Science.gov

Sample records for interactome predicts spatiotemporal

  1. A predicted interactome for Arabidopsis.

    PubMed

    Geisler-Lee, Jane; O'Toole, Nicholas; Ammar, Ron; Provart, Nicholas J; Millar, A Harvey; Geisler, Matt

    2007-10-01

    The complex cellular functions of an organism frequently rely on physical interactions between proteins. A map of all protein-protein interactions, an interactome, is thus an invaluable tool. We present an interactome for Arabidopsis (Arabidopsis thaliana) predicted from interacting orthologs in yeast (Saccharomyces cerevisiae), nematode worm (Caenorhabditis elegans), fruitfly (Drosophila melanogaster), and human (Homo sapiens). As an internal quality control, a confidence value was generated based on the amount of supporting evidence for each interaction. A total of 1,159 high confidence, 5,913 medium confidence, and 12,907 low confidence interactions were identified for 3,617 conserved Arabidopsis proteins. There was significant coexpression of genes whose proteins were predicted to interact, even among low confidence interactions. Interacting proteins were also significantly more likely to be found within the same subcellular location, and significantly less likely to be found in conflicting localizations than randomly paired proteins. A notable exception was that proteins located in the Golgi were more likely to interact with Golgi, vacuolar, or endoplasmic reticulum sorted proteins, indicating possible docking or trafficking interactions. These predictions can aid researchers by extending known complexes and pathways with candidate proteins. In addition we have predicted interactions for many previously unknown proteins in known pathways and complexes. We present this interactome, and an online Web interface the Arabidopsis Interactions Viewer, as a first step toward understanding global signaling in Arabidopsis, and to whet the appetite for those who are awaiting results from high-throughput experimental approaches.

  2. PTIR: Predicted Tomato Interactome Resource

    PubMed Central

    Yue, Junyang; Xu, Wei; Ban, Rongjun; Huang, Shengxiong; Miao, Min; Tang, Xiaofeng; Liu, Guoqing; Liu, Yongsheng

    2016-01-01

    Protein-protein interactions (PPIs) are involved in almost all biological processes and form the basis of the entire interactomics systems of living organisms. Identification and characterization of these interactions are fundamental to elucidating the molecular mechanisms of signal transduction and metabolic pathways at both the cellular and systemic levels. Although a number of experimental and computational studies have been performed on model organisms, the studies exploring and investigating PPIs in tomatoes remain lacking. Here, we developed a Predicted Tomato Interactome Resource (PTIR), based on experimentally determined orthologous interactions in six model organisms. The reliability of individual PPIs was also evaluated by shared gene ontology (GO) terms, co-evolution, co-expression, co-localization and available domain-domain interactions (DDIs). Currently, the PTIR covers 357,946 non-redundant PPIs among 10,626 proteins, including 12,291 high-confidence, 226,553 medium-confidence, and 119,102 low-confidence interactions. These interactions are expected to cover 30.6% of the entire tomato proteome and possess a reasonable distribution. In addition, ten randomly selected PPIs were verified using yeast two-hybrid (Y2H) screening or a bimolecular fluorescence complementation (BiFC) assay. The PTIR was constructed and implemented as a dedicated database and is available at http://bdg.hfut.edu.cn/ptir/index.html without registration. PMID:27121261

  3. PAIR: the predicted Arabidopsis interactome resource.

    PubMed

    Lin, Mingzhi; Shen, Xueling; Chen, Xin

    2011-01-01

    The predicted Arabidopsis interactome resource (PAIR, http://www.cls.zju.edu.cn/pair/), comprised of 5990 experimentally reported molecular interactions in Arabidopsis thaliana together with 145,494 predicted interactions, is currently the most comprehensive data set of the Arabidopsis interactome with high reliability. PAIR predicts interactions by a fine-tuned support vector machine model that integrates indirect evidences for interaction, such as gene co-expressions, domain interactions, shared GO annotations, co-localizations, phylogenetic profile similarities and homologous interactions in other organisms (interologs). These predictions were expected to cover 24% of the entire Arabidopsis interactome, and their reliability was estimated to be 44%. Two independent example data sets were used to rigorously validate the prediction accuracy. PAIR features a user-friendly query interface, providing rich annotation on the relationships between two proteins. A graphical interaction network browser has also been integrated into the PAIR web interface to facilitate mining of specific pathways.

  4. PRIN: a predicted rice interactome network

    PubMed Central

    2011-01-01

    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

  5. Computational prediction of the human-microbial oral interactome

    PubMed Central

    2014-01-01

    Background The oral cavity is a complex ecosystem where human chemical compounds coexist with a particular microbiota. However, shifts in the normal composition of this microbiota may result in the onset of oral ailments, such as periodontitis and dental caries. In addition, it is known that the microbial colonization of the oral cavity is mediated by protein-protein interactions (PPIs) between the host and microorganisms. Nevertheless, this kind of PPIs is still largely undisclosed. To elucidate these interactions, we have created a computational prediction method that allows us to obtain a first model of the Human-Microbial oral interactome. Results We collected high-quality experimental PPIs from five major human databases. The obtained PPIs were used to create our positive dataset and, indirectly, our negative dataset. The positive and negative datasets were merged and used for training and validation of a naïve Bayes classifier. For the final prediction model, we used an ensemble methodology combining five distinct PPI prediction techniques, namely: literature mining, primary protein sequences, orthologous profiles, biological process similarity, and domain interactions. Performance evaluation of our method revealed an area under the ROC-curve (AUC) value greater than 0.926, supporting our primary hypothesis, as no single set of features reached an AUC greater than 0.877. After subjecting our dataset to the prediction model, the classified result was filtered for very high confidence PPIs (probability ≥ 1-10−7), leading to a set of 46,579 PPIs to be further explored. Conclusions We believe this dataset holds not only important pathways involved in the onset of infectious oral diseases, but also potential drug-targets and biomarkers. The dataset used for training and validation, the predictions obtained and the network final network are available at http://bioinformatics.ua.pt/software/oralint. PMID:24576332

  6. A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize

    PubMed Central

    Musungu, Bryan; Bhatnagar, Deepak; Brown, Robert L.; Fakhoury, Ahmad M.; Geisler, Matt

    2015-01-01

    Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM) is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs) that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize. PMID:26089837

  7. Interactome of Radiation-Induced microRNA-Predicted Target Genes

    PubMed Central

    Lhakhang, Tenzin W.; Chaudhry, M. Ahmad

    2012-01-01

    The microRNAs (miRNAs) function as global negative regulators of gene expression and have been associated with a multitude of biological processes. The dysfunction of the microRNAome has been linked to various diseases including cancer. Our laboratory recently reported modulation in the expression of miRNA in a variety of cell types exposed to ionizing radiation (IR). To further understand miRNA role in IR-induced stress pathways, we catalogued a set of common miRNAs modulated in various irradiated cell lines and generated a list of predicted target genes. Using advanced bioinformatics tools we identified cellular pathways where miRNA predicted target genes function. The miRNA-targeted genes were found to play key roles in previously identified IR stress pathways such as cell cycle, p53 pathway, TGF-beta pathway, ubiquitin-mediated proteolysis, focal adhesion pathway, MAPK signaling, thyroid cancer pathway, adherens junction, insulin signaling pathway, oocyte meiosis, regulation of actin cytoskeleton, and renal cell carcinoma pathway. Interestingly, we were able to identify novel targeted pathways that have not been identified in cellular radiation response, such as aldosterone-regulated sodium reabsorption, long-term potentiation, and neutrotrophin signaling pathways. Our analysis indicates that the miRNA interactome in irradiated cells provides a platform for comprehensive modeling of the cellular stress response to IR exposure. PMID:22924026

  8. Spatiotemporal Patterns and Predictability of Cyberattacks

    PubMed Central

    Chen, Yu-Zhong; Huang, Zi-Gang; Xu, Shouhuai; Lai, Ying-Cheng

    2015-01-01

    A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term “spatio” refers to the IP address space. In particular, we focus on analyzing macroscopic properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack “fingerprints” and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches. PMID:25992837

  9. Evidence That a Psychopathology Interactome Has Diagnostic Value, Predicting Clinical Needs: An Experience Sampling Study

    PubMed Central

    van Os, Jim; Lataster, Tineke; Delespaul, Philippe; Wichers, Marieke; Myin-Germeys, Inez

    2014-01-01

    Background For the purpose of diagnosis, psychopathology can be represented as categories of mental disorder, symptom dimensions or symptom networks. Also, psychopathology can be assessed at different levels of temporal resolution (monthly episodes, daily fluctuating symptoms, momentary fluctuating mental states). We tested the diagnostic value, in terms of prediction of treatment needs, of the combination of symptom networks and momentary assessment level. Method Fifty-seven patients with a psychotic disorder participated in an ESM study, capturing psychotic experiences, emotions and circumstances at 10 semi-random moments in the flow of daily life over a period of 6 days. Symptoms were assessed by interview with the Positive and Negative Syndrome Scale (PANSS); treatment needs were assessed using the Camberwell Assessment of Need (CAN). Results Psychotic symptoms assessed with the PANSS (Clinical Psychotic Symptoms) were strongly associated with psychotic experiences assessed with ESM (Momentary Psychotic Experiences). However, the degree to which Momentary Psychotic Experiences manifested as Clinical Psychotic Symptoms was determined by level of momentary negative affect (higher levels increasing probability of Momentary Psychotic Experiences manifesting as Clinical Psychotic Symptoms), momentary positive affect (higher levels decreasing probability of Clinical Psychotic Symptoms), greater persistence of Momentary Psychotic Experiences (persistence predicting increased probability of Clinical Psychotic Symptoms) and momentary environmental stress associated with events and activities (higher levels increasing probability of Clinical Psychotic Symptoms). Similarly, the degree to which momentary visual or auditory hallucinations manifested as Clinical Psychotic Symptoms was strongly contingent on the level of accompanying momentary paranoid delusional ideation. Momentary Psychotic Experiences were associated with CAN unmet treatment needs, over and above PANSS

  10. DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome.

    PubMed

    Luo, Heng; Zhang, Ping; Cao, Xi Hang; Du, Dizheng; Ye, Hao; Huang, Hui; Li, Can; Qin, Shengying; Wan, Chunling; Shi, Leming; He, Lin; Yang, Lun

    2016-11-02

    The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational "drug candidate positioning" or "drug positioning", to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value. Since many therapeutic effects are mediated by unexpected drug-protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles to predict indications. Here we introduce the server DPDR-CPI, which can make real-time predictions based only on the structure of the small molecule. When a user submits a molecule, the server will dock it across 611 human proteins, generating a CPI profile of features that can be used for predictions. It can suggest the likelihood of relevance of the input molecule towards ~1,000 human diseases with top predictions listed. DPDR-CPI achieved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent validation. The server is freely accessible via http://cpi.bio-x.cn/dpdr/.

  11. DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome

    PubMed Central

    Luo, Heng; Zhang, Ping; Cao, Xi Hang; Du, Dizheng; Ye, Hao; Huang, Hui; Li, Can; Qin, Shengying; Wan, Chunling; Shi, Leming; He, Lin; Yang, Lun

    2016-01-01

    The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational “drug candidate positioning” or “drug positioning”, to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value. Since many therapeutic effects are mediated by unexpected drug-protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles to predict indications. Here we introduce the server DPDR-CPI, which can make real-time predictions based only on the structure of the small molecule. When a user submits a molecule, the server will dock it across 611 human proteins, generating a CPI profile of features that can be used for predictions. It can suggest the likelihood of relevance of the input molecule towards ~1,000 human diseases with top predictions listed. DPDR-CPI achieved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent validation. The server is freely accessible via http://cpi.bio-x.cn/dpdr/. PMID:27805045

  12. DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome.

    PubMed

    Luo, Heng; Zhang, Ping; Huang, Hui; Huang, Jialiang; Kao, Emily; Shi, Leming; He, Lin; Yang, Lun

    2014-07-01

    Drug-drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug-human protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/.

  13. Spatiotemporal Signatures of Lexical–Semantic Prediction

    PubMed Central

    Lau, Ellen F.; Weber, Kirsten; Gramfort, Alexandre; Hämäläinen, Matti S.; Kuperberg, Gina R.

    2016-01-01

    Although there is broad agreement that top-down expectations can facilitate lexical–semantic processing, the mechanisms driving these effects are still unclear. In particular, while previous electroencephalography (EEG) research has demonstrated a reduction in the N400 response to words in a supportive context, it is often challenging to dissociate facilitation due to bottom-up spreading activation from facilitation due to top-down expectations. The goal of the current study was to specifically determine the cortical areas associated with facilitation due to top-down prediction, using magnetoencephalography (MEG) recordings supplemented by EEG and functional magnetic resonance imaging (fMRI) in a semantic priming paradigm. In order to modulate expectation processes while holding context constant, we manipulated the proportion of related pairs across 2 blocks (10 and 50% related). Event-related potential results demonstrated a larger N400 reduction when a related word was predicted, and MEG source localization of activity in this time-window (350–450 ms) localized the differential responses to left anterior temporal cortex. fMRI data from the same participants support the MEG localization, showing contextual facilitation in left anterior superior temporal gyrus for the high expectation block only. Together, these results provide strong evidence that facilitatory effects of lexical–semantic prediction on the electrophysiological response 350–450 ms postonset reflect modulation of activity in left anterior temporal cortex. PMID:25316341

  14. Sensitivity to Spatiotemporal Percepts Predicts the Perception of Emotion

    PubMed Central

    Castro, Vanessa L.; Boone, R. Thomas

    2015-01-01

    The present studies examined how sensitivity to spatiotemporal percepts such as rhythm, angularity, configuration, and force predicts accuracy in perceiving emotion. In Study 1, participants (N = 99) completed a nonverbal test battery consisting of three nonverbal emotion perception tests and two perceptual sensitivity tasks assessing rhythm sensitivity and angularity sensitivity. Study 2 (N = 101) extended the findings of Study 1 with the addition of a fourth nonverbal test, a third configural sensitivity task, and a fourth force sensitivity task. Regression analyses across both studies revealed partial support for the association between perceptual sensitivity to spatiotemporal percepts and greater emotion perception accuracy. Results indicate that accuracy in perceiving emotions may be predicted by sensitivity to specific percepts embedded within channel- and emotion-specific displays. The significance of such research lies in the understanding of how individuals acquire emotion perception skill and the processes by which distinct features of percepts are related to the perception of emotion. PMID:26339111

  15. Functional Integrative Levels in the Human Interactome Recapitulate Organ Organization

    PubMed Central

    Prieto, Carlos; Benkahla, Alia; De Las Rivas, Javier; Brun, Christine

    2011-01-01

    Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This ‘Largest Common Interactome Network’ represents a ‘functional interactome core’. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization. PMID:21799769

  16. Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations

    SciTech Connect

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

    2009-04-03

    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.

  17. Spatiotemporal predictability of schooling and nonschooling prey of Pigeon Guillemots

    USGS Publications Warehouse

    Litzow, Michael A.; Piatt, John F.; Abookire, Alisa A.; Speckman, Suzann G.; Arimitsu, Mayumi L.; Figurski, Jared D.

    2004-01-01

    Low spatiotemporal variability in the abundance of nonschooling prey might allow Pigeon Guillemots (Cepphus columba) to maintain the high chick provisioning rates that are characteristic of the species. We tested predictions of this hypothesis with data collected with beach seines and scuba and hydroacoustic surveys in Kachemak Bay, Alaska, during 1996–1999. Coefficients of variability were 20–211% greater for schooling than nonschooling prey on day, seasonal, and km scales. However, the proportion of schooling prey in chick diets explained relatively little variability in Pigeon Guillemot meal delivery rates at the scale of hours (r2 = 0.07) and weeks (r2 = 0.19). Behavioral adaptations such as flexible time budgets likely ameliorate the negative effects of high resource variability, but we propose that these adaptations are only effective when schooling prey are available at distances well below the maximum foraging range of the species.

  18. Spatiotemporal properties of microsaccades: Model predictions and experimental tests

    NASA Astrophysics Data System (ADS)

    Zhou, Jian-Fang; Yuan, Wu-Jie; Zhou, Zhao

    2016-10-01

    Microsaccades are involuntary and very small eye movements during fixation. Recently, the microsaccade-related neural dynamics have been extensively investigated both in experiments and by constructing neural network models. Experimentally, microsaccades also exhibit many behavioral properties. It’s well known that the behavior properties imply the underlying neural dynamical mechanisms, and so are determined by neural dynamics. The behavioral properties resulted from neural responses to microsaccades, however, are not yet understood and are rarely studied theoretically. Linking neural dynamics to behavior is one of the central goals of neuroscience. In this paper, we provide behavior predictions on spatiotemporal properties of microsaccades according to microsaccade-induced neural dynamics in a cascading network model, which includes both retinal adaptation and short-term depression (STD) at thalamocortical synapses. We also successfully give experimental tests in the statistical sense. Our results provide the first behavior description of microsaccades based on neural dynamics induced by behaving activity, and so firstly link neural dynamics to behavior of microsaccades. These results indicate strongly that the cascading adaptations play an important role in the study of microsaccades. Our work may be useful for further investigations of the microsaccadic behavioral properties and of the underlying neural dynamical mechanisms responsible for the behavioral properties.

  19. Predicting and tracking spatiotemporal moments in electrical resistivity tomography

    NASA Astrophysics Data System (ADS)

    Ward, W. O. C.; Wilkinson, P. B.; Chambers, J.; Bai, L.

    2015-12-01

    Visualisation is an invaluable tool in the study of near sub-surface processes, whether by mathematical modelling or by geophysical imaging. Quantitative analysis can further assist interpretation of the ongoing physical processes, and it is clear that any reliable model should take direct observations into account. Using electrical resistivity tomography (ERT), localised areas can be surveyed to produce 2-D and 3-D time-lapse images. This study investigates combining quantitative results obtained via ERT with spatio-temporal motion models in tracer experiments to interpret and predict fluid flow. As with any indirect imaging technique, ERT suffers specific issues with resolution and smoothness as a result of its inversion process. In addition, artefacts are typical in the resulting volumes. Mathematical models are also a source of uncertainty - and in combining these with ERT images, a trade-off must be made between the theoretical and the observed. Using computational imaging, distinct regions of stable resistivity can be directly extracted from a time-slice of an ERT volume. The automated nature, as well the potential for more than one region-of-interest, means that multiple regions can be detected. Using Kalman filters, it is possible to convert the detections into a process state, taking into account pre-defined models and predicting progression. In consecutive time-steps, newly detected features are assigned, where possible, to existing predictions to create tracks that match the tracer model. Preliminary studies looked at a simple motion model, tracking the centre of mass of a tracer plume with assumed constant velocity and mean resistivity. Extending the model to factor in spatial distribution of the plume, an oriented semi-axis is used to represent the centralised second-order moment, with an increasing factor of magnitude to represent the plume dispersion. Initial results demonstrate the efficacy of the approach, significantly improving reliability as the

  20. Approach of the spatiotemporal prediction using vectorial geographic data

    NASA Astrophysics Data System (ADS)

    Mezzadri-Centeno, Tania; Saint-Joan, D.; Desachy, Jacky; Vidal, F.

    1996-12-01

    Spatial evolutions of the anthropized ecosystems and the progressive transformation of spaces in the course of time emerge more and more as a special interest issue in research about the environment. This evolution can present a large preoccupation in space accommodation and environmental domains, and it gives rise to a considerable problem in terms of prospective. How will be the conditions of a region area, between now and 15, 30, or 50 years? In fact, the time consists of hierarchical events and can produce transformations upon a terrain landscape as emergence, disappearing, union of spatial entities. These transformations are called temporal phenomena. We propose to predict the forestry evolution in the forthcoming years on an experimental area which reveals these spatial transformations. For these purposes, we have developed a specific spatio-temporal prediction approach. The idea we present here takes a first step in attacking this problematic, it turns out very interesting results in this domain. We describe in this paper a method for analysis and prediction of terrain landscape for an established date. This method is founded on n geographic maps representing the terrain conditions for distinct years. The basic idea is to employ the observation of the temporal phenomena evolution. In fact, results of this observation represent the evolution of each region area on maps in the course of time. The evolution modeling of the regions is obtained with the help of a sequence of aerial photographies compared through different dates. It allows the geographer interested in environmental prospective problems to get type cartographical documents showing the future conditions of a landscape. This method makes use of vectorial geographic data and it achieves a prediction by means of different comparisons between attributes of regions such as the surface, center and distance between regions. The final shapes and positions of the regions are determined by combining the

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

    PubMed Central

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

    2013-01-01

    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

  2. Impaired Spatio-Temporal Predictive Motor Timing Associated with Spinocerebellar Ataxia Type 6

    PubMed Central

    Onuki, Yoshiyuki; Abdelgabar, Abdel R.; Owens, Cullen B.; Picard, Samuel; Willems, Jessica; Boele, Henk-Jan; Gazzola, Valeria; Van der Werf, Ysbrand D.; De Zeeuw, Chris I.

    2016-01-01

    Many daily life activities demand precise integration of spatial and temporal information of sensory inputs followed by appropriate motor actions. This type of integration is carried out in part by the cerebellum, which has been postulated to play a central role in learning and timing of movements. Cerebellar damage due to atrophy or lesions may compromise forward-model processing, in which both spatial and temporal cues are used to achieve prediction for future motor states. In the present study we sought to further investigate the cerebellar contribution to predictive and reactive motor timing, as well as to learning of sequential order and temporal intervals in these tasks. We tested patients with spinocerebellar ataxia type 6 (SCA6) and healthy controls for two related motor tasks; one requiring spatio-temporal prediction of dynamic visual stimuli and another one requiring reactive timing only. We found that healthy controls established spatio-temporal prediction in their responses with high temporal precision, which was absent in the cerebellar patients. SCA6 patients showed lower predictive motor timing, coinciding with a reduced number of correct responses during the ‘anticipatory’ period on the task. Moreover, on the task utilizing reactive motor timing functions, control participants showed both sequence order and temporal interval learning, whereas patients only showed sequence order learning. These results suggest that SCA6 affects predictive motor timing and temporal interval learning. Our results support and highlight cerebellar contribution to timing and argue for cerebellar engagement during spatio-temporal prediction of upcoming events. PMID:27571363

  3. Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs

    PubMed Central

    Lee, Sungjun; Lim, Junseok; Park, Jonghun; Kim, Kwanho

    2016-01-01

    Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user’s next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user’s past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user’s next places than the previous approaches considered in most cases. PMID:26805850

  4. Retrospective prediction of intraurban spatiotemporal distribution of PM2.5 in Taipei

    NASA Astrophysics Data System (ADS)

    Hwa-Lung, Yu; Chih-Hsin, Wang

    2010-08-01

    Numerous studies have shown that fine airborne particulate matter particles (PM2.5) are more dangerous to human health than coarse particles, e.g. PM10. The assessment of the impacts to human health or ecological effects by long-term PM2.5 exposure is often limited by lack of PM2.5 measurements. In Taipei, PM2.5 was not systematically observed until August, 2005. Taipei is the largest metropolitan area in Taiwan, where a variety of industrial and traffic emissions are continuously generated and distributed across space and time. PM-related data, i.e., PM10 and Total Suspended Particles (TSP) are independently systematically collected by different central and local government institutes. In this study, the retrospective prediction of spatiotemporal distribution of monthly PM2.5 over Taipei will be performed by using Bayesian Maximum Entropy method (BME) to integrate (a) the spatiotemporal dependence among PM measurements (i.e. PM10, TSP, and PM2.5), (b) the site-specific information of PM measurements which can be certain or uncertain information, and (c) empirical evidence about the PM2.5/PM10 and PM10/TSP ratios. The performance assessment of the retrospective prediction for the spatiotemporal distribution of PM2.5 was performed over space and time during 2003-2004 by comparing the posterior pdf of PM2.5 with the observations. Results show that the incorporation of PM10 and TSP observations by BME method can effectively improve the spatiotemporal PM2.5 estimation in the sense of lower mean and standard deviation of estimation errors. Moreover, the spatiotemporal retrospective prediction with PM2.5/PM10 and PM2.5/TSP ratios can provide good estimations of the range of PM2.5 levels over space and time during 2003-2004 in Taipei.

  5. Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning.

    PubMed

    Reid, Colleen E; Jerrett, Michael; Petersen, Maya L; Pfister, Gabriele G; Morefield, Philip E; Tager, Ira B; Raffuse, Sean M; Balmes, John R

    2015-03-17

    Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

  6. Manifold learning in protein interactomes.

    PubMed

    Marras, Elisabetta; Travaglione, Antonella; Capobianco, Enrico

    2011-01-01

    Many studies and applications in the post-genomic era have been devoted to analyze complex biological systems by computational inference methods. We propose to apply manifold learning methods to protein-protein interaction networks (PPIN). Despite their popularity in data-intensive applications, these methods have received limited attention in the context of biological networks. We show that there is both utility and unexplored potential in adopting manifold learning for network inference purposes. In particular, the following advantages are highlighted: (a) fusion with diagnostic statistical tools designed to assign significance to protein interactions based on pre-selected topological features; (b) dissection into components of the interactome in order to elucidate global and local connectivity organization; (c) relevance of embedding the interactome in reduced dimensions for biological validation purposes. We have compared the performances of three well-known techniques--kernel-PCA, RADICAL ICA, and ISOMAP--relatively to their power of mapping the interactome onto new coordinate dimensions where important associations among proteins can be detected, and then back projected such that the corresponding sub-interactomes are reconstructed. This recovery has been done selectively, by using significant information according to a robust statistical procedure, and then standard biological annotation has been provided to validate the results. We expect that a byproduct of using subspace analysis by the proposed techniques is a possible calibration of interactome modularity studies. Supplementary Material is available online at www.libertonlinec.com.

  7. Learned spatiotemporal sequence recognition and prediction in primary visual cortex

    PubMed Central

    Gavornik, Jeffrey P.; Bear, Mark F.

    2014-01-01

    Learning to recognize and predict temporal sequences is fundamental to sensory perception, and is impaired in several neuropsychiatric disorders, but little is known about where and how this occurs in the brain. We discovered that repeated presentations of a visual sequence over a course of days causes evoked response potentiation in mouse V1 that is highly specific for stimulus order and timing. Remarkably, after V1 is trained to recognize a sequence, cortical activity regenerates the full sequence even when individual stimulus elements are omitted. This novel neurophysiological report of sequence learning advances the understanding of how the brain makes “intelligent guesses” based on limited information to form visual percepts and suggests that it is possible to study the mechanistic basis of this high–level cognitive ability by studying low–level sensory systems. PMID:24657967

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

    NASA Astrophysics Data System (ADS)

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

    2011-06-01

    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.

  9. Predictive Spatiotemporal Manipulation of Signaling Perturbations Using Optogenetics

    PubMed Central

    Valon, Leo; Etoc, Fred; Remorino, Amanda; di Pietro, Florencia; Morin, Xavier; Dahan, Maxime; Coppey, Mathieu

    2015-01-01

    Recently developed optogenetic methods promise to revolutionize cell biology by allowing signaling perturbations to be controlled in space and time with light. However, a quantitative analysis of the relationship between a custom-defined illumination pattern and the resulting signaling perturbation is lacking. Here, we characterize the biophysical processes governing the localized recruitment of the Cryptochrome CRY2 to its membrane-anchored CIBN partner. We develop a quantitative framework and present simple procedures that enable predictive manipulation of protein distributions on the plasma membrane with a spatial resolution of 5 μm. We show that protein gradients of desired levels can be established in a few tens of seconds and then steadily maintained. These protein gradients can be entirely relocalized in a few minutes. We apply our approach to the control of the Cdc42 Rho GTPase activity. By inducing strong localized signaling perturbation, we are able to monitor the initiation of cell polarity and migration with a remarkable reproducibility despite cell-to-cell variability. PMID:26536256

  10. Predicting saltwater intrusion into aquifers in vicinity of deserts using spatio-temporal kriging.

    PubMed

    Bahrami Jovein, E; Hosseini, S M

    2017-02-01

    The primary objective of this study was to provide a detailed framework to use the spatio-temporal kriging to model the spatio-temporal variations of salinity data and predict saltwater intrusion into freshwater aquifers in the vicinity of deserts. EC data, measured in extraction wells in the Mahvelat plain located in the Northeastern part of Iran, available from 2007 to 2013, were used to demonstrate the developed framework. The source of data was not a well-designed measurement network. Therefore, to homogenize the data, spatial analysis was used to find EC distribution in the area in each year of study. To conduct the spatial analysis, a guideline and a systematic process were developed to select an appropriate kriging method and optimize its parameters. This process can be applied to different variables. After spatial analysis of EC data for all the years of the analysis period using empirical Bayesian kriging (EBK) method with manually optimized parameters, spatio-temporal and corresponding variogram analysis was conducted using R software. This process was based on a separable product-sum model applied to the data from 2007 to 2012. The data of 2013 and the data available for the years 1999 and 2006 were used for evaluating the performance of the spatio-temporal model. The EC distribution maps, developed for different years until 2021, show a high level of EC in the north, south, and west of the study area and growing saltwater intrusion into the central freshwater aquifer. This result can be attributed to the over-exploitation of the aquifer and hydraulic head and gradient distribution in the area. The framework provided in this study for spatio-temporal analysis of unstructured EC data is useful for groundwater managers in making proper decisions.

  11. Viruses and interactomes in translation.

    PubMed

    Meyniel-Schicklin, Laurène; de Chassey, Benoît; André, Patrice; Lotteau, Vincent

    2012-07-01

    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.

  12. Spatio-temporal variation and prediction of ischemic heart disease hospitalizations in Shenzhen, China.

    PubMed

    Wang, Yanxia; Du, Qingyun; Ren, Fu; Liang, Shi; Lin, De-nan; Tian, Qin; Chen, Yan; Li, Jia-jia

    2014-05-06

    Ischemic heart disease (IHD) is a leading cause of death worldwide. Urban public health and medical management in Shenzhen, an international city in the developing country of China, is challenged by an increasing burden of IHD. This study analyzed the spatio-temporal variation of IHD hospital admissions from 2003 to 2012 utilizing spatial statistics, spatial analysis, and space-time scan statistics. The spatial statistics and spatial analysis measured the incidence rate (hospital admissions per 1,000 residents) and the standardized rate (the observed cases standardized by the expected cases) of IHD at the district level to determine the spatio-temporal distribution and identify patterns of change. The space-time scan statistics was used to identify spatio-temporal clusters of IHD hospital admissions at the district level. The other objective of this study was to forecast the IHD hospital admissions over the next three years (2013-2015) to predict the IHD incidence rates and the varying burdens of IHD-related medical services among the districts in Shenzhen. The results show that the highest hospital admissions, incidence rates, and standardized rates of IHD are in Futian. From 2003 to 2012, the IHD hospital admissions exhibited similar mean centers and directional distributions, with a slight increase in admissions toward the north in accordance with the movement of the total population. The incidence rates of IHD exhibited a gradual increase from 2003 to 2012 for all districts in Shenzhen, which may be the result of the rapid development of the economy and the increasing traffic pollution. In addition, some neighboring areas exhibited similar temporal change patterns, which were also detected by the spatio-temporal cluster analysis. Futian and Dapeng would have the highest and the lowest hospital admissions, respectively, although these districts have the highest incidence rates among all of the districts from 2013 to 2015 based on the prediction using the GM (1

  13. A climate-based spatiotemporal prediction for dengue fever epidemics: a case study in southern Taiwan

    NASA Astrophysics Data System (ADS)

    Yu, H.-L.; Yang, S.-J.; Lin, Y.-C.

    2012-04-01

    Dengue Fever (DF) has been identified by the World Health organization (WHO) as one of the most serious vector-borne infectious diseases in tropical and sub-tropical areas. DF has been one of the most important epidemics in Taiwan which occur annually especially in southern Taiwan during summer and autumn. Most DF studies have focused mainly on temporal DF patterns and its close association with climatic covariates, whereas few studies have investigated the spatial DF patterns (spatial dependence and clustering) and composite space-time effects of the DF epidemics. The present study proposes a spatio-temporal DF prediction approach based on stochastic Bayesian Maximum Entropy (BME) analysis. Core and site-specific knowledge bases are considered, including climate and health datasets under conditions of uncertainty, space-time dependence functions, and a Poisson regression model of climatic variables contributing to DF occurrences in southern Taiwan during 2007, when the highest number of DF cases was recorded in the history of Taiwan epidemics (over 2000). The obtained results show that the DF outbreaks in the study area are highly influenced by climatic conditions. Furthermore, the analysis can provide the required "one-week-ahead" outbreak warnings based on spatio-temporal predictions of DF distributions. Therefore, the proposed analysis can provide the Taiwan Disease Control Agency with a valuable tool to timely identify, control, and even efficiently prevent DF spreading across space-time.

  14. Microclimate Data Improve Predictions of Insect Abundance Models Based on Calibrated Spatiotemporal Temperatures

    PubMed Central

    Rebaudo, François; Faye, Emile; Dangles, Olivier

    2016-01-01

    A large body of literature has recently recognized the role of microclimates in controlling the physiology and ecology of species, yet the relevance of fine-scale climatic data for modeling species performance and distribution remains a matter of debate. Using a 6-year monitoring of three potato moth species, major crop pests in the tropical Andes, we asked whether the spatiotemporal resolution of temperature data affect the predictions of models of moth performance and distribution. For this, we used three different climatic data sets: (i) the WorldClim dataset (global dataset), (ii) air temperature recorded using data loggers (weather station dataset), and (iii) air crop canopy temperature (microclimate dataset). We developed a statistical procedure to calibrate all datasets to monthly and yearly variation in temperatures, while keeping both spatial and temporal variances (air monthly temperature at 1 km² for the WorldClim dataset, air hourly temperature for the weather station, and air minute temperature over 250 m radius disks for the microclimate dataset). Then, we computed pest performances based on these three datasets. Results for temperature ranging from 9 to 11°C revealed discrepancies in the simulation outputs in both survival and development rates depending on the spatiotemporal resolution of the temperature dataset. Temperature and simulated pest performances were then combined into multiple linear regression models to compare predicted vs. field data. We used an additional set of study sites to test the ability of the results of our model to be extrapolated over larger scales. Results showed that the model implemented with microclimatic data best predicted observed pest abundances for our study sites, but was less accurate than the global dataset model when performed at larger scales. Our simulations therefore stress the importance to consider different temperature datasets depending on the issue to be solved in order to accurately predict species

  15. The Effect of Rainfall Measurement Technique and Its Spatiotemporal Resolution on Discharge Predictions in the Netherlands

    NASA Astrophysics Data System (ADS)

    Uijlenhoet, R.; Brauer, C.; Overeem, A.; Sassi, M.; Rios Gaona, M. F.

