies that involve signal transduction such as cancer, developmental defects and metabolic disorders. This report describes interactions between SH2 domains and potential binding partners that comprise initial signaling downstream of activated fibroblast growth factor (FGF), insulin (Ins), and insulin-like growth factor-1 (IGF-1) receptors. A panel of 50 SH2 domains screened against a set of 192 phosphotyrosine peptides defines an extensive potential interactome while demonstrating the selectivity of individual SH2 domains. The interactions described confirm virtually all previously reported associations while describing a large set of potential novel interactions that imply additional complexity in the signaling networks initiated from activated receptors. This study of pTyr ligand binding by SH2 domains provides valuable insight into the selectivity that underpins complex signaling networks that are assembled using modular protein interaction domains.



Exploring off-targets and off-systems for adverse drug reactions via chemical-protein interactome--clozapine-induced agranulocytosis as a case study.  


In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs. PMID:21483481

Yang, Lun; Wang, Kejian; Chen, Jian; Jegga, Anil G; Luo, Heng; Shi, Leming; Wan, Chunling; Guo, Xizhi; Qin, Shengying; He, Guang; Feng, Guoyin; He, Lin



Exploring Off-Targets and Off-Systems for Adverse Drug Reactions via Chemical-Protein Interactome -- Clozapine-Induced Agranulocytosis as a Case Study  

PubMed Central

In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs.

Chen, Jian; Jegga, Anil G.; Luo, Heng; Shi, Leming; Wan, Chunling; Guo, Xizhi; Qin, Shengying; He, Guang; Feng, Guoyin; He, Lin



Challenges for modelling spatio-temporal variations of malaria risk in Malawi  

NASA Astrophysics Data System (ADS)

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.

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



Evaluation of Physically-based Model's Predictive Skill for Hurricane-triggered Landslides: Case Study in North Carolina and Indonesia  

Microsoft Academic Search

The key to advancing the predictability of rainfall-triggered landslides is to use physically-based, slope-stability models that simulate the dynamical response of the subsurface moisture to the spatiotemporal variability of rainfall in complex terrain. In the first study we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Analysis) in the Blue

Z. Liao; Y. Hong; H. Fukuoka; K. Sassa



Absolute Stability of Wavetrains Can Explain Spatiotemporal Dynamics in Reaction-Diffusion Systems of Lambda-Omega Type  

NASA Astrophysics Data System (ADS)

The lambda-omega class of reaction-diffusion equations has received considerable attention because they are more amenable to mathematical investigation than other oscillatory reaction-diffusion systems and include the normal form of any reaction-diffusion system with scalar diffusion close to a standard supercritical Hopf bifurcation. Despite this, detailed studies of the dynamics predicted by numerical simulations have mostly been restricted to regions of parameter space in which stable wavetrains (periodic traveling waves) are selected by the initial or boundary conditions; we use the term "stability" to denote spectral stability on the real line. Here we consider the emergent spatiotemporal dynamics on large bounded domains, with Dirichlet conditions at one boundary and Neumann conditions at the other. Previous studies have established a parameter threshold below which stable wavetrains are generated by the Dirichlet boundary condition. We use numerical continuation techniques to analyze the spectral stability of wavetrain solutions, and we identify a second stability threshold, above which the selected wavetrain is absolutely unstable. In addition, we prove that the onset of absolute stability always occurs through a complex conjugate pair of branch points in the absolute spectrum, which greatly simplifies the detection of this threshold. In the parameter region in which the spectra of the selected waves indicate instability but absolute stability, our numerical simulations predict so-called "source-sink" dynamics: bands of visibly regular periodic traveling waves that are separated by localized defects. Beyond the absolute stability threshold our simulations predict irregular spatiotemporal behavior.

Smith, Matthew J.; Rademacher, Jens D. M.; Sherratt, Jonathan A.



Ordering spatiotemporal chaos in complex thermosensitive neuron networks  

NASA Astrophysics Data System (ADS)

We have studied the effect of random long-range connections in chaotic thermosensitive neuron networks with each neuron being capable of exhibiting diverse bursting behaviors, and found stochastic synchronization and optimal spatiotemporal patterns. For a given coupling strength, the chaotic burst-firings of the neurons become more and more synchronized as the number of random connections (or randomness) is increased and, rather, the most pronounced spatiotemporal pattern appears for an optimal randomness. As the coupling strength is increased, the optimal randomness shifts towards a smaller strength. This result shows that random long-range connections can tame the chaos in the neural networks and make the neurons more effectively reach synchronization. Since the model studied can be used to account for hypothalamic neurons of dogfish, catfish, etc., this result may reflect the significant role of random connections in transferring biological information.

Gong, Yubing; Xu, Bo; Xu, Qiang; Yang, Chuanlu; Ren, Tingqi; Hou, Zhonghuai; Xin, Houwen



Local information transfer as a spatiotemporal filter for complex systems  

NASA Astrophysics Data System (ADS)

We present a measure of local information transfer, derived from an existing averaged information-theoretical measure, namely, transfer entropy. Local transfer entropy is used to produce profiles of the information transfer into each spatiotemporal point in a complex system. These spatiotemporal profiles are useful not only as an analytical tool, but also allow explicit investigation of different parameter settings and forms of the transfer entropy metric itself. As an example, local transfer entropy is applied to cellular automata, where it is demonstrated to be a useful method of filtering for coherent structure. More importantly, local transfer entropy provides the first quantitative evidence for the long-held conjecture that the emergent traveling coherent structures known as particles (both gliders and domain walls, which have analogs in many physical processes) are the dominant information transfer agents in cellular automata.

Lizier, Joseph T.; Prokopenko, Mikhail; Zomaya, Albert Y.



Dynamics of light propagation in spatiotemporal dielectric structures.  


Propagation, transmission and reflection properties of linearly polarized plane waves and arbitrarily short electromagnetic pulses in one-dimensional dispersionless dielectric media possessing an arbitrary space-time dependence of the refractive index are studied by using a two-component, highly symmetric version of Maxwell's equations. The use of any slow varying amplitude approximation is avoided. Transfer matrices of sharp nonstationary interfaces are calculated explicitly, together with the amplitudes of all secondary waves produced in the scattering. Time-varying multilayer structures and spatiotemporal lenses in various configurations are investigated analytically and numerically in a unified approach. Several effects are reported, such as pulse compression, broadening and spectral manipulation of pulses by a spatiotemporal lens, and the closure of the forbidden frequency gaps with the subsequent opening of wave number band gaps in a generalized Bragg reflector. PMID:17501007

Biancalana, Fabio; Amann, Andreas; Uskov, Alexander V; O'Reilly, Eoin P



Block-Copolymer Ordering with a Spatiotemporally Heterogeneous Mobility  

NASA Astrophysics Data System (ADS)

Motivated by recent zone annealing measurements on stripe-forming block-copolymer films [B. C. Berry , Nano Lett. 7, 2789 (2007)NALEFD1530-698410.1021/nl071354s], we study block-copolymer ordering with a spatiotemporally heterogeneous mobility. Specifically, we implement a time- and space-dependent mobility field in the relaxation of a diblock copolymer self-consistent field theory. The model includes a gradient in the local mobility and intrinsic nanoscale mobility variations characteristic of glass phenomenology. The simulations demonstrate that a spatiotemporally heterogeneous mobility can have a significant influence on microdomain ordering in block-copolymer systems, and that nanoscale dynamic heterogeneities associated with glass formation can impact the structure of the ordered block-copolymer microphase.

Bosse, August W.; Douglas, Jack F.; Berry, Brian C.; Jones, Ronald L.; Karim, Alamgir



Patterns of spatiotemporal organization in an "ambiquitous" enzyme model.  

PubMed Central

Many enzymes in pathways such as glycolysis associate reversibly with cellular substructures. The spatiotemporal behavior of a "limit-cycle" oscillation model is studied under the condition that the "ambiquitous" oscillophor, phosphofructokinase, is partitioned between "bulk-phase" and "bound" forms in a heterogeneous system. Computer simulation demonstrates the occurrence of sustained, wave-like spatiotemporal patterns of chemical concentration in the bulk medium. Kinetic dissimilarity among the localized populations of bound enzyme leads to a "polarity" effect in the wave phenomenon. It is suggested that a key physiological role of the limit-cycle regime is to engender a rapid, site-to-site, signal-transmission modality in large eukaryotic (e.g., mammalian) cells. Images

Marmillot, P; Hervagault, J F; Welch, G R



Vibration measurement by spatiotemporal analysis of shadow moiré fringes  

NASA Astrophysics Data System (ADS)

A 3D Fourier transform for vibration measurement of an object based on shadow moiré technique is presented. A sinusoidal grating is placed closed to a vibrating object. The moiré fringe patterns generated by the interference of grating lines and shadow lines are captured by a high-speed camera. The joint spatial and temporal information of the fringe sequence is processed by use of the 3D Fourier transform simultaneously rather than separately, then a 3D space-time phase distribution can be obtained from the filtered spatiotemporal spectra. From the phase values, the surface profile, and displacement of the object at different time can be retrieved. The experiment for measurement of a vibrating cantilever beam is used to demonstrate the validity of the technique. The results show that shadow moiré with spatiotemporal analysis can be applied to dynamic measurement.

Shi, Hongjian; Zhu, Feipeng; He, Xiaoyuan



Spatiotemporal modes of climatic variability: building blocks of complex networks?  

NASA Astrophysics Data System (ADS)

The theory of complex networks offers a rich set of tools aimed at understanding various aspects of high-dimensional spatiotemporal systems [1]. Recently, methods based on complex network theory have been applied to quantities characterizing climatic variability (e.g. [2]) with the aim of characterizing the behavior of the atmospheric system. Typically, complex network methods are applied directly to reanalysis data [3], which are available on a high resolution planetary grid. An important pre-requisite to the application of complex network methods is the quantification of pairwise interactions in the spatiotemporal process. When estimating such dependencies, multiple sources of bias must be taken into account, such as spatial smoothness [4] or dynamical memory variability [5]. The attenuation or mitigation of selected biases may be aided by a suitable reduced representation of the climatic spatiotemporal process. We study decompositions based on multivariate analysis methods such as PCA, ICA or spectral clustering [6] with the purpose of providing a reduced representation of the climate system amenable to analysis using symmetric and asymmetric dependence measures. Some of the key studied questions are robustness, temporal variability and method-induced bias of the obtained spatiotemporal components. Robustness of the components is an important facet of the general validity of the decomposition and strongly affects the suitability of the resulting components as nodes in a complex network. Temporal variability will be studied especially with respect to the activity of the North Atlantic Oscillation (NAO), a major climatic mode in the Northern hemisphere. Acknowledgment This study is supported by the Czech Science Foundation, Project No. P103/11/J068.

Vejmelka, M.; Hlinka, J.; Hartman, D.; Palus, M.



Modeling spatiotemporal noise covariance for MEG\\/EEG source analysis  

Microsoft Academic Search

We propose a new model for approximating spatiotemporal noise covariance for\\u000ause in MEG\\/EEG source analysis. Our model is an extension of an existing model\\u000a[1,2] that uses a single Kronecker product of a pair of matrices - temporal and\\u000aspatial covariance; we employ a series of Kronecker products in order to\\u000aconstruct a better approximation of the full covariance.

S. M. Plis; J. S. George; S. C. Jun; J. Pare-Blagoev; D. M. Ranken; D. M. Schmidt; C. C. Wood



Spatiotemporal Features for Action Recognition and Salient Event Detection  

Microsoft Academic Search

Although the mechanisms of human visual understanding remain partially unclear, computational models inspired by existing\\u000a knowledge on human vision have emerged and applied to several fields. In this paper, we propose a novel method to compute\\u000a visual saliency from video sequences by counting in the actual spatiotemporal nature of the video. The visual input is represented\\u000a by a volume in

Konstantinos Rapantzikos; Yannis S. Avrithis; Stefanos D. Kollias



Spatio-Temporal Quality Assessment for Home Videos  

Microsoft Academic Search

Compared with the video programs taken by professionals, home videos are always with low-quality content resulted from lack of professional capture skills. In this paper, we present a novel spatio-temporal quality assessment scheme in terms of low-level content features for home videos. In contrast to existing frame-level-based quality assessment ap- proaches, a type of temporal segment of video, sub-shot, is

Tao Mei; Cai-Zhi Zhu; He-Qin Zhou; Xian-Sheng Hua


Home Video Visual Quality Assessment With Spatiotemporal Factors  

Microsoft Academic Search

Compared with the video programs taken by professionals, home videos are always with low quality content resulted from non-professional capture skills. In this paper, we present a novel spatiotemporal quality assessment scheme in terms of low-level content features for home videos. In contrast to existing frame-level-based quality assessment approaches, a type of temporal segment of video, subshot, is selected as

Tao Mei; Xian-sheng Hua; Cai-zhi Zhu; He-qin Zhou; Shipeng Li



Spatio-temporal quality assessment for home videos  

Microsoft Academic Search

Compared with the video programs taken by professionals, home videos are always with low-quality content resulted from lack of professional capture skills. In this paper, we present a novel spatio-temporal quality assessment scheme in terms of low-level content features for home videos. In contrast to existing frame-level-based quality assessment approaches, a type of temporal segment of video, sub-shot, is selected

Tao Mei; Cai-Zhi Zhu; He-Qin Zhou; Xian-Sheng Hua



Spatiotemporal analysis of ERP data in emotional processing  

Microsoft Academic Search

The aim of this paper is to analyze spatiotemporal patterns of Event-related potential (ERP) in emotional processing by using fuzzy k-means clustering method to segment ERP data into microstates.108 pictures (categorized as positive, negative and neutral) were presented to 24 healthy, right-handed subjects while 128-channel EEG data were recorded. For each subject, 3 artifact-free ERPs were computed under each condition.

Jin Hu; Jie Tian; Lei Yang; Xiaohong Pan; Jianggang Liu



Arrhythmia induced by spatiotemporal overexpression of calreticulin in the heart  

Microsoft Academic Search

Calreticulin (CRT) is a Ca2+-binding protein of the endoplasmic reticulum essential for cardiac development. For further investigation of the functional mechanism of calreticulin, we generated transgenic mice with spatiotemporal overexpression of calreticulin using a cre-loxP system. To elucidate the role of the protein in cardiogenesis, we adopted Nkx2.5-cre mice for heart specific overexpression. The overexpression of calreticulin was associated with

Kiyoko Hattori; Kimitoshi Nakamura; Yuichiro Hisatomi; Shirou Matsumoto; Misao Suzuki; Richard P. Harvey; Hiroki Kurihara; Shinzaburo Hattori; Tetsuro Yamamoto; Marek Michalak; Fumio Endo



Spatiotemporal energy models for the perception of motion  

Microsoft Academic Search

A motion sequence may be represented as a single pattern in x-y-t space; a velocity of motion corresponds to a three-dimensional orientation in this space. Motion sinformation can be extracted by a system that responds to the oriented spatiotemporal energy. We discuss a class of models for human motion mechanisms in which the first stage consists of linear filters that

Edward H. Adelson; James R. Bergen



Considering Correlation between Variables to Improve Spatiotemporal Forecasting  

Microsoft Academic Search

The importance of forecasting cannot be overemphasized in modern environment surveillance applications, including flood control,\\u000a rainfall analysis, pollution study, nuclear leakage prevention and so on. That is why we proposed STIFF (SpatioTemporal Integrated\\u000a Forecasting Framework) in previous work [11], trying to answer such a challenging problem of doing forecasting in natural environment with both spatial and temporal\\u000a characteristics involved. However,

Zhigang Li; Liangang Liu; Margaret H. Dunham



Spatio-temporal Pattern Mining in Sports Video  

Microsoft Academic Search

\\u000a Sports video is characterized with strict game rules, numerable events and well defined structures. In this paper, we proposed\\u000a a generic framework for spatio-temporal pattern mining in sports video. Specifically, the periodicities in sports video are\\u000a identified using unsupervised clustering and data mining method. In this way sports video analysis never needs priori domain\\u000a knowledge about video genres, producers or

Dong-jun Lan; Yu-fei Ma; Wei-ying Ma; Hong-Jiang Zhang



Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data.  