    2014-12-01

    Several rainfall measurement techniques are available for hydrological applications, each with its own spatial and temporal resolution. We investigated the effect of these spatiotemporal resolutions on discharge simulations in lowland catchments by forcing a novel rainfall-runoff model (WALRUS) with rainfall data from gauges, radars and microwave links. The hydrological model used for this analysis is the recently developed Wageningen Lowland Runoff Simulator (WALRUS). WALRUS is a rainfall-runoff model accounting for hydrological processes relevant to areas with shallow groundwater (e.g. groundwater-surface water feedback). Here, we used WALRUS for case studies in a freely draining lowland catchment and a polder with controlled water levels. We used rain gauge networks with automatic (hourly resolution but low spatial density) and manual gauges (high spatial density but daily resolution). Operational (real-time) and climatological (gauge-adjusted) C-band radar products and country-wide rainfall maps derived from microwave link data from a cellular telecommunication network were also used. Discharges simulated with these different inputs were compared to observations. We also investigated the effect of spatiotemporal resolution with a high-resolution X-band radar data set for catchments with different sizes. Uncertainty in rainfall forcing is a major source of uncertainty in discharge predictions, both with lumped and with distributed models. For lumped rainfall-runoff models, the main source of input uncertainty is associated with the way in which (effective) catchment-average rainfall is estimated. When catchments are divided into sub-catchments, rainfall spatial variability can become more important, especially during convective rainfall events, leading to spatially varying catchment wetness and spatially varying contribution of quick flow routes. Improving rainfall measurements and their spatiotemporal resolution can improve the performance of rainfall

  16. Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula

    NASA Astrophysics Data System (ADS)

    Nowosad, Jakub

    2016-06-01

    Corylus, Alnus, and Betula trees are among the most important sources of allergic pollen in the temperate zone of the Northern Hemisphere and have a large impact on the quality of life and productivity of allergy sufferers. Therefore, it is important to predict high pollen concentrations, both in time and space. The aim of this study was to create and evaluate spatiotemporal models for predicting high Corylus, Alnus, and Betula pollen concentration levels, based on gridded meteorological data. Aerobiological monitoring was carried out in 11 cities in Poland and gathered, depending on the site, between 2 and 16 years of measurements. According to the first allergy symptoms during exposure, a high pollen count level was established for each taxon. An optimizing probability threshold technique was used for mitigation of the problem of imbalance in the pollen concentration levels. For each taxon, the model was built using a random forest method. The study revealed the possibility of moderately reliable prediction of Corylus and highly reliable prediction of Alnus and Betula high pollen concentration levels, using preprocessed gridded meteorological data. Cumulative growing degree days and potential evaporation proved to be two of the most important predictor variables in the models. The final models predicted not only for single locations but also for continuous areas. Furthermore, the proposed modeling framework could be used to predict high pollen concentrations of Corylus, Alnus, Betula, and other taxa, and in other countries.

  17. Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula.

    PubMed

    Nowosad, Jakub

    2016-06-01

    Corylus, Alnus, and Betula trees are among the most important sources of allergic pollen in the temperate zone of the Northern Hemisphere and have a large impact on the quality of life and productivity of allergy sufferers. Therefore, it is important to predict high pollen concentrations, both in time and space. The aim of this study was to create and evaluate spatiotemporal models for predicting high Corylus, Alnus, and Betula pollen concentration levels, based on gridded meteorological data. Aerobiological monitoring was carried out in 11 cities in Poland and gathered, depending on the site, between 2 and 16 years of measurements. According to the first allergy symptoms during exposure, a high pollen count level was established for each taxon. An optimizing probability threshold technique was used for mitigation of the problem of imbalance in the pollen concentration levels. For each taxon, the model was built using a random forest method. The study revealed the possibility of moderately reliable prediction of Corylus and highly reliable prediction of Alnus and Betula high pollen concentration levels, using preprocessed gridded meteorological data. Cumulative growing degree days and potential evaporation proved to be two of the most important predictor variables in the models. The final models predicted not only for single locations but also for continuous areas. Furthermore, the proposed modeling framework could be used to predict high pollen concentrations of Corylus, Alnus, Betula, and other taxa, and in other countries.

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

    PubMed Central

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

    2013-01-01

    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. PMID:23346056

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

    PubMed Central

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

    2013-01-01

    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

  20. Spatiotemporal neurodynamics underlying internally and externally driven temporal prediction: a high spatial resolution ERP study.

    PubMed

    Mento, Giovanni; Tarantino, Vincenza; Vallesi, Antonino; Bisiacchi, Patrizia Silvia

    2015-03-01

    Temporal prediction (TP) is a flexible and dynamic cognitive ability. Depending on the internal or external nature of information exploited to generate TP, distinct cognitive and brain mechanisms are engaged with the same final goal of reducing uncertainty about the future. In this study, we investigated the specific brain mechanisms involved in internally and externally driven TP. To this end, we employed an experimental paradigm purposely designed to elicit and compare externally and internally driven TP and a combined approach based on the application of a distributed source reconstruction modeling on a high spatial resolution electrophysiological data array. Specific spatiotemporal ERP signatures were identified, with significant modulation of contingent negative variation and frontal late sustained positivity in external and internal TP contexts, respectively. These different electrophysiological patterns were supported by the engagement of distinct neural networks, including a left sensorimotor and a prefrontal circuit for externally and internally driven TP, respectively.

  1. Climate change forecasts, long-term spatio-temporal prediction and the resilience of dry ecosystems

    NASA Astrophysics Data System (ADS)

    Shafran-Natan, Rakefet; Svoray, Tal; Avi, Perevolotsky

    2010-05-01

    Primary production is an important indicator to climatic changes in drylands, while reduction in productivity has many consequences on ecosystem functioning. We suggest that the response of dry ecosystems to climate change should lead to a change in spatial patterns of grasses without a substantial change in ecosystem resilience. We used field data and a recently published spatio-temporally explicit model to study factors affecting long-term variation in primary production in two dry ecosystems: semi-arid (SAE) and Mediterranean (DME) dominated by annual vegetation. The model was operated in both patch and landscape scales and was executed along 30 years (1979-2008) at SAE and along 21 years (1986-1990; 1993-2008) at DME. Model predictions were validated against samples that were harvested in each site at the end of the growing season, over 15 seasons (1994-2008) at SAE (0.63

  2. Serial interactome capture of the human cell nucleus.

    PubMed

    Conrad, Thomas; Albrecht, Anne-Susann; de Melo Costa, Veronica Rodrigues; Sauer, Sascha; Meierhofer, David; Ørom, Ulf Andersson

    2016-04-04

    Novel RNA-guided cellular functions are paralleled by an increasing number of RNA-binding proteins (RBPs). Here we present 'serial RNA interactome capture' (serIC), a multiple purification procedure of ultraviolet-crosslinked poly(A)-RNA-protein complexes that enables global RBP detection with high specificity. We apply serIC to the nuclei of proliferating K562 cells to obtain the first human nuclear RNA interactome. The domain composition of the 382 identified nuclear RBPs markedly differs from previous IC experiments, including few factors without known RNA-binding domains that are in good agreement with computationally predicted RNA binding. serIC extends the number of DNA-RNA-binding proteins (DRBPs), and reveals a network of RBPs involved in p53 signalling and double-strand break repair. serIC is an effective tool to couple global RBP capture with additional selection or labelling steps for specific detection of highly purified RBPs.

  3. The HTLV-1 Tax interactome

    PubMed Central

    Boxus, Mathieu; Twizere, Jean-Claude; Legros, Sébastien; Dewulf, Jean-François; Kettmann, Richard; Willems, Luc

    2008-01-01

    The Tax1 oncoprotein encoded by Human T-lymphotropic virus type I is a major determinant of viral persistence and pathogenesis. Tax1 affects a wide variety of cellular signalling pathways leading to transcriptional activation, proliferation and ultimately transformation. To carry out these functions, Tax1 interacts with and modulates activity of a number of cellular proteins. In this review, we summarize the present knowledge of the Tax1 interactome and propose a rationale for the broad range of cellular proteins identified so far. PMID:18702816

  4. An online spatio-temporal prediction model for dengue fever epidemic in Kaohsiung,Taiwan

    NASA Astrophysics Data System (ADS)

    Cheng, Ming-Hung; Yu, Hwa-Lung; Angulo, Jose; Christakos, George

    2013-04-01

    Dengue Fever (DF) is one of the most serious vector-borne infectious diseases in tropical and subtropical areas. DF epidemics occur in Taiwan annually especially during summer and fall seasons. Kaohsiung city has been one of the major DF hotspots in decades. The emergence and re-emergence of the DF epidemic is complex and can be influenced by various factors including space-time dynamics of human and vector populations and virus serotypes as well as the associated uncertainties. This study integrates a stochastic space-time "Susceptible-Infected-Recovered" model under Bayesian maximum entropy framework (BME-SIR) to perform real-time prediction of disease diffusion across space-time. The proposed model is applied for spatiotemporal prediction of the DF epidemic at Kaohsiung city during 2002 when the historical series of high DF cases was recorded. The online prediction by BME-SIR model updates the parameters of SIR model and infected cases across districts over time. Results show that the proposed model is rigorous to initial guess of unknown model parameters, i.e. transmission and recovery rates, which can depend upon the virus serotypes and various human interventions. This study shows that spatial diffusion can be well characterized by BME-SIR model, especially at the district surrounding the disease outbreak locations. The prediction performance at DF hotspots, i.e. Cianjhen and Sanmin, can be degraded due to the implementation of various disease control strategies during the epidemics. The proposed online disease prediction BME-SIR model can provide the governmental agency with a valuable reference to timely identify, control, and efficiently prevent DF spread across space-time.

  5. Geomagnetic imprinting predicts spatio-temporal variation in homing migration of pink and sockeye salmon

    PubMed Central

    Putman, Nathan F.; Jenkins, Erica S.; Michielsens, Catherine G. J.; Noakes, David L. G.

    2014-01-01

    Animals navigate using a variety of sensory cues, but how each is weighted during different phases of movement (e.g. dispersal, foraging, homing) is controversial. Here, we examine the geomagnetic and olfactory imprinting hypotheses of natal homing with datasets that recorded variation in the migratory routes of sockeye (Oncorhynchus nerka) and pink (Oncorhynchus gorbuscha) salmon returning from the Pacific Ocean to the Fraser River, British Columbia. Drift of the magnetic field (i.e. geomagnetic imprinting) uniquely accounted for 23.2% and 44.0% of the variation in migration routes for sockeye and pink salmon, respectively. Ocean circulation (i.e. olfactory imprinting) predicted 6.1% and 0.1% of the variation in sockeye and pink migration routes, respectively. Sea surface temperature (a variable influencing salmon distribution but not navigation, directly) accounted for 13.0% of the variation in sockeye migration but was unrelated to pink migration. These findings suggest that geomagnetic navigation plays an important role in long-distance homing in salmon and that consideration of navigation mechanisms can aid in the management of migratory fishes by better predicting movement patterns. Finally, given the diversity of animals that use the Earth's magnetic field for navigation, geomagnetic drift may provide a unifying explanation for spatio-temporal variation in the movement patterns of many species. PMID:25056214

  6. Geomagnetic imprinting predicts spatio-temporal variation in homing migration of pink and sockeye salmon.

    PubMed

    Putman, Nathan F; Jenkins, Erica S; Michielsens, Catherine G J; Noakes, David L G

    2014-10-06

    Animals navigate using a variety of sensory cues, but how each is weighted during different phases of movement (e.g. dispersal, foraging, homing) is controversial. Here, we examine the geomagnetic and olfactory imprinting hypotheses of natal homing with datasets that recorded variation in the migratory routes of sockeye (Oncorhynchus nerka) and pink (Oncorhynchus gorbuscha) salmon returning from the Pacific Ocean to the Fraser River, British Columbia. Drift of the magnetic field (i.e. geomagnetic imprinting) uniquely accounted for 23.2% and 44.0% of the variation in migration routes for sockeye and pink salmon, respectively. Ocean circulation (i.e. olfactory imprinting) predicted 6.1% and 0.1% of the variation in sockeye and pink migration routes, respectively. Sea surface temperature (a variable influencing salmon distribution but not navigation, directly) accounted for 13.0% of the variation in sockeye migration but was unrelated to pink migration. These findings suggest that geomagnetic navigation plays an important role in long-distance homing in salmon and that consideration of navigation mechanisms can aid in the management of migratory fishes by better predicting movement patterns. Finally, given the diversity of animals that use the Earth's magnetic field for navigation, geomagnetic drift may provide a unifying explanation for spatio-temporal variation in the movement patterns of many species.

  7. Spatiotemporal prediction applying fuzzy logic in a sequence of satellite images

    NASA Astrophysics Data System (ADS)

    Mezzadri-Centeno, Tania; Selleron, Gilles

    2002-01-01

    Spatial evolutions of anthropized ecosystems and the progressive transformation of spaces in the course of time emerge more and more as a special interest issue in researches about the environment. This evolution constitutes one of the major concerns in the domain of environmental space management. The landscape evolution of a region area and the perspectives for a future state rises an issue particularly important. What will be the state of the region in 15, 30 or 50 years? Time can produce transformations over a region area like emergence, disappearance or union of spatial entities... These transformations are called temporal phenomena. We propose to predict the forestry evolution in the forthcoming years on an experimental area, which reveals these spatial transformations. The proposed method is based on the analysis of terrain landscape given a sequence of n satellite images, which represent the state of a region area in different years. For these purposes, we have developed a specific spatio-temporal prediction approach, linking results of forestry evolution analysis and fuzzy logic. The method is supported by the analysis of the landscape dynamics of a test-site located in a tropical rain country: the oriental piedmont of Andes Mountain in Venezuela. This large area - at the scale of a spot satellite image - is typical of tropical deforestation in a pioneer front. The presented approach allows the geographer interested in environmental prospective problems to get type cartographical documents showing future conditions of a landscape. The experimental tests have showed promising results.

  8. Tumor site prediction using spatiotemporal detection of subclinical hyperemia in experimental photocarcinogenesis

    NASA Astrophysics Data System (ADS)

    Konger, Raymond L.; Xu, Zhengbin; Sahu, Ravi P.; Kim, Young L.

    2014-03-01

    We demonstrate that a spatial and temporal analysis of subclinical hyperemia reliably predicts specific areas at high risk for skin tumor development during photocarcinogenesis. To determine detailed spatiotemporal patterns of inflammatory angiogenesis foci in a relatively large area, we developed a mesoscopic (between microscopic and macroscopic) imaging approach. This method relies on our earlier finding that the combination of a spectral analysis of hemoglobin with diffuse-light-suppressed imaging can increase the image resolution, contrast and penetration depth to visualize microvasculature Hgb content in the large tissue area. In our recent study, SKH1 hairless albino mice were irradiated for 10 weeks with a carcinogen dose of UVB. Using our newly developed mesoscopic imaging methods, we imaged the mice over 20 - 30 weeks after stopping UVB, and excised hyperemic/non-hyperemic areas at several different timepoints. We show that persistent hyperemic foci can predict future tumor formation. In particular, our imaging approach allows us to assess the spatial and temporal extent of subclinical inflammatory foci, which in turn can predict sites of future overlying tumor formation. In addition, although COX-2 inhibitors are known to suppress skin cancer development in humans, it remains unclear whether the chemopreventive activity of COX-2 inhibitors are chiefly attributable to their anti-inflammatory effects. Our study provides evidence that subclinical subepithelial inflammatory foci occur prior to overt tumor formation, and that these areas are highly predictive for future tumor formation, that celecoxib's ability to suppress tumorigenesis is tightly linked to its ability to reduce the area of subclinical inflammatory foci.

  9. Proteomic-coupled-network analysis of T877A-androgen receptor interactomes can predict clinical prostate cancer outcomes between White (non-Hispanic) and African-American groups.

    PubMed

    Zaman, Naif; Giannopoulos, Paresa N; Chowdhury, Shafinaz; Bonneil, Eric; Thibault, Pierre; Wang, Edwin; Trifiro, Mark; Paliouras, Miltiadis

    2014-01-01

    The androgen receptor (AR) remains an important contributor to the neoplastic evolution of prostate cancer (CaP). CaP progression is linked to several somatic AR mutational changes that endow upon the AR dramatic gain-of-function properties. One of the most common somatic mutations identified is Thr877-to-Ala (T877A), located in the ligand-binding domain, that results in a receptor capable of promiscuous binding and activation by a variety of steroid hormones and ligands including estrogens, progestins, glucocorticoids, and several anti-androgens. In an attempt to further define somatic mutated AR gain-of-function properties, as a consequence of its promiscuous ligand binding, we undertook a proteomic/network analysis approach to characterize the protein interactome of the mutant T877A-AR in LNCaP cells under eight different ligand-specific treatments (dihydrotestosterone, mibolerone, R1881, testosterone, estradiol, progesterone, dexamethasone, and cyproterone acetate). In extending the analysis of our multi-ligand complexes of the mutant T877A-AR we observed significant enrichment of specific complexes between normal and primary prostatic tumors, which were furthermore correlated with known clinical outcomes. Further analysis of certain mutant T877A-AR complexes showed specific population preferences distinguishing primary prostatic disease between white (non-Hispanic) vs. African-American males. Moreover, these cancer-related AR-protein complexes demonstrated predictive survival outcomes specific to CaP, and not for breast, lung, lymphoma or medulloblastoma cancers. Our study, by coupling data generated by our proteomics to network analysis of clinical samples, has helped to define real and novel biological pathways in complicated gain-of-function AR complex systems.

  10. Predicting avian distributions to evaluate spatiotemporal overlap with locust control operations in eastern Australia.

    PubMed

    Szabo, Judit K; Davy, Pamela J; Hooper, Michael J; Astheimer, Lee B

    2009-12-01

    Locusts and grasshoppers cause considerable economic damage to agriculture worldwide. The Australian Plague Locust Commission uses multiple pesticides to control locusts in eastern Australia. Avian exposure to agricultural pesticides is of conservation concern, especially in the case of rare and threatened species. The aim of this study was to evaluate the probability of pesticide exposure of native avian species during operational locust control based on knowledge of species occurrence in areas and times of application. Using presence-absence data provided by the Birds Australia Atlas for 1998 to 2002, we developed a series of generalized linear models to predict avian occurrences on a monthly basis in 0.5 degrees grid cells for 280 species over 2 million km2 in eastern Australia. We constructed species-specific models relating occupancy patterns to survey date and location, rainfall, and derived habitat preference. Model complexity depended on the number of observations available. Model output was the probability of occurrence for each species at times and locations of past locust control operations within the 5-year study period. Given the high spatiotemporal variability of locust control events, the variability in predicted bird species presence was high, with 108 of the total 280 species being included at least once in the top 20 predicted species for individual space-time events. The models were evaluated using field surveys collected between 2000 and 2005, at sites with and without locust outbreaks. Model strength varied among species. Some species were under- or over-predicted as times and locations of interest typically did not correspond to those in the prediction data set and certain species were likely attracted to locusts as a food source. Field surveys demonstrated the utility of the spatially explicit species lists derived from the models but also identified the presence of a number of previously unanticipated species. These results also emphasize

  11. Disease networks. Uncovering disease-disease relationships through the incomplete interactome.

    PubMed

    Menche, Jörg; Sharma, Amitabh; Kitsak, Maksim; Ghiassian, Susan Dina; Vidal, Marc; Loscalzo, Joseph; Barabási, Albert-László

    2015-02-20

    According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes.

  12. Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFα and co-treatments

    NASA Astrophysics Data System (ADS)

    Loo, Lit-Hsin; Bougen-Zhukov, Nicola Michelle; Tan, Wei-Ling Cecilia

    2017-03-01

    Signaling pathways can generate different cellular responses to the same cytotoxic agents. Current quantitative models for predicting these differential responses are usually based on large numbers of intracellular gene products or signals at different levels of signaling cascades. Here, we report a study to predict cellular sensitivity to tumor necrosis factor alpha (TNFα) using high-throughput cellular imaging and machine-learning methods. We measured and compared 1170 protein phosphorylation events in a panel of human lung cancer cell lines based on different signals, subcellular regions, and time points within one hour of TNFα treatment. We found that two spatiotemporal-specific changes in an intermediate signaling protein, p90 ribosomal S6 kinase (RSK), are sufficient to predict the TNFα sensitivity of these cell lines. Our models could also predict the combined effects of TNFα and other kinase inhibitors, many of which are not known to target RSK directly. Therefore, early spatiotemporal-specific changes in intermediate signals are sufficient to represent the complex cellular responses to these perturbations. Our study provides a general framework for the development of rapid, signaling-based cytotoxicity screens that may be used to predict cellular sensitivity to a cytotoxic agent, or identify co-treatments that may sensitize or desensitize cells to the agent.

  13. Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFα and co-treatments

    PubMed Central

    Loo, Lit-Hsin; Bougen-Zhukov, Nicola Michelle; Tan, Wei-Ling Cecilia

    2017-01-01

    Signaling pathways can generate different cellular responses to the same cytotoxic agents. Current quantitative models for predicting these differential responses are usually based on large numbers of intracellular gene products or signals at different levels of signaling cascades. Here, we report a study to predict cellular sensitivity to tumor necrosis factor alpha (TNFα) using high-throughput cellular imaging and machine-learning methods. We measured and compared 1170 protein phosphorylation events in a panel of human lung cancer cell lines based on different signals, subcellular regions, and time points within one hour of TNFα treatment. We found that two spatiotemporal-specific changes in an intermediate signaling protein, p90 ribosomal S6 kinase (RSK), are sufficient to predict the TNFα sensitivity of these cell lines. Our models could also predict the combined effects of TNFα and other kinase inhibitors, many of which are not known to target RSK directly. Therefore, early spatiotemporal-specific changes in intermediate signals are sufficient to represent the complex cellular responses to these perturbations. Our study provides a general framework for the development of rapid, signaling-based cytotoxicity screens that may be used to predict cellular sensitivity to a cytotoxic agent, or identify co-treatments that may sensitize or desensitize cells to the agent. PMID:28272488

  14. Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem

    NASA Astrophysics Data System (ADS)

    Yao, Bing; Yang, Hui

    2016-12-01

    This paper presents a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time-varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two-sphere geometry and a realistic torso-heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods.

  15. Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem

    PubMed Central

    Yao, Bing; Yang, Hui

    2016-01-01

    This paper presents a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time-varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two-sphere geometry and a realistic torso-heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods. PMID:27966576

  16. Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem.

    PubMed

    Yao, Bing; Yang, Hui

    2016-12-14

    This paper presents a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time-varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two-sphere geometry and a realistic torso-heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods.

  17. Information flow analysis of interactome networks.

    PubMed

    Missiuro, Patrycja Vasilyev; Liu, Kesheng; Zou, Lihua; Ross, Brian C; Zhao, Guoyan; Liu, Jun S; Ge, Hui

    2009-04-01

    Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein-protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression

  18. Defining a Modular Signalling Network from the Fly Interactome

    PubMed Central

    Baudot, Anaïs; Angelelli, Jean-Baptiste; Guénoche, Alain; Jacq, Bernard; Brun, Christine

    2008-01-01

    Background Signalling pathways relay information by transmitting signals from cell surface receptors to intracellular effectors that eventually activate the transcription of target genes. Since signalling pathways involve several types of molecular interactions including protein-protein interactions, we postulated that investigating their organization in the context of the global protein-protein interaction network could provide a new integrated view of signalling mechanisms. Results Using a graph-theory based method to analyse the fly protein-protein interaction network, we found that each signalling pathway is organized in two to three different signalling modules. These modules contain canonical proteins of the signalling pathways, known regulators as well as other proteins thereby predicted to participate to the signalling mechanisms. Connections between the signalling modules are prominent as compared to the other network's modules and interactions within and between signalling modules are among the more central routes of the interaction network. Conclusion Altogether, these modules form an interactome sub-network devoted to signalling with particular topological properties: modularity, density and centrality. This finding reflects the integration of the signalling system into cell functioning and its important role connecting and coordinating different biological processes at the level of the interactome. PMID:18489752

  19. Utilizing the eigenvectors of freeway loop data spatiotemporal schematic for real time crash prediction.

    PubMed

    Fang, Shou'en; Xie, Wenjing; Wang, Junhua; Ragland, David R

    2016-09-01

    The concept of crash precursor identification is gaining more practicality due to the recent advancements in Advanced Transportation Management and Information Systems. Investigating the shortcomings of the existing models, this paper proposes a new method to model the real time crash likelihood based on loop data through schematic eigenvectors. Firstly, traffic volume, occupancy and density spatiotemporal schematics in certain duration before an accident occurrence were constructed to describe the traffic flow status. Secondly, eigenvectors and eigenvalues of the spatiotemporal schematics were extracted to represent traffic volume, occupancy and density situation before the crash occurrence. Thirdly, by setting the vectors in crash time as case and those at crash free time as control, a logistic model is constructed to identify the crash precursors. Results show that both the eigenvectors and eigenvalues can significantly impact the accident likelihood compared to the previous study, the proposed model has the advantage of avoiding multicollinearity, better reflection of the overall traffic flow status before the crash, and improving missing data problem of loop detectors.

  20. Prediction of fine particulate matter chemical components with a spatio-temporal model for the Multi-Ethnic Study of Atherosclerosis cohort

    PubMed Central

    Kim, Sun-Young; Sheppard, Lianne; Bergen, Silas; Szpiro, Adam A.; Sampson, Paul D.; Kaufman, Joel D.; Vedal, Sverre

    2016-01-01

    Although cohort studies of the health effects of PM2.5 have developed exposure prediction models to represent spatial variability across participant residences, few models exist for PM2.5 components. We aimed to develop a city-specific spatio-temporal prediction approach to estimate long-term average concentrations of four PM2.5 components including sulfur, silicon, and elemental and organic carbon for the Multi-Ethnic Study of Atherosclerosis cohort, and to compare predictions to those from a national spatial model. Using 2-week average measurements from a cohort-focused monitoring campaign, the spatio-temporal model employed selected geographic covariates in a universal kriging framework with the data-driven temporal trend. Relying on long-term means of daily measurements from regulatory monitoring networks, the national spatial model employed dimension-reduced predictors using universal kriging. For the spatio-temporal model, the cross-validated and temporally-adjusted R2 was relatively higher for EC and OC, and in the Los Angeles and Baltimore areas. The cross-validated R2s for both models across the six areas were reasonably high for all components except silicon. Predicted long-term concentrations at participant homes from the two models were generally highly correlated across cities but poorly correlated within cities. The spatio-temporal model may be preferred for city-specific health analyses, whereas both models could be used for multi-city studies. PMID:27189258

  1. Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003-2011.

    PubMed

    Lee, Mihye; Kloog, Itai; Chudnovsky, Alexandra; Lyapustin, Alexei; Wang, Yujie; Melly, Steven; Coull, Brent; Koutrakis, Petros; Schwartz, Joel

    2016-06-01

    Numerous studies have demonstrated that fine particulate matter (PM2.5, particles smaller than 2.5 μm in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM2.5 to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM2.5 exposures. In this paper, we used AOD data with other PM2.5 variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM2.5 at a 1-km(2) resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM2.5 measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R(2) values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 μg/m(3) for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM2.5 concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM2.5. Our model results will also extend the existing studies on PM2.5 which have mostly focused on urban areas because of the paucity of monitors in rural areas.

  2. Spatiotemporal prediction of fine particulate matter using high resolution satellite images in the southeastern U.S 2003–2011

    PubMed Central

    Lee, Mihye; Kloog, Itai; Chudnovsky, Alexandra; Lyapustin, Alexei; Wang, Yujie; Melly, Steven; Coull, Brent; Koutrakis, Petros; Schwartz, Joel

    2016-01-01

    Numerous studies have demonstrated that fine particulate matter (PM2.5, particles smaller than 2.5 μm in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM2.5 to assess personal exposure; however, induces measurement error. Land use regression provides spatially resolved predictions but land use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM2.5 exposures. In this paper, we used AOD data with other PM2.5 variables such as meteorological variables, land use regression, and spatial smoothing to predict daily concentrations of PM2.5 at a 1 km2 resolution of the southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 through 2011. We divided the study area into 3 regions and applied separate mixed-effect models to calibrate AOD using ground PM2.5 measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors (RMSPE) of 2.89, 2.51, and 2.82 μg/m3 for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM2.5 concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM2.5. Our model results will also extend the existing studies on PM2.5 which have mostly focused on urban areas due to the paucity of monitors in rural areas. PMID:26082149

  3. APID interactomes: providing proteome-based interactomes with controlled quality for multiple species and derived networks

    PubMed Central

    Alonso-López, Diego; Gutiérrez, Miguel A.; Lopes, Katia P.; Prieto, Carlos; Santamaría, Rodrigo; De Las Rivas, Javier

    2016-01-01

    APID (Agile Protein Interactomes DataServer) is an interactive web server that provides unified generation and delivery of protein interactomes mapped to their respective proteomes. This resource is a new, fully redesigned server that includes a comprehensive collection of protein interactomes for more than 400 organisms (25 of which include more than 500 interactions) produced by the integration of only experimentally validated protein–protein physical interactions. For each protein–protein interaction (PPI) the server includes currently reported information about its experimental validation to allow selection and filtering at different quality levels. As a whole, it provides easy access to the interactomes from specific species and includes a global uniform compendium of 90,379 distinct proteins and 678,441 singular interactions. APID integrates and unifies PPIs from major primary databases of molecular interactions, from other specific repositories and also from experimentally resolved 3D structures of protein complexes where more than two proteins were identified. For this purpose, a collection of 8,388 structures were analyzed to identify specific PPIs. APID also includes a new graph tool (based on Cytoscape.js) for visualization and interactive analyses of PPI networks. The server does not require registration and it is freely available for use at http://apid.dep.usal.es. PMID:27131791

  4. Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm

    NASA Astrophysics Data System (ADS)

    Zhan, Yu; Luo, Yuzhou; Deng, Xunfei; Chen, Huajin; Grieneisen, Michael L.; Shen, Xueyou; Zhu, Lizhong; Zhang, Minghua

    2017-04-01

    A high degree of uncertainty associated with the emission inventory for China tends to degrade the performance of chemical transport models in predicting PM2.5 concentrations especially on a daily basis. In this study a novel machine learning algorithm, Geographically-Weighted Gradient Boosting Machine (GW-GBM), was developed by improving GBM through building spatial smoothing kernels to weigh the loss function. This modification addressed the spatial nonstationarity of the relationships between PM2.5 concentrations and predictor variables such as aerosol optical depth (AOD) and meteorological conditions. GW-GBM also overcame the estimation bias of PM2.5 concentrations due to missing AOD retrievals, and thus potentially improved subsequent exposure analyses. GW-GBM showed good performance in predicting daily PM2.5 concentrations (R2 = 0.76, RMSE = 23.0 μg/m3) even with partially missing AOD data, which was better than the original GBM model (R2 = 0.71, RMSE = 25.3 μg/m3). On the basis of the continuous spatiotemporal prediction of PM2.5 concentrations, it was predicted that 95% of the population lived in areas where the estimated annual mean PM2.5 concentration was higher than 35 μg/m3, and 45% of the population was exposed to PM2.5 >75 μg/m3 for over 100 days in 2014. GW-GBM accurately predicted continuous daily PM2.5 concentrations in China for assessing acute human health effects.

  5. Detection of driver protein complexes in breast cancer metastasis by large-scale transcriptome-interactome integration.

    PubMed

    Garcia, Maxime; Finetti, Pascal; Bertucci, Francois; Birnbaum, Daniel; Bidaut, Ghislain

    2014-01-01

    With the development of high-throughput gene expression profiling technologies came the opportunity to define genomic signatures predicting clinical condition or cancer patient outcome. However, such signatures show dependency on training set, lack of generalization, and instability, partly due to microarray data topology. Additional issues for analyzing tumor gene expression are that subtle molecular perturbations in driver genes leading to cancer and metastasis (masked in typical differential expression analysis) may provoke expression changes of greater amplitude in downstream genes (easily detected). In this chapter, we are describing an interactome-based algorithm, Interactome-Transcriptome Integration (ITI) that is used to find a generalizable signature for prediction of breast cancer relapse by superimposition of a large-scale protein-protein interaction data (human interactome) over several gene expression datasets. ITI extracts regions in the interactome whose expression is discriminating for predicting relapse-free survival in cancer and allow detection of subnetworks that constitutes a generalizable and stable genomic signature. In this chapter, we describe the practical aspects of running the full ITI pipeline (subnetwork detection and classification) on six microarray datasets.

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

    PubMed Central

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

    2011-01-01

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

  7. Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection

    PubMed Central

    Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

    2015-01-01

    Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks. PMID:26496370

  8. Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

    PubMed

    Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

    2015-01-01

    Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

  9. Subject-specific liver motion modeling in MRI: a feasibility study on spatiotemporal prediction

    NASA Astrophysics Data System (ADS)

    Noorda, Yolanda H.; Bartels, Lambertus W.; Viergever, Max A.; Pluim, Josien P. W.

    2017-04-01

    A liver motion model based on registration of dynamic MRI data, as previously proposed by the authors, was extended with temporal prediction and respiratory signal data. The potential improvements of these extensions with respect to the original model were investigated. Additional evaluations were performed to investigate the limitations of the model regarding temporal prediction and extreme breathing motion. Data were acquired of four volunteers, with breathing instructions and a respiratory belt. The model was built from these data using spatial prediction only and using temporal forward prediction of 300 ms to 1200 ms, using the extended Kalman filter. From temporal prediction of 0 ms to 1200 ms ahead, the Dice coefficient of liver overlap decreased with 0.85%, the median liver surface distance increased with 20.6% and the vessel misalignment increased with 20%. The mean vessel misalignment was 2.9 mm for the original method, 3.42 mm for spatial prediction with a respiratory signal and 4.01 mm for prediction of 1200 ms ahead with a respiratory signal. Although the extension of the model to temporal prediction yields a decreased prediction accuracy, the results are still acceptable. The use of the breathing signal as input to the model is feasible. Sudden changes in the breathing pattern can yield large errors. However, these errors only persist during a short time interval, after which they can be corrected automatically. Therefore, this model could be useful in a clinical setting.

  10. Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN

    NASA Astrophysics Data System (ADS)

    Shwetha, H. R.; Kumar, D. Nagesh

    2016-07-01

    Land Surface Temperature (LST) with high spatio-temporal resolution is in demand for hydrology, climate change, ecology, urban climate and environmental studies, etc. Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the most commonly used sensors owing to its high spatial and temporal availability over the globe, but is incapable of providing LST data under cloudy conditions, resulting in gaps in the data. In contrast, microwave measurements have a capability to penetrate under clouds. The current study proposes a methodology by exploring this property to predict high spatio-temporal resolution LST under cloudy conditions during daytime and nighttime without employing in-situ LST measurements. To achieve this, Artificial Neural Networks (ANNs) based models are employed for different land cover classes, utilizing Microwave Polarization Difference Index (MPDI) at finer resolution with ancillary data. MPDI was derived using resampled (from 0.25° to 1 km) brightness temperatures (Tb) at 36.5 GHz channel of dual polarization from Advance Microwave Scanning Radiometer (AMSR)-Earth Observing System and AMSR2 sensors. The proposed methodology is tested over Cauvery basin in India and the performance of the model is quantitatively evaluated through performance measures such as correlation coefficient (r), Nash Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE). Results revealed that during daytime, AMSR-E(AMSR2) derived LST under clear sky conditions corresponds well with MODIS LST resulting in values of r ranging from 0.76(0.78) to 0.90(0.96), RMSE from 1.76(1.86) K to 4.34(4.00) K and NSE from 0.58(0.61) to 0.81(0.90) for different land cover classes. During nighttime, r values ranged from 0.76(0.56) to 0.87(0.90), RMSE from 1.71(1.70) K to 2.43(2.12) K and NSE from 0.43(0.28) to 0.80(0.81) for different land cover classes. RMSE values found between predicted LST and MODIS LST during daytime under clear sky conditions were within acceptable

  11. Interactome disassembly during apoptosis occurs independent of caspase cleavage.

    PubMed

    Scott, Nichollas E; Rogers, Lindsay D; Prudova, Anna; Brown, Nat F; Fortelny, Nikolaus; Overall, Christopher M; Foster, Leonard J

    2017-01-12

    Protein-protein interaction networks (interactomes) define the functionality of all biological systems. In apoptosis, proteolysis by caspases is thought to initiate disassembly of protein complexes and cell death. Here we used a quantitative proteomics approach, protein correlation profiling (PCP), to explore changes in cytoplasmic and mitochondrial interactomes in response to apoptosis initiation as a function of caspase activity. We measured the response to initiation of Fas-mediated apoptosis in 17,991 interactions among 2,779 proteins, comprising the largest dynamic interactome to date. The majority of interactions were unaffected early in apoptosis, but multiple complexes containing known caspase targets were disassembled. Nonetheless, proteome-wide analysis of proteolytic processing by terminal amine isotopic labeling of substrates (TAILS) revealed little correlation between proteolytic and interactome changes. Our findings show that, in apoptosis, significant interactome alterations occur before and independently of caspase activity. Thus, apoptosis initiation includes a tight program of interactome rearrangement, leading to disassembly of relatively few, select complexes. These early interactome alterations occur independently of cleavage of these protein by caspases.

  12. The interactome of CCT complex - A computational analysis.

    PubMed

    Narayanan, Aswathy; Pullepu, Dileep; Kabir, M Anaul

    2016-10-01

    The eukaryotic chaperonin, CCT (Chaperonin Containing TCP1 or TriC-TCP-1 Ring Complex) has been subjected to physical and genetic analyses in S. cerevisiae which can be extrapolated to human CCT (hCCT), owing to its structural and functional similarities with yeast CCT (yCCT). Studies on hCCT and its interactome acquire an additional dimension, as it has been implicated in several disease conditions like neurodegeneration and cancer. We attempt to study its stress response role in general, which will be reflected in the aspects of human diseases and yeast physiology, through computational analysis of the interactome. Towards consolidating and analysing the interactome data, we prepared and compared the unique CCT-interacting protein lists for S. cerevisiae and H. sapiens, performed GO term classification and enrichment studies which provide information on the diversity in CCT interactome, in terms of protein classes in the data set. Enrichment with disease-associated proteins and pathways highlight the medical importance of CCT. Different analyses converge, suggesting the significance of WD-repeat proteins, protein kinases and cytoskeletal proteins in the interactome. The prevalence of proteasomal subunits and ribosomal proteins suggest a possible cross-talk between protein-synthesis, folding and degradation machinery. A network of chaperones and chaperonins that function in combination can also be envisaged from the CCT interactome-Hsp70 interactome analysis.

  13. The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review

    NASA Astrophysics Data System (ADS)

    Aburas, Maher Milad; Ho, Yuek Ming; Ramli, Mohammad Firuz; Ash'aari, Zulfa Hanan

    2016-10-01

    In recent years, several types of simulation and prediction models have been used within a GIS environment to determine a realistic future for urban growth patterns. These models include quantitative and spatio-temporal techniques that are implemented to monitor urban growth. The results derived through these techniques are used to create future policies that take into account sustainable development and the demands of future generations. The aim of this paper is to provide a basis for a literature review of urban Cellular Automata (CA) models to find the most suitable approach for a realistic simulation of land use changes. The general characteristics of simulation models of urban growth and urban CA models are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various models are identified based on the analysis and discussion of the characteristics of these models. The results of the review confirm that the CA model is one of the strongest models for simulating urban growth patterns owing to its structure, simplicity, and possibility of evolution. Limitations of the CA model, namely weaknesses in the quantitative aspect, and the inability to include the driving forces of urban growth in the simulation process, may be minimized by integrating it with other quantitative models, such as via the Analytic Hierarchy Process (AHP), Markov Chain and frequency ratio models. Realistic simulation can be achieved when socioeconomic factors and spatial and temporal dimensions are integrated in the simulation process.

  14. Mining protein interactomes to improve their reliability and support the advancement of network medicine

    PubMed Central

    Alanis-Lobato, Gregorio

    2015-01-01

    High-throughput detection of protein interactions has had a major impact in our understanding of the intricate molecular machinery underlying the living cell, and has permitted the construction of very large protein interactomes. The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives. Fortunately, the structural properties observed in good quality social or technological networks are also present in biological systems. This has encouraged the development of tools, to improve the reliability of protein networks and predict new interactions based merely on the topological characteristics of their components. Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology. These system components can then be targeted with minimal collateral damage. In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed. In addition to the review, a few representative examples of how molecular and clinical data can be integrated to deepen our understanding of pathogenesis are discussed. PMID:26442112

  15. Interactome analysis of myeloid-derived suppressor cells in murine models of colon and breast cancer.

    PubMed

    Aliper, Alexander M; Frieden-Korovkina, Victoria P; Buzdin, Anton; Roumiantsev, Sergey A; Zhavoronkov, Alex

    2014-11-30

    In solid cancers, myeloid derived suppressor cells (MDSC) infiltrate (peri)tumoral tissues to induce immune tolerance and hence to establish a microenvironment permissive to tumor growth. Importantly, the mechanisms that facilitate such infiltration or a subsequent immune suppression are not fully understood. Hence, in this study, we aimed to delineate disparate molecular pathways which MDSC utilize in murine models of colon or breast cancer. Using pathways enrichment analysis, we completed interactome maps of multiple signaling pathways in CD11b+/Gr1(high/low) MDSC from spleens and tumor infiltrates of mice with c26GM colon cancer and tumor infiltrates of MDSC in 4T1 breast cancer. In both cancer models, infiltrating MDSC, but not CD11b+ splenic cells, have been found to be enriched in multiple signaling molecules suggestive of their enhanced proliferative and invasive phenotypes. The interactome data has been subsequently used to reconstruct a previously unexplored regulation of MDSC cell cycle by the c-myc transcription factor which was predicted by the analysis. Thus, this study represents a first interactome mapping of distinct multiple molecular pathways whereby MDSC sustain cancer progression.

  16. Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada

    NASA Astrophysics Data System (ADS)

    Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

    2016-09-01

    As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for

  17. Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

    PubMed

    Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

    2016-09-01

    As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for

  18. Towards a Predictive Theory of Malaria: Connections to Spatio-temporal Variability of Climate and Hydrology

    NASA Astrophysics Data System (ADS)

    Endo, N.; Eltahir, E. A. B.

    2015-12-01

    Malaria transmission is closely linked to climatology, hydrology, environment, and the biology of local vectors. These factors interact with each other and non-linearly influence malaria transmission dynamics, making prediction and prevention challenging. Our work attempts to find a universality in the multi-dimensional system of malaria transmission and to develop a theory to predict emergence of malaria given a limited set of environmental and biological inputs.A credible malaria transmission dynamics model, HYDREMATS (Bomblies et al., 2008), was used under hypothetical settings to investigate the role of spatial and temporal distribution of vector breeding pools. HYDREMATS is a mechanistic model and capable of simulating the basic reproduction rate (Ro) without bold assumptions even under dynamic conditions. The spatial distribution of pools is mainly governed by hydrological factors; the impact of pool persistence and rainy season length on malaria transmission were investigated. Also analyzed was the impact of the temporal distribution of pools relative to human houses. We developed non-dimensional variables combining the hydrological and biological parameters. Simulated values of Ro from HYDREMATS are presented in a newly-introduced non-dimensional plane, which leads to a some-what universal theory describing the condition for sustainable malaria transmission. The findings were tested against observations both from the West Africa and the Ethiopian Highland, representing diverse hydroclimatological conditions. Predicated Ro values from the theory over the two regions are in good agreement with the observed malaria transmission data.

  19. 5D Modelling: An Efficient Approach for Creating Spatiotemporal Predictive 3D Maps of Large-Scale Cultural Resources

    NASA Astrophysics Data System (ADS)

    Doulamis, A.; Doulamis, N.; Ioannidis, C.; Chrysouli, C.; Grammalidis, N.; Dimitropoulos, K.; Potsiou, C.; Stathopoulou, E.-K.; Ioannides, M.

    2015-08-01

    Outdoor large-scale cultural sites are mostly sensitive to environmental, natural and human made factors, implying an imminent need for a spatio-temporal assessment to identify regions of potential cultural interest (material degradation, structuring, conservation). On the other hand, in Cultural Heritage research quite different actors are involved (archaeologists, curators, conservators, simple users) each of diverse needs. All these statements advocate that a 5D modelling (3D geometry plus time plus levels of details) is ideally required for preservation and assessment of outdoor large scale cultural sites, which is currently implemented as a simple aggregation of 3D digital models at different time and levels of details. The main bottleneck of such an approach is its complexity, making 5D modelling impossible to be validated in real life conditions. In this paper, a cost effective and affordable framework for 5D modelling is proposed based on a spatial-temporal dependent aggregation of 3D digital models, by incorporating a predictive assessment procedure to indicate which regions (surfaces) of an object should be reconstructed at higher levels of details at next time instances and which at lower ones. In this way, dynamic change history maps are created, indicating spatial probabilities of regions needed further 3D modelling at forthcoming instances. Using these maps, predictive assessment can be made, that is, to localize surfaces within the objects where a high accuracy reconstruction process needs to be activated at the forthcoming time instances. The proposed 5D Digital Cultural Heritage Model (5D-DCHM) is implemented using open interoperable standards based on the CityGML framework, which also allows the description of additional semantic metadata information. Visualization aspects are also supported to allow easy manipulation, interaction and representation of the 5D-DCHM geometry and the respective semantic information. The open source 3DCity

  20. Identification of SRC as a potent drug target for asthma, using an integrative approach of protein interactome analysis and in silico drug discovery.

    PubMed

    Randhawa, Vinay; Bagler, Ganesh

    2012-10-01

    Network-biology inspired modeling of interactome data and computational chemistry have the potential to revolutionize drug discovery by complementing conventional methods. We consider asthma, a complex disease characterized by intricate molecular mechanisms, for our study. We aim to integrate prediction of potent drug targets using graph-theoretical methods and subsequent identification of small molecules capable of modulating activity of the best target. In this work, we construct the protein interactome underlying this disease: Asthma Protein Interactome (API). Using a strategy based on network analysis of the interactome, we identify a set of potential drug targets for asthma. Topologically and dynamically, v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (SRC) emerges as the most central target in API. SRC is known to play an important role in promoting airway smooth muscle cell growth and facilitating migration in airway remodeling. From interactome analysis, and with the reported role in respiratory mechanisms, SRC emerges as a promising drug target for asthma. Further, we proceed to identify leads for SRC from a public database of small molecules. We predict two potential leads for SRC using ligand-based virtual screening methodology.

  1. A complex-based reconstruction of the Saccharomyces cerevisiae interactome.

    PubMed

    Wang, Haidong; Kakaradov, Boyko; Collins, Sean R; Karotki, Lena; Fiedler, Dorothea; Shales, Michael; Shokat, Kevan M; Walther, Tobias C; Krogan, Nevan J; Koller, Daphne

    2009-06-01

    Most cellular processes are performed by proteomic units that interact with each other. These units are often stoichiometrically stable complexes comprised of several proteins. To obtain a faithful view of the protein interactome we must view it in terms of these basic units (complexes and proteins) and the interactions between them. This study makes two contributions toward this goal. First, it provides a new algorithm for reconstruction of stable complexes from a variety of heterogeneous biological assays; our approach combines state-of-the-art machine learning methods with a novel hierarchical clustering algorithm that allows clusters to overlap. We demonstrate that our approach constructs over 40% more known complexes than other recent methods and that the complexes it produces are more biologically coherent even compared with the reference set. We provide experimental support for some of our novel predictions, identifying both a new complex involved in nutrient starvation and a new component of the eisosome complex. Second, we provide a high accuracy algorithm for the novel problem of predicting transient interactions involving complexes. We show that our complex level network, which we call ComplexNet, provides novel insights regarding the protein-protein interaction network. In particular, we reinterpret the finding that "hubs" in the network are enriched for being essential, showing instead that essential proteins tend to be clustered together in essential complexes and that these essential complexes tend to be large.

  2. Predicting breeding habitat for amphibians: a spatiotemporal analysis across Yellowstone National Park.

    PubMed

    Bartelt, Paul E; Gallant, Alisa L; Klaver, Robert W; Wright, Chris K; Patla, Debra A; Peterson, Charles R

    2011-10-01

    The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000-2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probability, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from (1) field survey site data only, (2) field data combined with data from geospatial models, and (3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 to 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of "core" breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative areas that could be protected for each species

  3. Predicting breeding habitat for amphibians: a spatiotemporal analysis across Yellowstone National Park

    USGS Publications Warehouse

    Bartelt, Paul E.; Gallant, Alisa L.; Klaver, Robert W.; Wright, Christopher K.; Patla, Debra A.; Peterson, Charles R.

    2011-01-01

    The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000-2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probabililty, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from 1) field survey site data only, 2) field data combined with data from geospatial models, and 3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 - 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of "core" breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative for areas that could be protected for each species

  4. Predicting breeding habitat for amphibians: A spatiotemporal analysis across Yellowstone National Park

    USGS Publications Warehouse

    Bartelt, P.E.; Gallant, A.L.; Klaver, R.W.; Wright, C.K.; Patla, D.A.; Peterson, C.R.

    2011-01-01

    The ability to predict amphibian breeding across landscapes is important for informing land management decisions and helping biologists better understand and remediate factors contributing to declines in amphibian populations. We built geospatial models of likely breeding habitats for each of four amphibian species that breed in Yellowstone National Park (YNP). We used field data collected in 2000-2002 from 497 sites among 16 basins and predictor variables from geospatial models produced from remotely sensed data (e.g., digital elevation model, complex topographic index, landform data, wetland probability, and vegetative cover). Except for 31 sites in one basin that were surveyed in both 2000 and 2002, all sites were surveyed once. We used polytomous regression to build statistical models for each species of amphibian from (1) field survey site data only, (2) field data combined with data from geospatial models, and (3) data from geospatial models only. Based on measures of receiver operating characteristic (ROC) scores, models of the second type best explained likely breeding habitat because they contained the most information (ROC values ranged from 0.70 to 0.88). However, models of the third type could be applied to the entire YNP landscape and produced maps that could be verified with reserve field data. Accuracy rates for models built for single years were highly variable, ranging from 0.30 to 0.78. Accuracy rates for models built with data combined from multiple years were higher and less variable, ranging from 0.60 to 0.80. Combining results from the geospatial multiyear models yielded maps of "core" breeding areas (areas with high probability values for all three years) surrounded by areas that scored high for only one or two years, providing an estimate of variability among years. Such information can highlight landscape options for amphibian conservation. For example, our models identify alternative areas that could be protected for each species

  5. Two-dimensional transient model for prediction of arteriolar NO/O2 modulation by spatiotemporal variations in cell-free layer width.

    PubMed

    Ng, Yan Cheng; Namgung, Bumseok; Kim, Sangho

    2015-01-01

    Despite the significant roles of the cell-free layer (CFL) in balancing nitric oxide (NO) and oxygen (O2) bioavailability in arteriolar tissue, many previous numerical approaches have relied on a one-dimensional (1-D) steady-state model for simplicity. However, these models are unable to demonstrate the influence of spatiotemporal variations in the CFL on the NO/O2 transport under dynamic flow conditions. Therefore, the present study proposes a new two-dimensional (2-D) transient model capable of predicting NO/O2 transport modulated by the spatiotemporal variations in the CFL width. Our model predicted that NO bioavailability was inversely related to the CFL width as expected. The enhancement of NO production by greater wall shear stress with a thinner CFL could dominate the diffusion barrier role of the CFL. In addition, NO/O2 availability along the vascular wall was inhomogeneous and highly regulated by dynamic changes of local CFL width variation. The spatial variations of CFL widths on opposite sides of the arteriole exhibited a significant inverse relation. This asymmetric formation of CFL resulted in a significantly imbalanced NO/O2 bioavailability on opposite sides of the arteriole. The novel integrative methodology presented here substantially highlighted the significance of spatiotemporal variations of the CFL in regulating the bioavailability of NO/O2, and provided further insight about the opposing effects of the CFL on arteriolar NO production.

  6. Toward high-resolution flash flood prediction in large urban areas - Analysis of sensitivity to spatiotemporal resolution of rainfall input and hydrologic modeling

    NASA Astrophysics Data System (ADS)

    Rafieeinasab, Arezoo; Norouzi, Amir; Kim, Sunghee; Habibi, Hamideh; Nazari, Behzad; Seo, Dong-Jun; Lee, Haksu; Cosgrove, Brian; Cui, Zhengtao

    2015-12-01

    Urban flash flooding is a serious problem in large, highly populated areas such as the Dallas-Fort Worth Metroplex (DFW). Being able to monitor and predict flash flooding at a high spatiotemporal resolution is critical to providing location-specific early warnings and cost-effective emergency management in such areas. Under the idealized conditions of perfect models and precipitation input, one may expect that spatiotemporal specificity and accuracy of the model output improve as the resolution of the models and precipitation input increases. In reality, however, due to the errors in the precipitation input, and in the structures, parameters and states of the models, there are practical limits to the model resolution. In this work, we assess the sensitivity of streamflow simulation in urban catchments to the spatiotemporal resolution of precipitation input and hydrologic modeling to identify the resolution at which the simulation errors may be at minimum given the quality of the precipitation input and hydrologic models used, and the response time of the catchment. The hydrologic modeling system used in this work is the National Weather Service (NWS) Hydrology Laboratory's Research Distributed Hydrologic Model (HLRDHM) applied at spatiotemporal resolutions ranging from 250 m to 2 km and from 1 min to 1 h applied over the Cities of Fort Worth, Arlington and Grand Prairie in DFW. The high-resolution precipitation input is from the DFW Demonstration Network of the Collaborative Adaptive Sensing of the Atmosphere (CASA) radars. For comparison, the NWS Multisensor Precipitation Estimator (MPE) product, which is available at a 4-km 1-h resolution, was also used. The streamflow simulation results are evaluated for 5 urban catchments ranging in size from 3.4 to 54.6 km2 and from about 45 min to 3 h in time-to-peak in the Cities of Fort Worth, Arlington and Grand Prairie. The streamflow observations used in evaluation were obtained from water level measurements via rating

  7. hp-DPI: Helicobacter pylori database of protein interactomes--embracing experimental and inferred interactions.

    PubMed

    Lin, Chung-Yen; Chen, Chia-Ling; Cho, Chi-Shiang; Wang, Li-Ming; Chang, Chia-Ming; Chen, Pao-Yang; Lo, Chen-Zen; Hsiung, Chao A

    2005-04-01

    We implemented a statistical model into our protein interaction database for validation of two-hybrid assays of Helicobacter pylori, and prediction of putative protein interactions not yet discovered experimentally. To present the enormous amount of experimental and inferred protein interaction networking maps, the H.pylori Database of Protein Interactomes (hp-DPI) is developed with a succinct yet comprehensive visualization tool integrated with annotation from Genbank, GO, and KEGG. hp-DPI is first built with, but not limited to, H.pylori protein interactions and is expected to naturally include other organisms' protein interacting relationships in the future.

  8. A process-level evaluation of the spatiotemporal variability of CO2 fluxes predicted by terrestrial biosphere models using atmospheric data

    NASA Astrophysics Data System (ADS)

    Fang, Y.; Michalak, A. M.; Shiga, Y. P.; Yadav, V.

    2013-12-01

    Terrestrial biosphere models (TBMs) are used to extrapolate local observations and process-level understanding of land-atmosphere carbon exchange to larger regions, and can serve as a predictive tool for examining carbon-climate interactions and global change. Understanding and improving the performance of TBMs is thus crucial to the carbon cycle research community. In this work, we evaluate the spatiotemporal patterns of net ecosystem exchange (NEE) simulated by TBMs using atmospheric CO2 observations and a Geostatistical Inverse Modeling (GIM) framework. The evaluation methodology is based on the ability (or inability) of the spatiotemporal patterns in NEE estimates to explain the variability observed in atmospheric CO2 distribution. More simply, we examine whether the spatiotemporal patterns of NEE simulated by TBMs (including CASA-GFED, ORCHIDEE, VEGAS2 and SiB3) are consistent with the variations observed in the atmosphere. A similar GIM methodology is also applied using environmental variables (such as water availability, temperature, radiation, etc.) rather than TBMs, to explore the environmental processes associated with the variability of NEE, and determine which processes are associated with good/poor performance in TBMs. We find that NEE simulated by TBMs is consistent with that seen by atmospheric measurements more often during growing season months (Apr-Sept) than during the non-growing season. Over Temperate Broadleaf and Mixed Forests, Temperate Coniferous Forests and Temperate Grasslands, Savannas and Shrublands, atmospheric measurements are sufficiently sensitive to NEE fluxes to constrain the evaluation of model performance during the majority of the year (about 7-8 months in a year, mostly in growing season). For these regions and months, at least one of the TBMs is found to be able to reproduce the observed variability, but the most representative TBM varies by region and month. For the remaining months, none of the TBMs are able to reproduce

  9. "Fuzziness" in the celular interactome: a historical perspective.

    PubMed

    Welch, G Rickey

    2012-01-01

    Some historical background is given for appreciating the impact of the empirical construct known as the cellular protein-protein interactome, which is a seemingly de novo entity that has arisen of late within the context of postgenomic systems biology. The approach here builds on a generalized principle of "fuzziness" in protein behavior, proposed by Tompa and Fuxreiter.(1) Recent controversies in the analysis and interpretation of the interactome studies are rationalized historically under the auspices of this concept. There is an extensive literature on protein-protein interactions, dating to the mid-1900s, which may help clarify the "fuzziness" in the interactome picture and, also, provide a basis for understanding the physiological importance of protein-protein interactions in vivo.

  10. Hierarchical modularity and the evolution of genetic interactomes across species.

    PubMed

    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, Pádraig; Shokat, Kevan M; Cagney, Gerard; Svensson, J Peter; Guthrie, Christine; Espenshade, Peter J; Ideker, Trey; Krogan, Nevan J

    2012-06-08

    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 nonessential genome. Using these signatures, we generated a catalog of 297 functional modules, and we assigned function to 144 previously uncharacterized 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 crosstalk between biological processes, suggesting general design principles of genetic interactomes.

  11. Next-Generation Technologies for Multiomics Approaches Including Interactome Sequencing

    PubMed Central

    Ohashi, Hiroyuki; Miyamoto-Sato, Etsuko

    2015-01-01

    The development of high-speed analytical techniques such as next-generation sequencing and microarrays allows high-throughput analysis of biological information at a low cost. These techniques contribute to medical and bioscience advancements and provide new avenues for scientific research. Here, we outline a variety of new innovative techniques and discuss their use in omics research (e.g., genomics, transcriptomics, metabolomics, proteomics, and interactomics). We also discuss the possible applications of these methods, including an interactome sequencing technology that we developed, in future medical and life science research. PMID:25649523

  12. Evidence for network evolution in an arabidopsis interactome map

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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 pl...

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

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

    PubMed Central

    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

    2011-01-01

    The heterotrimeric G-protein complex is minimally composed of Gα, Gβ, and Gγ 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

  15. Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

    PubMed

    Vashisht, Rohit; Mondal, Anupam Kumar; Jain, Akanksha; Shah, Anup; Vishnoi, Priti; Priyadarshini, Priyanka; Bhattacharyya, Kausik; Rohira, Harsha; Bhat, Ashwini G; Passi, Anurag; Mukherjee, Keya; Choudhary, Kumari Sonal; Kumar, Vikas; Arora, Anshula; Munusamy, Prabhakaran; Subramanian, Ahalyaa; Venkatachalam, Aparna; Gayathri, S; Raj, Sweety; Chitra, Vijaya; Verma, Kaveri; Zaheer, Salman; Balaganesh, J; Gurusamy, Malarvizhi; Razeeth, Mohammed; Raja, Ilamathi; Thandapani, Madhumohan; Mevada, Vishal; Soni, Raviraj; Rana, Shruti; Ramanna, Girish Muthagadhalli; Raghavan, Swetha; Subramanya, Sunil N; Kholia, Trupti; Patel, Rajesh; Bhavnani, Varsha; Chiranjeevi, Lakavath; Sengupta, Soumi; Singh, Pankaj Kumar; Atray, Naresh; Gandhi, Swati; Avasthi, Tiruvayipati Suma; Nisthar, Shefin; Anurag, Meenakshi; Sharma, Pratibha; Hasija, Yasha; Dash, Debasis; Sharma, Arun; Scaria, Vinod; Thomas, Zakir; Chandra, Nagasuma; Brahmachari, Samir K; Bhardwaj, Anshu

    2012-01-01

    A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

  16. Improved microarray-based decision support with graph encoded interactome data.

    PubMed

    Daemen, Anneleen; Signoretto, Marco; Gevaert, Olivier; Suykens, Johan A K; De Moor, Bart

    2010-04-19

    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 aspects of biological systems. By exploiting the properties of kernel methods, relations between genes with similar functions but active in alternative pathways could be incorporated in a support vector machine classifier based on spectral graph theory. Using 10 microarray data sets, we first reduced the number of data sources relevant for multiple cancer types and outcomes. Three sources on metabolic pathway information (KEGG), protein-protein interactions (OPHID) and miRNA-gene targeting (microRNA.org) outperformed the other sources with regard to the considered class of models. Both fixed and adaptive approaches were subsequently considered to combine the three corresponding classifiers. Averaging the predictions of these classifiers performed best and was significantly better than the model based on microarray data only. These results were confirmed on 6 validation microarray sets, with a significantly improved performance in 4 of them. Integrating interactome data thus improves classification of cancer outcome for the investigated microarray technologies and cancer types. Moreover, this strategy can be incorporated in any kernel method or non-linear version of a non-kernel method.

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

    NASA Astrophysics Data System (ADS)

    Zhu, Qing; Zhou, Zhiwen; Duncan, Emily W.; Lv, Ligang; Liao, Kaihua; Feng, Huihui

    2017-02-01

    Spatio-temporal variability of soil moisture (θ) is a challenge that remains to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time θ monitoring methods. This restricted the comprehensive and intensive examination of θ dynamics. In this study, we integrated the manual and real-time monitored data to depict the hillslope θ dynamics with good spatial coverage and temporal resolution. Linear (stepwise multiple linear regression-SMLR) and non-linear (support vector machines-SVM) models were used to predict θ at 39 manual sites (collected 1-2 times per month) with θ collected at three real-time monitoring sites (collected every 5 mins). By comparing the accuracies of SMLR and SVM for each depth and manual site, an optimal prediction model was then determined at this depth of this site. Results showed that θ at the 39 manual sites can be reliably predicted (root mean square errors <0.028 m3 m-3) using both SMLR and SVM. The linear or non-linear relationship between θ at each manual site and at the three real-time monitoring sites was the main reason for choosing SMLR or SVM as the optimal prediction model. The subsurface flow dynamics was an important factor that determined whether the relationship was linear or non-linear. Depth to bedrock, elevation, topographic wetness index, profile curvature, and θ temporal stability influenced the selection of prediction model since they were related to the subsurface soil water distribution and movement. Using this approach, hillslope θ spatial distributions at un-sampled times and dates can be predicted. Missing information of hillslope θ dynamics can be acquired successfully.

  18. A human skeletal muscle interactome centered on proteins involved in muscular dystrophies: LGMD interactome

    PubMed Central

    2013-01-01

    Background The complexity of the skeletal muscle and the identification of numerous human disease-causing mutations in its constitutive proteins make it an interesting tissue for proteomic studies aimed at understanding functional relationships of interacting proteins in both health and diseases. Method We undertook a large-scale study using two-hybrid screens and a human skeletal-muscle cDNA library to establish a proteome-scale map of protein-protein interactions centered on proteins involved in limb-girdle muscular dystrophies (LGMD). LGMD is a group of more than 20 different neuromuscular disorders that principally affect the proximal pelvic and shoulder girdle muscles. Results and conclusion The interaction network we unraveled incorporates 1018 proteins connected by 1492 direct binary interactions and includes 1420 novel protein-protein interactions. Computational, experimental and literature-based analyses were performed to assess the overall quality of this network. Interestingly, LGMD proteins were shown to be highly interconnected, in particular indirectly through sarcomeric proteins. In-depth mining of the LGMD-centered interactome identified new candidate genes for orphan LGMDs and other neuromuscular disorders. The data also suggest the existence of functional links between LGMD2B/dysferlin and gene regulation, between LGMD2C/γ-sarcoglycan and energy control and between LGMD2G/telethonin and maintenance of genome integrity. This dataset represents a valuable resource for future functional investigations. PMID:23414517

  19. PINA v2.0: mining interactome modules.

    PubMed

    Cowley, Mark J; Pinese, Mark; Kassahn, Karin S; Waddell, Nic; Pearson, John V; Grimmond, Sean M; Biankin, Andrew V; Hautaniemi, Sampsa; Wu, Jianmin

    2012-01-01

    The Protein Interaction Network Analysis (PINA) platform is a comprehensive web resource, which includes a database of unified protein-protein interaction data integrated from six manually curated public databases, and a set of built-in tools for network construction, filtering, analysis and visualization. The second version of PINA enhances its utility for studies of protein interactions at a network level, by including multiple collections of interaction modules identified by different clustering approaches from the whole network of protein interactions ('interactome') for six model organisms. All identified modules are fully annotated by enriched Gene Ontology terms, KEGG pathways, Pfam domains and the chemical and genetic perturbations collection from MSigDB. Moreover, a new tool is provided for module enrichment analysis in addition to simple query function. The interactome data are also available on the web site for further bioinformatics analysis. PINA is freely accessible at http://cbg.garvan.unsw.edu.au/pina/.