Recently, we described a Bayesian inference approach to the MEG/EEG inverse problem that used numerical techniques to estimate the full posterior probability distributions of likely solutions upon which all inferences were based [Schmidt, D.M., George, J.S., Wood, C.C., 1999. Bayesian inference applied to the electromagnetic inverse problem. Human Brain Mapping 7, 195; Schmidt, D.M., George, J.S., Ranken, D.M., Wood, C.C., 2001. Spatial-temporal bayesian inference for MEG/EEG. In: Nenonen, J., Ilmoniemi, R. J., Katila, T. (Eds.), Biomag 2000: 12th International Conference on Biomagnetism. Espoo, Norway, p. 671]. Schmidt et al. (1999) focused on the analysis of data at a single point in time employing an extended region source model. They subsequently extended their work to a spatiotemporal Bayesian inference analysis of the full spatiotemporal MEG/EEG data set. Here, we formulate spatiotemporal Bayesian inference analysis using a multi-dipole model of neural activity. This approach is faster than the extended region model, does not require use of the subject's anatomical information, does not require prior determination of the number of dipoles, and yields quantitative probabilistic inferences. In addition, we have incorporated the ability to handle much more complex and realistic estimates of the background noise, which may be represented as a sum of Kronecker products of temporal and spatial noise covariance components. This reduces the effects of undermodeling noise. In order to reduce the rigidity of the multi-dipole formulation which commonly causes problems due to multiple local minima, we treat the given covariance of the background as uncertain and marginalize over it in the analysis. Markov Chain Monte Carlo (MCMC) was used to sample the many possible likely solutions. The spatiotemporal Bayesian dipole analysis is demonstrated using simulated and empirical whole-head MEG data. PMID:16023866

Jun, Sung C; George, John S; Paré-Blagoev, Juliana; Plis, Sergey M; Ranken, Doug M; Schmidt, David M; Wood, C C



Spatiotemporal nonlinear optics in arrays of subwavelength waveguides  

SciTech Connect

We report numerical and experimental investigations of spatiotemporal nonlinear optical effects leading to spectral broadening in an array of subwavelength silicon waveguides pumped with infrared femtosecond pulses. Adjusting an input pulse position across the array, we observe different patterns in the output spectra. We explain these observations using a theory of the resonant (Cherenkov) radiation emitted by temporal solitons belonging to different spatial supermodes of the array. We also demonstrate strong nonperturbative coupling of temporal dispersion and discrete diffraction in the subwavelength arrays.

Gorbach, A. V.; Ding, W.; Staines, O. K.; Nobriga, C. E. de; Hobbs, G. D.; Wadsworth, W. J.; Knight, J. C.; Skryabin, D. V.; Samarelli, A.; Sorel, M.; De La Rue, R. M. [Centre for Photonics and Photonic Materials, Department of Physics, University of Bath, Bath BA2 7AY (United Kingdom); Department of Electronics and Electrical Engineering, University of Glasgow, Glasgow G12 8LT (United Kingdom)



Reflection-antisymmetric spatiotemporal chaos under field-translational invariance  

NASA Astrophysics Data System (ADS)

We propose a route to spatiotemporal chaos, in which the system is assumed to have spatial reflection antisymmetry and field-translation symmetry. The lowest-order nonlinear equation that satisfies these symmetries is explored with the weak nonlinear analysis around the bifurcation point. We conclude that the nonlinear term ?x2u?x3u is important to make a nontrivial dynamics, and show that the nonlinear dynamical equation having this term produces a turbulent dynamics.

Matsuo, Miki Y.; Sano, Masaki



Location, Location, Location: Development of Spatiotemporal Sequence Learning in Infancy  

Microsoft Academic Search

We investigated infants' sensitivity to spatiotemporal structure.In Experiment 1, circlesappearedin astatistically defined spatial pattern. At test 11-month-olds, but not 8-month-olds, looked longer at a novel spatial sequence. Experiment 2 presented different color\\/shape stimuli, but only the location sequence was violated during test; 8-month-olds preferred the novel spatial structure, but 5-month-olds did not. In Experiment 3, the locations but not color\\/shape

Natasha Z. Kirkham; Jonathan A. Slemmer; Daniel C. Richardson; Scott P. Johnson



Global coupling effects on spatiotemporal patterns on a ring electrode  

Microsoft Academic Search

Experimental results on spatiotemporal pattern formation on a ring electrode during the oscillatory electrodissolution of cobalt in 1.0M phosphoric acid are reported; the ring is the area between two non-concentric circles and its width thus varies continuously with angle. The experiments were controlled potentiostatically with the tip of the capillary of the reference electrode placed in the center of the

R. D. Otterstedt; N. I. Jaeger; P. J. Plath; J. L. Hudson



Spatio-temporal chaos through ramp-induced Eckhaus instability  

Microsoft Academic Search

It is demonstrated that spatio-temporal chaos can be induced by applying smooth spatial ramps to systems which under homogeneous conditions exhibit only steady spatially-periodic structures. This dynamics is investigated by numerical simulations of a simple reaction-diffusion model. They show that nonadiabatic effects, which are not contained in the usual amplitude equations, are relevant in the dynamics. It is expected that

H. Riecke; H.-G. Paap



Spatio-Temporal Instabilities and Self-Organization  

Microsoft Academic Search

Spontaneous generation of patterns, pattern formation and complex spatiotemporal structures in nonequilibrium systems [1] are one of the most intriguing current topics in science. Spanning as diverse disciplines as biology, chemistry, sociology, economics, hydrodynamics, solid-state physics, and optics, the phenomena identified as effects of self-organization [2–5] have been actively researched for several decades. Spirals formed by chemical reactions, ripples in

Cornelia Denz; Philip Jander


Predicting shallow water table depth at regional scale from rainfall and soil data  

NASA Astrophysics Data System (ADS)

We model the spatio-temporal changes of shallow water table depth at regional scale. The model assumes a sinusoidal behavior of the water table with a bimodal yearly cycle. The predictive tool is based on cumulative rainfall data and long term water table characteristics. Integrating soil information will increase the accuracy of the model. Spatio-temporal maps of the water table depth will be used to optimize irrigation management.

Calzolari, Costanza; Ungaro, Fabrizio



Plasticity of recurring spatiotemporal activity patterns in cortical networks.  


How do neurons encode and store information for long periods of time? Recurring patterns of activity have been reported in various cortical structures and were suggested to play a role in information processing and memory. To study the potential role of bursts of action potentials in memory mechanisms, we investigated patterns of spontaneous multi-single-unit activity in dissociated rat cortical cultures in vitro. Spontaneous spikes were recorded from networks of approximately 50 000 neurons and glia cultured on a grid of 60 extracellular substrate- embedded electrodes (multi-electrode arrays). These networks expressed spontaneous culture- wide bursting from approximately one week in vitro. During bursts, a large portion of the active electrodes showed elevated levels of firing. Spatiotemporal activity patterns within spontaneous bursts were clustered using a correlation-based clustering algorithm, and the occurrences of these burst clusters were tracked over several hours. This analysis revealed spatiotemporally diverse bursts occurring in well-defined patterns, which remained stable for several hours. Activity evoked by strong local tetanic stimulation resulted in significant changes in the occurrences of spontaneous bursts belonging to different clusters, indicating that the dynamical flow of information in the neuronal network had been altered. The diversity of spatiotemporal structure and long-term stability of spontaneous bursts together with their plastic nature strongly suggests that such network patterns could be used as codes for information transfer and the expression of memories stored in cortical networks. PMID:17928657

Madhavan, Radhika; Chao, Zenas C; Potter, Steve M



Using GeoRSS to syndicate the spatiotemporal information  

NASA Astrophysics Data System (ADS)

This paper describes a number of ways to encode spatiotemporal information in RSS feeds. As RSS becomes more and more prevalent as a way to publish and share information, it becomes increasingly important that location and time is described in an interoperable manner so that applications can request, aggregate, share and map spatiotemporally tagged feeds. This paper describes the GeoRSS model and encodings. With every RSS item has a timestamp, GeoRSS can represent time property for free. There are three GeoRSS encoding standards, such as W3C Geo, GeoRSS Simple, and GeoRSS GML profile. These standards differ in the number of coordinate systems they can support, and in the number of different geometric shapes they can add to the map to show where the news or event of interest is taking place. Further more, this paper described how to add time attribute to GeoRSS and implement and visualization the GeoRSS feeds through Google Map and Timeline. A few apt illustrations were given to show the powerful functions of GeoRSS in syndicating the spatiotemporal information. GeoRSS leverages this teeming ecosystem for geospatial technology, and with OGC support, GeoRSS is on firm conceptual ground and gains exposure across the industry.

Zhao, Bo; Li, Manchun; Jiang, Zhixin



Spatiotemporal quantile regression for detecting distributional changes in environmental processes  

PubMed Central

Climate change may lead to changes in several aspects of the distribution of climate variables, including changes in the mean, increased variability, and severity of extreme events. In this paper, we propose using spatiotemporal quantile regression as a flexible and interpretable method for simultaneously detecting changes in several features of the distribution of climate variables. The spatiotemporal quantile regression model assumes that each quantile level changes linearly in time, permitting straight-forward inference on the time trend for each quantile level. Unlike classical quantile regression which uses model-free methods to analyze a single quantile or several quantiles separately, we take a model-based approach which jointly models all quantiles, and thus the entire response distribution. In the spatiotemporal quantile regression model, each spatial location has its own quantile function that evolves over time, and the quantile functions are smoothed spatially using Gaussian process priors. We propose a basis expansion for the quantile function that permits a closed-form for the likelihood, and allows for residual correlation modeling via a Gaussian spatial copula. We illustrate the methods using temperature data for the southeast US from the years 1931–2009. For these data, borrowing information across space identifies more significant time trends than classical non-spatial quantile regression. We find a decreasing time trend for much of the spatial domain for monthly mean and maximum temperatures. For the lower quantiles of monthly minimum temperature, we find a decrease in Georgia and Florida, and an increase in Virginia and the Carolinas.

Reich, Brian J



Spatiotemporal cGMP Dynamics in Living Mouse Rods  

PubMed Central

Signaling of single photons in rod photoreceptors decreases the concentration of the second messenger, cyclic GMP (cGMP), causing closure of cGMP-sensitive channels located in the plasma membrane. Whether the spatiotemporal profiles of the fall in cGMP are narrow and deep, or broad and shallow, has important consequences for the amplification and the fidelity of signaling. The factors that determine the cGMP profiles include the diffusion coefficient for cGMP, the spontaneous rate of cGMP hydrolysis, and the rate of cGMP synthesis, which is powerfully regulated by calcium feedback mechanisms. Here, using suction electrodes to record light-dependent changes in cGMP-activated current in living mouse rods lacking calcium feedback, we have determined the rate constant of spontaneous cGMP hydrolysis and the longitudinal cGMP diffusion coefficient. These measurements result in a fully constrained spatiotemporal model of phototransduction, which we used to determine the effect of feedback to cGMP synthesis in spatially constricting the fall of cGMP during the single-photon response of normal rods. We find that the spatiotemporal cGMP profiles during the single-photon response are optimized for maximal amplification and preservation of signal linearity, effectively operating within an axial signaling domain of ?2 ?m.

Gross, Owen P.; Pugh, Edward N.; Burns, Marie E.



Transfer of Predictive Signals Across Saccades  

PubMed Central

Predicting visual information facilitates efficient processing of visual signals. Higher visual areas can support the processing of incoming visual information by generating predictive models that are fed back to lower visual areas. Functional brain imaging has previously shown that predictions interact with visual input already at the level of the primary visual cortex (V1; Harrison et al., 2007; Alink et al., 2010). Given that fixation changes up to four times a second in natural viewing conditions, cortical predictions are effective in V1 only if they are fed back in time for the processing of the next stimulus and at the corresponding new retinotopic position. Here, we tested whether spatio-temporal predictions are updated before, during, or shortly after an inter-hemifield saccade is executed, and thus, whether the predictive signal is transferred swiftly across hemifields. Using an apparent motion illusion, we induced an internal motion model that is known to produce a spatio-temporal prediction signal along the apparent motion trace in V1 (Muckli et al., 2005; Alink et al., 2010). We presented participants with both visually predictable and unpredictable targets on the apparent motion trace. During the task, participants saccaded across the illusion whilst detecting the target. As found previously, predictable stimuli were detected more frequently than unpredictable stimuli. Furthermore, we found that the detection advantage of predictable targets is detectable as early as 50–100?ms after saccade offset. This result demonstrates the rapid nature of the transfer of a spatio-temporally precise predictive signal across hemifields, in a paradigm previously shown to modulate V1.

Vetter, Petra; Edwards, Grace; Muckli, Lars



Predicting the Fission Yeast Protein Interaction Network  

PubMed Central

A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein–protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70–80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (

Pancaldi, Vera; Sarac, Omer S.; Rallis, Charalampos; McLean, Janel R.; Prevorovsky, Martin; Gould, Kathleen; Beyer, Andreas; Bahler, Jurg



Spatiotemporal dynamics of dengue epidemics, southern Vietnam.  


An improved understanding of heterogeneities in dengue virus transmission might provide insights into biological and ecologic drivers and facilitate predictions of the magnitude, timing, and location of future dengue epidemics. To investigate dengue dynamics in urban Ho Chi Minh City and neighboring rural provinces in Vietnam, we analyzed a 10-year monthly time series of dengue surveillance data from southern Vietnam. The per capita incidence of dengue was lower in Ho Chi Minh City than in most rural provinces; annual epidemics occurred 1-3 months later in Ho Chi Minh City than elsewhere. The timing and the magnitude of annual epidemics were significantly more correlated in nearby districts than in remote districts, suggesting that local biological and ecologic drivers operate at a scale of 50-100 km. Dengue incidence during the dry season accounted for 63% of variability in epidemic magnitude. These findings can aid the targeting of vector-control interventions and the planning for dengue vaccine implementation. PMID:23735713

Cuong, Hoang Quoc; Vu, Nguyen Thanh; Cazelles, Bernard; Boni, Maciej F; Thai, Khoa T D; Rabaa, Maia A; Quang, Luong Chan; Simmons, Cameron P; Huu, Tran Ngoc; Anders, Katherine L



A Comparison Between Complexity and Temporal GIS Models for Spatio-temporal Urban Applications  

Microsoft Academic Search

Spatio-temporal modeling for urban applications has received special attention lately. Due to the recent advances in computer\\u000a and geospatial technologies, the temporal aspect of urban applications which was ignored in conventional systems, is under\\u000a consideration nowadays. This new interest in spatio-temporal modeling, in spite of all its deficiencies, has brought about\\u000a great advances in spatio-temporal modeling and will enhance the

Majeed Pooyandeh; Saadi Mesgari; Abbas Alimohammadi; Rouzbeh Shad



Clifford algebra-based spatio-temporal modelling and analysis for complex geo-simulation data  

NASA Astrophysics Data System (ADS)

The spatio-temporal data simulating Ice-Land-Ocean interaction of Antarctic are used to demonstrate the Clifford algebra-based data model construction, spatio-temporal query and data analysis. The results suggest that Clifford algebra provides a powerful mathematical tool for the whole modelling and analysis chains for complex geo-simulation data. It can also help implement spatio-temporal analysis algorithms more clearly and simply.

Luo, Wen; Yu, Zhaoyuan; Hu, Yong; Yuan, Linwang



catRAPID omics: a web server for large-scale prediction of protein-RNA interactions  

PubMed Central

Summary: Here we introduce catRAPID omics, a server for large-scale calculations of protein–RNA interactions. Our web server allows (i) predictions at proteomic and transcriptomic level; (ii) use of protein and RNA sequences without size restriction; (iii) analysis of nucleic acid binding regions in proteins; and (iv) detection of RNA motifs involved in protein recognition. Results: We developed a web server to allow fast calculation of ribonucleoprotein associations in Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae and Xenopus tropicalis (custom libraries can be also generated). The catRAPID omics was benchmarked on the recently published RNA interactomes of Serine/arginine-rich splicing factor 1 (SRSF1), Histone-lysine N-methyltransferase EZH2 (EZH2), TAR DNA-binding protein 43 (TDP43) and RNA-binding protein FUS (FUS) as well as on the protein interactomes of U1/U2 small nucleolar RNAs, X inactive specific transcript (Xist) repeat A region (RepA) and Crumbs homolog 3 (CRB3) 3?-untranslated region RNAs. Our predictions are highly significant (P < 0.05) and will help the experimentalist to identify candidates for further validation. Availability: catRAPID omics can be freely accessed on the Web at Documentation, tutorial and FAQs are available at Contact:

Agostini, Federico; Zanzoni, Andreas; Klus, Petr; Marchese, Domenica; Cirillo, Davide; Tartaglia, Gian Gaetano



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

NASA Astrophysics Data System (ADS)

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

Gollas, Frank; Tetzlaff, Ronald



Fine Scale Spatiotemporal Clustering of Dengue Virus Transmission in Children and Aedes aegypti in Rural Thai Villages.  