  20. Proteomic data on the nuclear interactome of human MCM9

    PubMed Central

    Hutchins, James R.A.; Traver, Sabine; Coulombe, Philippe; Peiffer, Isabelle; Kitzmann, Magali; Latreille, Daniel; Méchali, Marcel

    2015-01-01

    We present data relating to the interactome of MCM9 from the nuclei of human cells. MCM9 belongs to the AAA+ superfamily, and contains an MCM domain and motifs that may confer DNA helicase activity. MCM9 has been shown to bind MCM8, and has been implicated in DNA replication and homologous recombination. However, the mechanistic basis of MCM9’s role in DNA repair is poorly understood, and proteins with which it interacts were hitherto unknown. We performed tandem affinity purification of MCM9 and its interacting proteins from nuclear extracts of human cells, followed by proteomic analysis, thereby generating a set of mass spectrometry data corresponding to the MCM9 interactome [1]. The proteomic data set comprises 29 mass spectrometry RAW files, deposited to the ProteomeXchange Consortium, and freely available from the PRIDE partner repository with the data set identifier PXD000212. A set of 22 interacting proteins identified from the proteomic data was used to create an MCM9-centered interactive network diagram, using the Cytoscape program. These data allow the scientific community to access, mine and explore the human nuclear MCM9 interactome. PMID:26870752

  1. Charting the NF-κB Pathway Interactome Map

    PubMed Central

    Tieri, Paolo; Termanini, Alberto; Bellavista, Elena; Salvioli, Stefano; Capri, Miriam; Franceschi, Claudio

    2012-01-01

    Inflammation is part of a complex physiological response to harmful stimuli and pathogenic stress. The five components of the Nuclear Factor κB (NF-κB) family are prominent mediators of inflammation, acting as key transcriptional regulators of hundreds of genes. Several signaling pathways activated by diverse stimuli converge on NF-κB activation, resulting in a regulatory system characterized by high complexity. It is increasingly recognized that the number of components that impinges upon phenotypic outcomes of signal transduction pathways may be higher than those taken into consideration from canonical pathway representations. Scope of the present analysis is to provide a wider, systemic picture of the NF-κB signaling system. Data from different sources such as literature, functional enrichment web resources, protein-protein interaction and pathway databases have been gathered, curated, integrated and analyzed in order to reconstruct a single, comprehensive picture of the proteins that interact with, and participate to the NF-κB activation system. Such a reconstruction shows that the NF-κB interactome is substantially different in quantity and quality of components with respect to canonical representations. The analysis highlights that several neglected but topologically central proteins may play a role in the activation of NF-κB mediated responses. Moreover the interactome structure fits with the characteristics of a bow tie architecture. This interactome is intended as an open network resource available for further development, refinement and analysis. PMID:22403694

  2. A realistic large-scale model of the cerebellum granular layer predicts circuit spatio-temporal filtering properties.

    PubMed

    Solinas, Sergio; Nieus, Thierry; D'Angelo, Egidio

    2010-01-01

    The way the cerebellar granular layer transforms incoming mossy fiber signals into new spike patterns to be related to Purkinje cells is not yet clear. Here, a realistic computational model of the granular layer was developed and used to address four main functional hypotheses: center-surround organization, time-windowing, high-pass filtering in responses to spike bursts and coherent oscillations in response to diffuse random activity. The model network was activated using patterns inspired by those recorded in vivo. Burst stimulation of a small mossy fiber bundle resulted in granule cell bursts delimited in time (time windowing) and space (center-surround) by network inhibition. This burst-burst transmission showed marked frequency-dependence configuring a high-pass filter with cut-off frequency around 100 Hz. The contrast between center and surround properties was regulated by the excitatory-inhibitory balance. The stronger excitation made the center more responsive to 10-50 Hz input frequencies and enhanced the granule cell output (with spikes occurring earlier and with higher frequency and number) compared to the surround. Finally, over a certain level of mossy fiber background activity, the circuit generated coherent oscillations in the theta-frequency band. All these processes were fine-tuned by NMDA and GABA-A receptor activation and neurotransmitter vesicle cycling in the cerebellar glomeruli. This model shows that available knowledge on cellular mechanisms is sufficient to unify the main functional hypotheses on the cerebellum granular layer and suggests that this network can behave as an adaptable spatio-temporal filter coordinated by theta-frequency oscillations.

  3. Characterization of a Protein Interactome by Co-Immunoprecipitation and Shotgun Mass Spectrometry.

    PubMed

    Maccarrone, Giuseppina; Bonfiglio, Juan Jose; Silberstein, Susana; Turck, Christoph W; Martins-de-Souza, Daniel

    2017-01-01

    Identifying the partners of a given protein (the interactome) may provide leads about the protein's function and the molecular mechanisms in which it is involved. One of the alternative strategies used to characterize protein interactomes consists of co-immunoprecipitation (co-IP) followed by shotgun mass spectrometry. This enables the isolation and identification of a protein target in its native state and its interactome from cells or tissue lysates under physiological conditions. In this chapter, we describe a co-IP protocol for interactome studies that uses an antibody against a protein of interest bound to protein A/G plus agarose beads to isolate a protein complex. The interacting proteins may be further fractionated by SDS-PAGE, followed by in-gel tryptic digestion and nano liquid chromatography high-resolution tandem mass spectrometry (nLC ESI-MS/MS) for identification purposes. The computational tools, strategy for protein identification, and use of interactome databases also will be described.

  4. Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data.

    PubMed

    Sawada, Ryusuke; Iwata, Hiroaki; Mizutani, Sayaka; Yamanishi, Yoshihiro

    2015-12-28

    Drug repositioning, or the identification of new indications for known drugs, is a useful strategy for drug discovery. In this study, we developed novel computational methods to predict potential drug targets and new drug indications for systematic drug repositioning using large-scale chemical-protein interactome data. We explored the target space of drugs (including primary targets and off-targets) based on chemical structure similarity and phenotypic effect similarity by making optimal use of millions of compound-protein interactions. On the basis of the target profiles of drugs, we constructed statistical models to predict new drug indications for a wide range of diseases with various molecular features. The proposed method outperformed previous methods in terms of interpretability, applicability, and accuracy. Finally, we conducted a comprehensive prediction of the drug-target-disease association network for 8270 drugs and 1401 diseases and showed biologically meaningful examples of newly predicted drug targets and drug indications. The predictive model is useful to understand the mechanisms of the predicted drug indications.

  5. Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

    PubMed Central

    Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

    2014-01-01

    Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

  6. Understanding and Predicting Spatio-Temporal Variability of Temperature in Scotland's Rivers: Implications for Riparian Land Management

    NASA Astrophysics Data System (ADS)

    Jackson, F. L.; Malcolm, I.; Hannah, D. M.

    2015-12-01

    Freshwater fish are frequently the focus of river management, and rising water temperature (Tw) has the potential to negatively influence the suitability of habitats for many species, including salmonids. Consequently, an improved understanding of spatial and temporal variability in Tw is required at the river basin, hydrometric region and national scales concurrent with the scales at which management decisions are made. The Scotland River Temperature Monitoring Network (SRTMN) was designed to record Tw variability across the observed environmental range of a suite of landscape characteristics. These characteristics act as proxies for controls known to affect heat and water exchange processes. This national-scale monitoring network provides quality controlled data that enables the scaling up of small-scale process understanding to larger spatial scales. This dataset provided the input to statistical models which were used to investigate controls on summary metrics describing Tw and to predict future change. Following model selection procedures, the most significant and influential variables were found to vary seasonally and in relation to the temperature metric being described, reflecting the time varying importance of landscape controls and their influence on energy exchange processes. The presence of riparian woodland was a significant control on temperature that is also amenable to management control. Future work will focus on applying these models to unmonitored locations and highlighting sensitive areas where riparian management (planting or protecting woodland) could be beneficial to reduce the risks of potentially damaging high temperatures. This large-scale scientific understanding will be important for informing the management of Scottish rivers under a changing climate.

  7. HIV–host interactome revealed directly from infected cells

    PubMed Central

    Luo, Yang; Jacobs, Erica Y.; Greco, Todd M.; Mohammed, Kevin D.; Tong, Tommy; Keegan, Sarah; Binley, James M.; Cristea, Ileana M.; Fenyö, David; Rout, Michael P.; Chait, Brian T.; Muesing, Mark A.

    2016-01-01

    Although genetically compact, HIV-1 commandeers vast arrays of cellular machinery to sustain and protect it during cycles of viral outgrowth. Transposon-mediated saturation linker scanning mutagenesis was used to isolate fully replication-competent viruses harbouring a potent foreign epitope tag. Using these viral isolates, we performed differential isotopic labelling and affinity-capture mass spectrometric analyses on samples obtained from cultures of human lymphocytes to classify the vicinal interactomes of the viral Env and Vif proteins as they occur during natural infection. Importantly, interacting proteins were recovered without bias, regardless of their potential for positive, negative or neutral impact on viral replication. We identified specific host associations made with trimerized Env during its biosynthesis, at virological synapses, with innate immune effectors (such as HLA-E) and with certain cellular signalling pathways (for example, Notch1). We also defined Vif associations with host proteins involved in the control of nuclear transcription and nucleoside biosynthesis as well as those interacting stably or transiently with the cytoplasmic protein degradation apparatus. Our approach is broadly applicable to elucidating pathogen–host interactomes, providing high-certainty identification of interactors by their direct access during cycling infection. Understanding the pathophysiological consequences of these associations is likely to provide strategic targets for antiviral intervention. PMID:27375898

  8. Identification of the human testis protein phosphatase 1 interactome.

    PubMed

    Fardilha, Margarida; Esteves, Sara L C; Korrodi-Gregório, Luís; Vintém, Ana Paula; Domingues, Sara C; Rebelo, Sandra; Morrice, Nick; Cohen, Patricia T W; da Cruz e Silva, Odete A B; da Cruz e Silva, Edgar F

    2011-11-15

    Protein phosphorylation is a critical regulatory mechanism in cellular signalling. To this end, PP1 is a major eukaryotic serine/threonine-specific phosphatase whose cellular functions, in turn, depend on complexes it forms with PP1 interacting proteins-PIPs. The importance of the testis/sperm-enriched variant, PP1γ2, in sperm motility and spermatogenesis has previously been shown. Given the key role of PIPs, it is imperative to identify the physiologically relevant PIPs in testis and sperm. Hence, we performed Yeast Two-Hybrid screens of a human testis cDNA library using as baits the different PP1 isoforms and also a proteomic approach aimed at identifying PP1γ2 binding proteins. To the best of our knowledge this is the largest data set of the human testis PP1 interactome. We report the identification of 77 proteins in human testis and 7 proteins in human sperm that bind PP1. The data obtained increased the known PP1 interactome by reporting 72 novel interactions. Confirmation of the interaction of PP1 with 5 different proteins was also further validated by co-immunoprecipitation or protein overlays. The data here presented provides important insights towards the function of these proteins and opens new possibilities for future research. In fact, such diversity in PP1 regulators makes them excellent targets for pharmacological intervention.

  9. Centrality in the host–pathogen interactome is associated with pathogen fitness during infection

    PubMed Central

    Crua Asensio, Núria; Muñoz Giner, Elisabet; de Groot, Natalia Sánchez; Torrent Burgas, Marc

    2017-01-01

    To perform their functions proteins must interact with each other, but how these interactions influence bacterial infection remains elusive. Here we demonstrate that connectivity in the host–pathogen interactome is directly related to pathogen fitness during infection. Using Y. pestis as a model organism, we show that the centrality-lethality rule holds for pathogen fitness during infection but only when the host–pathogen interactome is considered. Our results suggest that the importance of pathogen proteins during infection is directly related to their number of interactions with the host. We also show that pathogen proteins causing an extensive rewiring of the host interactome have a higher impact in pathogen fitness during infection. Hence, we conclude that hubs in the host–pathogen interactome should be explored as promising targets for antimicrobial drug design. PMID:28090086

  10. Comparative influenza protein interactomes identify the role of plakophilin 2 in virus restriction

    PubMed Central

    Wang, Lingyan; Fu, Bishi; Li, Wenjun; Patil, Girish; Liu, Lin; Dorf, Martin E.; Li, Shitao

    2017-01-01

    Cellular protein interaction networks are integral to host defence and immune signalling pathways, which are often hijacked by viruses via protein interactions. However, the comparative virus–host protein interaction networks and how these networks control host immunity and viral infection remain to be elucidated. Here, we mapped protein interactomes between human host and several influenza A viruses (IAV). Comparative analyses of the interactomes identified common and unique interaction patterns regulating innate immunity and viral infection. Functional screening of the ‘core‘ interactome consisting of common interactions identified five novel host factors regulating viral infection. Plakophilin 2 (PKP2), an influenza PB1-interacting protein, restricts IAV replication and competes with PB2 for PB1 binding. The binding competition leads to perturbation of the IAV polymerase complex, thereby limiting polymerase activity and subsequent viral replication. Taken together, comparative analyses of the influenza–host protein interactomes identified PKP2 as a natural inhibitor of IAV polymerase complex. PMID:28169297

  11. Functional proteomic and interactome analysis of proteins associated with beef tenderness in angus cattle

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Beef is a source of high quality protein for the human population, and beef tenderness has significant influence on beef palatability, consumer expectation and industry profitability. To further elucidate the factors affecting beef tenderness, functional proteomics and bioinformatics interactome ana...

  12. Centrality in the host-pathogen interactome is associated with pathogen fitness during infection

    NASA Astrophysics Data System (ADS)

    Crua Asensio, Núria; Muñoz Giner, Elisabet; de Groot, Natalia Sánchez; Torrent Burgas, Marc

    2017-01-01

    To perform their functions proteins must interact with each other, but how these interactions influence bacterial infection remains elusive. Here we demonstrate that connectivity in the host-pathogen interactome is directly related to pathogen fitness during infection. Using Y. pestis as a model organism, we show that the centrality-lethality rule holds for pathogen fitness during infection but only when the host-pathogen interactome is considered. Our results suggest that the importance of pathogen proteins during infection is directly related to their number of interactions with the host. We also show that pathogen proteins causing an extensive rewiring of the host interactome have a higher impact in pathogen fitness during infection. Hence, we conclude that hubs in the host-pathogen interactome should be explored as promising targets for antimicrobial drug design.

  13. STPMiner: A Highperformance Spatiotemporal Pattern Mining Toolbox

    SciTech Connect

    Vatsavai, Raju

    2011-01-01

    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.

  14. Emergence of plant and rhizospheric microbiota as stable interactomes.

    PubMed

    Bandyopadhyay, Prasun; Bhuyan, Soubhagya Kumar; Yadava, Pramod Kumar; Varma, Ajit; Tuteja, Narendra

    2017-03-01

    The growing human population and depletion of resources have necessitated development of sustainable agriculture. Beneficial plant-microbe associations have been known for quite some time now. To maintain sustainability, one could show better reliance upon beneficial attributes of the rhizosphere microbiome. To harness the best agronomic traits, understanding the entire process of recruitment, establishment, and maintenance of microbiota as stable interactome within the rhizosphere is important. In this article, we highlight the process of recruitment and establishment of microbiota within rhizosphere. Further, we have discussed the interlinkages and the ability of multiple (microbial and plant) partners to interact with one another forming a stable plant holobiont system. Lastly, we address the possibility of exploring the knowledge gained from the holobiont system to tailor the rhizosphere microbiome for better productivity and maintenance of agroecosystems. The article provide new insights into the broad principles of stable plant-microbe interactions which could be useful for sustaining agriculture and food security.

  15. Characterization and interactome study of white spot syndrome virus envelope protein VP11.

    PubMed

    Liu, Wang-Jing; Shiung, Hui-Jui; Lo, Chu-Fang; Leu, Jiann-Horng; Lai, Ying-Jang; Lee, Tai-Lin; Huang, Wei-Tung; Kou, Guang-Hsiung; Chang, Yun-Shiang

    2014-01-01

    White spot syndrome virus (WSSV) is a large enveloped virus. The WSSV viral particle consists of three structural layers that surround its core DNA: an outer envelope, a tegument and a nucleocapsid. Here we characterize the WSSV structural protein VP11 (WSSV394, GenBank accession number AF440570), and use an interactome approach to analyze the possible associations between this protein and an array of other WSSV and host proteins. Temporal transcription analysis showed that vp11 is an early gene. Western blot hybridization of the intact viral particles and fractionation of the viral components, and immunoelectron microscopy showed that VP11 is an envelope protein. Membrane topology software predicted VP11 to be a type of transmembrane protein with a highly hydrophobic transmembrane domain at its N-terminal. Based on an immunofluorescence assay performed on VP11-transfected Sf9 cells and a trypsin digestion analysis of the virion, we conclude that, contrary to topology software prediction, the C-terminal of this protein is in fact inside the virion. Yeast two-hybrid screening combined with co-immunoprecipitation assays found that VP11 directly interacted with at least 12 other WSSV structural proteins as well as itself. An oligomerization assay further showed that VP11 could form dimers. VP11 is also the first reported WSSV structural protein to interact with the major nucleocapsid protein VP664.

  16. SONAR Discovers RNA-Binding Proteins from Analysis of Large-Scale Protein-Protein Interactomes.

    PubMed

    Brannan, Kristopher W; Jin, Wenhao; Huelga, Stephanie C; Banks, Charles A S; Gilmore, Joshua M; Florens, Laurence; Washburn, Michael P; Van Nostrand, Eric L; Pratt, Gabriel A; Schwinn, Marie K; Daniels, Danette L; Yeo, Gene W

    2016-10-20

    RNA metabolism is controlled by an expanding, yet incomplete, catalog of RNA-binding proteins (RBPs), many of which lack characterized RNA binding domains. Approaches to expand the RBP repertoire to discover non-canonical RBPs are currently needed. Here, HaloTag fusion pull down of 12 nuclear and cytoplasmic RBPs followed by quantitative mass spectrometry (MS) demonstrates that proteins interacting with multiple RBPs in an RNA-dependent manner are enriched for RBPs. This motivated SONAR, a computational approach that predicts RNA binding activity by analyzing large-scale affinity precipitation-MS protein-protein interactomes. Without relying on sequence or structure information, SONAR identifies 1,923 human, 489 fly, and 745 yeast RBPs, including over 100 human candidate RBPs that contain zinc finger domains. Enhanced CLIP confirms RNA binding activity and identifies transcriptome-wide RNA binding sites for SONAR-predicted RBPs, revealing unexpected RNA binding activity for disease-relevant proteins and DNA binding proteins.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  18. Uncover disease genes by maximizing information flow in the phenome–interactome network

    PubMed Central

    Chen, Yong; Jiang, Tao; Jiang, Rui

    2011-01-01

    Motivation: Pinpointing genes that underlie human inherited diseases among candidate genes in susceptibility genetic regions is the primary step towards the understanding of pathogenesis of diseases. Although several probabilistic models have been proposed to prioritize candidate genes using phenotype similarities and protein–protein interactions, no combinatorial approaches have been proposed in the literature. Results: We propose the first combinatorial approach for prioritizing candidate genes. We first construct a phenome–interactome network by integrating the given phenotype similarity profile, protein–protein interaction network and associations between diseases and genes. Then, we introduce a computational method called MAXIF to maximize the information flow in this network for uncovering genes that underlie diseases. We demonstrate the effectiveness of this method in prioritizing candidate genes through a series of cross-validation experiments, and we show the possibility of using this method to identify diseases with which a query gene may be associated. We demonstrate the competitive performance of our method through a comparison with two existing state-of-the-art methods, and we analyze the robustness of our method with respect to the parameters involved. As an example application, we apply our method to predict driver genes in 50 copy number aberration regions of melanoma. Our method is not only able to identify several driver genes that have been reported in the literature, it also shed some new biological insights on the understanding of the modular property and transcriptional regulation scheme of these driver genes. Contact: ruijiang@tsinghua.edu.cn PMID:21685067

  19. Exploring Bacterial Organelle Interactomes: A Model of the Protein-Protein Interaction Network in the Pdu Microcompartment

    PubMed Central

    Jorda, Julien; Liu, Yu; Bobik, Thomas A.; Yeates, Todd O.

    2015-01-01

    Bacterial microcompartments (MCPs) are protein-bound organelles that carry out diverse metabolic pathways in a wide range of bacteria. These supramolecular assemblies consist of a thin outer protein shell, reminiscent of a viral capsid, which encapsulates sequentially acting enzymes. The most complex MCP elucidated so far is the propanediol utilizing (Pdu) microcompartment. It contains the reactions for degrading 1,2-propanediol. While several experimental studies on the Pdu system have provided hints about its organization, a clear picture of how all the individual components interact has not emerged yet. Here we use co-evolution-based methods, involving pairwise comparisons of protein phylogenetic trees, to predict the protein-protein interaction (PPI) network governing the assembly of the Pdu MCP. We propose a model of the Pdu interactome, from which selected PPIs are further inspected via computational docking simulations. We find that shell protein PduA is able to serve as a “universal hub” for targeting an array of enzymes presenting special N-terminal extensions, namely PduC, D, E, L and P. The varied N-terminal peptides are predicted to bind in the same cleft on the presumptive luminal face of the PduA hexamer. We also propose that PduV, a protein of unknown function with remote homology to the Ras-like GTPase superfamily, is likely to localize outside the MCP, interacting with the protruding β-barrel of the hexameric PduU shell protein. Preliminary experiments involving a bacterial two-hybrid assay are presented that corroborate the existence of a PduU-PduV interaction. This first systematic computational study aimed at characterizing the interactome of a bacterial microcompartment provides fresh insight into the organization of the Pdu MCP. PMID:25646976

  20. Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays.

    PubMed

    Braun, Pascal

    2012-05-01

    Protein interactions mediate essentially all biological processes and analysis of protein-protein interactions using both large-scale and small-scale approaches has contributed fundamental insights to the understanding of biological systems. In recent years, interactome network maps have emerged as an important tool for analyzing and interpreting genetic data of complex phenotypes. Complementary experimental approaches to test for binary, direct interactions, and for membership in protein complexes are used to explore the interactome. The two approaches are not redundant but yield orthogonal perspectives onto the complex network of physical interactions by which proteins mediate biological processes. In recent years, several publications have demonstrated that interactions from high-throughput experiments can be equally reliable as the high quality subset of interactions identified in small-scale studies. Critical for this insight was the introduction of standardized experimental benchmarking of interaction and validation assays using reference sets. The data obtained in these benchmarking experiments have resulted in greater appreciation of the limitations and the complementary strengths of different assays. Moreover, benchmarking is a central element of a conceptual framework to estimate interactome sizes and thereby measure progress toward near complete network maps. These estimates have revealed that current large-scale data sets, although often of high quality, cover only a small fraction of a given interactome. Here, I review the findings of assay benchmarking and discuss implications for quality control, and for strategies toward obtaining a near-complete map of the interactome of an organism.

  1. Interactomic and pharmacological insights on human sirt-1.

    PubMed

    Sharma, Ankush; Gautam, Vasu; Costantini, Susan; Paladino, Antonella; Colonna, Giovanni

    2012-01-01

    Sirt-1 is defined as a nuclear protein involved in the molecular mechanisms of inflammation and neurodegeneration through the de-acetylation of many different substrates even if experimental data in mouse suggest both its cytoplasmatic presence and nucleo-cytoplasmic shuttling upon oxidative stress. Since the experimental structure of human Sirt-1 has not yet been reported, we have modeled its 3D structure, highlighted that it is composed by four different structural regions: N-terminal region, allosteric site, catalytic core and C-terminal region, and underlined that the two terminal regions have high intrinsic disorder propensity and numerous putative phosphorylation sites. Many different papers report experimental studies related to its functional activators because Sirt-1 is implicated in various diseases and cancers. The aim of this article is (i) to present interactomic studies based human Sirt-1 to understand its most important functional relationships in the light of the gene-protein interactions that control major metabolic pathways and (ii) to show by docking studies how this protein binds some activator molecules in order to evidence structural determinants, physico-chemical features and those residues involved in the formation of complexes.

  2. Transcriptional atlas of cardiogenesis maps congenital heart disease interactome.

    PubMed

    Li, Xing; Martinez-Fernandez, Almudena; Hartjes, Katherine A; Kocher, Jean-Pierre A; Olson, Timothy M; Terzic, Andre; Nelson, Timothy J

    2014-07-01

    Mammalian heart development is built on highly conserved molecular mechanisms with polygenetic perturbations resulting in a spectrum of congenital heart diseases (CHD). However, knowledge of cardiogenic ontogeny that regulates proper cardiogenesis remains largely based on candidate-gene approaches. Mapping the dynamic transcriptional landscape of cardiogenesis from a genomic perspective is essential to integrate the knowledge of heart development into translational applications that accelerate disease discovery efforts toward mechanistic-based treatment strategies. Herein, we designed a time-course transcriptome analysis to investigate the genome-wide dynamic expression landscape of innate murine cardiogenesis ranging from embryonic stem cells to adult cardiac structures. This comprehensive analysis generated temporal and spatial expression profiles, revealed stage-specific gene functions, and mapped the dynamic transcriptome of cardiogenesis to curated pathways. Reconciling known genetic underpinnings of CHD, we deconstructed a disease-centric dynamic interactome encoded within this cardiogenic atlas to identify stage-specific developmental disturbances clustered on regulation of epithelial-to-mesenchymal transition (EMT), BMP signaling, NF-AT signaling, TGFb-dependent EMT, and Notch signaling. Collectively, this cardiogenic transcriptional landscape defines the time-dependent expression of cardiac ontogeny and prioritizes regulatory networks at the interface between health and disease.

  3. SInCRe—structural interactome computational resource for Mycobacterium tuberculosis

    PubMed Central

    Metri, Rahul; Hariharaputran, Sridhar; Ramakrishnan, Gayatri; Anand, Praveen; Raghavender, Upadhyayula S.; Ochoa-Montaño, Bernardo; Higueruelo, Alicia P.; Sowdhamini, Ramanathan; Chandra, Nagasuma R.; Blundell, Tom L.; Srinivasan, Narayanaswamy

    2015-01-01

    We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein–protein and protein–small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein–protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host–pathogen protein–protein interactions. Together they provide prerequisites for identification of off-target binding. Database URL: http://proline.biochem.iisc.ernet.in/sincre PMID:26130660

  4. Proteomic Analysis of the Mediator Complex Interactome in Saccharomyces cerevisiae

    PubMed Central

    Uthe, Henriette; Vanselow, Jens T.; Schlosser, Andreas

    2017-01-01

    Here we present the most comprehensive analysis of the yeast Mediator complex interactome to date. Particularly gentle cell lysis and co-immunopurification conditions allowed us to preserve even transient protein-protein interactions and to comprehensively probe the molecular environment of the Mediator complex in the cell. Metabolic 15N-labeling thereby enabled stringent discrimination between bona fide interaction partners and nonspecifically captured proteins. Our data indicates a functional role for Mediator beyond transcription initiation. We identified a large number of Mediator-interacting proteins and protein complexes, such as RNA polymerase II, general transcription factors, a large number of transcriptional activators, the SAGA complex, chromatin remodeling complexes, histone chaperones, highly acetylated histones, as well as proteins playing a role in co-transcriptional processes, such as splicing, mRNA decapping and mRNA decay. Moreover, our data provides clear evidence, that the Mediator complex interacts not only with RNA polymerase II, but also with RNA polymerases I and III, and indicates a functional role of the Mediator complex in rRNA processing and ribosome biogenesis. PMID:28240253

  5. Current Approaches Toward Quantitative Mapping of the Interactome

    PubMed Central

    Buntru, Alexander; Trepte, Philipp; Klockmeier, Konrad; Schnoegl, Sigrid; Wanker, Erich E.

    2016-01-01

    Protein–protein interactions (PPIs) play a key role in many, if not all, cellular processes. Disease is often caused by perturbation of PPIs, as recently indicated by studies of missense mutations. To understand the associations of proteins and to unravel the global picture of PPIs in the cell, different experimental detection techniques for PPIs have been established. Genetic and biochemical methods such as the yeast two-hybrid system or affinity purification-based approaches are well suited to high-throughput, proteome-wide screening and are mainly used to obtain qualitative results. However, they have been criticized for not reflecting the cellular situation or the dynamic nature of PPIs. In this review, we provide an overview of various genetic methods that go beyond qualitative detection and allow quantitative measuring of PPIs in mammalian cells, such as dual luminescence-based co-immunoprecipitation, Förster resonance energy transfer or luminescence-based mammalian interactome mapping with bait control. We discuss the strengths and weaknesses of different techniques and their potential applications in biomedical research. PMID:27200083

  6. Interactomic and Pharmacological Insights on Human Sirt-1

    PubMed Central

    Sharma, Ankush; Gautam, Vasu; Costantini, Susan; Paladino, Antonella; Colonna, Giovanni

    2012-01-01

    Sirt-1 is defined as a nuclear protein involved in the molecular mechanisms of inflammation and neurodegeneration through the de-acetylation of many different substrates even if experimental data in mouse suggest both its cytoplasmatic presence and nucleo-cytoplasmic shuttling upon oxidative stress. Since the experimental structure of human Sirt-1 has not yet been reported, we have modeled its 3D structure, highlighted that it is composed by four different structural regions: N-terminal region, allosteric site, catalytic core and C-terminal region, and underlined that the two terminal regions have high intrinsic disorder propensity and numerous putative phosphorylation sites. Many different papers report experimental studies related to its functional activators because Sirt-1 is implicated in various diseases and cancers. The aim of this article is (i) to present interactomic studies based human Sirt-1 to understand its most important functional relationships in the light of the gene–protein interactions that control major metabolic pathways and (ii) to show by docking studies how this protein binds some activator molecules in order to evidence structural determinants, physico-chemical features and those residues involved in the formation of complexes. PMID:22470339

  7. Transcriptional atlas of cardiogenesis maps congenital heart disease interactome

    PubMed Central

    Li, Xing; Martinez-Fernandez, Almudena; Hartjes, Katherine A.; Kocher, Jean-Pierre A.; Olson, Timothy M.; Terzic, Andre

    2014-01-01

    Mammalian heart development is built on highly conserved molecular mechanisms with polygenetic perturbations resulting in a spectrum of congenital heart diseases (CHD). However, knowledge of cardiogenic ontogeny that regulates proper cardiogenesis remains largely based on candidate-gene approaches. Mapping the dynamic transcriptional landscape of cardiogenesis from a genomic perspective is essential to integrate the knowledge of heart development into translational applications that accelerate disease discovery efforts toward mechanistic-based treatment strategies. Herein, we designed a time-course transcriptome analysis to investigate the genome-wide dynamic expression landscape of innate murine cardiogenesis ranging from embryonic stem cells to adult cardiac structures. This comprehensive analysis generated temporal and spatial expression profiles, revealed stage-specific gene functions, and mapped the dynamic transcriptome of cardiogenesis to curated pathways. Reconciling known genetic underpinnings of CHD, we deconstructed a disease-centric dynamic interactome encoded within this cardiogenic atlas to identify stage-specific developmental disturbances clustered on regulation of epithelial-to-mesenchymal transition (EMT), BMP signaling, NF-AT signaling, TGFb-dependent EMT, and Notch signaling. Collectively, this cardiogenic transcriptional landscape defines the time-dependent expression of cardiac ontogeny and prioritizes regulatory networks at the interface between health and disease. PMID:24803680

  8. Oncogenic nature of a novel mutant AATF and its interactome existing within human cancer cells.

    PubMed

    Sharma, Shaveta; Kaul, Deepak; Arora, Mansi; Malik, Deepti

    2015-03-01

    Since apoptosis presents a natural defense in cancer development, the anti-apoptotic factor AATF/Che-1 has emerged as a crucial 'Epigenomic-Switch'. We have tried to understand the double-edged nature of AATF, showing for the first time the conspicuous existence of an aberrant AATF/Che-1 transcriptome encoding for 23 kDa mutant AATF protein, which evolves its unique interactome within human cancer cells derived from different tissue origins. This mutant AATF along with its interactome consisting of SP1, DNMT3B and Par-4 ensures cancer cell DNA methylation required for down-regulation of tumor suppressor genes. Hence, the proposed mutant AATF interactome-based pathway can have the inherent ability to ensure human cells become and remain cancerous.

  9. Protein interactome analysis of 12 mitogen-activated protein kinase kinase kinase in rice using a yeast two-hybrid system.

    PubMed

    Singh, Raksha; Lee, Jae-Eun; Dangol, Sarmina; Choi, Jihyun; Yoo, Ran Hee; Moon, Jae Sun; Shim, Jae-Kyung; Rakwal, Randeep; Agrawal, Ganesh Kumar; Jwa, Nam-Soo

    2014-01-01

    The mitogen-activated protein kinase (MAPK) cascade is composed at least of MAP3K (for MAPK kinase kinase), MAP2K, and MAPK family modules. These components together play a central role in mediating extracellular signals to the cell and vice versa by interacting with their partner proteins. However, the MAP3K-interacting proteins remain poorly investigated in plants. Here, we utilized a yeast two-hybrid system and bimolecular fluorescence complementation in the model crop rice (Oryza sativa) to map MAP3K-interacting proteins. We identified 12 novel nonredundant interacting protein pairs (IPPs) representing 11 nonredundant interactors using 12 rice MAP3Ks (available as full-length cDNA in the rice KOME (http://cdna01.dna.affrc.go.jp/cDNA/) at the time of experimental design and execution) as bait and a rice seedling cDNA library as prey. Of the 12 MAP3Ks, only six had interacting protein partners. The established MAP3K interactome consisted of two kinases, three proteases, two forkhead-associated domain-containing proteins, two expressed proteins, one E3 ligase, one regulatory protein, and one retrotransposon protein. Notably, no MAP3K showed physical interaction with either MAP2K or MAPK. Seven IPPs (58.3%) were confirmed in vivo by bimolecular fluorescence complementation. Subcellular localization of 14 interactors, together involved in nine IPPs (75%) further provide prerequisite for biological significance of the IPPs. Furthermore, GO of identified interactors predicted their involvement in diverse physiological responses, which were supported by a literature survey. These findings increase our knowledge of the MAP3K-interacting proteins, help in proposing a model of MAPK modules, provide a valuable resource for developing a complete map of the rice MAPK interactome, and allow discussion for translating the interactome knowledge to rice crop improvement against environmental factors.

  10. Exploring a structural protein-drug interactome for new therapeutics in lung cancer.

    PubMed

    Peng, Xiaodong; Wang, Fang; Li, Liwei; Bum-Erdene, Khuchtumur; Xu, David; Wang, Bo; Sinn, Anthony A; Pollok, Karen E; Sandusky, George E; Li, Lang; Turchi, John J; Jalal, Shadia I; Meroueh, Samy O

    2014-03-04

    The pharmacology of drugs is often defined by more than one protein target. This property can be exploited to use approved drugs to uncover new targets and signaling pathways in cancer. Towards enabling a rational approach to uncover new targets, we expand a structural protein-ligand interactome () by scoring the interaction among 1000 FDA-approved drugs docked to 2500 pockets on protein structures of the human genome. This afforded a drug-target network whose properties compared favorably with previous networks constructed using experimental data. Among drugs with the highest degree and betweenness two are cancer drugs and one is currently used for treatment of lung cancer. Comparison of predicted cancer and non-cancer targets reveals that the most cancer-specific compounds were also the most selective compounds. Analysis of compound flexibility, hydrophobicity, and size showed that the most selective compounds were low molecular weight fragment-like heterocycles. We use a previously-developed screening approach using the cancer drug erlotinib as a template to screen other approved drugs that mimic its properties. Among the top 12 ranking candidates, four are cancer drugs, two of them kinase inhibitors (like erlotinib). Cellular studies using non-small cell lung cancer (NSCLC) cells revealed that several drugs inhibited lung cancer cell proliferation. We mined patient records at the Regenstrief Medical Record System to explore the possible association of exposure to three of these drugs with occurrence of lung cancer. Preliminary in vivo studies using the non-small cell lung cancer (NCLSC) xenograft model showed that losartan- and astemizole-treated mice had tumors that weighed 50 (p < 0.01) and 15 (p < 0.01) percent less than the treated controls. These results set the stage for further exploration of these drugs and to uncover new drugs for lung cancer therapy.