National Technical Information Service (NTIS)

Based on spatiotemporal clustering of human dengue virus (DENV) infections, transmission is thought to occur at fine spatiotemporal scales by horizontal transfer of virus between humans and mosquito vectors. To define the dimensions of local transmission ...

A. Getis A. L. Rothman D. Tannitisupawong I. Yoon J. Aldstadt



Refined estimate of China's CO2 emissions in spatiotemporal distributions  

NASA Astrophysics Data System (ADS)

Being the largest contributor to the global source of fossil-fuel CO2 emissions, China's emissions need to be accurately quantified and well understood. Previous studies have usually focused on the amount of national emissions and rarely discussed their spatiotemporal distributions, which are also crucial for both carbon flux and carbon management. In this study, we calculated China's CO2 emissions from fossil fuel use and industrial processes using provincial statistics and then mapped those emissions at 0.25° resolution on monthly basis. Several key steps have been implemented to gain a better understanding of the spatiotemporal distributions, including (1) development and application of China's CO2 emission inventories using provincial statistics; (2) separate calculations of emissions from large point sources and accurate identification of their geographical locations; (3) development of 1 km×1 km gridded population and GDP data for China from 2000 to 2009 and application of them as dynamic spatial proxies to allocate emissions; and (4) monthly variation curves of CO2 emissions from various sectors were developed for each province and applied to our inventory. China's total CO2 emission from fossil fuel and industrial process have increased from 3.6 billion tons in 2000 to 8.6 billion tons in 2009, which may be off by 14-18% and are enough to skew global totals. And the resulting spatiotemporal distributions of our inventories also differed greatly in several ways from those derived using national statistics and population-based approach for the various economic development levels, industrial and energy structures, and even large point emissions sources within China and each province.

Liu, M.-M.; Wang, H.-K.; Wang, H.-M.; Oda, T.; Zhao, Y.; Yang, X.-H.; Zhang, R.-R.; Zhang, B.; Bi, J.; Chen, J.-M.



Manipulating spatiotemporal degrees of freedom of waves in random media.  


We show that all the spatiotemporal degrees of freedom available in a complex medium can be harnessed and converted into spatial ones. This is demonstrated experimentally through an instantaneous spatial inversion, using broadband ultrasonic waves in a multiple scattering sample. We show theoretically that the inversion convergence is governed by the total number of degrees of freedom available in the medium for a fixed bandwidth and demonstrate experimentally its use for complex media investigation. We believe our approach has potential in sensing, imagery, focusing, and telecommunication. PMID:19905758

Lemoult, Fabrice; Lerosey, Geoffroy; de Rosny, Julien; Fink, Mathias



Spatiotemporal Stochastic Resonance and its consequences in a neural system  

NASA Astrophysics Data System (ADS)

Biological neurons are good examples of a threshold device-this is why neural systems are in the focus when looking for realization of Stochastic Resonance (SR) and Spatiotemporal Stochastic Resonance (STSR) phenomena. There are two different ways to simulate neural systems-one based on differential equations, the other based on a simple threshold model. In this talk the effect of noise on neural systems will be discussed using both ways of modelling. The results so far suggest that SR and STSR do occur in models of neural systems. However, how significant is the role played by these phenomena and what implications might they have on neurobiology is still a question. .

Balázsi, Gábor; Kiss, László B.; Moss, Frank E.



Spatio-temporal dynamics in the origin of genetic information  

NASA Astrophysics Data System (ADS)

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.

Kim, Pan-Jun; Jeong, Hawoong



Large scale stochastic spatio-temporal modelling with PCRaster  

NASA Astrophysics Data System (ADS)

PCRaster is a software framework for building spatio-temporal models of land surface processes ( Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations are available to model builders as Python functions. The software comes with Python framework classes providing control flow for spatio-temporal modelling, Monte Carlo simulation, and data assimilation (Ensemble Kalman Filter and Particle Filter). Models are built by combining the spatial operations in these framework classes. This approach enables modellers without specialist programming experience to construct large, rather complicated models, as many technical details of modelling (e.g., data storage, solving spatial operations, data assimilation algorithms) are taken care of by the PCRaster toolbox. Exploratory modelling is supported by routines for prompt, interactive visualisation of stochastic spatio-temporal data generated by the models. The high computational requirements for stochastic spatio-temporal modelling, and an increasing demand to run models over large areas at high resolution, e.g. in global hydrological modelling, require an optimal use of available, heterogeneous computing resources by the modelling framework. Current work in the context of the eWaterCycle project is on a parallel implementation of the modelling engine, capable of running on a high-performance computing infrastructure such as clusters and supercomputers. Model runs will be distributed over multiple compute nodes and multiple processors (GPUs and CPUs). Parallelization will be done by parallel execution of Monte Carlo realizations and sub regions of the modelling domain. In our approach we use multiple levels of parallelism, improving scalability considerably. On the node level we will use OpenCL, the industry standard for low-level high performance computing kernels. To combine multiple nodes we will use software from the eScience Technology Platform (eSTeP), developed at the Netherlands eScience Center. This will allow us to scale up to hundreds of machines, with thousands of compute cores. A key requirement is not to change the user experience of the software. PCRaster operations and the use of the Python framework classes should work in a similar manner on machines ranging from a laptop to a supercomputer. This enables a seamless transfer of models from small machines, where model development is done, to large machines used for large-scale model runs. Domain specialists from a large range of disciplines, including hydrology, ecology, sedimentology, and land use change studies, currently use the PCRaster Python software within research projects. Applications include global scale hydrological modelling and error propagation in large-scale land use change models. The software runs on MS Windows, Linux operating systems, and OS X.

Karssenberg, Derek; Drost, Niels; Schmitz, Oliver; de Jong, Kor; Bierkens, Marc F. P.



Segmented Waves from a Spatiotemporal Transverse Wave Instability  

NASA Astrophysics Data System (ADS)

We observe traveling waves emitted from Turing spots in the chlorine dioxide-iodine-malonic acid reaction. The newborn waves are continuous, but they break into segments as they propagate, and the propagation of these segments ultimately gives rise to spatiotemporal chaos. We model the wave-breaking process and the motion of the chaotic segments. We find stable segmented spirals as well. We attribute the segmentation to an interaction between front rippling via a transverse instability and front symmetry breaking by a fast-diffusing inhibitor far from the codimension-2 Hopf-Turing bifurcation, and the chaos to a secondary instability of the periodic segmentation.

Yang, Lingfa; Berenstein, Igal; Epstein, Irving R.



A spatio-temporal model of housing prices based on individual sales transactions over time  

Microsoft Academic Search

A spatio-temporal model of housing price trends is developed that focuses on individual housing sales over time. The model allows for both the spatio-temporal lag effects of previous sales in the vicinity of each housing sale, and for general autocorrelation effects over time. A key feature of this model is the recognition of the unequal spacing between individual housing sales

Tony E. Smith; Peggy Wu



Implementation and evaluation of a hypercube-based method for spatiotemporal exploration and analysis  

Microsoft Academic Search

This paper presents the results obtained with a new type of spatiotemporal topological dimension implemented within a hypercube, i.e., within a multidimensional database (MDDB) structure formed by the conjunction of several thematic, spatial and temporal dimensions. Our goal is to support efficient SpatioTemporal Exploration and Analysis (STEA) in the context of Automatic Position Reporting System (APRS), the worldwide amateur radio

Pierre Marchand; Alexandre Brisebois; Yvan Bédard; Geoffrey Edwards



Establishing object correspondence across eye movements: Flexible use of spatiotemporal and surface feature information  

Microsoft Academic Search

Visual input is frequently disrupted by eye movements, blinks, and occlusion. The visual system must be able to establish correspondence between objects visible before and after a disruption. Current theories hold that correspondence is established solely on the basis of spatiotemporal information, with no contribution from surface features. In five experiments, we tested the relative contributions of spatiotemporal and surface

Ashleigh M. Richard; Steven J. Luck; Andrew Hollingworth



Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil  

NASA Astrophysics Data System (ADS)

This paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2.5°×2.5° longitude-latitude grid with time lags relevant to dengue transmission, an El Niño Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM—generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.

Lowe, Rachel; Bailey, Trevor C.; Stephenson, David B.; Graham, Richard J.; Coelho, Caio A. S.; Sá Carvalho, Marilia; Barcellos, Christovam



Oscillations, complex spatiotemporal behavior, and information transport in networks of excitatory and inhibitory neurons  

SciTech Connect

Various types of spatiotemporal behavior are described for two-dimensional networks of excitatory and inhibitory neurons with time delayed interactions. It is described how the network behaves as several structural parameters are varied, such as the number of neurons, the connectivity, and the values of synaptic weights. A transition from spatially uniform oscillations to spatiotemporal chaos via intermittentlike behavior is observed. The properties of spatiotemporally chaotic solutions are investigated by evaluating the largest positive Lyapunov exponent and the loss of correlation with distance. Finally, properties of information transport are evaluated during uniform oscillations and spatiotemporal chaos. It is shown that the diffusion coefficient increases significantly in the spatiotemporal phase similar to the increase of transport coefficients at the onset of fluid turbulence. It is proposed that such a property should be seen in other media, such as chemical turbulence or networks of oscillators. The possibility of measuring information transport from appropriate experiments is also discussed.

Destexhe, A. (Universite Libre de Bruxelles, Code Postal 231, Campus Plaine, Boulevard du Triomphe, B-1050 Bruxelles (Belgium))



Spatio-temporal topography of saccadic overestimation of time.  


Rapid eye movements (saccades) induce visual misperceptions. A number of studies in recent years have investigated the spatio-temporal profiles of effects like saccadic suppression or perisaccadic mislocalization and revealed substantial functional similarities. Saccade induced chronostasis describes the subjective overestimation of stimulus duration when the stimulus onset falls within a saccade. In this study we aimed to functionally characterize saccade induced chronostasis in greater detail. Specifically we tested if chronostasis is influenced by or functionally related to saccadic suppression. In a first set of experiments, we measured the perceived duration of visual stimuli presented at different spatial positions as a function of presentation time relative to the saccade. We further compared perceived duration during saccades for isoluminant and luminant stimuli. Finally, we investigated whether or not saccade induced chronostasis is dependent on the execution of a saccade itself. We show that chronostasis occurs across the visual field with a clear spatio-temporal tuning. Furthermore, we report chronostasis during simulated saccades, indicating that spurious retinal motion induced by the saccade is a prime origin of the phenomenon. PMID:23458677

Knöll, Jonas; Morrone, M Concetta; Bremmer, Frank



Gait recognition using spatio-temporal silhouette-based features  

NASA Astrophysics Data System (ADS)

This paper presents a new algorithm for human gait recognition based on Spatio-temporal body biometric features using wavelet transforms. The proposed algorithm extracts the Gait cycle depending on the width of boundary box from a sequence of Silhouette images. Gait recognition is based on feature level fusion of three feature vectors: the gait spatio-temporal feature represented by the distances between (feet, knees, hands, shoulders, and height); binary difference between consecutive frames of the silhouette for each leg detected separately based on hamming distance; a vector of statistical parameters captured from the wavelet low frequency domain. The fused feature vector is subjected to dimension reduction using linear discriminate analysis. The Nearest Neighbour with a certain threshold used for classification. The threshold is obtained by experiment from a set of data captured from the CASIA database. We shall demonstrate that our method provides a non-traditional identification based on certain threshold to classify the outsider members as non-classified members.

Sabir, Azhin; Al-jawad, Naseer; Jassim, Sabah



A unified framework for gesture recognition and spatiotemporal gesture segmentation.  


Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American Sign Language (ASL). PMID:19574627

Alon, Jonathan; Athitsos, Vassilis; Yuan, Quan; Sclaroff, Stan



Spatiotemporal continuous wavelets applied to missile warhead detection and tracking  

NASA Astrophysics Data System (ADS)

This paper addresses the problem of tracking a ballistic missile warhead. In this scenario, the ballistic missile is assumed to be fragmented into many pieces. The goal of the algorithm presented here is to track the warhead that is among the fragments. It is assumed that images are acquired from an optical sensor located in the interceptor nose cone. This imagery is used by the algorithm to steer the course of interception. The algorithm proposed in this paper is based on continuous spatio-temporal wavelet transforms (CWTs). Two different energy densities of the CWT are used to perform velocity detection and filtering. Additional post-processing is applied to discriminate among objects traveling at similar velocities. Particular attention is given to achieving robust performance on noisy sensor data and under conditions of temporary occlusions. First we introduce the spatio-temporal CWT and stress the relationships with classical orientation filters. Then we describe the CWT- based algorithm for target tracking, and present results on synthetically generated sequences.

Mujica, Fernando; Leduc, Jean-Pierre; Smith, Mark J.; Murenzi, Romain



Spatiotemporal regulation of chemical reactions by active cytoskeletal remodeling  

PubMed Central

Efficient and reproducible construction of signaling and sorting complexes, both on the surface and within the living cell, is contingent on local regulation of biochemical reactions by the cellular milieu. We propose that in many cases this spatiotemporal regulation can be mediated by interaction with components of the dynamic cytoskeleton. We show how the interplay between active contractility and remodeling of the cytoskeleton can result in transient focusing of passive molecules to form clusters, leading to a dramatic increase in the reaction efficiency and output levels. The dynamic cytoskeletal elements that drive focusing behave as quasienzymes catalyzing the chemical reaction. These ideas are directly applicable to the cortical actin-dependent clustering of cell surface proteins such as lipid-tethered GPI-anchored proteins, Ras proteins, as well as many proteins that have domains that confer the ability to interact with the actin cytoskeleton. In general such cytoskeletal driven clustering of proteins could be a cellular mechanism to spatiotemporally regulate and amplify local chemical reaction rates in a variety of contexts such as signaling, transcription, sorting, and endocytosis.

Chaudhuri, Abhishek; Bhattacharya, Bhaswati; Gowrishankar, Kripa; Mayor, Satyajit; Rao, Madan



Spatiotemporal Mapping the Neural Correlates of Acupuncture with MEG  

PubMed Central

Abstract Acupuncture is an ancient Eastern healing modality with putative therapeutic applications. Unfortunately, little is known about the central mechanisms by which acupuncture may exert its effects. In this study, 16 healthy subjects were evaluated with magnetoencephalography (MEG) to map the location and timing of brain activity during low-frequency electroacupuncture (EA) and mechanical, noninsertive, sham acupuncture (SA) given at acupoint PC-6. Both EA and SA evoked brain responses that localized to contralateral primary somatosensory (SI) cortex. However, initial responses for EA peaked slightly earlier than those for SA and were located inferiorly within SI. Average equivalent current dipole strength was stronger (particularly at latencies?>60?ms) for SA. These spatiotemporal differences between activations elicited by EA and SA are likely attributable to stimulus modality (electrical versus mechanical) and differences in the underlying somatosensory fibers transmitting these signals. The present data confirm that acupuncture modulates activity within somatosensory cortex, providing support for previous studies that suggest that the therapeutic effects of acupuncture are linked to SI modulation. Thus, MEG provides excellent spatiotemporal characterization of the somatosensory component of acupuncture, and future studies can contrast derived brain response parameters in healthy controls with those found in a diseased state.

Witzel, Thomas; Hamalainen, Matti; Kettner, Norman; Napadow, Vitaly



Deep Spatiotemporal Feature Learning with Application to Image Classification  

SciTech Connect

Deep machine learning is an emerging framework for dealing with complex high-dimensionality data in a hierarchical fashion which draws some inspiration from biological sources. Despite the notable progress made in the field, there remains a need for an architecture that can represent temporal information with the same ease that spatial information is discovered. In this work, we present new results using a recently introduced deep learning architecture called Deep Spatio-Temporal Inference Network (DeSTIN). DeSTIN is a discriminative deep learning architecture that combines concepts from unsupervised learning for dynamic pattern representation together with Bayesian inference. In DeSTIN the spatiotemporal dependencies that exist within the observations are modeled inherently in an unguided manner. Each node models the inputs by means of clustering and simple dynamics modeling while it constructs a belief state over the distribution of sequences using Bayesian inference. We demonstrate that information from the different layers of this hierarchical system can be extracted and utilized for the purpose of pattern classification. Earlier simulation results indicated that the framework is highly promising, consequently in this work we expand DeSTIN to a popular problem, the MNIST data set of handwritten digits. The system as a preprocessor to a neural network achieves a recognition accuracy of 97.98% on this data set. We further show related experimental results pertaining to automatic cluster adaptation and termination.

Karnowski, Thomas Paul [ORNL; Arel, Itamar [ORNL; Rose, Derek C [ORNL



Spatiotemporal dynamics of drift wave turbulence in a helicon discharge  

SciTech Connect

This paper presents results of experimental investigations about the spatiotemporal evolution of turbulent structures in weakly developed turbulence in the linearly magnetized helicon discharge VINETA. On the basis of the dispersion behavior the governing instability has been identified as the drift wave instability. A key quantity of drift waves are the associated parallel currents, which are measured with high sensitive B-probes. The fluctuating parallel currents are correlated with the drift wave density fluctuations. Drift waves are driven into a weakly developed turbulent state, which is characterized by strong intermittency of plasma density fluctuations in the far plasma edge. Spatiotemporal measurements revealed that this intermittency can be ascribed to radially propagating turbulent structures, which form in the region of large radial plasma pressure gradient and propagate radially outwards. The radial propagation speeds are typically 10% of the local ion sound speed. Measurements of the parallel electron dynamics associated with the radially propagating structures strongly suggest that the parallel electron flux contributes significantly to the polarization of turbulent structures.

Grulke, O.; Klinger, T. [MPI for Plasma Physics, EURATOM Association, D-17491 Greifswald (Germany); Ernst-Moritz-Arndt University, D-17489 Greifswald (Germany); Ullrich, S.; Windisch, T. [MPI for Plasma Physics, EURATOM Association, D-17491 Greifswald (Germany)



Cell Population Tracking and Lineage Construction with Spatiotemporal Context 1  

PubMed Central

Automated visual-tracking of cell populations in vitro using phase contrast time-lapse microscopy enables quantitative, systematic and high-throughput measurements of cell behaviors. These measurements include the spatiotemporal quantification of cell migration, mitosis, apoptosis, and the construction of cell lineages. The combination of low signal-to-noise ratio of phase contrast microscopy images, high and varying densities of the cell cultures, topological complexities of cell shapes, and wide range of cell behaviors pose many challenges to existing tracking techniques. This paper presents a fully-automated multi-target tracking system that can efficiently cope with these challenges while simultaneously tracking and analyzing thousands of cells observed using time-lapse phase contrast microscopy. The system combines bottom-up and top-down image analysis by integrating multiple collaborative modules, which exploit a fast geometric active contour tracker in conjunction with adaptive interacting multiple models (IMM) motion filtering and spatiotemporal trajectory optimization. The system, which was tested using a variety of cell populations, achieved tracking accuracy in the range of 86.9%–92.5%.

Li, Kang; Chen, Mei; Kanade, Takeo; Miller, Eric D.; Weiss, Lee E.; Campbell, Phil G.



Measurement of spatio-temporal transport in live cells  

NASA Astrophysics Data System (ADS)

The live cell is a highly dynamical system with complicated biophysical and biochemical processes taking place at diverse spatiotemporal scales. Though it is well known that microtubules and actin filaments play important roles in intracellular transport, their dynamic behavior is not entirely understood. We propose a unified approach to studying transport in live cells. We used Spatial Light Interference Microscopy, a quantitative phase imaging method developed in our laboratory, to extract cell mass distributions over broad spatiotemporal scales. The dispersion relations for this transport dynamics, i.e. frequency bandwidth vs. spatial frequencies, reveal deterministic mass transport at large spatial scales (w˜q) and diffusive transport at small spatial scales (w˜q?2). At submicron scales, we observed a w˜q?3 behavior, which indicates whip-like movements of protein filaments. Further control experiments where both the microtubule and actin polymerization were blocked suggests that essentially actin governs the long spatial scales behavior and microtubules the short scales. This label-free method enables us to access different components of cell dynamics and quantify diffusion coefficients and speed of motor proteins.

Wang, Ru; Wang, Zhuo; Millet, Larry; Gillette, Martha U.; Popescu, Gabriel



Spatio-temporal modeling of perimetric test data.  


This work describes the application of a spatio-temporal modeling to the study of glaucoma, a very serious ocular illness. The aim of this modeling is to solve various significant medical problems, namely the forecasting of future observations, the classification of observations as normal or defective, and the simulation of new longitudinal data sets. In order to ascertain whether a patient suffers from glaucoma, a perimetry is performed. The output of a perimetry is called a visual field and consists of a map with 52 numerical values plotted on a regular grid. In this work, a data set of healthy patients' visual fields is used. The work begins with an exploratory spatial data analysis. A semi-parametric approach is used to model the mean, and the variogram is fitted using a Matérn function. Once the spatial structure has been analysed, the spatial mean is subtracted from all the observations in the data set and the spatio-temporal correlation of the residuals is explored. All this information is used to build a space-time model, the parameters of which are estimated by maximum likelihood. Different methods are used to check the goodness of fit. PMID:17698932

Ibáñez, M V; Simó, A



Use of Spatial Information to Predict Multidrug Resistance in Tuberculosis Patients, Peru  

PubMed Central

To determine whether spatiotemporal information could help predict multidrug resistance at the time of tuberculosis diagnosis, we investigated tuberculosis patients who underwent drug susceptibility testing in Lima, Peru, during 2005–2007. We found that crude representation of spatial location at the level of the health center improved prediction of multidrug resistance.

Lin, Hsien-Ho; Shin, Sonya S.; Contreras, Carmen; Asencios, Luis; Paciorek, Christopher J.



Analysis of image color and effective bandwidth as a tool for assessing air pollution at urban spatiotemporal scale  

NASA Astrophysics Data System (ADS)

Size and concentration of airborne particulate matter (PM) are important indicators of air pollution events and public health risks. It is therefore important to monitor size resolved PM concentrations in the ambient air. This task, however, is hindered by the highly dynamic spatiotemporal variations of the PM concentrations. Satellite remote sensing is a common approach for gathering spatiotemporal data regarding aerosol events but its current spatial resolution is limited to a large grid that does not fit high varying urban areas. Moreover, satellite-borne remote sensing has limited revisit periods and it measures along vertical atmospheric columns. Thus, linking satellite-borne aerosol products to ground PM measurements is extremely challenging. In the last two decades visibility analysis is used by the US Environmental Protection Agency (US-EPA) to obtain quantitative representation of air quality in rural areas by horizontal imaging. However, significantly fewer efforts have been given to utilize the acquired scene characteristics (color, contrast, etc.) for quantitative parametric modeling of PM concentrations. We suggest utilizing the image effective bandwidth, a quantitative measure of image characteristics, for predicting PM concentrations. For validating the suggested method, we have assembled a large dataset that consists of time series imaging as well as measurements from air quality monitoring stations located in the study area that report PM concentrations and meteorological data (wind direction and velocity, relative humidity, etc.). Quantitative and qualitative statistical evaluation of the suggested method shows that dynamic changes of PM concentrations can be inferred from the acquired images.

Etzion, Yael; Broday, David M.; Fishbain, Barak



Gaussian predictive process models for large spatial data sets  

PubMed Central

Summary With scientific data available at geocoded locations, investigators are increasingly turning to spatial process models for carrying out statistical inference. Over the last decade, hierarchical models implemented through Markov chain Monte Carlo methods have become especially popular for spatial modelling, given their flexibility and power to fit models that would be infeasible with classical methods as well as their avoidance of possibly inappropriate asymptotics. However, fitting hierarchical spatial models often involves expensive matrix decompositions whose computational complexity increases in cubic order with the number of spatial locations, rendering such models infeasible for large spatial data sets. This computational burden is exacerbated in multivariate settings with several spatially dependent response variables. It is also aggravated when data are collected at frequent time points and spatiotemporal process models are used. With regard to this challenge, our contribution is to work with what we call predictive process models for spatial and spatiotemporal data. Every spatial (or spatiotemporal) process induces a predictive process model (in fact, arbitrarily many of them). The latter models project process realizations of the former to a lower dimensional subspace, thereby reducing the computational burden. Hence, we achieve the flexibility to accommodate non-stationary, non-Gaussian, possibly multivariate, possibly spatiotemporal processes in the context of large data sets. We discuss attractive theoretical properties of these predictive processes. We also provide a computational template encompassing these diverse settings. Finally, we illustrate the approach with simulated and real data sets.

Banerjee, Sudipto; Gelfand, Alan E.; Finley, Andrew O.; Sang, Huiyan



Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal adjustment scale  

NASA Astrophysics Data System (ADS)

State updating of distributed rainfall-runoff models via streamflow assimilation is subject to overfitting because large dimensionality of the state space of the model may render the assimilation problem seriously under-determined. To examine the issue in the context of operational hydrology, we carry out a set of real-world experiments in which streamflow data is assimilated into gridded Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic-wave routing models of the US National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM) with the variational data assimilation technique. Study basins include four basins in Oklahoma and five basins in Texas. To assess the sensitivity of data assimilation performance to dimensionality reduction in the control vector, we used nine different spatiotemporal adjustment scales, where state variables are adjusted in a lumped, semi-distributed, or distributed fashion and biases in precipitation and potential evaporation (PE) are adjusted hourly, 6-hourly, or kept time-invariant. For each adjustment scale, three different streamflow assimilation scenarios are explored, where streamflow observations at basin interior points, at the basin outlet, or at both interior points and the outlet are assimilated. The streamflow assimilation experiments with nine different basins show that the optimum spatiotemporal adjustment scale varies from one basin to another and may be different for streamflow analysis and prediction in all of the three streamflow assimilation scenarios. The most preferred adjustment scale for seven out of nine basins is found to be the distributed, hourly scale, despite the fact that several independent validation results at this adjustment scale indicated the occurrence of overfitting. Basins with highly correlated interior and outlet flows tend to be less sensitive to the adjustment scale and could benefit more from streamflow assimilation. In comparison to outlet flow assimilation, interior flow assimilation at any adjustment scale produces streamflow predictions with a spatial correlation structure more consistent with that of streamflow observations. We also describe diagnosing the complexity of the assimilation problem using the spatial correlation information associated with the streamflow process, and discuss the effect of timing errors in a simulated hydrograph on the performance of the data assimilation procedure.

Lee, H.; Seo, D.-J.; Liu, Y.; Koren, V.; McKee, P.; Corby, R.



Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal scale of adjustment  

NASA Astrophysics Data System (ADS)

State updating of distributed rainfall-runoff models via streamflow assimilation is subject to overfitting because large dimensionality of the state space of the model may render the assimilation problem seriously under-determined. To examine the issue in the context of operational hydrologic forecasting, we carried out a set of real-world experiments in which streamflow data is assimilated into the gridded Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic-wave routing models of the US National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM) via variational data assimilation (DA). The nine study basins include four in Oklahoma and five in Texas. To assess the sensitivity of the performance of DA to the dimensionality of the control vector, we used nine different spatiotemporal adjustment scales, with which the state variables are adjusted in a lumped, semi-distributed, or distributed fashion and biases in precipitation and PE are adjusted at hourly or 6-hourly scale, or at the scale of the fast response of the basin. For each adjustment scale, three different assimilation scenarios were carried out in which streamflow observations are assumed to be available at basin interior points only, at the basin outlet only, or at all locations. The results for the nine basins show that the optimum spatiotemporal adjustment scale varies from basin to basin and between streamflow analysis and prediction for all three streamflow assimilation scenarios. The most preferred adjustment scale for seven out of the nine basins is found to be distributed and hourly. It was found that basins with highly correlated flows between interior and outlet locations tend to be less sensitive to the adjustment scale and could benefit more from streamflow assimilation. In comparison with outlet flow assimilation, interior flow assimilation produced streamflow predictions whose spatial correlation structure is more consistent with that of observed flow for all adjustment scales. We also describe diagnosing the complexity of the assimilation problem using spatial correlation of streamflow and discuss the effect of timing errors in hydrograph simulation on the performance of the DA procedure.

Lee, H.; Seo, D.-J.; Liu, Y.; Koren, V.; McKee, P.; Corby, R.



Reference ranges of ductus arteriosus derived by cardio-spatiotemporal image correlation from 14 to 40 weeks of gestation.  


Objective: To construct reference ranges of fetal ductus arteriosus (DA) derived by volume datasets of cardio-spatiotemporal image correlation (cardio-STIC). Methods: Cardio-STIC volume datasets were acquired from low-risk singleton pregnancies with a reliable gestational age from 14 to 40 weeks. In offline analysis with 4D View version 9, fetal DA was measured in the transverse ductal arch view with orthogonal control in the multiplanar view. The reference ranges of the DA and Z-score equation were constructed against gestational weeks and biparietal diameter (BPD) as independent variables. Results: A total of satisfactory 606 volumes were analyzed. The reference ranges for predicting means and SDs of fetal DA were constructed based on the best-fit regression model. Mean DA (mm) was best predicted by linear model as a function of GA (weeks) and BPD (cm) as follows: Predicted DA diameter (cm) = -0.051 + 0.014 × GA (weeks) (r = 0.84) and Predicted DA diameter (cm) = -0.015 + 0.053 × BPD (cm) (r = 0.83). Models for Z-score calculation and centile charts for predicting fetal DA were also provided. Conclusion: Reference ranges of the fetal DA and Z-score model are provided. These may serve as a useful tool in the assessment of fetal DA, especially in fetal cardiac anomalies or in monitoring fetuses exposed to maternal indomethacin. PMID:23635389

Traisrisilp, Kuntharee; Tongprasert, Fuanglada; Srisupundit, Kasemsri; Luewan, Suchaya; Tongsong, Theera



Quantized vortices in a rotating Bose-Einstein condensate with spatiotemporally modulated interaction  

SciTech Connect

We present theoretical analysis and numerical studies of the quantized vortices in a rotating Bose-Einstein condensate with spatiotemporally modulated interaction in harmonic and anharmonic potentials, respectively. The exact quantized vortex and giant vortex solutions are constructed explicitly by similarity transformation. Their stability behavior has been examined by numerical simulation, which shows that a new series of stable vortex states (defined by radial and angular quantum numbers) can be supported by the spatiotemporally modulated interaction in this system. We find that there exist stable quantized vortices with large topological charges in repulsive condensates with spatiotemporally modulated interaction. We also give an experimental protocol to observe these vortex states in future experiments.

Wang Dengshan; Song Shuwei; Xiong Bo; Liu, W. M. [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China)



Pattern dynamics and spatiotemporal chaos in the quantum Zakharov equations  

SciTech Connect

The dynamical behavior of the nonlinear interaction of quantum Langmuir waves (QLWs) and quantum ion-acoustic waves (QIAWs) is studied in the one-dimensional quantum Zakharov equations. Numerical simulations of coupled QLWs and QIAWs reveal that many coherent solitary patterns can be excited and saturated via the modulational instability of unstable harmonic modes excited by a modulation wave number of monoenergetic QLWs. The evolution of such solitary patterns may undergo the states of spatially partial coherence (SPC), coexistence of temporal chaos and spatiotemporal chaos (STC), as well as STC. The SPC state is essentially due to ion-acoustic wave emission and due to quantum diffraction, while the STC is caused by the combined effects of SPC and quantum diffraction, as well as by collisions and fusions among patterns in stochastic motion. The energy in the system is strongly redistributed, which may switch on the onset of weak turbulence in dense quantum plasmas.

Misra, A. P.; Shukla, P. K. [Department of Mathematics, Visva-Bharati University, Santiniketan 731 235 (India); Institut fuer Theoretische Physik IV and Centre for Plasma Science and Astrophysics, Fakultaet fuer Physik and Astronomie, Ruhr-Universitaet Bochum, D-44780 Bochum (Germany)



Spatiotemporal alignment of multi-sensor aerial video sequences  

NASA Astrophysics Data System (ADS)

This paper presents a method for spatiotemporal alignment of two video sequences recorded by aerial sensors using different modularity. It recovers the inter-video temporal synchronization and spatial transformation from two sequences of intra-video transformations between successive frames within each video. Since it needs no directly comparison of images across two videos, and the intra-video transformation is easy to obtain, the alignment is possible even when the two videos have very different appearances. We make best use of the geometry between rigidly connected aerial video sensors and the ground scene, treat the intra-video and inter-video spatial transformations as similar ones, and develop a simple alignment method. Both theoretical analysis and experimental results demonstrate the stability and efficiency advantages over previous methods.