  11. Spatio-temporal clustering

    NASA Astrophysics Data System (ADS)

    Kisilevich, Slava; Mansmann, Florian; Nanni, Mirco; Rinzivillo, Salvatore

    Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal clustering in geographic space. First, we provide a classification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal clustering - trajectory clustering, provide an overview of the state-of-the-art approaches and methods of spatio-temporal clustering and finally present several scenarios in different application domains such as movement, cellular networks and environmental studies.

  12. The role of the interactome in the maintenance of deleterious variability in human populations

    PubMed Central

    Garcia-Alonso, Luz; Jiménez-Almazán, Jorge; Carbonell-Caballero, Jose; Vela-Boza, Alicia; Santoyo-López, Javier; Antiñolo, Guillermo; Dopazo, Joaquin

    2014-01-01

    Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins. PMID:25261458

  13. High Quality Binary Protein Interaction Map of the Yeast Interactome Network

    PubMed Central

    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-Franćois; 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

    2009-01-01

    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 datasets, demonstrating that high-throughput yeast two-hybrid (Y2H) provides high-quality binary interaction information. As 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 dataset covering ~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 inter-complex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy. PMID:18719252

  14. A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework

    NASA Astrophysics Data System (ADS)

    Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

    2015-01-01

    The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.

  15. ΔF508 CFTR interactome remodeling promotes rescue of Cystic Fibrosis

    PubMed Central

    Pankow, Sandra; Bamberger, Casimir; Calzolari, Diego; Martínez-Bartolomé, Salvador; Lavallée-Adam, Mathieu; Balch, William E.; Yates, John R.

    2015-01-01

    Summary Deletion of phenylalanine 508 of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is the major cause of Cystic Fibrosis (CF), one of the most common inherited childhood diseases. The mutated CFTR anion channel is not fully glycosylated and shows minimal activity in bronchial epithelial cells of CF patients. Low temperature or inhibition of histone deacetylases (HDACi) can partially rescue ΔF508 CFTR cellular processing defects and function. A favorable change of ΔF508 CFTR protein-protein interactions was proposed as mechanism of rescue, however CFTR interactome dynamics during temperature-shift and HDACi rescue are unknown. Here, we report the first comprehensive analysis of the wt and ΔF508 CFTR interactome and its dynamics during temperature shift and HDACi. By using a novel deep proteomic analysis method (CoPIT), we identified 638 individual high-confidence CFTR interactors and discovered a mutation-specific interactome, which is extensively remodeled upon rescue. Detailed analysis of the interactome remodeling identified key novel interactors, whose loss promoted enhanced CFTR channel function in primary CF epithelia or which were critical for normal CFTR biogenesis. Our results demonstrate that global remodeling of ΔF508 CFTR interactions is crucial for rescue, and provide comprehensive insight into the molecular disease mechanisms of CF caused by deletion of F508. PMID:26618866

  16. Integrating the interactome and the transcriptome of Drosophila

    PubMed Central

    2014-01-01

    networks are frequently organized into hubs of widely expressed proteins to which are attached various tissue- or stage-specific proteins. This is consistent with earlier analyses of human PPI data and suggests a similar organization of interaction networks across species. This organization implies that tissue or stage specific networks can be best identified from interactome data by using filters designed to include both ubiquitously expressed and specifically expressed genes and proteins. PMID:24913703

  17. Simultaneously measuring multiple protein interactions and their correlations in a cell by Protein-interactome Footprinting

    PubMed Central

    Luo, Si-Wei; Liang, Zhi; Wu, Jia-Rui

    2017-01-01

    Quantitatively detecting correlations of multiple protein-protein interactions (PPIs) in vivo is a big challenge. Here we introduce a novel method, termed Protein-interactome Footprinting (PiF), to simultaneously measure multiple PPIs in one cell. The principle of PiF is that each target physical PPI in the interactome is simultaneously transcoded into a specific DNA sequence based on dimerization of the target proteins fused with DNA-binding domains. The interaction intensity of each target protein is quantified as the copy number of the specific DNA sequences bound by each fusion protein dimers. Using PiF, we quantitatively reveal dynamic patterns of PPIs and their correlation network in E. coli two-component systems. PMID:28338015

  18. Serum Amyloid P Component (SAP) Interactome in Human Plasma Containing Physiological Calcium Levels.

    PubMed

    Poulsen, Ebbe Toftgaard; Pedersen, Kata Wolff; Marzeda, Anna Maria; Enghild, Jan J

    2017-02-14

    The pentraxin serum amyloid P component (SAP) is secreted by the liver and found in plasma at a concentration of approximately 30 mg/L. SAP is a 25 kDa homopentamer known to bind both protein and nonprotein ligands, all in a calcium-dependent manner. The function of SAP is unclear but likely involves the humoral innate immune system spanning the complement system, inflammation, and coagulation. Also, SAP is known to bind to the generic structure of amyloid deposits and possibly to protect them against proteolysis. In this study, we have characterized the SAP interactome in human plasma containing the physiological Ca(2+) concentration using SAP affinity pull-down and co-immunoprecipitation experiments followed by mass spectrometry analyses. The analyses resulted in the identification of 33 proteins, of which 24 were direct or indirect interaction partners not previously reported. The SAP interactome can be divided into categories that include apolipoproteins, the complement system, coagulation, and proteolytic regulation.

  19. Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder.

    PubMed

    Martins-de-Souza, Daniel

    2014-03-01

    Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types-prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

  20. Mapping interactomes with high coverage and efficiency using the shifted transversal design.

    PubMed

    Xin, Xiaofeng; Boone, Charles; Thierry-Mieg, Nicolas

    2012-01-01

    "Smart-pooling" is a strategy to achieve high efficiency, sensitivity, and specificity in large-scale yeast two-hybrid screening. In smart-pooling, reagents are multiplexed in a highly redundant manner and the positives can be read out on the final selection plates without the requirement of any extra experimental steps. We have shown that the Shifted Transversal Design (STD), a powerful theoretical construction for smart-pooling, can be used in yeast two-hybrid interactome mapping. STD pooling can achieve similar levels of sensitivity and specificity as one-on-one array-based yeast two-hybrid, while the costs and workloads are much lower. This chapter focuses on the construction and usage of STD arrays for large-scale yeast two-hybrid interactome mapping.

  1. Next Generation Protein Interactomes for Plant Systems Biology and Biomass Feedstock Research

    SciTech Connect

    Ecker, Joseph Robert; Trigg, Shelly; Garza, Renee; Song, Haili; MacWilliams, Andrew; Nery, Joseph; Reina, Joaquin; Bartlett, Anna; Castanon, Rosa; Goubil, Adeline; Feeney, Joseph; O'Malley, Ronan; Huang, Shao-shan Carol; Zhang, Zhuzhu; Galli, Mary

    2016-11-30

    Biofuel crop cultivation is a necessary step in heading towards a sustainable future, making their genomic studies a priority. While technology platforms that currently exist for studying non-model crop species, like switch-grass or sorghum, have yielded large quantities of genomic and expression data, still a large gap exists between molecular mechanism and phenotype. The aspect of molecular activity at the level of protein-protein interactions has recently begun to bridge this gap, providing a more global perspective. Interactome analysis has defined more specific functional roles of proteins based on their interaction partners, neighborhoods, and other network features, making it possible to distinguish unique modules of immune response to different plant pathogens(Jiang, Dong, and Zhang 2016). As we work towards cultivating heartier biofuel crops, interactome data will lead to uncovering crop-specific defense and development networks. However, the collection of protein interaction data has been limited to expensive, time-consuming, hard-to-scale assays that mostly require cloned ORF collections. For these reasons, we have successfully developed a highly scalable, economical, and sensitive yeast two-hybrid assay, ProCREate, that can be universally applied to generate proteome-wide primary interactome data. ProCREate enables en masse pooling and massively paralleled sequencing for the identification of interacting proteins by exploiting Cre-lox recombination. ProCREate can be used to screen ORF/cDNA libraries from feedstock plant tissues. The interactome data generated will yield deeper insight into many molecular processes and pathways that can be used to guide improvement of feedstock productivity and sustainability.

  2. Perturbation of the mutated EGFR interactome identifies vulnerabilities and resistance mechanisms

    PubMed Central

    Li, Jiannong; Bennett, Keiryn; Stukalov, Alexey; Fang, Bin; Zhang, Guolin; Yoshida, Takeshi; Okamoto, Isamu; Kim, Jae-Young; Song, Lanxi; Bai, Yun; Qian, Xiaoning; Rawal, Bhupendra; Schell, Michael; Grebien, Florian; Winter, Georg; Rix, Uwe; Eschrich, Steven; Colinge, Jacques; Koomen, John; Superti-Furga, Giulio; Haura, Eric B

    2013-01-01

    We hypothesized that elucidating the interactome of epidermal growth factor receptor (EGFR) forms that are mutated in lung cancer, via global analysis of protein–protein interactions, phosphorylation, and systematically perturbing the ensuing network nodes, should offer a new, more systems-level perspective of the molecular etiology. Here, we describe an EGFR interactome of 263 proteins and offer a 14-protein core network critical to the viability of multiple EGFR-mutated lung cancer cells. Cells with acquired resistance to EGFR tyrosine kinase inhibitors (TKIs) had differential dependence of the core network proteins based on the underlying molecular mechanisms of resistance. Of the 14 proteins, 9 are shown to be specifically associated with survival of EGFR-mutated lung cancer cell lines. This included EGFR, GRB2, MK12, SHC1, ARAF, CD11B, ARHG5, GLU2B, and CD11A. With the use of a drug network associated with the core network proteins, we identified two compounds, midostaurin and lestaurtinib, that could overcome drug resistance through direct EGFR inhibition when combined with erlotinib. Our results, enabled by interactome mapping, suggest new targets and combination therapies that could circumvent EGFR TKI resistance. PMID:24189400

  3. Computer vision profiling of neurite outgrowth dynamics reveals spatiotemporal modularity of Rho GTPase signaling

    PubMed Central

    Fusco, Ludovico; Lefort, Riwal; Smith, Kevin; Benmansour, Fethallah; Gonzalez, German; Barillari, Caterina; Rinn, Bernd; Fleuret, Francois; Fua, Pascal

    2016-01-01

    Rho guanosine triphosphatases (GTPases) control the cytoskeletal dynamics that power neurite outgrowth. This process consists of dynamic neurite initiation, elongation, retraction, and branching cycles that are likely to be regulated by specific spatiotemporal signaling networks, which cannot be resolved with static, steady-state assays. We present NeuriteTracker, a computer-vision approach to automatically segment and track neuronal morphodynamics in time-lapse datasets. Feature extraction then quantifies dynamic neurite outgrowth phenotypes. We identify a set of stereotypic neurite outgrowth morphodynamic behaviors in a cultured neuronal cell system. Systematic RNA interference perturbation of a Rho GTPase interactome consisting of 219 proteins reveals a limited set of morphodynamic phenotypes. As proof of concept, we show that loss of function of two distinct RhoA-specific GTPase-activating proteins (GAPs) leads to opposite neurite outgrowth phenotypes. Imaging of RhoA activation dynamics indicates that both GAPs regulate different spatiotemporal Rho GTPase pools, with distinct functions. Our results provide a starting point to dissect spatiotemporal Rho GTPase signaling networks that regulate neurite outgrowth. PMID:26728857

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

    PubMed Central

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

    2013-01-01

    The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of NOx in the Los Angeles area during a ten year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated R2 of approximately 0.7 at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy. PMID:25264424

  5. Spatiotemporal Optical Vortices

    NASA Astrophysics Data System (ADS)

    Jhajj, N.; Larkin, I.; Rosenthal, E. W.; Zahedpour, S.; Wahlstrand, J. K.; Milchberg, H. M.

    2016-07-01

    We present the first experimental evidence, supported by theory and simulation, of spatiotemporal optical vortices (STOVs). A STOV is an optical vortex with phase and energy circulation in a spatiotemporal plane. Depending on the sign of the material dispersion, the local electromagnetic energy flow is saddle or spiral about the STOV. STOVs are a fundamental element of the nonlinear collapse and subsequent propagation of short optical pulses in material media, and conserve topological charge, constraining their birth, evolution, and annihilation. We measure a self-generated STOV consisting of a ring-shaped null in the electromagnetic field about which the phase is spiral, forming a dynamic torus that is concentric with and tracks the propagating pulse. Our results, here obtained for optical pulse collapse and filamentation in air, are generalizable to a broad class of nonlinearly propagating waves.

  6. Spatiotemporal multipartite entanglement

    SciTech Connect

    Kolobov, Mikhail I.; Patera, Giuseppe

    2011-05-15

    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 ({rho}-vector,t) and ({rho}-vector{sup '},t{sup '}), with {rho}-vector 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.

  7. Interactome of E. piscicida and grouper liver proteins reveals strategies of bacterial infection and host immune response

    PubMed Central

    Li, Hui; Zhu, Qing-feng; Peng, Xuan-xian; Peng, Bo

    2017-01-01

    The occurrence of infectious diseases is related to heterogeneous protein interactions between a host and a microbe. Therefore, elucidating the host-pathogen interplay is essential. We previously revealed the protein interactome between Edwardsiella piscicida and fish gill cells, and the present study identified the protein interactome between E. piscicida and E. drummondhayi liver cells. E. drummondhayi liver cells and bacterial pull-down approaches were used to identify E. piscicida outer membrane proteins that bind to liver cells and fish liver cell proteins that interact with bacterial cells, respectively. Eight bacterial proteins and 11 fish proteins were characterized. Heterogeneous protein-protein interactions between these bacterial cells and fish liver cells were investigated through far-Western blotting and co-immunoprecipitation. A network was constructed based on 42 heterogeneous protein-protein interactions between seven bacterial proteins and 10 fish proteins. A comparison of the new interactome with the previously reported interactome showed that four bacterial proteins overlapped, whereas all of the identified fish proteins were new, suggesting a difference between bacterial tricks for evading host immunity and the host strategy for combating bacterial infection. Furthermore, these bacterial proteins were found to regulate the expression of host innate immune-related proteins. These findings indicate that the interactome contributes to bacterial infection and host immunity. PMID:28045121

  8. Columbia University: Computational Human High-grade Glioblastoma Multiforme Interactome - miRNA (Post-transcriptional) Layer | Office of Cancer Genomics

    Cancer.gov

    The Human High-Grade Glioma Interactome (HGi) contains a genome-wide complement of molecular interactions that are Glioblastoma Multiforme (GBM)-specific. HGi v3 contains the post-transcriptional layer of the HGi, which includes the miRNA-target (RNA-RNA) layer of the interactome. Read the Abstract

  9. Impaired autophagy and APP processing in Alzheimer's disease: The potential role of Beclin 1 interactome.

    PubMed

    Salminen, Antero; Kaarniranta, Kai; Kauppinen, Anu; Ojala, Johanna; Haapasalo, Annakaisa; Soininen, Hilkka; Hiltunen, Mikko

    2013-01-01

    The accumulation of amyloid-β-containing neuritic plaques and intracellular tau protein tangles are key histopathological hallmarks of Alzheimer's disease (AD). This type of pathology clearly indicates that the mechanisms of neuronal housekeeping and protein quality control are compromised in AD. There is mounting evidence that the autophagosome-lysosomal degradation is impaired, which could disturb the processing of APP and provoke AD pathology. Beclin 1 is a molecular platform assembling an interactome with stimulating and suppressive components which regulate the initiation of the autophagosome formation. Recent studies have indicated that the expression Beclin 1 is reduced in AD brain. Moreover, the deficiency of Beclin 1 in cultured neurons and transgenic mice provokes the deposition of amyloid-β peptides whereas its overexpression reduces the accumulation of amyloid-β. There are several potential mechanisms, which could inhibit the function of Beclin 1 interactome and thus impair autophagy and promote AD pathology. The mechanisms include (i) reduction of Beclin 1 expression or its increased proteolytic cleavage by caspases, (ii) sequestration of Beclin 1 to non-functional locations, such as tau tangles, (iii) formation of inhibitory complexes between Beclin 1 and antiapoptotic Bcl-2 proteins or inflammasomes, (iv) interaction of Beclin 1 with inhibitory neurovirulent proteins, e.g. herpex simplex ICP34.5, or (v) inhibition of the Beclin 1/Vps34 complex through the activation of CDK1 and CDK5. We will shortly introduce the function of Beclin 1 interactome in autophagy and phagocytosis, review the recent evidence indicating that Beclin 1 regulates autophagy and APP processing in AD, and finally examine the potential mechanisms through which Beclin 1 dysfunction could be involved in the pathogenesis of AD.

  10. Comprehensive Identification of mRNA-Binding Proteins of Leishmania donovani by Interactome Capture

    PubMed Central

    Nandan, Devki; Thomas, Sneha A.; Nguyen, Anne; Moon, Kyung-Mee; Foster, Leonard J.; Reiner, Neil E.

    2017-01-01

    Leishmania are unicellular eukaryotes responsible for leishmaniasis in humans. Like other trypanosomatids, leishmania regulate protein coding gene expression almost exclusively at the post-transcriptional level with the help of RNA binding proteins (RBPs). Due to the presence of polycystronic transcription units, leishmania do not regulate RNA polymerase II-dependent transcription initiation. Recent evidence suggests that the main control points in gene expression are mRNA degradation and translation. Protein-RNA interactions are involved in every aspect of RNA biology, such as mRNA splicing, polyadenylation, localization, degradation, and translation. A detailed picture of these interactions would likely prove to be highly informative in understanding leishmania biology and virulence. We developed a strategy involving covalent UV cross-linking of RBPs to mRNA in vivo, followed by interactome capture using oligo(dT) magnetic beads to define comprehensively the mRNA interactome of growing L. donovani amastigotes. The protein mass spectrometry analysis of captured proteins identified 79 mRNA interacting proteins which withstood very stringent washing conditions. Strikingly, we found that 49 of these mRNA interacting proteins had no orthologs or homologs in the human genome. Consequently, these may represent high quality candidates for selective drug targeting leading to novel therapeutics. These results show that this unbiased, systematic strategy has the promise to be applicable to study the mRNA interactome during various biological settings such as metabolic changes, stress (low pH environment, oxidative stress and nutrient deprivation) or drug treatment. PMID:28135300

  11. Neuregulin 1-erbB4 pathway in schizophrenia: From genes to an interactome.

    PubMed

    Banerjee, Anamika; Macdonald, Mathew L; Borgmann-Winter, Karin E; Hahn, Chang-Gyu

    2010-09-30

    Recently identified candidate susceptibility genes for schizophrenia are likely to play, important roles in the pathophysiology of the illness. It is also clear, however, that the etiologic, contribution of these genes is not only via their own functions but also through interactions with other, genes and environmental factors. Genetic, transgenic and postmortem brain studies support a, potential role for NRG1-erbB4 signaling in schizophrenia. Embedded in the results of these studies, however, are clues to the notion that NRG1-erbB4 signaling does not act alone but in conjunction with, other pathways. This article aims to re-evaluate the evidence for the role of neuregulin 1 (NRG1)-erbB4 signaling in schizophrenia by focusing on its interactions with other candidate susceptibility, pathways. In addition, we consider molecular substrates upon which the NRG1-erbB4 and other, candidate pathways converge contributing to susceptibility for the illness (schizophrenia interactome). Glutamatergic signaling can be an interesting candidate for schizophrenia interactome. Schizophrenia is associated with NMDA receptor hypofunction and moreover, several susceptibility genes for, schizophrenia converge on NMDA receptor signaling. These candidate genes influence NMDA receptor, signaling via diverse mechanisms, yet all eventually impact on protein composition of NMDA receptor, complexes. Likewise, the protein associations in the receptor complexes can themselves modulate, signaling molecules of candidate genes and their pathways. Therefore, protein-protein interactions in the NMDA receptor complexes can mediate reciprocal interactions between NMDA receptor function, and susceptibility candidate pathways including NRG1-erbB4 signaling and thus can be a, schizophrenia interactome.

  12. A computational interactome and functional annotation for the human proteome

    PubMed Central

    Garzón, José Ignacio; Deng, Lei; Murray, Diana; Shapira, Sagi; Petrey, Donald; Honig, Barry

    2016-01-01

    We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function. DOI: http://dx.doi.org/10.7554/eLife.18715.001 PMID:27770567

  13. The chicken B-cell line DT40 proteome, beadome and interactomes

    PubMed Central

    Rees, Johanna S.; Lilley, Kathryn S.; Jackson, Antony P.

    2015-01-01

    In developing a new quantitative AP-MS method for exploring interactomes in the chicken B-cell line DT40, we also surveyed the most abundant proteins in this organism and explored the likely contaminants that bind to a variety of affinity resins that would later be confirmed quantitatively [1]. We present the ‘Top 150 abundant DT40 proteins list’, the DT40 beadomes as well as protein interaction lists for the Phosphatidyl inositol 5-phosphate 4-kinase 2β and Fanconi anaemia protein complexes. PMID:26217713

  14. Ocean plankton. Determinants of community structure in the global plankton interactome.

    PubMed

    Lima-Mendez, Gipsi; Faust, Karoline; Henry, Nicolas; Decelle, Johan; Colin, Sébastien; Carcillo, Fabrizio; Chaffron, Samuel; Ignacio-Espinosa, J Cesar; Roux, Simon; Vincent, Flora; Bittner, Lucie; Darzi, Youssef; Wang, Jun; Audic, Stéphane; Berline, Léo; Bontempi, Gianluca; Cabello, Ana M; Coppola, Laurent; Cornejo-Castillo, Francisco M; d'Ovidio, Francesco; De Meester, Luc; Ferrera, Isabel; Garet-Delmas, Marie-José; Guidi, Lionel; Lara, Elena; Pesant, Stéphane; Royo-Llonch, Marta; Salazar, Guillem; Sánchez, Pablo; Sebastian, Marta; Souffreau, Caroline; Dimier, Céline; Picheral, Marc; Searson, Sarah; Kandels-Lewis, Stefanie; Gorsky, Gabriel; Not, Fabrice; Ogata, Hiroyuki; Speich, Sabrina; Stemmann, Lars; Weissenbach, Jean; Wincker, Patrick; Acinas, Silvia G; Sunagawa, Shinichi; Bork, Peer; Sullivan, Matthew B; Karsenti, Eric; Bowler, Chris; de Vargas, Colomban; Raes, Jeroen

    2015-05-22

    Species interaction networks are shaped by abiotic and biotic factors. Here, as part of the Tara Oceans project, we studied the photic zone interactome using environmental factors and organismal abundance profiles and found that environmental factors are incomplete predictors of community structure. We found associations across plankton functional types and phylogenetic groups to be nonrandomly distributed on the network and driven by both local and global patterns. We identified interactions among grazers, primary producers, viruses, and (mainly parasitic) symbionts and validated network-generated hypotheses using microscopy to confirm symbiotic relationships. We have thus provided a resource to support further research on ocean food webs and integrating biological components into ocean models.

  15. Spatio-temporal modeling of Active Layer Thickness

    NASA Astrophysics Data System (ADS)

    Touyz, J.; Apanasovich, T. V.; Streletskiy, D. A.; Shiklomanov, N. I.

    2015-12-01

    Arctic Regions are experiencing an unprecedented rate of environmental and climate change. The active layer (the uppermost layer of soil between the atmosphere and permafrost that freezes in winter and thaws in summer) is sensitive to both climate and environmental changes and plays an important role in the functioning of Arctic ecosystems, planning, and economic activities. Knowledge about spatio-temporal variability of ALT is crucial for environmental and engineering applications. The objective of this study is to provide the methodology to model and estimate spatio-temporal variation in the active layer thickness (ALT) at several sites located in the Circumpolar region spanning the Alaska North Slope, and to demonstrate its use in spatio-temporal interpolation as well as time-forward prediction. In our data analysis we estimate a parametric trend and examine residuals for the presence of spatial and temporal dependence. We propose models that provide a description of residual space-time variability in ALT. Formulations that take into account interaction among spatial and temporal components are also developed. Moreover, we compare our models to naive models in which residual spatio-temporal and temporal correlations are not considered. The predicted root mean squared and absolute errors are significantly reduced when our approach is employed. While the methodology is developed in the context of ALT, it can also be applied to model and predict other environmental variables which use similar spatio-temporal sampling designs.

  16. Mapping RNA–RNA interactome and RNA structure in vivo by MARIO

    PubMed Central

    Nguyen, Tri C.; Cao, Xiaoyi; Yu, Pengfei; Xiao, Shu; Lu, Jia; Biase, Fernando H.; Sridhar, Bharat; Huang, Norman; Zhang, Kang; Zhong, Sheng

    2016-01-01

    The pervasive transcription of our genome presents a possibility of revealing new genomic functions by investigating RNA interactions. Current methods for mapping RNA–RNA interactions have to rely on an ‘anchor' protein or RNA and often require molecular perturbations. Here we present the MARIO (Mapping RNA interactome in vivo) technology to massively reveal RNA–RNA interactions from unperturbed cells. We mapped tens of thousands of endogenous RNA–RNA interactions from mouse embryonic stem cells and brain. We validated seven interactions by RNA antisense purification and one interaction using single-molecule RNA–FISH. The experimentally derived RNA interactome is a scale-free network, which is not expected from currently perceived promiscuity in RNA–RNA interactions. Base pairing is observed at the interacting regions between long RNAs, including transposon transcripts, suggesting a class of regulatory sequences acting in trans. In addition, MARIO data reveal thousands of intra-molecule interactions, providing in vivo data on high-order RNA structures. PMID:27338251

  17. Systematic interactome mapping and genetic perturbation analysis of a C. elegans TGF-beta signaling network.

    PubMed

    Tewari, Muneesh; Hu, Patrick J; Ahn, Jin Sook; Ayivi-Guedehoussou, Nono; Vidalain, Pierre-Olivier; Li, Siming; Milstein, Stuart; Armstrong, Chris M; Boxem, Mike; Butler, Maurice D; Busiguina, Svetlana; Rual, Jean-François; Ibarrola, Nieves; Chaklos, Sabrina T; Bertin, Nicolas; Vaglio, Philippe; Edgley, Mark L; King, Kevin V; Albert, Patrice S; Vandenhaute, Jean; Pandey, Akhilesh; Riddle, Donald L; Ruvkun, Gary; Vidal, Marc

    2004-02-27

    To initiate a system-level analysis of C. elegans DAF-7/TGF-beta signaling, we combined interactome mapping with single and double genetic perturbations. Yeast two-hybrid (Y2H) screens starting with known DAF-7/TGF-beta pathway components defined a network of 71 interactions among 59 proteins. Coaffinity purification (co-AP) assays in mammalian cells confirmed the overall quality of this network. Systematic perturbations of the network using RNAi, both in wild-type and daf-7/TGF-beta pathway mutant animals, identified nine DAF-7/TGF-beta signaling modifiers, seven of which are conserved in humans. We show that one of these has functional homology to human SNO/SKI oncoproteins and that mutations at the corresponding genetic locus daf-5 confer defects in DAF-7/TGF-beta signaling. Our results reveal substantial molecular complexity in DAF-7/TGF-beta signal transduction. Integrating interactome maps with systematic genetic perturbations may be useful for developing a systems biology approach to this and other signaling modules.

  18. Chronically dysregulated NOTCH1 interactome in the dentate gyrus after traumatic brain injury

    PubMed Central

    Puhakka, Noora; Bot, Anna Maria; Vuokila, Niina; Debski, Konrad Jozef; Lukasiuk, Katarzyna; Pitkänen, Asla

    2017-01-01

    Traumatic brain injury (TBI) can result in several dentate gyrus-regulated disabilities. Almost nothing is known about the chronic molecular changes after TBI, and their potential as treatment targets. We hypothesized that chronic transcriptional alterations after TBI are under microRNA (miRNA) control. Expression of miRNAs and their targets in the dentate gyrus was analyzed using microarrays at 3 months after experimental TBI. Of 305 miRNAs present on the miRNA-array, 12 were downregulated (p<0.05). In parallel, 75 of their target genes were upregulated (p<0.05). A bioinformatics analysis of miRNA targets highlighted the dysregulation of the transcription factor NOTCH1 and 39 of its target genes (NOTCH1 interactome). Validation assays confirmed downregulation of miR-139-5p, upregulation of Notch1 and its activated protein, and positive enrichment of NOTCH1 target gene expression. These findings demonstrate that miRNA-based transcriptional regulation can be present at chronic time points after TBI, and highlight the NOTCH1 interactome as one of the mechanisms behind the dentate gyrus pathology-related morbidities. PMID:28273100

  19. Mapping transcription factor interactome networks using HaloTag protein arrays

    PubMed Central

    Yazaki, Junshi; Galli, Mary; Kim, Alice Y.; Nito, Kazumasa; Aleman, Fernando; Chang, Katherine N.; Quan, Rosa; Nguyen, Hien; Song, Liang; Alvarez, José M.; Huang, Shao-shan Carol; Chen, Huaming; Ramachandran, Niroshan; Altmann, Stefan; Gutiérrez, Rodrigo A.; Schroeder, Julian I.; Chory, Joanne; LaBaer, Joshua; Vidal, Marc; Braun, Pascal; Ecker, Joseph R.

    2016-01-01

    Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein–protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thousands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literature-curated interactions. Moreover, using a bimolecular fluorescence complementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor–protein interactions and led to the development of a proteome-wide plant hormone TF interactome network. PMID:27357687

  20. Mapping transcription factor interactome networks using HaloTag protein arrays.

    PubMed

    Yazaki, Junshi; Galli, Mary; Kim, Alice Y; Nito, Kazumasa; Aleman, Fernando; Chang, Katherine N; Carvunis, Anne-Ruxandra; Quan, Rosa; Nguyen, Hien; Song, Liang; Alvarez, José M; Huang, Shao-Shan Carol; Chen, Huaming; Ramachandran, Niroshan; Altmann, Stefan; Gutiérrez, Rodrigo A; Hill, David E; Schroeder, Julian I; Chory, Joanne; LaBaer, Joshua; Vidal, Marc; Braun, Pascal; Ecker, Joseph R

    2016-07-19

    Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein-protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thousands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literature-curated interactions. Moreover, using a bimolecular fluorescence complementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor-protein interactions and led to the development of a proteome-wide plant hormone TF interactome network.

  1. Caspofungin exposure alters the core septin AspB interactome of Aspergillus fumigatus.

    PubMed

    Vargas-Muñiz, José M; Renshaw, Hilary; Waitt, Greg; Soderblom, Erik J; Moseley, M Arthur; Palmer, Jonathan M; Juvvadi, Praveen R; Keller, Nancy P; Steinbach, William J

    2017-04-01

    Aspergillus fumigatus, the main etiological agent of invasive aspergillosis, is a leading cause of death in immunocompromised patients. Septins, a conserved family of GTP-binding proteins, serve as scaffolding proteins to recruit enzymes and key regulators to different cellular compartments. Deletion of the A. fumigatus septin aspB increases susceptibility to the echinocandin antifungal caspofungin. However, how AspB mediates this response to caspofungin is unknown. Here, we characterized the AspB interactome under basal conditions and after exposure to a clinically relevant concentration of caspofungin. While A. fumigatus AspB interacted with 334 proteins, including kinases, cell cycle regulators, and cell wall synthesis-related proteins under basal growth conditions, caspofungin exposure altered AspB interactions. A total of 69 of the basal interactants did not interact with AspB after exposure to caspofungin, and 54 new interactants were identified following caspofungin exposure. We generated A. fumigatus deletion strains for 3 proteins (ArpB, Cyp4, and PpoA) that only interacted with AspB following exposure to caspofungin that were previously annotated as induced after exposure to antifungal agents, yet only PpoA was implicated in the response to caspofungin. Taken together, we defined how the septin AspB interactome is altered in the presence of a clinically relevant antifungal.

  2. Targeted cross-linking-mass spectrometry determines vicinal interactomes within heterogeneous RNP complexes

    PubMed Central

    Trahan, Christian; Oeffinger, Marlene

    2016-01-01

    Proteomic and RNomic approaches have identified many components of different ribonucleoprotein particles (RNPs), yet still little is known about the organization and protein proximities within these heterogeneous and highly dynamic complexes. Here we describe a targeted cross-linking approach, which combines cross-linking from a known anchor site with affinity purification and mass spectrometry (MS) to identify the changing vicinity interactomes along RNP maturation pathways. Our method confines the reaction radius of a heterobifunctional cross-linker to a specific interaction surface, increasing the probability to capture low abundance conformations and transient vicinal interactors too infrequent for identification by traditional cross-linking-MS approaches, and determine protein proximities within RNPs. Applying the method to two conserved RNA-associated complexes in Saccharomyces cerevisae, the mRNA export receptor Mex67:Mtr2 and the pre-ribosomal Nop7 subcomplex, we identified dynamic vicinal interactomes within those complexes and along their changing pathway milieu. Our results therefore show that this method provides a new tool to study the changing spatial organization of heterogeneous dynamic RNP complexes. PMID:26657640

  3. Interactome-transcriptome analysis discovers signatures complementary to GWAS Loci of Type 2 Diabetes

    PubMed Central

    Li, Jing-Woei; Lee, Heung-Man; Wang, Ying; Tong, Amy Hin-Yan; Yip, Kevin Y.; Tsui, Stephen Kwok-Wing; Lok, Si; Ozaki, Risa; Luk, Andrea O; Kong, Alice P. S.; So, Wing-Yee; Ma, Ronald C. W.; Chan, Juliana C. N.; Chan, Ting-Fung

    2016-01-01

    Protein interactions play significant roles in complex diseases. We analyzed peripheral blood mononuclear cells (PBMC) transcriptome using a multi-method strategy. We constructed a tissue-specific interactome (T2Di) and identified 420 molecular signatures associated with T2D-related comorbidity and symptoms, mainly implicated in inflammation, adipogenesis, protein phosphorylation and hormonal secretion. Apart from explaining the residual associations within the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study, the T2Di signatures were enriched in pathogenic cell type-specific regulatory elements related to fetal development, immunity and expression quantitative trait loci (eQTL). The T2Di revealed a novel locus near a well-established GWAS loci AChE, in which SRRT interacts with JAZF1, a T2D-GWAS gene implicated in pancreatic function. The T2Di also included known anti-diabetic drug targets (e.g. PPARD, MAOB) and identified possible druggable targets (e.g. NCOR2, PDGFR). These T2Di signatures were validated by an independent computational method, and by expression data of pancreatic islet, muscle and liver with some of the signatures (CEBPB, SREBF1, MLST8, SRF, SRRT and SLC12A9) confirmed in PBMC from an independent cohort of 66 T2D and 66 control subjects. By combining prior knowledge and transcriptome analysis, we have constructed an interactome to explain the multi-layered regulatory pathways in T2D. PMID:27752041

  4. A core of kinase-regulated interactomes defines the neoplastic MDSC lineage

    PubMed Central

    Zudaire, Isabel; Liechtenstein, Therese; Arasanz, Hugo; Lozano, Teresa; Casares, Noelia; Chaikuad, Apirat; Knapp, Stefan; Guerrero-Setas, David; Escors, David; Kochan, Grazyna; Santamaría, Enrique

    2015-01-01

    Myeloid-derived suppressor cells (MDSCs) differentiate from bone marrow precursors, expand in cancer-bearing hosts and accelerate tumor progression. MDSCs have become attractive therapeutic targets, as their elimination strongly enhances anti-neoplastic treatments. Here, immature myeloid dendritic cells (DCs), MDSCs modeling tumor-infiltrating subsets or modeling non-cancerous (NC)-MDSCs were compared by in-depth quantitative proteomics. We found that neoplastic MDSCs differentially expressed a core of kinases which controlled lineage-specific (PI3K-AKT and SRC kinases) and cancer-induced (ERK and PKC kinases) protein interaction networks (interactomes). These kinases contributed to some extent to myeloid differentiation. However, only AKT and ERK specifically drove MDSC differentiation from myeloid precursors. Interfering with AKT and ERK with selective small molecule inhibitors or shRNAs selectively hampered MDSC differentiation and viability. Thus, we provide compelling evidence that MDSCs constitute a distinct myeloid lineage distinguished by a “kinase signature” and well-defined interactomes. Our results define new opportunities for the development of anti-cancer treatments targeting these tumor-promoting immune cells. PMID:26320174

  5. Clathrin complexes with the inhibitor kappa B kinase signalosome: imaging the interactome

    PubMed Central

    Gamboni, Fabia; Escobar, Guillermo A.; Moore, Ernest E.; Dzieciatkowska, Monika; Hansen, Kirk C.; Mitra, Sanchayita; Nydam, Trevor A.; Silliman, Christopher C.; Banerjee, Anirban

    2014-01-01

    Abstract Many receptors involved with innate immunity activate the inhibitor kappa B kinase signalosome (IKK). The active complex appears to be assembled from the two kinase units, IKKα and IKKβ with the regulatory protein NEMO. Because we previously found that RNA silencing of clathrin heavy chains (CHC), in transformed human lung pneumocytes (A549), decreased TNFα‐induced signaling and phosphorylation of inhibitor kappa B (IκB), we hypothesized that CHC forms cytoplasmic complexes with members of the IKK signalosome. Widely available antibodies were used to immunoprecipitate IKKα and NEMO interactomes. Analysis of the affinity interactomes by mass spectrometry detected clathrin with both baits with high confidence. Using the same antibodies for indirect digital immunofluorescence microscopy and FRET, the CHC–IKK complexes were visualized together with NEMO or HSP90. The natural variability of protein amounts in unsynchronized A549 cells was used to obtain statistical correlation for several complexes, at natural levels and without invasive labeling. Analyses of voxel numbers indicated that: (i) CHC–IKK complexes are not part of the IKK signalosome itself but, likely, precursors of IKK–NEMO complexes. (ii) CHC–IKKβ complexes may arise from IKKβ–HSP90 complexes. PMID:24994893

  6. A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human.

    PubMed

    Vo, Tommy V; Das, Jishnu; Meyer, Michael J; Cordero, Nicolas A; Akturk, Nurten; Wei, Xiaomu; Fair, Benjamin J; Degatano, Andrew G; Fragoza, Robert; Liu, Lisa G; Matsuyama, Akihisa; Trickey, Michelle; Horibata, Sachi; Grimson, Andrew; Yamano, Hiroyuki; Yoshida, Minoru; Roth, Frederick P; Pleiss, Jeffrey A; Xia, Yu; Yu, Haiyuan

    2016-01-14

    Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ∼ 50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution.

  7. Mapping RNA-RNA interactome and RNA structure in vivo by MARIO.

    PubMed

    Nguyen, Tri C; Cao, Xiaoyi; Yu, Pengfei; Xiao, Shu; Lu, Jia; Biase, Fernando H; Sridhar, Bharat; Huang, Norman; Zhang, Kang; Zhong, Sheng

    2016-06-24

    The pervasive transcription of our genome presents a possibility of revealing new genomic functions by investigating RNA interactions. Current methods for mapping RNA-RNA interactions have to rely on an 'anchor' protein or RNA and often require molecular perturbations. Here we present the MARIO (Mapping RNA interactome in vivo) technology to massively reveal RNA-RNA interactions from unperturbed cells. We mapped tens of thousands of endogenous RNA-RNA interactions from mouse embryonic stem cells and brain. We validated seven interactions by RNA antisense purification and one interaction using single-molecule RNA-FISH. The experimentally derived RNA interactome is a scale-free network, which is not expected from currently perceived promiscuity in RNA-RNA interactions. Base pairing is observed at the interacting regions between long RNAs, including transposon transcripts, suggesting a class of regulatory sequences acting in trans. In addition, MARIO data reveal thousands of intra-molecule interactions, providing in vivo data on high-order RNA structures.

  8. Interactome Mapping Reveals the Evolutionary History of the Nuclear Pore Complex

    PubMed Central

    Obado, Samson O.; Brillantes, Marc; Uryu, Kunihiro; Zhang, Wenzhu; Ketaren, Natalia E.; Chait, Brian T.; Field, Mark C.; Rout, Michael P.

    2016-01-01

    The nuclear pore complex (NPC) is responsible for nucleocytoplasmic transport and constitutes a hub for control of gene expression. The components of NPCs from several eukaryotic lineages have been determined, but only the yeast and vertebrate NPCs have been extensively characterized at the quaternary level. Significantly, recent evidence indicates that compositional similarity does not necessarily correspond to homologous architecture between NPCs from different taxa. To address this, we describe the interactome of the trypanosome NPC, a representative, highly divergent eukaryote. We identify numerous new NPC components and report an exhaustive interactome, allowing assignment of trypanosome nucleoporins to discrete NPC substructures. Remarkably, despite retaining similar protein composition, there are exceptional architectural dissimilarities between opisthokont (yeast and vertebrates) and excavate (trypanosomes) NPCs. Whilst elements of the inner core are conserved, numerous peripheral structures are highly divergent, perhaps reflecting requirements to interface with divergent nuclear and cytoplasmic functions. Moreover, the trypanosome NPC has almost complete nucleocytoplasmic symmetry, in contrast to the opisthokont NPC; this may reflect divergence in RNA export processes at the NPC cytoplasmic face, as we find evidence supporting Ran-dependent mRNA export in trypanosomes, similar to protein transport. We propose a model of stepwise acquisition of nucleocytoplasmic mechanistic complexity and demonstrate that detailed dissection of macromolecular complexes provides fuller understanding of evolutionary processes. PMID:26891179

  9. Chronically dysregulated NOTCH1 interactome in the dentate gyrus after traumatic brain injury.

    PubMed

    Puhakka, Noora; Bot, Anna Maria; Vuokila, Niina; Debski, Konrad Jozef; Lukasiuk, Katarzyna; Pitkänen, Asla

    2017-01-01

    Traumatic brain injury (TBI) can result in several dentate gyrus-regulated disabilities. Almost nothing is known about the chronic molecular changes after TBI, and their potential as treatment targets. We hypothesized that chronic transcriptional alterations after TBI are under microRNA (miRNA) control. Expression of miRNAs and their targets in the dentate gyrus was analyzed using microarrays at 3 months after experimental TBI. Of 305 miRNAs present on the miRNA-array, 12 were downregulated (p<0.05). In parallel, 75 of their target genes were upregulated (p<0.05). A bioinformatics analysis of miRNA targets highlighted the dysregulation of the transcription factor NOTCH1 and 39 of its target genes (NOTCH1 interactome). Validation assays confirmed downregulation of miR-139-5p, upregulation of Notch1 and its activated protein, and positive enrichment of NOTCH1 target gene expression. These findings demonstrate that miRNA-based transcriptional regulation can be present at chronic time points after TBI, and highlight the NOTCH1 interactome as one of the mechanisms behind the dentate gyrus pathology-related morbidities.

  10. ImmunemiR - a database of prioritized immune miRNA disease associations and its interactome.

    PubMed

    Prabahar, Archana; Natarajan, Jeyakumar

    2017-01-17

    MicroRNAs are the key regulators of gene expression and their abnormal expression in the immune system may be associated with several human diseases such as inflammation, cancer and autoimmune diseases. Elucidation of microRNA (miRNA) disease association through the interactome will deepen the understanding of its disease mechanisms. In this present study, miRNAs specific to immune related diseases were retrieved from curated databases and literature based on MeSH classification of immune system diseases. In total 245 immune miRNAs associated with 92 OMIM disease categories were identified and they are prioritized to specific immune diseases using random walk ranking algorithm. These data were compiled as ImmunemiR, a database of prioritized immune miRNA disease associations. This database provides both text based annotation information and network visualization of its interactome network. To our knowledge, ImmunemiR is the first available database to provide a comprehensive repository of human immune disease associated miRNAs with network visualization options of its target genes, protein-protein interactions (PPI).

  11. A proteome-wide fission yeast interactome reveals network evolution principles from yeasts to human

    PubMed Central

    Vo, Tommy V.; Das, Jishnu; Meyer, Michael J.; Cordero, Nicolas A.; Akturk, Nurten; Wei, Xiaomu; Fair, Benjamin J.; Degatano, Andrew G.; Fragoza, Robert; Liu, Lisa G.; Matsuyama, Akihisa; Trickey, Michelle; Horibata, Sachi; Grimson, Andrew; Yamano, Hiroyuki; Yoshida, Minoru; Roth, Frederick P.; Pleiss, Jeffrey A.; Xia, Yu; Yu, Haiyuan

    2015-01-01

    SUMMARY Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ~50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution. PMID:26771498

  12. The timeline of corona formation around silica nanocarriers highlights the role of the protein interactome.

    PubMed

    Pisani, Cédric; Gaillard, Jean-Charles; Odorico, Michaël; Nyalosaso, Jeff L; Charnay, Clarence; Guari, Yannick; Chopineau, Joël; Devoisselle, Jean-Marie; Armengaud, Jean; Prat, Odette

    2017-02-02

    Magnetic mesoporous silica nanoparticles (M-MSNs) represent promising targeting tools for theranostics. Engineering the interaction of nanoparticles (NPs) with biological systems requires an understanding of protein corona formation around the nanoparticles as this drives the biological fate of nanocarriers. We investigated the behavior of proteins in contact with M-MSNs by high-throughput comparative proteomics, using human and bovine sera as biological fluids, in order to assess the adsorption dynamics of proteins in these media. Using system biology tools, and especially protein-protein interaction databases, we demonstrated how the protein network builds up within the corona over the course of the experiment. Based on these results, we introduce and discuss the role of the "corona interactome" as an important factor influencing protein corona evolution. The concept of the "corona interactome" is an original methodology which could be generalized to all NP candidates. Based on this, pre-coating nanocarriers with specific proteins presenting minimal interactions with opsonins might provide them with properties such as stealth.

  13. Spatial and Spatiotemporal Data Mining: Recent Advances

    SciTech Connect

    Shekhar, Shashi; Vatsavai, Raju; Celik, Mete

    2008-01-01

    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.

  14. Spatiotemporal chaos from bursting dynamics

    SciTech Connect

    Berenstein, Igal; De Decker, Yannick

    2015-08-14

    In this paper, we study the emergence of spatiotemporal chaos from mixed-mode oscillations, by using an extended Oregonator model. We show that bursting dynamics consisting of fast/slow mixed mode oscillations along a single attractor can lead to spatiotemporal chaotic dynamics, although the spatially homogeneous solution is itself non-chaotic. This behavior is observed far from the Hopf bifurcation and takes the form of a spatiotemporal intermittency where the system locally alternates between the fast and the slow phases of the mixed mode oscillations. We expect this form of spatiotemporal chaos to be generic for models in which one or several slow variables are coupled to activator-inhibitor type of oscillators.

  15. Contrast Adaptation Implies Two Spatiotemporal Channels but Three Adapting Processes

    ERIC Educational Resources Information Center

    Langley, Keith; Bex, Peter J.

    2007-01-01

    The contrast gain control model of adaptation predicts that the effects of contrast adaptation correlate with contrast sensitivity. This article reports that the effects of high contrast spatiotemporal adaptors are maximum when adapting around 19 Hz, which is a factor of two or more greater than the peak in contrast sensitivity. To explain the…

  16. Spatiotemporal dynamics of HIV infection

    NASA Astrophysics Data System (ADS)

    Strain, Matthew Carl

    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.

  17. Time-space Kriging to address the spatiotemporal misalignment in the large datasets.

    PubMed

    Liang, Dong; Kumar, Naresh

    2013-06-01

    This paper presents a Bayesian hierarchical spatiotemporal method of interpolation, termed as Markov Cube Kriging (MCK). The classical Kriging methods become computationally prohibitive, especially for large datasets due to the O(n(3)) matrix decomposition. MCK offers novel and computationally efficient solutions to address spatiotemporal misalignment, mismatch in the spatiotemporal scales and missing values across space and time in large spatiotemporal datasets. MCK is flexible in that it allows for non-separable spatiotemporal structure and nonstationary covariance at the hierarchical spatiotemporal scales. Employing MCK we developed estimates of daily concentration of fine particulates matter ≤2.5 μm in aerodynamic diameter (PM2.5) at 2.5 km spatial grid for the Cleveland Metropolitan Statistical Area, 2000 to 2009. Our validation and cross-validation suggest that MCK achieved robust prediction of spatiotemporal random effects and underlying hierarchical and nonstationary spatiotemporal structure in air pollution data. MCK has important implications for environmental epidemiology and environmental sciences for exposure quantification and collocation of data from different sources, available at different spatiotemporal scales.

  18. Time-space Kriging to address the spatiotemporal misalignment in the large datasets

    PubMed Central

    Liang, Dong; Kumar, Naresh

    2013-01-01

    This paper presents a Bayesian hierarchical spatiotemporal method of interpolation, termed as Markov Cube Kriging (MCK). The classical Kriging methods become computationally prohibitive, especially for large datasets due to the O(n3) matrix decomposition. MCK offers novel and computationally efficient solutions to address spatiotemporal misalignment, mismatch in the spatiotemporal scales and missing values across space and time in large spatiotemporal datasets. MCK is flexible in that it allows for non-separable spatiotemporal structure and nonstationary covariance at the hierarchical spatiotemporal scales. Employing MCK we developed estimates of daily concentration of fine particulates matter ≤2.5 μm in aerodynamic diameter (PM2.5) at 2.5 km spatial grid for the Cleveland Metropolitan Statistical Area, 2000 to 2009. Our validation and cross-validation suggest that MCK achieved robust prediction of spatiotemporal random effects and underlying hierarchical and nonstationary spatiotemporal structure in air pollution data. MCK has important implications for environmental epidemiology and environmental sciences for exposure quantification and collocation of data from different sources, available at different spatiotemporal scales. PMID:24039539

  19. Influenza Virus-Host Interactomes as a Basis for Antiviral Drug Development

    PubMed Central

    Watanabe, Tokiko; Kawaoka, Yoshihiro

    2016-01-01

    Currently, antiviral drugs that target specific viral protein functions are available for the treatment of influenza; however, concern regarding the emergence of drug-resistant viruses is warranted, as is the urgent need for new antiviral targets, including non-viral targets, such as host cellular factors. Viruses rely on host cellular functions to replicate, and therefore a thorough understanding of the roles of virus-host interactions during influenza virus replication is essential to develop novel anti-influenza drugs that target the host factors involved in virus replication. Here, we review recent studies that used several approaches to identify host factors involved in influenza virus replication. These studies have permitted the construction of an interactome map of virus-host interactions in the influenza virus life cycle, clarifying the entire life cycle of this virus and accelerating the development of new antiviral drugs with a low propensity for the development of resistance. PMID:26364134

  20. Genotype Correlation Analysis Reveals Pathway-Based Functional Disequilibrium and Potential Epistasis in the Human Interactome

    PubMed Central

    Bush, William S.; Haines, Jonathan L.

    2016-01-01

    Epistasis is thought to be a pervasive part of complex phenotypes due to the dynamics and complexity of biological systems, and a further understanding of epistasis in the context of biological pathways may provide insight into the etiology of complex disease. In this study, we use genotype data from the International HapMap Project to characterize the functional dependencies between alleles in the human interactome as defined by KEGG pathways. We performed chi-square tests to identify non-independence between functionally-related SNP pairs within parental Caucasian and Yoruba samples. We further refine this list by testing for skewed transmission of pseudo-haplotypes to offspring using a haplotype-based TDT test. From these analyses, we identify pathways enriched for functional disequilibrium, and a set of 863 SNP pairs (representing 453 gene pairs) showing consistent non-independence and transmission distortion. These results represent gene pairs with strong evidence of epistasis within the context of a biological function.

  1. Native Piezo2 Interactomics Identifies Pericentrin as a Novel Regulator of Piezo2 in Somatosensory Neurons.

    PubMed

    Narayanan, Pratibha; Sondermann, Julia; Rouwette, Tom; Karaca, Samir; Urlaub, Henning; Mitkovski, Mišo; Gomez-Varela, David; Schmidt, Manuela

    2016-08-05

    The ability of somatosensory neurons to perceive mechanical stimuli relies on specialized mechanotransducing proteins and their molecular environment. Only recently has the identity of a major transducer of mechanical forces in vertebrates been revealed by the discovery of Piezo2. Further work has established its pivotal role for innocuous touch in mice. Therefore, Piezo2 offers a unique platform for the molecular investigation of somatosensory mechanosensation. We performed a mass spectrometry-based interactomics screen on native Piezo2 in somatosensory neurons of mouse dorsal root ganglia (DRG). Stringent and quantitative data analysis yielded the identity of 36 novel binding partners of Piezo2. The biological significance of this data set is reflected by functional experiments demonstrating a role for Pericentrin in modulating Piezo2 activity and membrane expression in somatosensory neurons. Collectively, our findings provide a framework for understanding Piezo2 physiology and serve as a rich resource for the molecular dissection of mouse somatosensation.

  2. Chromosomes. A comprehensive Xist interactome reveals cohesin repulsion and an RNA-directed chromosome conformation.

    PubMed

    Minajigi, Anand; Froberg, John E; Wei, Chunyao; Sunwoo, Hongjae; Kesner, Barry; Colognori, David; Lessing, Derek; Payer, Bernhard; Boukhali, Myriam; Haas, Wilhelm; Lee, Jeannie T

    2015-07-17

    The inactive X chromosome (Xi) serves as a model to understand gene silencing on a global scale. Here, we perform "identification of direct RNA interacting proteins" (iDRiP) to isolate a comprehensive protein interactome for Xist, an RNA required for Xi silencing. We discover multiple classes of interactors-including cohesins, condensins, topoisomerases, RNA helicases, chromatin remodelers, and modifiers-that synergistically repress Xi transcription. Inhibiting two or three interactors destabilizes silencing. Although Xist attracts some interactors, it repels architectural factors. Xist evicts cohesins from the Xi and directs an Xi-specific chromosome conformation. Upon deleting Xist, the Xi acquires the cohesin-binding and chromosomal architecture of the active X. Our study unveils many layers of Xi repression and demonstrates a central role for RNA in the topological organization of mammalian chromosomes.

  3. A comprehensive Xist interactome reveals cohesin repulsion and an RNA-directed chromosome conformation

    PubMed Central

    Wei, Chunyao; Sunwoo, Hongjae; Kesner, Barry; Colognori, David; Lessing, Derek; Payer, Bernhard; Boukhali, Myriam; Haas, Wilhelm; Lee, Jeannie T.

    2016-01-01

    The inactive X chromosome (Xi) serves as a model to understand gene silencing on a global scale. Here, we perform “identification of direct RNA interacting proteins” (iDRiP) to isolate a comprehensive protein interactome for Xist, an RNA required for Xi silencing. We discover multiple classes of interactors, including cohesins, condensins, topoisomerases, RNA helicases, chromatin remodelers and modifiers, which synergistically repress Xi transcription. Inhibiting two or three interactors destabilizes silencing. While Xist attracts some interactors, it repels architectural factors. Xist evicts cohesins from the Xi and directs an Xi-specific chromosome conformation. Upon deleting Xist, the Xi acquires the cohesin-binding and chromosomal architecture of the active X. Our study unveils many layers of Xi repression and demonstrates a central role for RNA in the topological organization of mammalian chromosomes. PMID:26089354

  4. A systems biology approach for the investigation of the heparin/heparan sulfate interactome.

    PubMed

    Ori, Alessandro; Wilkinson, Mark C; Fernig, David G

    2011-06-03

    A large body of evidence supports the involvement of heparan sulfate (HS) proteoglycans in physiological processes such as development and diseases including cancer and neurodegenerative disorders. The role of HS emerges from its ability to interact and regulate the activity of a vast number of extracellular proteins including growth factors and extracellular matrix components. A global view on how protein-HS interactions influence the extracellular proteome and, consequently, cell function is currently lacking. Here, we systematically investigate the functional and structural properties that characterize HS-interacting proteins and the network they form. We collected 435 human proteins interacting with HS or the structurally related heparin by integrating literature-derived and affinity proteomics data. We used this data set to identify the topological features that distinguish the heparin/HS-interacting network from the rest of the extracellular proteome and to analyze the enrichment of gene ontology terms, pathways, and domain families in heparin/HS-binding proteins. Our analysis revealed that heparin/HS-binding proteins form a highly interconnected network, which is functionally linked to physiological and pathological processes that are characteristic of higher organisms. Therefore, we then investigated the existence of a correlation between the expansion of domain families characteristic of the heparin/HS interactome and the increase in biological complexity in the metazoan lineage. A strong positive correlation between the expansion of the heparin/HS interactome and biosynthetic machinery and organism complexity emerged. The evolutionary role of HS was reinforced by the presence of a rudimentary HS biosynthetic machinery in a unicellular organism at the root of the metazoan lineage.

  5. Endogenous Protein Interactome of Human UDP-Glucuronosyltransferases Exposed by Untargeted Proteomics

    PubMed Central

    Rouleau, Michèle; Audet-Delage, Yannick; Desjardins, Sylvie; Rouleau, Mélanie; Girard-Bock, Camille; Guillemette, Chantal

    2017-01-01

    The conjugative metabolism mediated by UDP-glucuronosyltransferase enzymes (UGTs) significantly influences the bioavailability and biological responses of endogenous molecule substrates and xenobiotics including drugs. UGTs participate in the regulation of cellular homeostasis by limiting stress induced by toxic molecules, and by controlling hormonal signaling networks. Glucuronidation is highly regulated at genomic, transcriptional, post-transcriptional and post-translational levels. However, the UGT protein interaction network, which is likely to influence glucuronidation, has received little attention. We investigated the endogenous protein interactome of human UGT1A enzymes in main drug metabolizing non-malignant tissues where UGT expression is most prevalent, using an unbiased proteomics approach. Mass spectrometry analysis of affinity-purified UGT1A enzymes and associated protein complexes in liver, kidney and intestine tissues revealed an intricate interactome linking UGT1A enzymes to multiple metabolic pathways. Several proteins of pharmacological importance such as transferases (including UGT2 enzymes), transporters and dehydrogenases were identified, upholding a potential coordinated cellular response to small lipophilic molecules and drugs. Furthermore, a significant cluster of functionally related enzymes involved in fatty acid β-oxidation, as well as in the glycolysis and glycogenolysis pathways were enriched in UGT1A enzymes complexes. Several partnerships were confirmed by co-immunoprecipitations and co-localization by confocal microscopy. An enhanced accumulation of lipid droplets in a kidney cell model overexpressing the UGT1A9 enzyme supported the presence of a functional interplay. Our work provides unprecedented evidence for a functional interaction between glucuronidation and bioenergetic metabolism. PMID:28217095

  6. Regulating the regulator: Insights into the cardiac protein phosphatase 1 interactome.

    PubMed

    Chiang, David Y; Heck, Albert J R; Dobrev, Dobromir; Wehrens, Xander H T

    2016-12-01

    Reversible phosphorylation of proteins is a delicate yet dynamic balancing act between kinases and phosphatases, the disturbance of which underlies numerous disease processes. While our understanding of protein kinases has grown tremendously over the past decades, relatively little is known regarding protein phosphatases. This may be because protein kinases are great in number and relatively specific in function, and thereby amenable to be studied in isolation, whereas protein phosphatases are much less abundant and more nonspecific in their function. To achieve subcellular localization and substrate specificity, phosphatases depend on partnering with a large number of regulatory subunits, protein scaffolds and/or other interactors. This added layer of complexity presents a significant barrier to their study, but holds the key to unexplored opportunities for novel pharmacologic intervention. In this review we focus on serine/threonine protein phosphatase type-1 (PP1), which plays an important role in cardiac physiology and pathophysiology. Although much work has been done to investigate the role of PP1 in cardiac diseases including atrial fibrillation and heart failure, most of these studies were limited to examining and manipulating the catalytic subunit(s) of PP1 without adequately considering the PP1 interactors, which give specificity to PP1's functions. To complement these studies, three unbiased methods have been developed and applied to the mapping of the PP1 interactome: bioinformatics approaches, yeast two-hybrid screens, and affinity-purification mass spectrometry. The application of these complementary methods has the potential to generate a detailed cardiac PP1 interactome, which is an important step in identifying novel and targeted pharmacological interventions.

  7. A Transporter Interactome Is Essential for the Acquisition of Antimicrobial Resistance to Antibiotics

    PubMed Central

    Shuster, Yonatan; Steiner-Mordoch, Sonia; Alon Cudkowicz, Noemie; Schuldiner, Shimon

    2016-01-01

    Awareness of the problem of antimicrobial resistance (AMR) has escalated and drug-resistant infections are named among the most urgent problems facing clinicians today. Our experiments here identify a transporter interactome and portray its essential function in acquisition of antimicrobial resistance. By exposing E. coli cells to consecutive increasing concentrations of the fluoroquinolone norfloxacin we generated in the laboratory highly resistant strains that carry multiple mutations, most of them identical to those identified in clinical isolates. With this experimental paradigm, we show that the MDTs function in a coordinated mode to provide an essential first-line defense mechanism, preventing the drug reaching lethal concentrations, until a number of stable efficient alterations occur that allow survival. Single-component efflux transporters remove the toxic compounds from the cytoplasm to the periplasmic space where TolC-dependent transporters expel them from the cell. We postulate a close interaction between the two types of transporters to prevent rapid leak of the hydrophobic substrates back into the cell. The findings change the prevalent concept that in Gram-negative bacteria a single multidrug transporter, AcrAB-TolC type, is responsible for the resistance. The concept of a functional interactome, the process of identification of its members, the elucidation of the nature of the interactions and its role in cell physiology will change the existing paradigms in the field. We anticipate that our work will have an impact on the present strategy searching for inhibitors of AcrAB-TolC as adjuvants of existing antibiotics and provide novel targets for this urgent undertaking. PMID:27050393

  8. Spatiotemporal integration of tactile patterns along and across fingers.

    PubMed

    Trojan, Jörg; Heil, Maruschka; Maihöfner, Christian; Hölzl, Rupert; Kleinböhl, Dieter; Flor, Herta; Benrath, Justus

    2014-01-01

    The volar sides of the fingers can be seen as the haptic counterpart to the fovea for visual perception. This study assessed the localisation of individual tactile stimuli and spatiotemporal patterns presented to the volar side of the fingers. Participants performed the localisation task by pointing at the perceived positions with a 3D tracker. Based on the pointing data, perceptual maps were devised in which perceived positions, their relationship to each other and to veridical stimulus positions could be analysed. Participants were able to accurately and consistently report the locations of the stimuli. Localisation of stimuli presented within a spatiotemporal pattern generally differed from localization of individual stimuli presented to the same positions. In most cases, stimuli were perceived as being spatially closer when they were presented within a spatiotemporal pattern compared to when being presented individually. Spatiotemporal integration along the fingers followed the predictions of the sensory saltation paradigm: The shorter the temporal delay between the two stimuli, the closer together they were perceived. For spatiotemporal patterns across fingers, the results were inconclusive: No general relationship between temporal delay and the difference between the perceived positions could be demonstrated, presumably because the effect could only be elicited in some finger combinations. Temporal delay did have, however, an effect on overall lateral shifts in localisation.

  9. The L1TD1 protein interactome reveals the importance of post-transcriptional regulation in human pluripotency.

    PubMed

    Emani, Maheswara Reddy; Närvä, Elisa; Stubb, Aki; Chakroborty, Deepankar; Viitala, Miro; Rokka, Anne; Rahkonen, Nelly; Moulder, Robert; Denessiouk, Konstantin; Trokovic, Ras; Lund, Riikka; Elo, Laura L; Lahesmaa, Riitta

    2015-03-10

    The RNA-binding protein L1TD1 is one of the most specific and abundant proteins in pluripotent stem cells and is essential for the maintenance of pluripotency in human cells. Here, we identify the protein interaction network of L1TD1 in human embryonic stem cells (hESCs) and provide insights into the interactome network constructed in human pluripotent cells. Our data reveal that L1TD1 has an important role in RNA splicing, translation, protein traffic, and degradation. L1TD1 interacts with multiple stem-cell-specific proteins, many of which are still uncharacterized in the context of development. Further, we show that L1TD1 is a part of the pluripotency interactome network of OCT4, SOX2, and NANOG, bridging nuclear and cytoplasmic regulation and highlighting the importance of RNA biology in pluripotency.

  10. Systematic Determination of Human Cyclin Dependent Kinase (CDK)-9 Interactome Identifies Novel Functions in RNA Splicing Mediated by the DEAD Box (DDX)-5/17 RNA Helicases.