Chai, Dengfeng; Peng, Qunsheng



Spatiotemporal temperature patterns during hydrogen oxidation on a nickel disk  

SciTech Connect

Spatiotemporal temperature patterns on a polycrystalline nickel disk were recorded using infrared video imaging during atmospheric hydrogen oxidation and characterized by the proper orthogonal decomposition pattern analysis technique. The system was studied at two different residence times, 3.2 s and 6.4 s. At moderate feed temperatures, steady-state multiplicity and rate oscillations were found. Oscillations at a residence time of 6.4 s were periodic and essentially spatially uniform. At a residence time of 3.2 s, however, the surface temperature became nonuniform, and rate oscillations occurred via traveling temperature waves which emanated from pacemakers (locally active regions) on the edge of the catalyst. During periodic oscillations, the waves were emitted synchronously from the pacemakers, while during chaotic oscillations, the pacemakers were desynchronized and emitted waves independently of each other. Nonlocal gas-phase coupling between distance surface elements caused spatial desynchronization during rate oscillations.

Lane, S.L.; Graham, M.D.; Luss, D. (Univ. of Houston, Houston, TX (United States))



Spatiotemporal clustering and temporal order in the excitable BZ reaction  

NASA Astrophysics Data System (ADS)

The prototype experimental example of ``spontaneous'' pattern formation in an unstirred chemical medium is the oscillatory Belousov-Zhabotinsky (BZ) reaction: target patterns of outward-moving concentric rings are readily observed when the reaction is run in a thin layer in a Petri dish. In many experimental runs, new target centers appeared to form closer to pre-existing target centers than expected in a randomized model. Here we describe a simple direct test for the presence of temporal order in the spatiotemporal dynamics of target nucleation, and apply this test to detect significant temporal order in target formation in the ferroin-catalyzed BZ reaction. We also describe how mixing heterogeneity can generate temporal order, even in the absence of heterogeneous physical nucleating centers.

Hastings, Harold M.; Sobel, Sabrina G.; Lemus, Arely; Yuen, Fiona; Peralta, Catalina; Cammalleri, Carolyn; Chabrel, Johan; Chaterpaul, Stephen; Frank, Claudia; Hilaire, Christian; Lang, Daniel; Ravinovitch, Daniel; Zaharakis, Alex



Fuzzy segmentation spatiotemporal patterns of cognitive potential into microstates.  


Fuzzy c-mean algorithm was applied to segment spatiotemporal patterns of brainwave into microstates and memberships. The optimal clustering number was estimated with both the trends of objective function and the eigenvalue number of microstates. Comparable spatial patterns may occur at different temporal moments in consideration of fuzzy index that is beyond the limit of serial processing. Those techniques were illustrated with multichannel event-related potentials recorded from 9 subjects during Stroop test. Statistical parametric map of F value suggested that significant task (color decision and word decision) effect involve widespread cortical regions after stimulus onset 280 ms and this result supports the hypothesis that Stroop interference derives from response competition during post-perception stage. As significant stimulus (congruent stimulus and incongruent stimulus) effect only involves several separate visual regions within 100 ms after stimulus presentation, it may reflect top-down attentional regulation. PMID:10582566

Zhou, S; Wang, C; Wei, J; Wu, S



Spatio-temporal distribution of malaria in Yunnan Province, China.  


The spatio-temporal distribution pattern of malaria in Yunnan Province, China was studied using a geographic information system technique. Both descriptive and temporal scan statistics revealed seasonal fluctuation in malaria incidences in Yunnan Province with only one peak during 1995-2000, and two apparent peaks from 2001 to 2005. Spatial autocorrelation analysis indicated that malaria incidence was not randomly distributed in the province. Further analysis using spatial scan statistics discovered that the high risk areas were mainly clustered at the bordering areas with Myanmar and Laos, and in Yuanjiang River Basin. There were obvious associations between Plasmodium vivax and Plasmodoium falciparum malaria incidences and climatic factors with a clear 1-month lagged effect, especially in cluster areas. All these could provide information on where and when malaria prevention and control measures would be applied. These findings imply that countermeasures should target high risk areas at suitable times, when climatic factors facilitate the transmission of malaria. PMID:19706922

Hui, Feng-Ming; Xu, Bing; Chen, Zhang-Wei; Cheng, Xiao; Liang, Lu; Huang, Hua-Bing; Fang, Li-Qun; Yang, Hong; Zhou, Hong-Ning; Yang, Heng-Lin; Zhou, Xiao-Nong; Cao, Wu-Chun; Gong, Peng



Spatiotemporal mode structure of nonlinearly coupled drift wave modes  

SciTech Connect

This paper presents full cross-section measurements of drift waves in the linear magnetized plasma of the Mirabelle device. Drift wave modes are studied in regimes of weakly developed turbulence. The drift wave modes develop azimuthal space-time structures of plasma density, plasma potential, and visible light fluctuations. A fast camera diagnostic is used to record visible light fluctuations of the plasma column in an azimuthal cross section with a temporal resolution of 10 {mu}s corresponding approximately to 10% of the typical drift wave period. Mode coupling and drift wave dispersion are studied by spatiotemporal Fourier decomposition of the camera frames. The observed coupling between modes is compared to calculations of nonlinearly coupled oscillators described by the Kuramoto model.

Brandt, Christian; Grulke, Olaf; Klinger, Thomas; Negrete, Jose Jr.; Bousselin, Guillaume; Brochard, Frederic; Bonhomme, Gerard; Oldenbuerger, Stella [Max-Planck-Institut fuer Plasmaphysik, Wendelsteinstrasse 1, D-17489 Greifswald (Germany); Max-Planck-Institut fuer Dynamik und Selbstorganisation, Am Fassberg 17, D-37077 Goettingen (Germany); Jean-Lamour Institute, Unite Mixte de Recherche No. 7198 associee au Centre National de la Recherche Scientifique, Henri Poincare University, Boite Postale No. 70239, F-54506 Vandoeuvre-les-Nancy (France); Itoh Research Center for Plasma Turbulence, Kyushu University, Kasuga (Japan)



Spatiotemporal control of microtubule nucleation and assembly using magnetic nanoparticles  

NASA Astrophysics Data System (ADS)

Decisions on the fate of cells and their functions are dictated by the spatiotemporal dynamics of molecular signalling networks. However, techniques to examine the dynamics of these intracellular processes remain limited. Here, we show that magnetic nanoparticles conjugated with key regulatory proteins can artificially control, in time and space, the Ran/RCC1 signalling pathway that regulates the cell cytoskeleton. In the presence of a magnetic field, RanGTP proteins conjugated to superparamagnetic nanoparticles can induce microtubule fibres to assemble into asymmetric arrays of polarized fibres in Xenopus laevis egg extracts. The orientation of the fibres is dictated by the direction of the magnetic force. When we locally concentrated nanoparticles conjugated with the upstream guanine nucleotide exchange factor RCC1, the assembly of microtubule fibres could be induced over a greater range of distances than RanGTP particles. The method shows how bioactive nanoparticles can be used to engineer signalling networks and spatial self-organization inside a cell environment.

Hoffmann, Céline; Mazari, Elsa; Lallet, Sylvie; Le Borgne, Roland; Marchi, Valérie; Gosse, Charlie; Gueroui, Zoher



Data-derived spatiotemporal resolution constraints for global auroral imagers  

NASA Astrophysics Data System (ADS)

We present new data-derived constraints on spatiotemporal resolution of global auroral imagers. The reported results are based on an extensive set of images from two previously flown instruments, POLAR UVI and IMAGE WIC, processed using the event detection methodology developed by Uritsky et al. (2002, 2003, 2006). We use the cross-scale analysis of ground-based and spacecraft observations of auroral emission regions by Kozelov et al. (2004) to derive the power law exponent relating spatial and temporal scales of auroral precipitation events, and estimate the normalization factor entering this relation using the satellite data. Our results show the existence of a nontrivial scaling relation between the relaxation time and the spatial dimension of auroral emission events. We use this relation as a proxi to the resolution scaling function providing non-redundant combinations of spatial and temporal resolution of an optimized auroral imager consistent with the dynamics of multiscale auroral precipitation.

Uritsky, Vadim M.; Donovan, Eric; Trondsen, Trond; Pineau, Deanna; Kozelov, Boris V.



Distillation and Visualization of Spatiotemporal Structures in Turbulent Flow Fields  

NASA Astrophysics Data System (ADS)

Although turbulence suggests randomness and disorder, organized motions that cause spatiotemporal 'coherent structures' are of particular interest. Revealing such structures in numerically given turbulent or semi-turbulent flows is of interest both for practically working engineers and theoretically oriented physicists. However, as long as there is no common agreement about the mathematical definition of coherent structures, extracting such structures is a vaguely defined task. Instead of searching for a general definition, the data visualization community takes a pragmatic approach and provides various tool chains implemented in flexible software frameworks that allow the user to extract distinct flow field structures. Thus physicists or engineers can select those flow structures which might advance their insight best. We present different approaches to distill important features from turbulent flows and discuss the necessary steps to be taken on the example of Lagrangian coherent structures.

Hege, Hans-Christian; Hotz, Ingrid; Kasten, Jens



Spatiotemporally Resolved Acoustics in a Photoelastic Granular Material  

NASA Astrophysics Data System (ADS)

In granular materials, stress transmission is manifested as force chains that propagate through the material in a branching structure. We send acoustic pulses into a two dimensional photoelastic granular material in which force chains are visible and investigate how the force chains influence the amplitude, speed, and dispersion of the sound waves. We observe particle scale dynamics using two methods, movies which provide spatiotemporally resolved measurements and accelerometers within individual grains. The movies allow us to visualize the sound's path through the material, revealing that the sound travels primarily along the force chains. Using the brightness of the photoelastic particles as a measure of the force chain strength, we observe that the sound travels both faster and at higher amplitude along the strong force chains. An exception to this trend is seen in transient force chains that only exist while the sound is closing particle contacts. We also measure the frequency dependence of the amplitude, speed, and dispersion of the sound wave.

Owens, Eli; Daniels, Karen



Collaborative Assistance with Spatio-temporal Planning Problems  

NASA Astrophysics Data System (ADS)

The paper describes a collaborative assistance approach with spatio-temporal planning, which requires user's active participation in the problem solving task. The proposed collaborative assistance system operates on a region-based representation structure, which allows for partial specification of constraints at different levels of granularity. Weakly specified constraints contribute on the one hand to high computational complexity when generating alternative solutions and on the other hand to large solution spaces. The paper introduces Partial Order, Neighboring Regions and Partial Order of Neighboring Regions heuristics, which allow for pruning of significant parts of the search space, and produce hierarchical structuring of the solution space. Resulting hierarchical organization of the solution space reflects human mental processing of geographic information. To reduce cognitive load during observation of solution space, filtering of certain aspects, set-oriented structuring and case-based reasoning approaches are introduced.

Seifert, Inessa


Delay-driven irregular spatiotemporal patterns in a plankton system.  


An inhomogeneous distribution of species density over physical space is a widely observed scenario in plankton systems. Understanding the mechanisms resulting in these spatial patterns is a central topic in plankton ecology. In this paper we explore the impact of time delay on spatiotemporal patterns in a prey-predator plankton system. We find that time delay can trigger the emergence of irregular spatial patterns via a Hopf bifurcation. Moreover, a phase transition from a regular spiral pattern to an irregular one was observed and the latter gradually replaced the former and persisted indefinitely. The characteristic length of the emergent spatial pattern is consistent with the scale of plankton patterns observed in field studies. PMID:23944497

Tian, Canrong; Zhang, Lai



Spatiotemporal attention operator using isotropic contrast and regional homogeneity  

NASA Astrophysics Data System (ADS)

A multiscale operator for spatiotemporal isotropic attention is proposed to reliably extract attention points during image sequence analysis. Its consecutive local maxima indicate attention points as the centers of image fragments of variable size with high intensity contrast, region homogeneity, regional shape saliency, and temporal change presence. The scale-adaptive estimation of temporal change (motion) and its aggregation with the regional shape saliency contribute to the accurate determination of attention points in image sequences. Multilocation descriptors of an image sequence are extracted at the attention points in the form of a set of multidimensional descriptor vectors. A fast recursive implementation is also proposed to make the operator's computational complexity independent from the spatial scale size, which is the window size in the spatial averaging filter. Experiments on the accuracy of attention-point detection have proved the operator consistency and its high potential for multiscale feature extraction from image sequences.

Palenichka, Roman; Lakhssassi, Ahmed; Zaremba, Marek



Spatio-temporal patterns of Campylobacter colonization in Danish broilers.  


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. PMID:22814565

Chowdhury, S; Themudo, G E; Sandberg, M; Ersbøll, A K



Spatiotemporal dynamics of condensins I and II: evolutionary insights from the primitive red alga Cyanidioschyzon merolae  

PubMed Central

Condensins are multisubunit complexes that play central roles in chromosome organization and segregation in eukaryotes. Many eukaryotic species have two different condensin complexes (condensins I and II), although some species, such as fungi, have condensin I only. Here we use the red alga Cyanidioschyzon merolae as a model organism because it represents the smallest and simplest organism that is predicted to possess both condensins I and II. We demonstrate that, despite the great evolutionary distance, spatiotemporal dynamics of condensins in C. merolae is strikingly similar to that observed in mammalian cells: condensin II is nuclear throughout the cell cycle, whereas condensin I appears on chromosomes only after the nuclear envelope partially dissolves at prometaphase. Unlike in mammalian cells, however, condensin II is confined to centromeres in metaphase, whereas condensin I distributes more broadly along arms. We firmly establish a targeted gene disruption technique in this organism and find, to our surprise, that condensin II is not essential for mitosis under laboratory growth conditions, although it plays a crucial role in facilitating sister centromere resolution in the presence of a microtubule drug. The results provide fundamental insights into the evolution of condensin-based chromosome architecture and dynamics.

Fujiwara, Takayuki; Tanaka, Kan; Kuroiwa, Tsuneyoshi; Hirano, Tatsuya



Spatiotemporal evolution of cavitation dynamics exhibited by flowing microbubbles during ultrasound exposure.  


Ultrasound and microbubble-based therapies utilize cavitation to generate bioeffects, yet cavitation dynamics during individual pulses and across consecutive pulses remain poorly understood under physiologically relevant flow conditions. SonoVue(®) microbubbles were made to flow (fluid velocity: 10-40 mm/s) through a vessel in a tissue-mimicking material and were exposed to ultrasound [frequency: 0.5 MHz, peak-rarefactional pressure (PRP): 150-1200 kPa, pulse length: 1-100,000 cycles, pulse repetition frequency (PRF): 1-50 Hz, number of pulses: 10-250]. Radiated emissions were captured on a linear array, and passive acoustic mapping was used to spatiotemporally resolve cavitation events. At low PRPs, stable cavitation was maintained throughout several pulses, thus generating a steady rise in energy with low upstream spatial bias within the focal volume. At high PRPs, inertial cavitation was concentrated in the first 6.3 ± 1.3 ms of a pulse, followed by an energy reduction and high upstream bias. Multiple pulses at PRFs below a flow-dependent critical rate (PRF(crit)) produced predictable and consistent cavitation dynamics. Above the PRF(crit), energy generated was unpredictable and spatially biased. In conclusion, key parameters in microbubble-seeded flow conditions were matched with specific types, magnitudes, distributions, and durations of cavitation; this may help in understanding empirically observed in vivo phenomena and guide future pulse sequence designs. PMID:23145633

Choi, James J; Coussios, Constantin-C



The influence of spatio-temporal resource fluctuations on insular rat population dynamics  

PubMed Central

Local spatio-temporal resource variations can strongly influence the population dynamics of small mammals. This is particularly true on islands which are bottom-up driven systems, lacking higher order predators and with high variability in resource subsidies. The influence of resource fluctuations on animal survival may be mediated by individual movement among habitat patches, but simultaneously analysing survival, resource availability and habitat selection requires sophisticated analytical methods. We use a Bayesian multi-state capture–recapture model to estimate survival and movement probabilities of non-native black rats (Rattus rattus) across three habitats seasonally varying in resource availability. We find that survival varies most strongly with temporal rainfall patterns, overwhelming minor spatial variation among habitats. Surprisingly for a generalist forager, movement between habitats was rare, suggesting individuals do not opportunistically respond to spatial resource subsidy variations. Climate is probably the main driver of rodent population dynamics on islands, and even substantial habitat and seasonal spatial subsidies are overwhelmed in magnitude by predictable annual patterns in resource pulses. Marked variation in survival and capture has important implications for the timing of rat control.