    PubMed

    Yang, Jun; Zhao, Yingxin; Kalita, Mridul; Li, Xueling; Jamaluddin, Mohammad; Tian, Bing; Edeh, Chukwudi B; Wiktorowicz, John E; Kudlicki, Andrzej; Brasier, Allan R

    2015-10-01

    Inducible transcriptional elongation is a rapid, stereotypic mechanism for activating immediate early immune defense genes by the epithelium in response to viral pathogens. Here, the recruitment of a multifunctional complex containing the cyclin dependent kinase 9 (CDK9) triggers the process of transcriptional elongation activating resting RNA polymerase engaged with innate immune response (IIR) genes. To identify additional functional activity of the CDK9 complex, we conducted immunoprecipitation (IP) enrichment-stable isotope labeling LC-MS/MS of the CDK9 complex in unstimulated cells and from cells activated by a synthetic dsRNA, polyinosinic/polycytidylic acid [poly (I:C)]. 245 CDK9 interacting proteins were identified with high confidence in the basal state and 20 proteins in four functional classes were validated by IP-SRM-MS. These data identified that CDK9 interacts with DDX 5/17, a family of ATP-dependent RNA helicases, important in alternative RNA splicing of NFAT5, and mH2A1 mRNA two proteins controlling redox signaling. A direct comparison of the basal versus activated state was performed using stable isotope labeling and validated by IP-SRM-MS. Recruited into the CDK9 interactome in response to poly(I:C) stimulation are HSPB1, DNA dependent kinases, and cytoskeletal myosin proteins that exchange with 60S ribosomal structural proteins. An integrated human CDK9 interactome map was developed containing all known human CDK9- interacting proteins. These data were used to develop a probabilistic global map of CDK9-dependent target genes that predicted two functional states controlling distinct cellular functions, one important in immune and stress responses. The CDK9-DDX5/17 complex was shown to be functionally important by shRNA-mediated knockdown, where differential accumulation of alternatively spliced NFAT5 and mH2A1 transcripts and alterations in downstream redox signaling were seen. The requirement of CDK9 for DDX5 recruitment to NFAT5 and mH2A1

  11. Sequential Elution Interactome Analysis of the Mind Bomb 1 Ubiquitin Ligase Reveals a Novel Role in Dendritic Spine Outgrowth*

    PubMed Central

    Mertz, Joseph; Tan, Haiyan; Pagala, Vishwajeeth; Bai, Bing; Chen, Ping-Chung; Li, Yuxin; Cho, Ji-Hoon; Shaw, Timothy; Wang, Xusheng; Peng, Junmin

    2015-01-01

    The mind bomb 1 (Mib1) ubiquitin ligase is essential for controlling metazoan development by Notch signaling and possibly the Wnt pathway. It is also expressed in postmitotic neurons and regulates neuronal morphogenesis and synaptic activity by mechanisms that are largely unknown. We sought to comprehensively characterize the Mib1 interactome and study its potential function in neuron development utilizing a novel sequential elution strategy for affinity purification, in which Mib1 binding proteins were eluted under different stringency and then quantified by the isobaric labeling method. The strategy identified the Mib1 interactome with both deep coverage and the ability to distinguish high-affinity partners from low-affinity partners. A total of 817 proteins were identified during the Mib1 affinity purification, including 56 high-affinity partners and 335 low-affinity partners, whereas the remaining 426 proteins are likely copurified contaminants or extremely weak binding proteins. The analysis detected all previously known Mib1-interacting proteins and revealed a large number of novel components involved in Notch and Wnt pathways, endocytosis and vesicle transport, the ubiquitin-proteasome system, cellular morphogenesis, and synaptic activities. Immunofluorescence studies further showed colocalization of Mib1 with five selected proteins: the Usp9x (FAM) deubiquitinating enzyme, alpha-, beta-, and delta-catenins, and CDKL5. Mutations of CDKL5 are associated with early infantile epileptic encephalopathy-2 (EIEE2), a severe form of mental retardation. We found that the expression of Mib1 down-regulated the protein level of CDKL5 by ubiquitination, and antagonized CDKL5 function during the formation of dendritic spines. Thus, the sequential elution strategy enables biochemical characterization of protein interactomes; and Mib1 analysis provides a comprehensive interactome for investigating its role in signaling networks and neuronal development. PMID:25931508

  12. Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

    NASA Astrophysics Data System (ADS)

    Perez-Lopez, Áron R.; Szalay, Kristóf Z.; Türei, Dénes; Módos, Dezső; Lenti, Katalin; Korcsmáros, Tamás; Csermely, Peter

    2015-05-01

    Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

  13. Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

    PubMed Central

    Perez-Lopez, Áron R.; Szalay, Kristóf Z.; Türei, Dénes; Módos, Dezső; Lenti, Katalin; Korcsmáros, Tamás; Csermely, Peter

    2015-01-01

    Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates. PMID:25960144

  14. Vascular Endothelial Growth Factor (VEGF) Promotes Assembly of the p130Cas Interactome to Drive Endothelial Chemotactic Signaling and Angiogenesis*

    PubMed Central

    Evans, Ian M.; Kennedy, Susan A.; Paliashvili, Ketevan; Santra, Tapesh; Yamaji, Maiko; Lovering, Ruth C.; Britton, Gary; Frankel, Paul; Kolch, Walter; Zachary, Ian C.

    2017-01-01

    p130Cas is a polyvalent adapter protein essential for cardiovascular development, and with a key role in cell movement. In order to identify the pathways by which p130Cas exerts its biological functions in endothelial cells we mapped the p130Cas interactome and its dynamic changes in response to VEGF using high-resolution mass spectrometry and reconstruction of protein interaction (PPI) networks with the aid of multiple PPI databases. VEGF enriched the p130Cas interactome in proteins involved in actin cytoskeletal dynamics and cell movement, including actin-binding proteins, small GTPases and regulators or binders of GTPases. Detailed studies showed that p130Cas association of the GTPase-binding scaffold protein, IQGAP1, plays a key role in VEGF chemotactic signaling, endothelial polarization, VEGF-induced cell migration, and endothelial tube formation. These findings indicate a cardinal role for assembly of the p130Cas interactome in mediating the cell migratory response to VEGF in angiogenesis, and provide a basis for further studies of p130Cas in cell movement. PMID:28007913

  15. Identification of novel prognostic indicators for triple-negative breast cancer patients through integrative analysis of cancer genomics data and protein interactome data

    PubMed Central

    Zhao, Hengqiang; Yang, Lei; Su, Fei; Zhou, Ming-Ming; Han, Junwei; Sobie, Eric A.; Walsh, Martin J.

    2016-01-01

    Triple negative breast cancers (TNBCs) are highly heterogeneous and aggressive without targeted treatment. Here, we aim to systematically dissect TNBCs from a prognosis point of view by building a subnetwork atlas for TNBC prognosis through integrating multi-dimensional cancer genomics data from The Cancer Genome Atlas (TCGA) project and the interactome data from three different interaction networks. The subnetworks are represented as the protein-protein interaction modules perturbed by multiple genetic and epigenetic interacting mechanisms contributing to patient survival. Predictive power of these subnetwork-derived prognostic models is evaluated using Monte Carlo cross-validation and the concordance index (C-index). We uncover subnetwork biomarkers of low oncogenic GTPase activity, low ubiquitin/proteasome degradation, effective protection from oxidative damage, and tightly immune response are linked to better prognosis. Such a systematic approach to integrate massive amount of cancer genomics data into clinical practice for TNBC prognosis can effectively dissect the molecular mechanisms underlying TNBC patient outcomes and provide potential opportunities to optimize treatment and develop therapeutics. PMID:27690302

  16. Video quality assessment based on correlation between spatiotemporal motion energies

    NASA Astrophysics Data System (ADS)

    Yan, Peng; Mou, Xuanqin

    2016-09-01

    Video quality assessment (VQA) has been a hot research topic because of rapid increase of huge demand of video communications. From the earliest PSNR metric to advanced models that are perceptual aware, researchers have made great progress in this field by introducing properties of human vision system (HVS) into VQA model design. Among various algorithms that model the property of HVS perceiving motion, the spatiotemporal energy model has been validated to be high consistent with psychophysical experiments. In this paper, we take the spatiotemporal energy model into VQA model design by the following steps. 1) According to the pristine spatiotemporal energy model proposed by Adelson et al, we apply the linear filters, which are oriented in space-time and tuned in spatial frequency, to filter the reference and test videos respectively. The outputs of quadrature pairs of above filters are then squared and summed to give two measures of motion energy, which are named rightward and leftward energy responses, respectively. 2) Based on the pristine model, we calculate summation of the rightward and leftward energy responses as spatiotemporal features to represent perceptual quality information for videos, named total spatiotemporal motion energy maps. 3) The proposed FR-VQA model, named STME, is calculated with statistics based on the pixel-wise correlation between the total spatiotemporal motion energy maps of the reference and distorted videos. The STME model was validated on the LIVE VQA Database by comparing with existing FR-VQA models. Experimental results show that STME performs with excellent prediction accuracy and stays in state-of-the-art VQA models.

  17. Auditory Perception of Spatiotemporal Patterns

    ERIC Educational Resources Information Center

    Tolkmitt, Frank J.; Brindley, Robin

    1977-01-01

    To test the tendency of subjects to perceptually organize discrete temporal patterns with regard to runs of identical stimulus events, spatiotemporal patterns of white noise were presented for reproduction. It is suggested that changes in runs of auditory patterns are perceptually analogous to changes in contours of visual patterns. (Editor/RK)

  18. Spatiotemporal Wave Patterns: Information Dynamics

    SciTech Connect

    Mikhail Rabinovich; Lev Tsimring

    2006-01-20

    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

  19. ppiTrim: constructing non-redundant and up-to-date interactomes.

    PubMed

    Stojmirović, Aleksandar; Yu, Yi-Kuo

    2011-01-01

    Robust advances in interactome analysis demand comprehensive, non-redundant and consistently annotated data sets. By non-redundant, we mean that the accounting of evidence for every interaction should be faithful: each independent experimental support is counted exactly once, no more, no less. While many interactions are shared among public repositories, none of them contains the complete known interactome for any model organism. In addition, the annotations of the same experimental result by different repositories often disagree. This brings up the issue of which annotation to keep while consolidating evidences that are the same. The iRefIndex database, including interactions from most popular repositories with a standardized protein nomenclature, represents a significant advance in all aspects, especially in comprehensiveness. However, iRefIndex aims to maintain all information/annotation from original sources and requires users to perform additional processing to fully achieve the aforementioned goals. Another issue has to do with protein complexes. Some databases represent experimentally observed complexes as interactions with more than two participants, while others expand them into binary interactions using spoke or matrix model. To avoid untested interaction information buildup, it is preferable to replace the expanded protein complexes, either from spoke or matrix models, with a flat list of complex members. To address these issues and to achieve our goals, we have developed ppiTrim, a script that processes iRefIndex to produce non-redundant, consistently annotated data sets of physical interactions. Our script proceeds in three stages: mapping all interactants to gene identifiers and removing all undesired raw interactions, deflating potentially expanded complexes, and reconciling for each interaction the annotation labels among different source databases. As an illustration, we have processed the three largest organismal data sets: yeast, human and

  20. Intranuclear interactomic inhibition of NF-κB suppresses LPS-induced severe sepsis

    SciTech Connect

    Park, Sung-Dong; Cheon, So Yeong; Park, Tae-Yoon; Shin, Bo-Young; Oh, Hyunju; Ghosh, Sankar; Koo, Bon-Nyeo; Lee, Sang-Kyou

    2015-08-28

    Suppression of nuclear factor-κB (NF-κB) activation, which is best known as a major regulator of innate and adaptive immune responses, is a potent strategy for the treatment of endotoxic sepsis. To inhibit NF-κB functions, we designed the intra-nuclear transducible form of transcription modulation domain (TMD) of RelA (p65), called nt-p65-TMD, which can be delivered effectively into the nucleus without influencing the cell viability, and work as interactomic inhibitors via disruption of the endogenous p65-mediated transcription complex. nt-p65-TMD effectively inhibited the secretion of pro-inflammatory cytokines, including TNF-α, IL-1β, or IL-6 from BV2 microglia cells stimulated by lipopolysaccharide (LPS). nt-p65-TMD did not inhibit tyrosine phosphorylation of signaling mediators such as ZAP-70, p38, JNK, or ERK involved in T cell activation, but was capable of suppressing the transcriptional activity of NF-κB without the functional effect on that of NFAT upon T-cell receptor (TCR) stimulation. The transduced nt-p65-TMD in T cell did not affect the expression of CD69, however significantly inhibited the secretion of T cell-specific cytokines such as IL-2, IFN-γ, IL-4, IL-17A, or IL-10. Systemic administration of nt-p65-TMD showed a significant therapeutic effect on LPS-induced sepsis model by inhibiting pro-inflammatory cytokines secretion. Therefore, nt-p65-TMD can be a novel therapeutics for the treatment of various inflammatory diseases, including sepsis, where a transcription factor has a key role in pathogenesis, and further allows us to discover new functions of p65 under normal physiological condition without genetic alteration. - Highlights: • The nt-p65-TMD is intra-nuclear interactomic inhibitor of endogenous p65. • The nt-p65-TMD effectively inhibited the secretion of pro-inflammatory cytokines. • The excellent therapeutic potential of nt-p65-TMD was confirmed in sepsis model.

  1. Optimum spatiotemporal receptive fields for vision in dim light.

    PubMed

    Klaus, Andreas; Warrant, Eric J

    2009-04-22

    Many nocturnal insects depend on vision for daily life and have evolved different strategies to improve their visual capabilities in dim light. Neural summation of visual signals is one strategy to improve visual performance, and this is likely to be especially important for insects with apposition compound eyes. Here we develop a model to determine the optimum spatiotemporal sampling of natural scenes at gradually decreasing light levels. Image anisotropy has a strong influence on the receptive field properties predicted to be optimal at low light intensities. Spatial summation between visual channels is predicted to extend more strongly in the direction with higher correlations between the input signals. Increased spatiotemporal summation increases signal-to-noise ratio at low frequencies but sacrifices signal-to-noise ratio at higher frequencies. These results, while obtained from a model of the insect visual system, are likely to apply to visual systems in general.

  2. Influenza virus-host interactome screen as a platform for antiviral drug development

    PubMed Central

    Watanabe, Tokiko; Kawakami, Eiryo; Shoemaker, Jason E.; Lopes, Tiago J. S.; Matsuoka, Yukiko; Tomita, Yuriko; Kozuka-Hata, Hiroko; Gorai, Takeo; Kuwahara, Tomoko; Takeda, Eiji; Nagata, Atsushi; Takano, Ryo; Kiso, Maki; Yamashita, Makoto; Sakai-Tagawa, Yuko; Katsura, Hiroaki; Nonaka, Naoki; Fujii, Hiroko; Fujii, Ken; Sugita, Yukihiko; Noda, Takeshi; Goto, Hideo; Fukuyama, Satoshi; Watanabe, Shinji; Neumann, Gabriele; Oyama, Masaaki; Kitano, Hiroaki; Kawaoka, Yoshihiro

    2015-01-01

    SUMMARY Host factors required for viral replication are ideal drug targets because they are less likely than viral proteins to mutate under drug-mediated selective pressure. Although genome-wide screens have identified host proteins involved in influenza virus replication, limited mechanistic understanding of how these factors affect influenza has hindered potential drug development. We conducted a systematic analysis to identify and validate host factors that associate with influenza virus proteins and affect viral replication. After identifying over one thousand host factors that co-immunoprecipitate with specific viral proteins, we generated a network of virus-host protein interactions based on the stage of the viral lifecycle affected upon host factor down-regulation. Using compounds that inhibit these host factors, we validated several proteins, notably Golgi-specific brefeldin A resistant guanine nucleotide exchange factor (GBF1) and JAK1, as potential antiviral drug targets. Thus, virus-host interactome screens are powerful strategies to identify targetable host factors and guide antiviral drug development. PMID:25464832

  3. The extracellular interactome of the human adenovirus family reveals diverse strategies for immunomodulation

    PubMed Central

    Martinez-Martin, Nadia; Ramani, Sree R.; Hackney, Jason A.; Tom, Irene; Wranik, Bernd J.; Chan, Michelle; Wu, Johnny; Paluch, Maciej T.; Takeda, Kentaro; Hass, Philip E.; Clark, Hilary; Gonzalez, Lino C.

    2016-01-01

    Viruses encode secreted and cell-surface expressed proteins essential to modulate host immune defenses and establish productive infections. However, to date there has been no systematic study of the extracellular interactome of any human virus. Here we utilize the E3 proteins, diverse and rapidly evolving transmembrane-containing proteins encoded by human adenoviruses, as a model system to survey the extracellular immunomodulatory landscape. From a large-scale protein interaction screen against a microarray of more than 1,500 human proteins, we find and validate 51 previously unidentified virus–host interactions. Our results uncover conserved strategies as well as substantial diversity and multifunctionality in host targeting within and between viral species. Prominent modulation of the leukocyte immunoglobulin-like and signalling lymphocyte activation molecule families and a number of inhibitory receptors were identified as hubs for viral perturbation, suggesting unrecognized immunoregulatory strategies. We describe a virus–host extracellular interaction map of unprecedented scale that provides new insights into viral immunomodulation. PMID:27145901

  4. Quantitative study of the interactome of PKCζ involved in the EGF-induced tumor cell chemotaxis.

    PubMed

    Chen, Ruibing; Wang, Yanping; Liu, Yan; Zhang, Qing; Zhang, Xiaofang; Zhang, Fei; Shieh, Chia-Hui Paul; Yang, De; Zhang, Ning

    2013-03-01

    Chemotaxis plays an important role in metastasis. In our previous studies, we reported that protein kinase C ζ (PKCζ) mediated cancer cell chemotaxis by regulating cytoskeleton rearrangement and cell adhesion. To further study the molecular mechanism of chemotaxis, mass spectrometry-based approaches were employed to investigate the interactome of PKCζ and its changes upon stimulation by epidermal growth factor (EGF). As a result, 233 proteins were identified as potential PKCζ binding partners. Label free quantification was applied to examine the quantitative changes of these interactions involved in the EGF induced chemotaxis. Fifteen identified proteins were enriched and 9 proteins were reduced in the presence of EGF (≥ 1.5 folds, p ≤ 0.05). The interaction between cofilin-1 (CFL1) and PKCζ was evidenced and this interaction was enhanced in the EGF induced chemotaxis signaling transduction. In addition, novel PKCζ interacting proteins potentially related with chemotaxis were characterized, such as isoform 1 of nucleophosmin (NPM1). Furthermore, Western blotting and chemotaxis assays were also applied to validate the proteomics result and explore its biological implications. Collectively, the combination of quantitative proteomics and biological assays provides a powerful strategy for elucidating the signaling pathway of tumor cell chemotaxis.

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

    PubMed

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

    2013-04-01

    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.

  6. Inhibitory activities of short linear motifs underlie Hox interactome specificity in vivo

    PubMed Central

    Baëza, Manon; Viala, Séverine; Heim, Marjorie; Dard, Amélie; Hudry, Bruno; Duffraisse, Marilyne; Rogulja-Ortmann, Ana; Brun, Christine; Merabet, Samir

    2015-01-01

    Hox proteins are well-established developmental regulators that coordinate cell fate and morphogenesis throughout embryogenesis. In contrast, our knowledge of their specific molecular modes of action is limited to the interaction with few cofactors. Here, we show that Hox proteins are able to interact with a wide range of transcription factors in the live Drosophila embryo. In this context, specificity relies on a versatile usage of conserved short linear motifs (SLiMs), which, surprisingly, often restrains the interaction potential of Hox proteins. This novel buffering activity of SLiMs was observed in different tissues and found in Hox proteins from cnidarian to mouse species. Although these interactions remain to be analysed in the context of endogenous Hox regulatory activities, our observations challenge the traditional role assigned to SLiMs and provide an alternative concept to explain how Hox interactome specificity could be achieved during the embryonic development. DOI: http://dx.doi.org/10.7554/eLife.06034.001 PMID:25869471

  7. Elucidation of the Cellular Interactome of Ebola Virus Nucleoprotein and Identification of Therapeutic Targets.

    PubMed

    García-Dorival, Isabel; Wu, Weining; Armstrong, Stuart D; Barr, John N; Carroll, Miles W; Hewson, Roger; Hiscox, Julian A

    2016-12-02

    Ebola virus (EBOV) infection results in severe disease and in some cases lethal hemorrhagic fever. The infection is directed by seven viral genes that encode nine viral proteins. By definition, viruses are obligate intracellular parasites and require aspects of host cell biology in order to replicate their genetic material, assemble new virus particles, and subvert host cell antiviral responses. Currently licensed antivirals are targeted against viral proteins to inhibit their function. However, experience with treating HIV and influenza virus demonstrates that resistant viruses are soon selected. An emerging area in virology is to transiently target host cell proteins that play critical proviral roles in virus biology, especially for acute infections. This has the advantage that the protein being targeted is evolutionary removed from the genome of the virus. Proteomics can aid in discovery biology and identify cellular proteins that may be utilized by the virus to facilitate infection. This work focused on defining the interactome of the EBOV nucleoprotein and identified that cellular chaperones, including HSP70, associate with this protein to promote stability. Utilization of a mini-genome replication system based on a recent Makona isolate demonstrated that disrupting the stability of NP had an adverse effect on viral RNA synthesis.

  8. Auxotrophy and intrapopulation complementary in the 'interactome' of a cultivated freshwater model community.

    PubMed

    Garcia, Sarahi L; Buck, Moritz; McMahon, Katherine D; Grossart, Hans-Peter; Eiler, Alexander; Warnecke, Falk

    2015-09-01

    Microorganisms are usually studied either in highly complex natural communities or in isolation as monoclonal model populations that we manage to grow in the laboratory. Here, we uncover the biology of some of the most common and yet-uncultured bacteria in freshwater environments using a mixed culture from Lake Grosse Fuchskuhle. From a single shotgun metagenome of a freshwater mixed culture of low complexity, we recovered four high-quality metagenome-assembled genomes (MAGs) for metabolic reconstruction. This analysis revealed the metabolic interconnectedness and niche partitioning of these naturally dominant bacteria. In particular, vitamin- and amino acid biosynthetic pathways were distributed unequally with a member of Crenarchaeota most likely being the sole producer of vitamin B12 in the mixed culture. Using coverage-based partitioning of the genes recovered from a single MAG intrapopulation metabolic complementarity was revealed pointing to 'social' interactions for the common good of populations dominating freshwater plankton. As such, our MAGs highlight the power of mixed cultures to extract naturally occurring 'interactomes' and to overcome our inability to isolate and grow the microbes dominating in nature.

  9. Mapping the Interactome of a Major Mammalian Endoplasmic Reticulum Heat Shock Protein 90

    PubMed Central

    Hong, Feng; Mohammad Rachidi, Saleh; Lundgren, Debbie; Han, David; Huang, Xiu; Zhao, Hongyu; Kimura, Yayoi; Hirano, Hisashi; Ohara, Osamu; Udono, Heichiiro; Meng, Songdong; Liu, Bei; Li, Zihai

    2017-01-01

    Up to 10% of cytosolic proteins are dependent on the mammalian heat shock protein 90 (HSP90) for folding. However, the interactors of its endoplasmic reticulum (ER) paralogue (gp96, Grp94 and HSP90b1) has not been systematically identified. By combining genetic and biochemical approaches, we have comprehensively mapped the interactome of gp96 in macrophages and B cells. A total of 511 proteins were reduced in gp96 knockdown cells, compared to levels observed in wild type cells. By immunoprecipitation, we found that 201 proteins associated with gp96. Gene Ontology analysis indicated that these proteins are involved in metabolism, transport, translation, protein folding, development, localization, response to stress and cellular component biogenesis. While known gp96 clients such as integrins, Toll-like receptors (TLRs) and Wnt co-receptor LRP6, were confirmed, cell surface HSP receptor CD91, TLR4 pathway protein CD180, WDR1, GANAB and CAPZB were identified as potentially novel substrates of gp96. Taken together, our study establishes gp96 as a critical chaperone to integrate innate immunity, Wnt signaling and organ development. PMID:28056051

  10. An extracellular interactome of immunoglobulin and LRR proteins reveals receptor-ligand networks.

    PubMed

    Özkan, Engin; Carrillo, Robert A; Eastman, Catharine L; Weiszmann, Richard; Waghray, Deepa; Johnson, Karl G; Zinn, Kai; Celniker, Susan E; Garcia, K Christopher

    2013-07-03

    Extracellular domains of cell surface receptors and ligands mediate cell-cell communication, adhesion, and initiation of signaling events, but most existing protein-protein "interactome" data sets lack information for extracellular interactions. We probed interactions between receptor extracellular domains, focusing on a set of 202 proteins composed of the Drosophila melanogaster immunoglobulin superfamily (IgSF), fibronectin type III (FnIII), and leucine-rich repeat (LRR) families, which are known to be important in neuronal and developmental functions. Out of 20,503 candidate protein pairs tested, we observed 106 interactions, 83 of which were previously unknown. We "deorphanized" the 20 member subfamily of defective-in-proboscis-response IgSF proteins, showing that they selectively interact with an 11 member subfamily of previously uncharacterized IgSF proteins. Both subfamilies interact with a single common "orphan" LRR protein. We also observed interactions between Hedgehog and EGFR pathway components. Several of these interactions could be visualized in live-dissected embryos, demonstrating that this approach can identify physiologically relevant receptor-ligand pairs.

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

    PubMed Central

    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.; Martínez-Climent, Jose A.; Sánchez-García, Isidro; Melnick, Ari

    2012-01-01

    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. PMID:22517897

  12. The molecular basis for ANE syndrome revealed by the large ribosomal subunit processome interactome

    PubMed Central

    McCann, Kathleen L; Teramoto, Takamasa; Zhang, Jun; Tanaka Hall, Traci M; Baserga, Susan J

    2016-01-01

    ANE syndrome is a ribosomopathy caused by a mutation in an RNA recognition motif of RBM28, a nucleolar protein conserved to yeast (Nop4). While patients with ANE syndrome have fewer mature ribosomes, it is unclear how this mutation disrupts ribosome assembly. Here we use yeast as a model system and show that the mutation confers growth and pre-rRNA processing defects. Recently, we found that Nop4 is a hub protein in the nucleolar large subunit (LSU) processome interactome. Here we demonstrate that the ANE syndrome mutation disrupts Nop4’s hub function by abrogating several of Nop4’s protein-protein interactions. Circular dichroism and NMR demonstrate that the ANE syndrome mutation in RRM3 of human RBM28 disrupts domain folding. We conclude that the ANE syndrome mutation generates defective protein folding which abrogates protein-protein interactions and causes faulty pre-LSU rRNA processing, thus revealing one aspect of the molecular basis of this human disease. DOI: http://dx.doi.org/10.7554/eLife.16381.001 PMID:27077951

  13. Characterization of the B-Raf interactome in mouse hippocampal neuronal cells.

    PubMed

    Bonfiglio, Juan J; Maccarrone, Giuseppina; Rewerts, Christiane; Holsboer, Florian; Arzt, Eduardo; Turck, Christoph W; Silberstein, Susana

    2011-02-01

    B-Raf links a variety of extracellular stimuli downstream of cell surface receptors, constituting a determining factor in the ability of neurons to activate ERK. A detailed study of the B-Raf interactome is necessary to clarify the intricacy of B-Raf-dependent signal transduction. We used a mouse hippocampal cell line (HT22) that expresses B-Raf at high levels, to identify B-Raf associated proteins under endogenous expression conditions, avoiding artificial interactions from overexpression studies. We used stringent procedures to co-immunoprecipitate proteins that specifically associate with endogenous B-Raf with the help of gel electrophoresis separation and off-line LC-MALDI-MS/MS proteomic analysis. Our stringent protein identification criteria allowed confident identification of B-Raf interacting proteins under non-stimulating conditions. The presence of previously reported B-Raf interactors among the list of proteins identified confirms the quality of proteomic data. We identified tubulin and actin as B-Raf interactors for the first time, among structural and accessory proteins of cell cytoskeleton, molecular chaperones (Hsc70, GRP78), and cellular components involved in aspects of mRNA metabolism and translation. Interactions were validated in HT22 cells and in the neuronal cell line Neuro-2a providing further evidence that the identified proteins are B-Raf interactors, which constitute a basis for understanding MAPK pathway regulation in neurons.

  14. Mapping wild-type and R345W fibulin-3 intracellular interactomes.

    PubMed

    Hulleman, John D; Genereux, Joseph C; Nguyen, Annie

    2016-12-01

    Fibulin-3 (F3) is an important, disulfide-rich, extracellular matrix glycoprotein that has been associated with a number of diseases ranging from cancer to retinal degeneration. An Arg345Trp (R345W) mutation in F3 causes the rare, autosomal dominant macular dystrophy, Malattia Leventinese. The purpose of this study was to identify and validate novel intracellular interacting partners of wild-type (WT) and R345W F3 in retinal pigment epithelium cells. We used stable isotope labeling by amino acids in cell culture (SILAC) to generate 'heavy' and 'light' isotopically labeled ARPE-19 cell populations which were subsequently infected with adenovirus encoding for FLAG-tagged WT or R345W F3. After immunoprecipitation, interacting proteins were identified by multidimensional protein identification technology (MudPIT). We identified sixteen new intracellular F3 interacting partners, the vast majority of which are involved in protein folding and/or degradation in the endoplasmic reticulum (ER). Eight of these interactions (ANXA5, ERdj5, PDIA4, P4HB, PDIA6, RCN1, SDF2L1, and TXNDC5) were verified at the western blotting level. These F3 interactome results can serve as the basis for pursuing targeted genetic or pharmacologic approaches in an effort to alter the fate of either WT or mutant F3.

  15. Spatiotemporal Interpolation for Environmental Modelling

    PubMed Central

    Susanto, Ferry; de Souza, Paulo; He, Jing

    2016-01-01

    A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method. In this study, we utilised one year of Tasmania’s South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW) method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications. PMID:27509497

  16. Identification and functional analysis of the BIM interactome; new clues on its possible involvement in Epstein-Barr Virus-associated diseases.

    PubMed

    Rouka, Erasmia; Kyriakou, Despoina

    2015-12-01

    Epigenetic deregulation is a common feature in the pathogenesis of Epstein-Barr Virus (EBV)-related lymphomas and carcinomas. Previous studies have demonstrated a strong association between EBV latency in B-cells and epigenetic silencing of the tumor suppressor gene BIM. This study aimed to the construction and functional analysis of the BIM interactome in order to identify novel host genes that may be targeted by EBV. Fifty-nine unique interactors were found to compose the BIM gene network. Ontological analysis at the pathway level highlighted infectious diseases along with neuropathologies. These results underline the possible interplay between the BIM interactome and EBV-associated disorders.

  17. Interactomes to Biological Phase Space: a call to begin thinking at a new level in computational biology.

    SciTech Connect

    Davidson, George S.; Brown, William Michael

    2007-09-01

    Techniques for high throughput determinations of interactomes, together with high resolution protein collocalizations maps within organelles and through membranes will soon create a vast resource. With these data, biological descriptions, akin to the high dimensional phase spaces familiar to physicists, will become possible. These descriptions will capture sufficient information to make possible realistic, system-level models of cells. The descriptions and the computational models they enable will require powerful computing techniques. This report is offered as a call to the computational biology community to begin thinking at this scale and as a challenge to develop the required algorithms and codes to make use of the new data.3

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

    NASA Astrophysics Data System (ADS)

    Manga, Edna; Awang, Norhashidah

    2016-06-01

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

  19. Defining the Na(+)/H(+) exchanger NHE1 interactome in triple-negative breast cancer cells.

    PubMed

    Amith, Schammim Ray; Vincent, Krista Marie; Wilkinson, Jodi Marie; Postovit, Lynne Marie; Fliegel, Larry

    2017-01-01

    Mounting evidence supports a major role for the Na(+)/H(+) exchanger NHE1 in cancer progression and metastasis. NHE1 is hyperactive at the onset of oncogenic transformation, resulting in intracellular alkalinization and extracellular microenvironmental acidification. These conditions promote invasion and facilitate metastasis. However, the signal pathways governing the regulation of exchanger activity are still unclear. This is especially important in the aggressively metastatic, triple-negative basal breast cancer subtype. We used affinity chromatography followed by mass spectrometry to identify novel and putative interaction partners of NHE1 in MDA-MB-231 triple-negative breast cancer cells. NHE1 associated with several types of proteins including cytoskeletal proteins and chaperones. We validated protein interactions by co-immunoprecipitation for: 14-3-3, AKT, α-enolase, CHP1, HSP70 and HSP90. Additionally, we used The Cancer Genome Atlas (TCGA) to study NHE1 gene expression in primary patient breast tumours versus adjacent normal tissue. NHE1 expression was elevated in breast tumour samples and, when broken down by breast cancer subtype, NHE1 gene expression was significantly lower in tumours of the basal subtype compared to luminal and HER2+ subtypes. Reverse phase protein array (RPPA) analysis showed that NHE1 expression positively correlated with p90(RSK) expression in basal, but not luminal, primary tumours. Other proteins were negatively correlated with NHE1 expression in basal breast cancer tumours. Taken together, our data provides the first insight into the signalling molecules that form the NHE1 interactome in triple-negative breast cancer cells. These results will focus our search for novel targeted therapies.

  20. Heterogeneous interactome between Litopenaeus vannamei plasma proteins and Vibrio parahaemolyticus outer membrane proteins.

    PubMed

    Liu, Xiang; She, Xin-Tao; Zhu, Qing-Feng; Li, Hui; Peng, Xuan-Xian

    2013-01-01

    A great loss has been suffered by microbial infectious diseases under intensive shrimp farming in recent years. In this background, the understanding of shrimp innate immunity becomes an importantly scientific issue, but little is known about the heterogeneous protein-protein interaction between pathogenic cells and hosts, which is a key step for the invading microbes to infect internet organs through bloodstream. In the present study, bacterial outer membrane (OM) protein array and pull-down approaches are used to isolate both Vibrio parahaemolyticus OM proteins that bind to shrimp serum proteins and the shrimp serum proteins that interact with bacterial cells, respectively. Three interacting shrimp serum proteins, hemocyanin, β-1,3-glucan binding protein and LV_HP_RA36F08r and thirty interacting OM proteins were determined. They form 63 heterogeneous protein-protein interactions. Nine out of the 30 OM proteins were randomly demonstrated to be up-regulated or down-regulated when bacterial cells were cultured with shrimp sera, indicating the biological significance of the network. The interesting findings uncover the complexity of struggle between host immunity and bacterial infection. Compared with our previous report on heterogeneous interactome between fish grill and bacterial OM proteins, the present study further extends the investigation from lower vertebrates to invertebrates and develops a bacterial OM protein array to identify the OM proteins bound with shrimp serum proteins, which elevates the frequencies of the bound OM proteins. Our results highlight the way to determine and understand the heterogeneous interaction between hosts and microbes.