Russell, James C.; Ruffino, Lise



Spatiotemporal change and ecological modelling of malaria in Turkey by means of geographic information systems.  


We described the spatiotemporal change of malaria (Plasmodium vivax) in Turkey over 34 years (1975-2008), and assessed the role of environmental variables in this change. We developed seven 5-year-period raster maps by using geo-referenced malaria case data from the centres of 81 provinces and the kriging method with a spherical variogram model in a geographic information systems (GIS) model. We also modelled malaria incidence in GIS by using our average malaria incidence raster map, and complementary spatial database including the raster map layers of 14 environmental variables. We chose linear regression analysis with backward method to investigate relationships among variables and develop a model. The model was run in GIS to obtain a model incidence raster map. We tested the reliability of the model map by residual statistics, and found the model map dependable. Five-year-period maps revealed that the distribution of malaria cases moved from the East Mediterranean region to the Southeast Anatolia region due to changing human activities. The latitude, minimum temperature, distance to seas and elevation variables were found to have significant impacts on malaria. Consequently, the model incidence map established a good background for early warning systems to predict epidemics of malaria following environmental changes. PMID:20888613

Dogan, Hakan Mete; Cetin, Ilhan; Egri, Mucahit



The influence of spatio-temporal resource fluctuations on insular rat population dynamics.  


Local spatio-temporal resource variations can strongly influence the population dynamics of small mammals. This is particularly true on islands which are bottom-up driven systems, lacking higher order predators and with high variability in resource subsidies. The influence of resource fluctuations on animal survival may be mediated by individual movement among habitat patches, but simultaneously analysing survival, resource availability and habitat selection requires sophisticated analytical methods. We use a Bayesian multi-state capture-recapture model to estimate survival and movement probabilities of non-native black rats (Rattus rattus) across three habitats seasonally varying in resource availability. We find that survival varies most strongly with temporal rainfall patterns, overwhelming minor spatial variation among habitats. Surprisingly for a generalist forager, movement between habitats was rare, suggesting individuals do not opportunistically respond to spatial resource subsidy variations. Climate is probably the main driver of rodent population dynamics on islands, and even substantial habitat and seasonal spatial subsidies are overwhelmed in magnitude by predictable annual patterns in resource pulses. Marked variation in survival and capture has important implications for the timing of rat control. PMID:21775327

Russell, James C; Ruffino, Lise



A linear merging methodology for high-resolution precipitation products using spatiotemporal regression  

SciTech Connect

Currently, the only viable option for a global precipitation product is the merger of several precipitation products from different modalities. In this article, we develop a linear merging methodology based on spatiotemporal regression. Four highresolution precipitation products (HRPPs), obtained through methods including the Climate Prediction Center's Morphing (CMORPH), Geostationary Operational Environmental Satellite-Based Auto-Estimator (GOES-AE), GOES-Based Hydro-Estimator (GOES-HE) and Self-Calibrating Multivariate Precipitation Retrieval (SCAMPR) algorithms, are used in this study. The merged data are evaluated against the Arkansas Red Basin River Forecast Center's (ABRFC's) ground-based rainfall product. The evaluation is performed using the Heidke skill score (HSS) for four seasons, from summer 2007 to spring 2008, and for two different rainfall detection thresholds. It is shown that the merged data outperform all the other products in seven out of eight cases. A key innovation of this machine learning method is that only 6% of the validation data are used for the initial training. The sensitivity of the algorithm to location, distribution of training data, selection of input data sets and seasons is also analysed and presented.

Turlapaty, Anish C. [Mississippi State University (MSU); Younan, Nicolas H. [Mississippi State University (MSU); Anantharaj, Valentine G [ORNL



Spatiotemporal characterization of interfacial Faraday waves by means of a light absorption technique.  


We present measurements of the complete spatiotemporal Fourier spectrum of Faraday waves. The Faraday waves are generated at the interface of two immiscible index matched liquids of different density. By use of a light absorption technique we are able to determine the bifurcation scenario from the flat surface to the patterned state for each complex spatial and temporal Fourier component separately. The surface spectra at onset are found to be in good agreement with the predictions from the linear stability analysis. For the nonlinear state our measurements show in a direct manner how energy is transferred from lower to higher harmonics and we quantify the nonlinear coupling coefficients. Furthermore we find that the nonlinear coupling generates static components in the temporal Fourier spectrum leading thus to a contribution of a nonoscillating permanent sinusoidal deformed surface state. A comparison of hexagonal and rectangular patterns reveals that spatial resonance can give rise to a spectrum that violates the temporal resonance conditions given by the weakly nonlinear theory. PMID:16241550

Kityk, A V; Embs, J; Mekhonoshin, V V; Wagner, C



Spatiotemporal characterization of interfacial Faraday waves by means of a light absorption technique  

NASA Astrophysics Data System (ADS)

We present measurements of the complete spatiotemporal Fourier spectrum of Faraday waves. The Faraday waves are generated at the interface of two immiscible index matched liquids of different density. By use of a light absorption technique we are able to determine the bifurcation scenario from the flat surface to the patterned state for each complex spatial and temporal Fourier component separately. The surface spectra at onset are found to be in good agreement with the predictions from the linear stability analysis. For the nonlinear state our measurements show in a direct manner how energy is transferred from lower to higher harmonics and we quantify the nonlinear coupling coefficients. Furthermore we find that the nonlinear coupling generates static components in the temporal Fourier spectrum leading thus to a contribution of a nonoscillating permanent sinusoidal deformed surface state. A comparison of hexagonal and rectangular patterns reveals that spatial resonance can give rise to a spectrum that violates the temporal resonance conditions given by the weakly nonlinear theory.

Kityk, A. V.; Embs, J.; Mekhonoshin, V. V.; Wagner, C.



Mathematical modelling of spatio-temporal glioma evolution  

PubMed Central

Background Gliomas are the most common types of brain cancer, well known for their aggressive proliferation and the invasive behavior leading to a high mortality rate. Several mathematical models have been developed for identifying the interactions between glioma cells and tissue microenvironment, which play an important role in the mechanism of the tumor formation and progression. Methods Building and expanding on existing approaches, this paper develops a continuous three-dimensional model of avascular glioma spatio-temporal evolution. The proposed spherical model incorporates the interactions between the populations of four different glioma cell phenotypes (proliferative, hypoxic, hypoglychemic and necrotic) and their tissue microenvironment, in order to investigate how they affect tumor growth and invasion in an isotropic and homogeneous medium. The model includes two key variables involved in the proliferation and invasion processes of cancer cells; i.e. the extracellular matrix and the matrix-degradative enzymes concentrations inside the tumor and its surroundings. Additionally, the proposed model focuses on innovative features, such as the separate and independent impact of two vital nutrients, namely oxygen and glucose, in tumor growth, leading to the formation of cell populations with different metabolic profiles. The model implementation takes under consideration the variations of particular factors, such as the local cell proliferation rate, the variable conversion rates of cells from one category to another and the nutrient-dependent thresholds of conversion. All model variables (cell densities, ingredients concentrations) are continuous and described by reaction-diffusion equations. Results Several simulations were performed using combinations of growth and invasion rates, for different evolution times. The model results were evaluated by medical experts and validated on experimental glioma models available in the literature, revealing high agreement between simulated and experimental results. Conclusions Based on the experimental validation, as well as the evaluation by clinical experts, the proposed model may provide an essential tool for the patient-specific simulation of different tumor evolution scenarios and reliable prognosis of glioma spatio-temporal progression.



Fast Spatiotemporal Smoothing of Calcium Measurements in Dendritic Trees  

PubMed Central

We discuss methods for fast spatiotemporal smoothing of calcium signals in dendritic trees, given single-trial, spatially localized imaging data obtained via multi-photon microscopy. By analyzing the dynamics of calcium binding to probe molecules and the effects of the imaging procedure, we show that calcium concentration can be estimated up to an affine transformation, i.e., an additive and multiplicative constant. To obtain a full spatiotemporal estimate, we model calcium dynamics within the cell using a functional approach. The evolution of calcium concentration is represented through a smaller set of hidden variables that incorporate fast transients due to backpropagating action potentials (bAPs), or other forms of stimulation. Because of the resulting state space structure, inference can be done in linear time using forward-backward maximum-a-posteriori methods. Non-negativity constraints on the calcium concentration can also be incorporated using a log-barrier method that does not affect the computational scaling. Moreover, by exploiting the neuronal tree structure we show that the cost of the algorithm is also linear in the size of the dendritic tree, making the approach applicable to arbitrarily large trees. We apply this algorithm to data obtained from hippocampal CA1 pyramidal cells with experimentally evoked bAPs, some of which were paired with excitatory postsynaptic potentials (EPSPs). The algorithm recovers the timing of the bAPs and provides an estimate of the induced calcium transient throughout the tree. The proposed methods could be used to further understand the interplay between bAPs and EPSPs in synaptic strength modification. More generally, this approach allows us to infer the concentration on intracellular calcium across the dendritic tree from noisy observations at a discrete set of points in space.

Pnevmatikakis, Eftychios A.; Kelleher, Keith; Chen, Rebecca; Saggau, Petter; Josic, Kresimir; Paninski, Liam



[Spatiotemporal pattern of alpine grassland productivity in Qiangtang Plateau].  


Based on the meteorological data and remote sensing data, and by using vegetation-climate comprehensive model and CASA model, this paper analyzed the climate change trend and the spatiotemporal pattern of alpine grassland potential and actual net primary productivity (NPP) in Qiantang Plateau. In 1955-2004, the mean annual temperature and annual cumulated precipitation in the Plateau increased by 1.37 degrees C and 63 mm, respectively. The climate in the central and eastern parts of the Plateau became warmer and wetter, whereas it was warmer and dryer in the western part. However, the regional climate change did not yet result in grassland degradation. The mean potential NPP of alpine grassland was in the order of eastern part > central part > western part. From 1982 to 2004, the potential NPP in the central part had the largest increment (0.55 t x hm(-2) x a(-1)), followed by in the eastern part (0.51 t x hm(-2) x a(-1)) and western part (0.21 t x hm(-2) x a(-1)), which was consequent with the spatiotemporal pattern of climate change in the study area. In contrast, the actual NPP in the eastern, central, and western parts in the past two decades was -0.19, -0.03, and 0.20 t x hm(-2) x a(-1), respectively. Overgrazing was the main reason of grassland degradation in the central and eastern parts, and the central part was the key layout area for the implement of 'grazing withdrawal and management of grassland' project. PMID:20873612

Wang, Jing-Sheng; Zhang, Xian-Zhou; Zhao, Yu-Ping; Qin, Si-Guo; Wu, Jian-Shuang



Spatio-temporal clustering of wildfires in Portugal  

NASA Astrophysics Data System (ADS)

Several studies have shown that wildfires in Portugal presenthigh temporal as well as high spatial variability (Pereira et al., 2005, 2011). The identification and characterization of spatio-temporal clusters contributes to a comprehensivecharacterization of the fire regime and to improve the efficiency of fire prevention and combat activities. The main goalsin this studyare: (i) to detect the spatio-temporal clusters of burned area; and, (ii) to characterize these clusters along with the role of human and environmental factors. The data were supplied by the National Forest Authority(AFN, 2011) and comprises: (a)the Portuguese Rural Fire Database, PRFD, (Pereira et al., 2011) for the 1980-2007period; and, (b) the national mapping burned areas between 1990 and 2009. In this work, in order to complement the more common cluster analysis algorithms, an alternative approach based onscan statistics and on the permutation modelwas used. This statistical methodallows the detection of local excess events and to test if such an excess can reasonably have occurred by chance.Results obtained for different simulations performed for different spatial and temporal windows are presented, compared and interpreted.The influence of several fire factors such as (climate, vegetation type, etc.) is also assessed. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005:"Synoptic patterns associated with large summer forest fires in Portugal".Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 AFN, 2011: AutoridadeFlorestalNacional (National Forest Authority). Available at

Costa, R.; Pereira, M. G.; Caramelo, L.; Vega Orozco, C.; Kanevski, M.



Spatiotemporal integration of tactile information in human somatosensory cortex  

PubMed Central

Background Our goal was to examine the spatiotemporal integration of tactile information in the hand representation of human primary somatosensory cortex (anterior parietal somatosensory areas 3b and 1), secondary somatosensory cortex (S2), and the parietal ventral area (PV), using high-resolution whole-head magnetoencephalography (MEG). To examine representational overlap and adaptation in bilateral somatosensory cortices, we used an oddball paradigm to characterize the representation of the index finger (D2; deviant stimulus) as a function of the location of the standard stimulus in both right- and left-handed subjects. Results We found that responses to deviant stimuli presented in the context of standard stimuli with an interstimulus interval (ISI) of 0.33s were significantly and bilaterally attenuated compared to deviant stimulation alone in S2/PV, but not in anterior parietal cortex. This attenuation was dependent upon the distance between the deviant and standard stimuli: greater attenuation was found when the standard was immediately adjacent to the deviant (D3 and D2 respectively), with attenuation decreasing for non-adjacent fingers (D4 and opposite D2). We also found that cutaneous mechanical stimulation consistently elicited not only a strong early contralateral cortical response but also a weak ipsilateral response in anterior parietal cortex. This ipsilateral response appeared an average of 10.7 ± 6.1 ms later than the early contralateral response. In addition, no hemispheric differences either in response amplitude, response latencies or oddball responses were found, independent of handedness. Conclusion Our findings are consistent with the large receptive fields and long neuronal recovery cycles that have been described in S2/PV, and suggest that this expression of spatiotemporal integration underlies the complex functions associated with this region. The early ipsilateral response suggests that anterior parietal fields also receive tactile input from the ipsilateral hand. The lack of a hemispheric difference in responses to digit stimulation supports a lack of any functional asymmetry in human somatosensory cortex.

Zhu, Zhao; Disbrow, Elizabeth A; Zumer, Johanna M; McGonigle, David J; Nagarajan, Srikantan S



Hand, foot and mouth disease: spatiotemporal transmission and climate  

PubMed Central

Background The Hand-Foot-Mouth Disease (HFMD) is the most common infectious disease in China, its total incidence being around 500,000 ~1,000,000 cases per year. The composite space-time disease variation is the result of underlining attribute mechanisms that could provide clues about the physiologic and demographic determinants of disease transmission and also guide the appropriate allocation of medical resources to control the disease. Methods and Findings HFMD cases were aggregated into 1456 counties and during a period of 11 months. Suspected climate attributes to HFMD were recorded monthly at 674 stations throughout the country and subsequently interpolated within 1456 × 11 cells across space-time (same as the number of HFMD cases) using the Bayesian Maximum Entropy (BME) method while taking into consideration the relevant uncertainty sources. The dimensionalities of the two datasets together with the integrated dataset combining the two previous ones are very high when the topologies of the space-time relationships between cells are taken into account. Using a self-organizing map (SOM) algorithm the dataset dimensionality was effectively reduced into 2 dimensions, while the spatiotemporal attribute structure was maintained. 16 types of spatiotemporal HFMD transmission were identified, and 3-4 high spatial incidence clusters of the HFMD types were found throughout China, which are basically within the scope of the monthly climate (precipitation) types. Conclusions HFMD propagates in a composite space-time domain rather than showing a purely spatial and purely temporal variation. There is a clear relationship between HFMD occurrence and climate. HFMD cases are geographically clustered and closely linked to the monthly precipitation types of the region. The occurrence of the former depends on the later.



Spatiotemporal analysis of DNA repair using charged particle radiation.  


Approaches to visualise the dynamics of the DNA lesion processing substantially contributes to the understanding of the hierarchical organisation of the DNA damage response pathways. Charged particle irradiation has recently emerged as a tool to generate discrete sites of subnuclear damage by its means of extremely localised dose deposition at low energies, thus facilitating the spatiotemporal analysis of repair events. In addition, they are of high interest for risk estimations of human space exploration (e.g. mars mission) in the high energy regime (HZE). In this short review we will give examples for the application of charged particle irradiation to study spatiotemporal aspects of DNA damage recognition and repair in the context of recent achievements in this field. Beamline microscopy allows determining the exact kinetics of repair-related proteins after irradiation with different charged particles that induce different lesion densities. The classification into fast recruited proteins like DNA-PK or XRCC1 or slower recruited ones like 53BP1 or MDC1 helps to establish the hierarchical organisation of damage recognition and subsequent repair events. Additionally, motional analysis of DNA lesions induced by traversing particles proved information about the mobility of DSBs. Increased mobility or the absence of large scale motion has direct consequences on the formation of chromosomal translocations and, thus, on mechanisms of cancer formation. Charged particle microbeams offer the interesting perspective of precise nuclear or subnuclear targeting with a defined number of ions, avoiding the Poisson distribution of traversals inherent to broad beam experiments. With the help of the microbeam, geometrical patterns of traversing ions can be applied facilitating the analysis of spatial organisation of repair. PMID:19944777

Tobias, F; Durante, M; Taucher-Scholz, G; Jakob, B



Spatio-temporal modeling with GIS and remote sensing for schistosomiasis control in Sichuan, China  

NASA Astrophysics Data System (ADS)

Schistosomiasis is a water-borne parasitic disease endemic in tropical and subtropical areas. Its transmission requires certain kind of snail as the intermediate host. Some efforts have been made to mapping snail habitats with remote sensing and schistosomiasis transmission modeling. However, the modeling is limited to isolated residential groups and does not include spatial interaction among those groups. Remotely sensed data are only used in snail habitat classification, not in estimation of snail abundance that is an important parameter in schistosomiasis transmission modeling. This research overcomes the above two problems using innovative geographic information system (GIS) and remote sensing technology. A mountainous environment near Xichang, China, is chosen as the test site. Environmental and epidemiological data are stored in a GIS to support modeling. Snail abundance is estimated from land-cover and land-use fractions derived from high spatial resolution IKONOS satellite data. Spatial interaction is determined in consideration of neighborhoods, group areas, relative slopes among groups, and natural barriers. Land-cover and land-use information extracted from 4 m high resolution IKONOS data is used as reference in scaling up to the regional level. The scale-up is done with coarser resolution satellite data including Landsat Thematic Mapper (TM), EO-1 Advanced Land Imager (ALI) and Hyperion data all at 30 m resolution. Snail abundance is estimated by regressing snail survey data with land-cover and land-use fractions. An R2 of 0.87 is obtained between the average snail density predicted and that surveyed at the group level. With such a model, a snail density map is generated for all residential groups in the study area. A spatio-temporal model of schistosomiasis transmission is finally built to incorporate the spatial interaction caused by miracidia and cercaria migration. Comparing the model results with and without spatial interaction has revealed a number of advantages of the spatio-temporal model. Particularly, with the inclusion of spatial interaction, more effective control of schistosomiasis transmission over the whole study area can be achieved.

Xu, Bing


Spatio-temporal dynamics of relativistic electron bunches during the micro-bunching instability in storage rings  

NASA Astrophysics Data System (ADS)

The intense Coherent Synchrotron Radiation emitted in the Terahertz range by relativistic electron bunches circulating in a storage ring is an attractive source for spectroscopy. Its stability is related to the electron bunch dynamics, and can exhibit a bursting behavior resulting from the irregular presence of micro-structures in the bunch. We evidence here the existence of two thresholds in the electron bunch spatio-temporal dynamics, associated with different levels of Terahertz signal fluctuations, with increasing number of electrons. The first threshold indicates the presence of micro-structures drifting in the bunch profile, and the second one appears when those micro-structures are strong enough to persist after about half a revolution period of the electron-bunch in the phase-space. Their prediction thanks to numerical simulations are confirmed by experiments at the synchrotron SOLEIL.

Evain, C.; Barros, J.; Loulergue, A.; Tordeux, M. A.; Nagaoka, R.; Labat, M.; Cassinari, L.; Creff, G.; Manceron, L.; Brubach, J. B.; Roy, P.; Couprie, M. E.



Spatiotemporal chaos near the onset of cellular growth during thin-film solidification of a binary alloy  

NASA Astrophysics Data System (ADS)

Thin-film solidification experiments with a succinonitrile-acetone alloy are used to observe the long time-scale dynamics of cellular crystal growth at growth rates only slightly above the critical value VC = Vc(lambda sub c) for the onset of morphological instability. Under these conditions only very small amplitude cells are observed with wavelengths near the value predicted by linear stability theory lambda = lambda sub c. At long times, microstructures with wavelengths significantly finer than lambda suc c form by nucleation at defects across the interface. These interfaces do not have a unique microstructure, but seem to exhibit spatiotemporal chaos on a long time scale caused by the continual birth and death of cells by tip splitting and cell annihilation in grooves.

Lee, J. T. C.; Tsiveriotis, K.; Brown, R. A.



Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study  

PubMed Central

Background Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments. Methods National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM2.5 and NO2) and a chemical transport model (for O3). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions. Results Calibration of the national PM2.5 surface using annual spatiotemporal interpolation predicted historical PM2.5 measurement data best (R2 = 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO2 (R2 = 0.38) and O3 (R2 = 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM2.5, NO2 and O3 exposures of 11.3 ?g/m3 (SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM2.5, NO2 and O3 exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls. Conclusions We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments. For submission to: Environmental Health



Optimizing Spatio-Temporal Sampling Designs of Synchronous, Static, or Clustered Measurements  

NASA Astrophysics Data System (ADS)

When sampling spatio-temporal random variables, the cost of a measurement may differ according to the setup of the whole sampling design: static measurements, i.e. repeated measurements at the same location, synchronous measurements or clustered measurements may be cheaper per measurement than completely individual sampling. Such "grouped" measurements may however not be as good as individually chosen ones because of redundancy. Often, the overall cost rather than the total number of measurements is fixed. A sampling design with grouped measurements may allow for a larger number of measurements thus outweighing the drawback of redundancy. The focus of this paper is to include the tradeoff between the number of measurements and the freedom of their location in sampling design optimisation. For simple cases, optimal sampling designs may be fully determined. To predict e.g. the mean over a spatio-temporal field having known covariance, the optimal sampling design often is a grid with density determined by the sampling costs [1, Ch. 15]. For arbitrary objective functions sampling designs can be optimised relocating single measurements, e.g. by Spatial Simulated Annealing [2]. However, this does not allow to take advantage of lower costs when using grouped measurements. We introduce a heuristic that optimises an arbitrary objective function of sampling designs, including static, synchronous, or clustered measurements, to obtain better results at a given sampling budget. Given the cost for a measurement, either within a group or individually, the algorithm first computes affordable sampling design configurations. The number of individual measurements as well as kind and number of grouped measurements are determined. Random locations and dates are assigned to the measurements. Spatial Simulated Annealing is used on each of these initial sampling designs (in parallel) to improve them. In grouped measurements either the whole group is moved or single measurements within the group, e.g. static measurements may be moved to another location or the sampling times may be rearranged. After several optimisation steps, the objective functions of the sampling designs are compared. Only for the best ones optimisation is pursued. After several iterations the sampling designs are selected again. Thus more and more of the low performing sampling designs are deleted and computational effort is concentrated on the most promising candidates. The use case is optimisation of a monitoring sampling design for a river. We use a flow model to simulate the spread of a pollutant that enters the system at different locations with known, location-dependent probabilities and at random times. The objective function to be minimised is the amount of pollution that is not detected. Keywords: spatio-temporal sampling design, static sample, synchronous sample, spatial simulated annealing, cost function References [1] Jaap de Gruijter, Dick Brus, Marc Bierkens, and Martin Knotters. Sampling for Natural Ressource Monitoring. Springer, 2006. [2] J. W. van Groenigen. Spatial simulated annealing for optimizing sampling, In: GeoENV I Geostatistics for environmental applications, pages 351 - 361, 1997.

Helle, Kristina; Pebesma, Edzer



Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic 'Gold' Standard  

PubMed Central

Background Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques. Methodology/Principal Findings We obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA), from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People's Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the ‘true’ prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River. Conclusion/Significance Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken into consideration when making risk prediction at small scales.

Wang, Xian-Hong; Zhou, Xiao-Nong; Vounatsou, Penelope; Chen, Zhao; Utzinger, Jurg; Yang, Kun; Steinmann, Peter; Wu, Xiao-Hua



Computing Spatio-Temporal Multiple View Geometry from Mutual Projections of Multiple Cameras  

NASA Astrophysics Data System (ADS)

The spatio-temporal multiple view geometry can represent the geometry of multiple images in the case where non-rigid arbitrary motions are viewed from multiple translational cameras. However, it requires many corresponding points and is sensitive to the image noise. In this paper, we investigate mutual projections of cameras in four-dimensional space and show that it enables us to reduce the number of corresponding points required for computing the spatio-temporal multiple view geometry. Surprisingly, take three views for instance, we no longer need any corresponding point to calculate the spatio-temporal multiple view geometry, if all the cameras are projected to the other cameras mutually for two time intervals. We also show that the stability of the computation of spatio-temporal multiple view geometry is drastically improved by considering the mutual projections of cameras.

Wan, Cheng; Sato, Jun


Comparing Single Pollutant and Multipollutant Traffic Indicators in Different Urban Environments Based on their Spatiotemporal Variability  

EPA Science Inventory

Single pollutant (NOx, CO, EC) and multipollutant metrics (source apportionment factors and emission-based indicators) were evaluated as traffic surrogates in three urban environments (Atlanta, Denver, and Houston). For each metric, trends in spatiotemporal variability are compar...


Yes/No Discrimination With Spatio-Temporal Characteristics of EEG.  

National Technical Information Service (NTIS)

Yes/No discrimination using spatio-temporal characteristics of EEG is investigated. For the correlation between EEG signals, we introduce two new representations useful in time domain calculation, synchronization rate and polarity. Using synchronization r...

M. Kim S. Shin Y. Song C. S. Ryu



Retrieval Method for Video Content in Different Format Based on Spatiotemporal Features  

Microsoft Academic Search

In this paper a robust video content retrieval method based on spatiotemporal features is proposed. To date, most video retrieval\\u000a methods are using the character of video key frames. This kind of frame based methods is not robust enough for different video\\u000a format. With our method, the temporal variation of visual information is presented using spatiotemporal slice. Then the DCT

Xuefeng Pan; Jintao Li; Yongdong Zhang; Sheng Tang; Juan Cao



Exploring spatio-temporal dynamics of an optically pumped semiconductor laser with intracavity second harmonic generation  

NASA Astrophysics Data System (ADS)

We experimentally observe an intriguing phenomenon of complex spatio-temporal dynamics in a commercial optically pumped semiconductor laser with intracavity second harmonic generation. We numerically verify that the experimental results come from the total mode locking of TEM00 and higher-order modes with significant astigmatism. The scenarios of the spatio-temporal dynamics are quite similar to the phenomena in soft-aperture Kerr-lens mode locked Ti:sapphire lasers.

Lee, Y. C.; Liang, H. C.; Tung, J. C.; Su, K. W.; Chen, Y. F.; Huang, K. F.



Ultrafast Spatiotemporal Dynamics of Terahertz Generation by Ionizing Two-Color Femtosecond Pulses in Gases  

SciTech Connect

We present a combined theoretical and experimental study of spatiotemporal propagation effects in terahertz (THz) generation in gases using two-color ionizing laser pulses. The observed strong broadening of the THz spectra with increasing gas pressure reveals the prominent role of spatiotemporal reshaping and of a plasma-induced blueshift of the pump pulses in the generation process. Results obtained from (3+1)-dimensional simulations are in good agreement with experimental findings and clarify the mechanisms responsible for THz emission.

Babushkin, I. [Weierstrass-Institut fuer Angewandte Analysis und Stochastik, 10117 Berlin (Germany); Kuehn, W.; Reimann, K.; Woerner, M.; Herrmann, J.; Elsaesser, T. [Max-Born-Institut fuer Nichtlineare Optik und Kurzzeitspektroskopie, 12489 Berlin (Germany); Koehler, C. [Max Planck Institute for the Physics of Complex Systems, 01187 Dresden (Germany); Skupin, S. [Max Planck Institute for the Physics of Complex Systems, 01187 Dresden (Germany); Friedrich Schiller University, Institute of Condensed Matter Theory and Optics, 07743 Jena (Germany); Berge, L. [CEA-DAM, DIF, 91297 Arpajon (France)



Spatio-temporal Source Localization and Granger Causality in Ictal Source Analysis  

Microsoft Academic Search

We have proposed a new ictal source analysis approach by combining a spatio-temporal source localization approach, and causal interaction estimation technique. The FINE approach is used to identify neural electrical sources from spatio-temporal scalp-EEGs. The Granger causality estimation uses source waveforms estimated by FINE to characterize the causal interaction between the neural electrical sources in order to distinguish primary sources,

L. Ding; G. A. Worrell; T. D. Lagerlund; B. He



Spatiotemporal contrast enhancement and feature extraction in the bat auditory midbrain and cortex.  


Navigating on the wing in complete darkness is a challenging task for echolocating bats. It requires the detailed analysis of spatial and temporal information gained through echolocation. Thus neural encoding of spatiotemporal echo information is a major function in the bat auditory system. In this study we presented echoes in virtual acoustic space and used a reverse-correlation technique to investigate the spatiotemporal response characteristics of units in the inferior colliculus (IC) and the auditory cortex (AC) of the bat Phyllostomus discolor. Spatiotemporal response maps (STRMs) of IC units revealed an organization of suppressive and excitatory regions that provided pronounced contrast enhancement along both the time and azimuth axes. Most IC units showed either spatially centralized short-latency excitation spatiotemporally imbedded in strong suppression, or the opposite, i.e., central short-latency suppression imbedded in excitation. This complementary arrangement of excitation and suppression was very rarely seen in AC units. In contrast, STRMs in the AC revealed much less suppression, sharper spatiotemporal tuning, and often a special spatiotemporal arrangement of two excitatory regions. Temporal separation of excitatory regions ranged up to 25 ms and was thus in the range of temporal delays occurring in target ranging in bats in a natural situation. Our data indicate that spatiotemporal processing of echo information in the bat auditory midbrain and cortex serves very different purposes: Whereas the spatiotemporal contrast enhancement provided by the IC contributes to echo-feature extraction, the AC reflects the result of this processing in terms of a high selectivity and task-oriented recombination of the extracted features. PMID:23785132

Hoffmann, Susanne; Warmbold, Alexander; Wiegrebe, Lutz; Firzlaff, Uwe



Spatio-temporal foraging patterns of a giant zooplanktivore, the leatherback turtle  

Microsoft Academic Search

Understanding food web functioning through the study of natural bio-indicators may constitute a valuable and original approach. In the context of jellyfish proliferation in many overexploited marine ecosystems studying the spatio-temporal foraging patterns of the giant “jellyvore” leatherback turtle turns out to be particularly relevant. Here we analyzed long-term tracking data to assess spatio-temporal foraging patterns in 21 leatherback turtles

Sabrina Fossette; Victoria J. Hobson; Charlotte Girard; Beatriz Calmettes; Philippe Gaspar; Jean-Yves Georges; Graeme C. Hays



Gaze-Contingent Spatio-temporal Filtering in a Head-Mounted Display  

Microsoft Academic Search

The spatio-temporal characteristics of the human visual system vary widely across the visual field. Recently, we have developed a display capable of sim-ulating arbitrary visual fields on high-resolution natural videos in real time by means of a gaze-contingent spatio-temporal filtering [1]. While such a system can also be a useful tool for psychophysical research, our main motivation is to develop

Michael Dorr; Martin Böhme; Thomas Martinetz; Erhardt Barth



Concepts and Applications of Spatiotemporal Interoperability in Environmental and Emergency Management  

Microsoft Academic Search

Interoperability of Systems and Services is gaining importance, but is mostly limited to a specific domain, e.g. the geospatial or modeling & simulation (M&S) domains. Spatiotemporal Interoperability describes an approach to exploit the synergies of coupling OGC-compliant services and HLA-based simulations in a standardized manner. The paper describes the current status of the Distributed spAtiotemporaL Interoperability Architecture (DALI), potential aplications

Ulrich Raape; Ingo Simonis; Thomas Schulze



Spatio-Temporal Prediction Modulates the Perception of Self-Produced Stimuli  

Microsoft Academic Search

We investigated why self-produced tactile stimulation is perceived as less intense than the same stimulus produced externally. A tactile stimulus on the palm of the right hand was either externally produced, by a robot or self-produced by the subject. In the conditions in which the tactile stimulus was self-produced, subjects moved the arm of a robot with their left hand

Sarah-J. Blakemore; Chris D. Frith; Daniel M. Wolpert



Detection of Dynamic Spatiotemporal Response to Periodic Chemical Stimulation in a Xenopus Embryonic Tissue  

PubMed Central

Embryonic development is guided by a complex and integrated set of stimuli that results in collective system-wide organization that is both time and space regulated. These regulatory interactions result in the emergence of highly functional units, which are correlated to frequency-modulated stimulation profiles. We have determined the dynamic response of vertebrate embryonic tissues to highly controlled, time-varying localized chemical stimulation using a microfluidic system with feedback control. Our approach has enabled localized spatiotemporal manipulation of the steroid hormone dexamethasone (DEX) in Animal Cap (AC) tissues isolated from gastrulating Xenopus embryos. Using this approach we investigated cell-scale responses to precisely controlled stimulation by tracking the redistribution of a GFP-tagged DEX-reporter constructed from the human glucocorticoid receptor (GR). We exposed defined regions of a single AC explant to different stimulation conditions—continuous stimulation, periodic stimulation, and no stimulation. We observed collective behavior of the GR transport into the nucleus was first-order. Furthermore, the dynamic response was well-modeled by a first-order differential equation with a single time derivative. The model predicted that responses to periodic stimulations closely matched the results of the frequency-based experiments. We find that stimulation with localized bursts versus continuous stimulation can result in highly distinct responses. This finding is critical as controlled space and time exposure to growth factors is a hallmark of complex processes in embryonic development. These complex responses to cellular signaling and transport machinery were similar to emergent behaviors in other complex systems, suggesting that even within a complex embryonic tissue, the overall system can converge toward a predictive first-order response.

Messner, William C.; LeDuc, Philip R.; Davidson, Lance A.



Regionalised spatiotemporal rainfall and temperature models for flood studies in the Basque Country, Spain  

NASA Astrophysics Data System (ADS)

A spatial-temporal point process model of rainfall is fitted to data taken from three homogeneous regions in the Basque Country, Spain. The model is the superposition of two spatial-temporal Neyman-Scott processes, in which rain cells are modelled as discs with radii that follow exponential distributions. In addition, the model includes a parameter for the radius of storm discs, so that rain only occurs when both a cell and a storm disc overlap a point. The model is fitted to data for each month, taken from each of the three homogeneous regions, using a modified method of moments procedure that ensures a smooth seasonal variation in the parameter estimates. Daily temperature data from twenty three sites are used to fit a stochastic temperature model. A principal component analysis of the maximum daily temperatures across the sites indicates that 92% of the variance is explained by the first component, implying that this component can be used to account for spatial variation. A harmonic equation with autoregressive error terms is fitted to the first principal component. The temperature model is obtained by regressing the maximum daily temperature on the first principal component, an indicator variable for the region, and altitude. This, together with scaling and a regression model of temperature range, enables hourly temperatures to be predicted. Rainfall is included as an explanatory variable but has only a marginal influence when predicting temperatures. A distributed model (TETIS; Francés et al., 2007) is calibrated for a selected catchment. Five hundred years of data are simulated using the rainfall and temperature models and used as input to the calibrated TETIS model to obtain simulated discharges to compare with observed discharges. Kolmogorov-Smirnov tests indicate that there is no significant difference in the distributions of observed and simulated maximum flows at the same sites, thus supporting the use of the spatiotemporal models for the intended application.

Cowpertwait, P.; Ocio, D.; Collazos, G.; de Cos, O.; Stocker, C.



Spatiotemporal clustering of the epigenome reveals rules of dynamic gene regulation  

PubMed Central

Spatial organization of different epigenomic marks was used to infer functions of the epigenome. It remains unclear what can be learned from the temporal changes of the epigenome. Here, we developed a probabilistic model to cluster genomic sequences based on the similarity of temporal changes of multiple epigenomic marks during a cellular differentiation process. We differentiated mouse embryonic stem (ES) cells into mesendoderm cells. At three time points during this differentiation process, we used high-throughput sequencing to measure seven histone modifications and variants—H3K4me1/2/3, H3K27ac, H3K27me3, H3K36me3, and H2A.Z; two DNA modifications—5-mC and 5-hmC; and transcribed mRNAs and noncoding RNAs (ncRNAs). Genomic sequences were clustered based on the spatiotemporal epigenomic information. These clusters not only clearly distinguished gene bodies, promoters, and enhancers, but also were predictive of bidirectional promoters, miRNA promoters, and piRNAs. This suggests specific epigenomic patterns exist on piRNA genes much earlier than germ cell development. Temporal changes of H3K4me2, unmethylated CpG, and H2A.Z were predictive of 5-hmC changes, suggesting unmethylated CpG and H3K4me2 as potential upstream signals guiding TETs to specific sequences. Several rules on combinatorial epigenomic changes and their effects on mRNA expression and ncRNA expression were derived, including a simple rule governing the relationship between 5-hmC and gene expression levels. A Sox17 enhancer containing a FOXA2 binding site and a Foxa2 enhancer containing a SOX17 binding site were identified, suggesting a positive feedback loop between the two mesendoderm transcription factors. These data illustrate the power of using epigenome dynamics to investigate regulatory functions.

Yu, Pengfei; Xiao, Shu; Xin, Xiaoyun; Song, Chun-Xiao; Huang, Wei; McDee, Darina; Tanaka, Tetsuya; Wang, Ting; He, Chuan; Zhong, Sheng



Modulation of V1 Spike Response by Temporal Interval of Spatiotemporal Stimulus Sequence  

PubMed Central

The spike activity of single neurons of the primary visual cortex (V1) becomes more selective and reliable in response to wide-field natural scenes compared to smaller stimuli confined to the classical receptive field (RF). However, it is largely unknown what aspects of natural scenes increase the selectivity of V1 neurons. One hypothesis is that modulation by surround interaction is highly sensitive to small changes in spatiotemporal aspects of RF surround. Such a fine-tuned modulation would enable single neurons to hold information about spatiotemporal sequences of oriented stimuli, which extends the role of V1 neurons as a simple spatiotemporal filter confined to the RF. In the current study, we examined the hypothesis in the V1 of awake behaving monkeys, by testing whether the spike response of single V1 neurons is modulated by temporal interval of spatiotemporal stimulus sequence encompassing inside and outside the RF. We used two identical Gabor stimuli that were sequentially presented with a variable stimulus onset asynchrony (SOA): the preceding one (S1) outside the RF and the following one (S2) in the RF. This stimulus configuration enabled us to examine the spatiotemporal selectivity of response modulation from a focal surround region. Although S1 alone did not evoke spike responses, visual response to S2 was modulated for SOA in the range of tens of milliseconds. These results suggest that V1 neurons participate in processing spatiotemporal sequences of oriented stimuli extending outside the RF.

Kim, Taekjun; Kim, HyungGoo R.; Kim, Kayeon; Lee, Choongkil



Network-oriented massive spatio-temporal data model and its applications  

NASA Astrophysics Data System (ADS)

It is foundation and key of developing GIS platforms of new generation to study the network-oriented massive spatial and spatio-temporal data model. But the research has met many difficulties. The paper combines two models of massive spatial data and spatio-temporal data seemed to be independent to study together in theory and technique. On the base of analyzing the limitations of present geographical spatial data model and spatio-temporal data model, a new model with characteristics of new generation's GIS platform, that is, Feature-Oriented Massive Spatio-temporal Object Tree (FOMSOT) with four-tier architectures is presented. The FOMSOT breaks down the constraint of map layer. It can deal with the massive spatio-temporal data better. The dynamic multi-base state with amendment (DMSA), fast index of base state with amendment in section, storage factors of variable granularity (SFVG) are used in FOMSOT which can manage the massive spatio-temporal data in high efficiency. A prototype "LyranMap" of new generation's GIS platform with the theory and technical method of FOMSOT has been realized, and it has been used in some application systems, for example, the land planning system "LandPlanner", land investigation system "LandExplorer" and land cadastral system "LandReGIS". These verify the correctness and effectiveness of the FOMSOT.

Liu, Renyi; Liu, Nan; Bao, Weizheng; Zhu, Yan



Localized nonlinear matter waves in Bose-Einstein condensates with spatially and spatiotemporally modulated nonlinearities  

NASA Astrophysics Data System (ADS)

The novel phenomena arising from Bose-Einstein condensates with spatially and spatiotemporally modulated nonlinearities in external potential is reviewed from a theoretical viewpoint. We first present theoretical analysis and numerical studies of the localized nonlinear matter waves in one-dimensional single and two-component BECs with spatially and spatiotemporally modulated nonlinearities, respectively. It is shown that the spatially or spatiotemporally modulated nonlinearity can support stable novel localized nonlinear matter waves such as the breathing solitons and moving solitons. Then the quasi-two-dimensional BEC with spatially modulated nonlinearity is investigated and we show that all of the BECs, similar to the linear harmonic oscillator, can have an arbitrary number of localized nonlinear matter waves with discrete energies. Their properties are determined by the principal quantum number and secondary quantum number. Moreover, we investigate the quantized vortices in a rotating BEC with spatiotemporally modulated interaction in harmonic and anharmonic potentials, respectively. The exact quantized vortex and giant vortex solutions are constructed explicitly by similarity transformation. Their stability behavior is examined by numerical simulation, which shows that a new series of stable vortex states which are defined by radial and angular quantum numbers, can be supported by the spatiotemporally modulated interaction in this system. We find that there exist stable quantized vortices with large topological charges in repulsive condensates with spatiotemporally modulated interaction.

Wang, Deng-Shan; Song, Shu-Wei; Liu, W. M.



Mining Association Rules in Spatio-Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change  

Microsoft Academic Search

This research demonstrates the application of association rule mining to spatio-temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A ? B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio-temporal association rule occurs when there is a spatio-temporal relationship in the antecedent

Jeremy L. Mennis; Jun Wei Liu



Sensitivity Analysis of a Spatio-Temporal Avalanche Forecasting Model Based on Support Vector Machines  

NASA Astrophysics Data System (ADS)

The recent progress in environmental monitoring technologies allows capturing extensive amount of data that can be used to assist in avalanche forecasting. While it is not straightforward to directly obtain the stability factors with the available technologies, the snow-pack profiles and especially meteorological parameters are becoming more and more available at finer spatial and temporal scales. Being very useful for improving physical modelling, these data are also of particular interest regarding their use involving the contemporary data-driven techniques of machine learning. Such, the use of support vector machine classifier opens ways to discriminate the ``safe'' and ``dangerous'' conditions in the feature space of factors related to avalanche activity based on historical observations. The input space of factors is constructed from the number of direct and indirect snowpack and weather observations pre-processed with heuristic and physical models into a high-dimensional spatially varying vector of input parameters. The particular system presented in this work is implemented for the avalanche-prone site of Ben Nevis, Lochaber region in Scotland. A data-driven model for spatio-temporal avalanche danger forecasting provides an avalanche danger map for this local (5x5 km) region at the resolution of 10m based on weather and avalanche observations made by forecasters on a daily basis at the site. We present the further work aimed at overcoming the ``black-box'' type modelling, a disadvantage the machine learning methods are often criticized for. It explores what the data-driven method of support vector machine has to offer to improve the interpretability of the forecast, uncovers the properties of the developed system with respect to highlighting which are the important features that led to the particular prediction (both in time and space), and presents the analysis of sensitivity of the prediction with respect to the varying input parameters. The purpose of the sensitivity analysis is to shed light on the particular abilities of the model in assessing the likelihood of avalanche releases under evolving meteorological/snowpack conditions. Both spatial resolution (the abilities to produce reliable forecasts for individual avalanche paths) and temporal behaviour of the model are explored in details. Based on the sensitivity analysis, the uncertainty estimation for the provided forecasts is discussed. Particularly, the ensembles of prediction models are run and analysed in order to estimate the variability of the provided forecast and assess the uncertainty coming from the variety of sources: imprecise input data, uncertainty in weather forecast, sub-optimal parameters of the prediction model and variability in the choice of the training dataset.

Matasci, G.; Pozdnoukhov, A.; Kanevski, M.



Spatiotemporal dynamics of Ca2+ signaling and its physiological roles.  


Changes in the intracellular Ca(2+) concentration regulate numerous cell functions and display diverse spatiotemporal dynamics, which underlie the versatility of Ca(2+) in cell signaling. In many cell types, an increase in the intracellular Ca(2+) concentration starts locally, propagates within the cell (Ca(2+) wave) and makes oscillatory changes (Ca(2+) oscillation). Studies of the intracellular Ca(2+) release mechanism from the endoplasmic reticulum (ER) showed that the Ca(2+) release mechanism has inherent regenerative properties, which is essential for the generation of Ca(2+) waves and oscillations. Ca(2+) may shuttle between the ER and mitochondria, and this appears to be important for pacemaking of Ca(2+) oscillations. Importantly, Ca(2+) oscillations are an efficient mechanism in regulating cell functions, having effects supra-proportional to the sum of duration of Ca(2+) increase. Furthermore, Ca(2+) signaling mechanism studies have led to the development of a method for specific inhibition of Ca(2+) signaling, which has been used to identify hitherto unrecognized functions of Ca(2+) signals. PMID:20228624

Iino, Masamitsu



Spatio-temporal self-organization in mudstones.  

SciTech Connect

Shales and other mudstones are the most abundant rock types in sedimentary basins, yet have received comparatively little attention. Common as hydrocarbon seals, these are increasingly being targeted as unconventional gas reservoirs, caprocks for CO2 sequestration, and storage repositories for waste. The small pore and grain size, large specific surface areas, and clay mineral structures lend themselves to rapid reaction rates, high capillary pressures, and semi-permeable membrane behavior accompanying changes in stress, pressure, temperature and chemical conditions. Under far from equilibrium conditions, mudrocks display a variety of spatio-temporal self-organized phenomena arising from nonlinear thermo-mechano-chemo-hydro coupling. Beginning with a detailed examination of nano-scale pore network structures in mudstones, we discuss the dynamics behind such self-organized phenomena as pressure solitons in unconsolidated muds, chemically-induced flow self focusing and permeability transients, localized compaction, time dependent well-bore failure, and oscillatory osmotic fluxes as they occur in clay-bearing sediments. Examples are draw from experiments, numerical simulation, and the field. These phenomena bear on the ability of these rocks to serve as containment barriers.

Dewers, Thomas A.



Functional MRI using super-resolved spatiotemporal encoding.  


Recently, new ultrafast imaging sequences such as rapid acquisition by sequential excitation and refocusing (RASER) and hybrid spatiotemporal encoding (SPEN) magnetic resonance imaging (MRI) have been proposed, in which the phase encoding of conventional echo planar imaging (EPI) is replaced with a SPEN. In contrast to EPI, SPEN provides significantly higher immunity to frequency heterogeneities including those caused by B(0) inhomogeneities and chemical shift offsets. Utilizing the inherent robustness of SPEN, it was previously shown that RASER can be used to successfully perform functional MRI (fMRI) experiments in the orbitofrontal cortex--a task which is challenging using EPI due to strong magnetic susceptibility variation near the air-filled sinuses. Despite this superior performance, systematic analyses have shown that, in its initial implementation, the use of SPEN was penalized by lower signal-to-noise ratio (SNR) and higher radiofrequency power deposition as compared to EPI-based methods. A recently developed reconstruction algorithm based on super-resolution principles is able to alleviate both of these shortcomings; the use of this algorithm is hereby explored within an fMRI context. Specifically, a series of fMRI measurements on the human visual cortex confirmed that the super-resolution algorithm retains the statistical significance of the blood oxygenation level dependent (BOLD) response, while significantly reducing the power deposition associated with SPEN and restoring the SNR to levels that are comparable with those of EPI. PMID:22789843

Ben-Eliezer, Noam; Goerke, Ute; Ugurbil, Kamil; Frydman, Lucio