  1. Expanding the Interactome of the Noncanonical NF-κB Signaling Pathway

    PubMed Central

    Willmann, Katharina L.; Krolo, Ana; Knapp, Sylvia; Bennett, Keiryn L.; Boztug, Kaan

    2017-01-01

    NF-κB signaling is a central pathway of immunity and integrates signal transduction upon a wide array of inflammatory stimuli. Noncanonical NF-κB signaling is activated by a small subset of TNF family receptors and characterized by NF-κB2/p52 transcriptional activity. The medical relevance of this pathway has recently re-emerged from the discovery of primary immunodeficiency patients that have loss-of-function mutations in the MAP3K14 gene encoding NIK. Nevertheless, knowledge of protein interactions that regulate noncanonical NF-κB signaling is sparse. Here we report a detailed state-of-the-art mass spectrometry-based protein−protein interaction network including the non-canonical NF-κB signaling nodes TRAF2, TRAF3, IKKα, NIK, and NF-κB2/p100. The value of the data set was confirmed by the identification of interactions already known to regulate this pathway. In addition, a remarkable number of novel interactors were identified. We provide validation of the novel NIK and IKKα interactor FKBP8, which may regulate processes downstream of noncanonical NF-κB signaling. To understand perturbed noncanonical NF-κB signaling in the context of misregulated NIK in disease, we also provide a differential interactome of NIK mutants that cause immunodeficiency. Altogether, this data set not only provides critical insight into how protein−protein interactions can regulate immune signaling but also offers a novel resource on noncanonical NF-κB signaling. PMID:27416764

  2. Angiogenic growth factors interactome and drug discovery: The contribution of surface plasmon resonance.

    PubMed

    Rusnati, Marco; Presta, Marco

    2015-06-01

    Angiogenesis is implicated in several pathological conditions, including cancer, and in regenerative processes, including the formation of collateral blood vessels after stroke. Physiological angiogenesis is the outcome of a fine balance between the action of angiogenic growth factors (AGFs) and anti-angiogenic molecules, while pathological angiogenesis occurs when this balance is pushed toward AGFs. AGFs interact with multiple endothelial cell (EC) surface receptors inducing cell proliferation, migration and proteases upregulation. On the contrary, free or extracellular matrix-associated molecules inhibit angiogenesis by sequestering AGFs (thus hampering EC stimulation) or by interacting with specific EC receptors inducing apoptosis or decreasing responsiveness to AGFs. Thus, angiogenesis results from an intricate network of interactions among pro- and anti-angiogenic molecules, EC receptors and various modulators. All these interactions represent targets for the development of pro- or anti-angiogenic therapies. These aims call for suitable technologies to study the countless interactions occurring during neovascularization. Surface plasmon resonance (SPR) is a label-free optical technique to study biomolecular interactions in real time. It has become the golden standard technology for interaction analysis in biomedical research, including angiogenesis. From a survey of the literature it emerges that SPR has already contributed substantially to the better understanding of the neovascularization process, laying the basis for the decoding of the angiogenesis "interactome" and the identification of "hub molecules" that may represent preferential targets for an efficacious modulation of angiogenesis. Here, the still unexploited full potential of SPR is enlightened, pointing to improvements in its use for a deeper understanding of the mechanisms of neovascularization and the identification of novel anti-angiogenic drugs.

  3. Neural mechanisms of spatiotemporal signal processing

    NASA Astrophysics Data System (ADS)

    Khanbabaie Shoub, Shaban (Reza)

    We have studied the synaptic, dendritic, and network mechanisms of spatiotemporal signal processing underlying the computation of visual motion in the avian tectum. Such mechanisms are critical for information processing in all vertebrates, but have been difficult to elucidate in mammals because of anatomical limitations. We have therefore developed a chick tectal slice preparation, which has features that help us circumvent these limitations. Using single-electrode multi-pulse synaptic stimulation experiments we found that the SGC-I cell responds to synaptic stimulation in a binary manner and its response is phasic in a time dependent probabilistic manner over large time scales. Synaptic inputs at two locations typically interact in a mutually exclusive manner when delivered within the "interaction time" of approximately 30 ms. Then we constructed a model of SGC-I cell and the retinal inputs to examine the role of the observed non-linear cellular properties in shaping the response of SGC-I neurons to assumed retinal representations of dynamic spatiotemporal visual stimuli. We found that by these properties, SGC-I cells can classify different stimuli. Especially without the phasic synaptic signal transfer the model SGC-I cell fails to distinguish between the static stationary stimuli and dynamic spatiotemporal stimuli. Based on one-site synaptic response probability and the assumption of independent neighboring dendritic endings we predicted the response probability of SGC-I cells to multiple synaptic inputs. We tested this independence-based model prediction and found that the independency assumption is not valid. The measured SGC-I response probability to multiple synaptic inputs does not increase with the number of synaptic inputs. The presence of GABAergic horizontal cells in layer 5 suggest an inhibitory effect of these cells on the SGC-I retino-tectal synaptic responses. In our experiment we found that the measured SGC-I response probability to multiple

  4. A semiparametric spatio-temporal model for solar irradiance data

    SciTech Connect

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

    2016-03-01

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

  5. Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources

    PubMed Central

    Gershman, Samuel J.; Blei, David M.; Norman, Kenneth A.; Sederberg, Per B.

    2014-01-01

    This paper extends earlier work on spatial modeling of fMRI data to the temporal domain, providing a framework for analyzing high temporal resolution brain imaging modalities such as electroencapholography (EEG). The central idea is to decompose brain imaging data into a covariate-dependent superposition of functions defined over continuous time and space (what we refer to as topographic latent sources). The continuous formulation allows us to parametrically model spatiotemporally localized activations. To make group-level inferences, we elaborate the model hierarchically by sharing sources across subjects. We describe a variational algorithm for parameter estimation that scales efficiently to large data sets. Applied to three EEG data sets, we find that the model produces good predictive performance and reproduces a number of classic findings. Our results suggest that topographic latent sources serve as an effective hypothesis space for interpreting spatiotemporal brain imaging data. PMID:24791745

  6. A semiparametric spatio-temporal model for solar irradiance data

    DOE PAGES

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

    2016-03-01

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

  7. Noise tolerant spatiotemporal chaos computing

    SciTech Connect

    Kia, Behnam; Kia, Sarvenaz; Ditto, William L.; Lindner, John F.; Sinha, Sudeshna

    2014-12-01

    We introduce and design a noise tolerant chaos computing system based on a coupled map lattice (CML) and the noise reduction capabilities inherent in coupled dynamical systems. The resulting spatiotemporal chaos computing system is more robust to noise than a single map chaos computing system. In this CML based approach to computing, under the coupled dynamics, the local noise from different nodes of the lattice diffuses across the lattice, and it attenuates each other's effects, resulting in a system with less noise content and a more robust chaos computing architecture.

  8. Enhancing chaoticity of spatiotemporal chaos.

    PubMed

    Li, Xiaowen; Zhang, Heqiao; Xue, Yu; Hu, Gang

    2005-01-01

    In some practical situations strong chaos is needed. This introduces the task of chaos control with enhancing chaoticity rather than suppressing chaoticity. In this paper a simple method of linear amplifications incorporating modulo operations is suggested to make spatiotemporal systems, which may be originally chaotic or nonchaotic, strongly chaotic. Specifically, this control can eliminate periodic windows, increase the values and the number of positive Lyapunov exponents, make the probability distributions of the output chaotic sequences more homogeneous, and reduce the correlations of chaotic outputs for different times and different space units. The applicability of the method to practical tasks, in particular to random number generators and secure communications, is briefly discussed.

  9. Noise tolerant spatiotemporal chaos computing.

    PubMed

    Kia, Behnam; Kia, Sarvenaz; Lindner, John F; Sinha, Sudeshna; Ditto, William L

    2014-12-01

    We introduce and design a noise tolerant chaos computing system based on a coupled map lattice (CML) and the noise reduction capabilities inherent in coupled dynamical systems. The resulting spatiotemporal chaos computing system is more robust to noise than a single map chaos computing system. In this CML based approach to computing, under the coupled dynamics, the local noise from different nodes of the lattice diffuses across the lattice, and it attenuates each other's effects, resulting in a system with less noise content and a more robust chaos computing architecture.

  10. Spatiotemporal chaos involving wave instability

    NASA Astrophysics Data System (ADS)

    Berenstein, Igal; Carballido-Landeira, Jorge

    2017-01-01

    In this paper, we investigate pattern formation in a model of a reaction confined in a microemulsion, in a regime where both Turing and wave instability occur. In one-dimensional systems, the pattern corresponds to spatiotemporal intermittency where the behavior of the systems alternates in both time and space between stationary Turing patterns and traveling waves. In two-dimensional systems, the behavior initially may correspond to Turing patterns, which then turn into wave patterns. The resulting pattern also corresponds to a chaotic state, where the system alternates in both space and time between standing wave patterns and traveling waves, and the local dynamics may show vanishing amplitude of the variables.

  11. Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy

    PubMed Central

    Cheng, Feixiong; Jia, Peilin; Wang, Quan; Zhao, Zhongming

    2014-01-01

    The human kinome is gaining importance through its promising cancer therapeutic targets, yet no general model to address the kinase inhibitor resistance has emerged. Here, we constructed a systems biology-based framework to catalogue the human kinome, including 538 kinase genes, in the broader context of the human interactome. Specifically, we constructed three networks: a kinase-substrate interaction network containing 7,346 pairs connecting 379 kinases to 36,576 phosphorylation sites in 1,961 substrates, a protein-protein interaction network (PPIN) containing 92,699 pairs, and an atomic resolution PPIN containing 4,278 pairs. We identified the conserved regulatory phosphorylation motifs (e.g., Ser/Thr-Pro) using a sequence logo analysis. We found the typical anticancer target selection strategy that uses network hubs as drug targets, might lead to a high adverse drug reaction risk. Furthermore, we found the distinct network centrality of kinases creates a high anticancer drug resistance risk by feedback or crosstalk mechanisms within cellular networks. This notion is supported by the systematic network and pathway analyses that anticancer drug resistance genes are significantly enriched as hubs and heavily participate in multiple signaling pathways. Collectively, this comprehensive human kinome interactome map sheds light on anticancer drug resistance mechanisms and provides an innovative resource for rational kinase inhibitor design. PMID:25003367

  12. Mapping the H+ (V)-ATPase interactome: identification of proteins involved in trafficking, folding, assembly and phosphorylation

    PubMed Central

    Merkulova, Maria; Păunescu, Teodor G.; Azroyan, Anie; Marshansky, Vladimir; Breton, Sylvie; Brown, Dennis

    2015-01-01

    V-ATPases (H+ ATPases) are multisubunit, ATP-dependent proton pumps that regulate pH homeostasis in virtually all eukaryotes. They are involved in key cell biological processes including vesicle trafficking, endosomal pH sensing, membrane fusion and intracellular signaling. They also have critical systemic roles in renal acid excretion and blood pH balance, male fertility, bone remodeling, synaptic transmission, olfaction and hearing. Furthermore, V-ATPase dysfunction either results in or aggravates various other diseases, but little is known about the complex protein interactions that regulate these varied V-ATPase functions. Therefore, we performed a proteomic analysis to identify V-ATPase associated proteins and construct a V-ATPase interactome. Our analysis using kidney tissue revealed V-ATPase-associated protein clusters involved in protein quality control, complex assembly and intracellular trafficking. ARHGEF7, DMXL1, EZR, NCOA7, OXR1, RPS6KA3, SNX27 and 9 subunits of the chaperonin containing TCP1 complex (CCT) were found to interact with V-ATPase for the first time in this study. Knockdown of two interacting proteins, DMXL1 and WDR7, inhibited V-ATPase-mediated intracellular vesicle acidification in a kidney cell line, providing validation for the utility of our interactome as a screen for functionally important novel V-ATPase-regulating proteins. Our data, therefore, provide new insights and directions for the analysis of V-ATPase cell biology and (patho)physiology. PMID:26442671

  13. Comprehensively Characterizing the Thioredoxin Interactome In Vivo Highlights the Central Role Played by This Ubiquitous Oxidoreductase in Redox Control.

    PubMed

    Arts, Isabelle S; Vertommen, Didier; Baldin, Francesca; Laloux, Géraldine; Collet, Jean-François

    2016-06-01

    Thioredoxin (Trx) is a ubiquitous oxidoreductase maintaining protein-bound cysteine residues in the reduced thiol state. Here, we combined a well-established method to trap Trx substrates with the power of bacterial genetics to comprehensively characterize the in vivo Trx redox interactome in the model bacterium Escherichia coli Using strains engineered to optimize trapping, we report the identification of a total 268 Trx substrates, including 201 that had never been reported to depend on Trx for reduction. The newly identified Trx substrates are involved in a variety of cellular processes, ranging from energy metabolism to amino acid synthesis and transcription. The interaction between Trx and two of its newly identified substrates, a protein required for the import of most carbohydrates, PtsI, and the bacterial actin homolog MreB was studied in detail. We provide direct evidence that PtsI and MreB contain cysteine residues that are susceptible to oxidation and that participate in the formation of an intermolecular disulfide with Trx. By considerably expanding the number of Trx targets, our work highlights the role played by this major oxidoreductase in a variety of cellular processes. Moreover, as the dependence on Trx for reduction is often conserved across species, it also provides insightful information on the interactome of Trx in organisms other than E. coli.

  14. Bacterial interactomes: Interacting protein partners share similar function and are validated in independent assays more frequently than previously reported.

    DOE PAGES

    Shatsky, Maxim; Allen, Simon; Gold, Barbara; ...

    2016-05-01

    Numerous affinity purification – mass-spectrometry (AP-MS) and yeast two hybrid (Y2H) screens have each defined thousands of pairwise protein-protein interactions (PPIs), most between functionally unrelated proteins. The accuracy of these networks, however, is under debate. Here we present an AP-MS survey of the bacterium Desulfovibrio vulgaris together with a critical reanalysis of nine published bacterial Y2H and AP-MS screens. We have identified 459 high confidence PPIs from D. vulgaris and 391 from Escherichia coli. Compared to the nine published interactomes, our two networks are smaller; are much less highly connected; have significantly lower false discovery rates; and are much moremore » enriched in protein pairs that are encoded in the same operon, have similar functions, and are reproducibly detected in other physical interaction assays. Lastly, our work establishes more stringent benchmarks for the properties of protein interactomes and suggests that bona fide PPIs much more frequently involve protein partners that are annotated with similar functions or that can be validated in independent assays than earlier studies suggested.« less

  15. Bacterial interactomes: Interacting protein partners share similar function and are validated in independent assays more frequently than previously reported.

    SciTech Connect

    Shatsky, Maxim; Allen, Simon; Gold, Barbara; Liu, Nancy L.; Juba, Thomas R.; Elias, Dwayne A; Reveco, Sonia A.; Prathapam, Ramadevi; He, Jennifer; Yang, Wenhong; Szakal, Evelin D.; Liu, Haichuan; Singer, Mary E.; Geller, Jil T.; Lam, Bonita R.; Saini, Avneesh; Trotter, Valentine V.; Hall, Steven C.; Fisher, Susan J.; Brenner, Steven E.; Chhabra, Swapnil; Hazen, Terry C.; Wall, Judy D.; Witkowska, Ewa; Biggin, Mark D.; Chandonia, John-Marc; Butland, Gareth

    2016-05-01

    Numerous affinity purification – mass-spectrometry (AP-MS) and yeast two hybrid (Y2H) screens have each defined thousands of pairwise protein-protein interactions (PPIs), most between functionally unrelated proteins. The accuracy of these networks, however, is under debate. Here we present an AP-MS survey of the bacterium Desulfovibrio vulgaris together with a critical reanalysis of nine published bacterial Y2H and AP-MS screens. We have identified 459 high confidence PPIs from D. vulgaris and 391 from Escherichia coli. Compared to the nine published interactomes, our two networks are smaller; are much less highly connected; have significantly lower false discovery rates; and are much more enriched in protein pairs that are encoded in the same operon, have similar functions, and are reproducibly detected in other physical interaction assays. Lastly, our work establishes more stringent benchmarks for the properties of protein interactomes and suggests that bona fide PPIs much more frequently involve protein partners that are annotated with similar functions or that can be validated in independent assays than earlier studies suggested.

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

    PubMed Central

    Carter, C. J.

    2013-01-01

    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

  17. Spatiotemporal Thinking in the Geosciences

    NASA Astrophysics Data System (ADS)

    Shipley, T. F.; Manduca, C. A.; Ormand, C. J.; Tikoff, B.

    2011-12-01

    Reasoning about spatial relations is a critical skill for geoscientists. Within the geosciences different disciplines may reason about different sorts of relationships. These relationships may span vastly different spatial and temporal scales (from the spatial alignment in atoms in crystals to the changes in the shape of plates). As part of work in a research center on spatial thinking in STEM education, we have been working to classify the spatial skills required in geology, develop tests for each spatial skill, and develop the cognitive science tools to promote the critical spatial reasoning skills. Research in psychology, neurology and linguistics supports a broad classification of spatial skills along two dimensions: one versus many objects (which roughly translates to object- focused and navigation focused skills) and static versus dynamic spatial relations. The talk will focus on the interaction of space and time in spatial cognition in the geosciences. We are working to develop measures of skill in visualizing spatiotemporal changes. A new test developed to measure visualization of brittle deformations will be presented. This is a skill that has not been clearly recognized in the cognitive science research domain and thus illustrates the value of interdisciplinary work that combines geosciences with cognitive sciences. Teaching spatiotemporal concepts can be challenging. Recent theoretical work suggests analogical reasoning can be a powerful tool to aid student learning to reason about temporal relations using spatial skills. Recent work in our lab has found that progressive alignment of spatial and temporal scales promotes accurate reasoning about temporal relations at geological time scales.

  18. A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case

    PubMed Central

    2015-01-01

    Background Computational pharmacology can uniquely address some issues in the process of drug development by providing a macroscopic view and a deeper understanding of drug action. Specifically, network-assisted approach is promising for the inference of drug repurposing. However, the drug-target associations coming from different sources and various assays have much noise, leading to an inflation of the inference errors. To reduce the inference errors, it is necessary and critical to create a comprehensive and weighted data set of drug-target associations. Results In this study, we created a weighted and integrated drug-target interactome (WinDTome) to provide a comprehensive resource of drug-target associations for computational pharmacology. We first collected drug-target interactions from six commonly used drug-target centered data sources including DrugBank, KEGG, TTD, MATADOR, PDSP Ki Database, and BindingDB. Then, we employed the record linkage method to normalize drugs and targets to the unique identifiers by utilizing the public data sources including PubChem, Entrez Gene, and UniProt. To assess the reliability of the drug-target associations, we assigned two scores (Score_S and Score_R) to each drug-target association based on their data sources and publication references. Consequently, the WinDTome contains 546,196 drug-target associations among 303,018 compounds and 4,113 genes. To assess the application of the WinDTome, we designed a network-based approach for drug repurposing using mental disorder schizophrenia (SCZ) as a case. Starting from 41 known SCZ drugs and their targets, we inferred a total of 264 potential SCZ drugs through the associations of drug-target with Score_S higher than two in WinDTome and human protein-protein interactions. Among the 264 SCZ-related drugs, 39 drugs have been investigated in clinical trials for SCZ treatment and 74 drugs for the treatment of other mental disorders, respectively. Compared with the results using other

  19. Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation.

    PubMed

    Aram, Parham; Kadirkamanathan, Visakan; Anderson, Sean R

    2015-11-01

    We present a framework for the identification of spatiotemporal linear dynamical systems. We use a state-space model representation that has the following attributes: 1) the number of spatial observation locations are decoupled from the model order; 2) the model allows for spatial heterogeneity; 3) the model representation is continuous over space; and 4) the model parameters can be identified in a simple and sparse estimation procedure. The model identification procedure we propose has four steps: 1) decomposition of the continuous spatial field using a finite set of basis functions where spatial frequency analysis is used to determine basis function width and spacing, such that the main spatial frequency contents of the underlying field can be captured; 2) initialization of states in closed form; 3) initialization of state-transition and input matrix model parameters using sparse regression-the least absolute shrinkage and selection operator method; and 4) joint state and parameter estimation using an iterative Kalman-filter/sparse-regression algorithm. To investigate the performance of the proposed algorithm we use data generated by the Kuramoto model of spatiotemporal cortical dynamics. The identification algorithm performs successfully, predicting the spatiotemporal field with high accuracy, whilst the sparse regression leads to a compact model.

  20. Spatiotemporal behavior and nonlinear dynamics in a phase conjugate resonator

    NASA Technical Reports Server (NTRS)

    Liu, Siuying Raymond

    1993-01-01

    The work described can be divided into two parts. The first part is an investigation of the transient behavior and stability property of a phase conjugate resonator (PCR) below threshold. The second part is an experimental and theoretical study of the PCR's spatiotemporal dynamics above threshold. The time-dependent coupled wave equations for four-wave mixing (FWM) in a photorefractive crystal, with two distinct interaction regions caused by feedback from an ordinary mirror, was used to model the transient dynamics of a PCR below threshold. The conditions for self-oscillation were determined and the solutions were used to define the PCR's transfer function and analyze its stability. Experimental results for the buildup and decay times confirmed qualitatively the predicted behavior. Experiments were carried out above threshold to study the spatiotemporal dynamics of the PCR as a function of Pragg detuning and the resonator's Fresnel number. The existence of optical vortices in the wavefront were identified by optical interferometry. It was possible to describe the transverse dynamics and the spatiotemporal instabilities by modeling the three-dimensional-coupled wave equations in photorefractive FWM using a truncated modal expansion approach.

  1. Spatiotemporal behavior and nonlinear dynamics in a phase conjugate resonator

    NASA Astrophysics Data System (ADS)

    Liu, Siuying Raymond

    1993-12-01

    The work described can be divided into two parts. The first part is an investigation of the transient behavior and stability property of a phase conjugate resonator (PCR) below threshold. The second part is an experimental and theoretical study of the PCR's spatiotemporal dynamics above threshold. The time-dependent coupled wave equations for four-wave mixing (FWM) in a photorefractive crystal, with two distinct interaction regions caused by feedback from an ordinary mirror, was used to model the transient dynamics of a PCR below threshold. The conditions for self-oscillation were determined and the solutions were used to define the PCR's transfer function and analyze its stability. Experimental results for the buildup and decay times confirmed qualitatively the predicted behavior. Experiments were carried out above threshold to study the spatiotemporal dynamics of the PCR as a function of Pragg detuning and the resonator's Fresnel number. The existence of optical vortices in the wavefront were identified by optical interferometry. It was possible to describe the transverse dynamics and the spatiotemporal instabilities by modeling the three-dimensional-coupled wave equations in photorefractive FWM using a truncated modal expansion approach.

  2. Graph-theoretical model of global human interactome reveals enhanced long-range communicability in cancer networks

    PubMed Central

    Gladilin, Evgeny

    2017-01-01

    Malignant transformation is known to involve substantial rearrangement of the molecular genetic landscape of the cell. A common approach to analysis of these alterations is a reductionist one and consists of finding a compact set of differentially expressed genes or associated signaling pathways. However, due to intrinsic tumor heterogeneity and tissue specificity, biomarkers defined by a small number of genes/pathways exhibit substantial variability. As an alternative to compact differential signatures, global features of genetic cell machinery are conceivable. Global network descriptors suggested in previous works are, however, known to potentially be biased by overrepresentation of interactions between frequently studied genes-proteins. Here, we construct a cellular network of 74538 directional and differential gene expression weighted protein-protein and gene regulatory interactions, and perform graph-theoretical analysis of global human interactome using a novel, degree-independent feature—the normalized total communicability (NTC). We apply this framework to assess differences in total information flow between different cancer (BRCA/COAD/GBM) and non-cancer interactomes. Our experimental results reveal that different cancer interactomes are characterized by significant enhancement of long-range NTC, which arises from circulation of information flow within robustly organized gene subnetworks. Although enhancement of NTC emerges in different cancer types from different genomic profiles, we identified a subset of 90 common genes that are related to elevated NTC in all studied tumors. Our ontological analysis shows that these genes are associated with enhanced cell division, DNA replication, stress response, and other cellular functions and processes typically upregulated in cancer. We conclude that enhancement of long-range NTC manifested in the correlated activity of genes whose tight coordination is required for survival and proliferation of all tumor cells

  3. Spatio-Temporal Clustering of Monitoring Network

    NASA Astrophysics Data System (ADS)

    Hussain, I.; Pilz, J.

    2009-04-01

    Pakistan has much diversity in seasonal variation of different locations. Some areas are in desserts and remain very hot and waterless, for example coastal areas are situated along the Arabian Sea and have very warm season and a little rainfall. Some areas are covered with mountains, have very low temperature and heavy rainfall; for instance Karakoram ranges. The most important variables that have an impact on the climate are temperature, precipitation, humidity, wind speed and elevation. Furthermore, it is hard to find homogeneous regions in Pakistan with respect to climate variation. Identification of homogeneous regions in Pakistan can be useful in many aspects. It can be helpful for prediction of the climate in the sub-regions and for optimizing the number of monitoring sites. In the earlier literature no one tried to identify homogeneous regions of Pakistan with respect to climate variation. There are only a few papers about spatio-temporal clustering of monitoring network. Steinhaus (1956) presented the well-known K-means clustering method. It can identify a predefined number of clusters by iteratively assigning centriods to clusters based. Castro et al. (1997) developed a genetic heuristic algorithm to solve medoids based clustering. Their method is based on genetic recombination upon random assorting recombination. The suggested method is appropriate for clustering the attributes which have genetic characteristics. Sap and Awan (2005) presented a robust weighted kernel K-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data. Soltani and Modarres (2006) used hierarchical and divisive cluster analysis to categorize patterns of rainfall in Iran. They only considered rainfall at twenty-eight monitoring sites and concluded that eight clusters

  4. Front dynamics in the presence of spatiotemporal structured noises.

    PubMed

    Santos, M A; Sancho, J M

    2001-07-01

    Front dynamics modeled by a reaction-diffusion equation are studied under the influence of spatiotemporal structured noises. An effective deterministic model is analytical derived where the noise parameters, intensity, correlation time, and correlation length appear explicitly. The different effects of these parameters are discussed for the Ginzburg-Landau and Schlögl models. We obtain an analytical expression for the front velocity as a function of the noise parameters. Numerical simulation results are in a good agreement with the theoretical predictions.

  5. A Systems Biology Methodology Combining Transcriptome and Interactome Datasets to Assess the Implications of Cytokinin Signaling for Plant Immune Networks.

    PubMed

    Kunz, Meik; Dandekar, Thomas; Naseem, Muhammad

    2017-01-01

    Cytokinins (CKs) play an important role in plant growth and development. Also, several studies highlight the modulatory implications of CKs for plant-pathogen interaction. However, the underlying mechanisms of CK mediating immune networks in plants are still not fully understood. A detailed analysis of high-throughput transcriptome (RNA-Seq and microarrays) datasets under modulated conditions of plant CKs and its mergence with cellular interactome (large-scale protein-protein interaction data) has the potential to unlock the contribution of CKs to plant defense. Here, we specifically describe a detailed systems biology methodology pertinent to the acquisition and analysis of various omics datasets that delineate the role of plant CKs in impacting immune pathways in Arabidopsis.

  6. A centrosome interactome provides insight into organelle assembly and reveals a non-duplication role for Plk4

    PubMed Central

    Galletta, Brian J.; Fagerstrom, Carey J.; Schoborg, Todd A.; McLamarrah, Tiffany A.; Ryniawec, John M.; Buster, Daniel W.; Slep, Kevin C.; Rogers, Gregory C.; Rusan, Nasser M.

    2016-01-01

    The centrosome is the major microtubule-organizing centre of many cells, best known for its role in mitotic spindle organization. How the proteins of the centrosome are accurately assembled to carry out its many functions remains poorly understood. The non-membrane-bound nature of the centrosome dictates that protein–protein interactions drive its assembly and functions. To investigate this massive macromolecular organelle, we generated a ‘domain-level' centrosome interactome using direct protein–protein interaction data from a focused yeast two-hybrid screen. We then used biochemistry, cell biology and the model organism Drosophila to provide insight into the protein organization and kinase regulatory machinery required for centrosome assembly. Finally, we identified a novel role for Plk4, the master regulator of centriole duplication. We show that Plk4 phosphorylates Cep135 to properly position the essential centriole component Asterless. This interaction landscape affords a critical framework for research of normal and aberrant centrosomes. PMID:27558293

  7. Notch3 Interactome Analysis Identified WWP2 as a Negative Regulator of Notch3 Signaling in Ovarian Cancer

    PubMed Central

    Guan, Bin; Wu, Ren-Chin; Zhu, Heng; Blackshaw, Seth; Shih, Ie-Ming; Wang, Tian-Li

    2014-01-01

    The Notch3 signaling pathway is thought to play a critical role in cancer development, as evidenced by the Notch3 amplification and rearrangement observed in human cancers. However, the molecular mechanism by which Notch3 signaling contributes to tumorigenesis is largely unknown. In an effort to identify the molecular modulators of the Notch3 signaling pathway, we screened for Notch3-intracellular domain (N3-ICD) interacting proteins using a human proteome microarray. Pathway analysis of the Notch3 interactome demonstrated that ubiquitin C was the molecular hub of the top functional network, suggesting the involvement of ubiquitination in modulating Notch3 signaling. Thereby, we focused on functional characterization of an E3 ubiquitin-protein ligase, WWP2, a top candidate in the Notch3 interactome list. Co-immunoprecipitation experiments showed that WWP2 interacted with N3-ICD but not with intracellular domains from other Notch receptors. Wild-type WWP2 but not ligase-deficient mutant WWP2 increases mono-ubiquitination of the membrane-tethered Notch3 fragment, therefore attenuating Notch3 pathway activity in cancer cells and leading to cell cycle arrest. The mono-ubiquitination by WWP2 may target an endosomal/lysosomal degradation fate for Notch3 as suggested by the fact that the process could be suppressed by the endosomal/lysosomal inhibitor. Analysis of The Cancer Genome Atlas dataset showed that the majority of ovarian carcinomas harbored homozygous or heterozygous deletions in WWP2 locus, and there was an inverse correlation in the expression levels between WWP2 and Notch3 in ovarian carcinomas. Furthermore, ectopic expression of WWP2 decreased tumor development in a mouse xenograft model and suppressed the Notch3-induced phenotypes including increase in cancer stem cell-like cell population and platinum resistance. Taken together, our results provide evidence that WWP2 serves as a tumor suppressor by negatively regulating Notch3 signaling in ovarian cancer

  8. Virus-host interactome: Putting the accent on how it changes.

    PubMed

    Rodrigo, Guillermo; Daròs, José-Antonio; Elena, Santiago F

    2017-03-06

    Viral infections are extremely complex processes that could only be well understood by precisely characterizing the interaction networks between the virus and the host components. In recent years, much effort has gone in this direction with the aim of unveiling the molecular basis of viral pathology. These networks are mostly formed by viral and host proteins, and are expected to be dynamic both with time and space (i.e., with the progression of infection, as well as with the virus and host genotypes; what we call plastodynamic). This largely overlooked spatio-temporal evolution urgently calls for a change both in the conceptual paradigms and experimental techniques used so far to characterize virus-host interactions. More generally, molecular plasticity and temporal dynamics are unavoidable components of the mechanisms that underlie any complex disease; components whose understanding will eventually enhance our ability to modulate those networks with the aim of improving disease treatments.

  9. Proteomic Profiling and the Predicted Interactome of Host Proteins in Compatible and Incompatible Interactions Between Soybean and Fusarium virguliforme.

    PubMed

    Iqbal, M Javed; Majeed, Maryam; Humayun, Maheen; Lightfoot, David A; Afzal, Ahmed J

    2016-12-01

    Sudden death syndrome (SDS) is a complex of two diseases of soybean (Glycine max), caused by the soil borne pathogenic fungus Fusarium virguliforme. The root rot and leaf scorch diseases both result in significant yield losses worldwide. Partial SDS resistance has been demonstrated in multiple soybean cultivars. This study aimed to highlight proteomic changes in soybean roots by identifying proteins which are differentially expressed in near isogenic lines (NILs) contrasting at the Rhg1/Rfs2 locus for partial resistance or susceptibility to SDS. Two-dimensional gel electrophoresis resolved approximately 1000 spots on each gel; 12 spots with a significant (P < 0.05) difference in abundance of 1.5-fold or more were picked, trypsin-digested, and analyzed using quadruple time-of-flight tandem mass spectrometry. Several spots contained more than one protein, so that 18 distinct proteins were identified overall. A functional analysis performed to categorize the proteins depicted that the major pathways altered by fungal infection include disease resistance, stress tolerance, and metabolism. This is the first report which identifies proteins whose abundances are altered in response to fungal infection leading to SDS. The results provide valuable information about SDS resistance in soybean plants, and plant partial resistance responses in general. More importantly, several of the identified proteins could be good candidates for the development of SDS-resistant soybean plants.

  10. Spatiotemporally controlled single cell sonoporation

    PubMed Central

    Fan, Zhenzhen; Liu, Haiyan; Mayer, Michael; Deng, Cheri X.

    2012-01-01

    This paper presents unique approaches to enable control and quantification of ultrasound-mediated cell membrane disruption, or sonoporation, at the single-cell level. Ultrasound excitation of microbubbles that were targeted to the plasma membrane of HEK-293 cells generated spatially and temporally controlled membrane disruption with high repeatability. Using whole-cell patch clamp recording combined with fluorescence microscopy, we obtained time-resolved measurements of single-cell sonoporation and quantified the size and resealing rate of pores. We measured the intracellular diffusion coefficient of cytoplasmic RNA/DNA from sonoporation-induced transport of an intercalating fluorescent dye into and within single cells. We achieved spatiotemporally controlled delivery with subcellular precision and calcium signaling in targeted cells by selective excitation of microbubbles. Finally, we utilized sonoporation to deliver calcein, a membrane-impermeant substrate of multidrug resistance protein-1 (MRP1), into HEK-MRP1 cells, which overexpress MRP1, and monitored the calcein efflux by MRP1. This approach made it possible to measure the efflux rate in individual cells and to compare it directly to the efflux rate in parental control cells that do not express MRP1. PMID:23012425

  11. Spatiotemporal recurrences of sandpile avalanches

    NASA Astrophysics Data System (ADS)

    Tarun, Anjali B.; Paguirigan, Antonino A.; Batac, Rene C.

    2015-10-01

    We study the space and time properties of avalanches in a continuous sandpile model by constructing a temporally directed network linking together the recurrent avalanche events based on their spatial separation. We use two different criteria for network construction: a later event is connected to a previous one if it is either nearest or farthest from it among all the later events. With this, we observe scale-free regimes emerge as characterized by the following power-law exponents: (a) α = 1.7 for the avalanche size distributions; (b) βF = 2.1 in the in-degree distribution of farthest recurrences; (c) δ = 1 for the separation distances; and (d) γ = 1 for the temporal separations of recurrences. Our results agree with earlier observations that describe the sandpile avalanches as repulsive events, i.e. the next avalanche is more likely to be physically separated from an earlier one. These observations, which are not captured by usual interoccurrence statistics and by random connection mechanisms, suggest an underlying spatiotemporal organization in the sandpile that makes it useful for modeling real-world systems.

  12. Host-Mycobacterium avium subsp. paratuberculosis interactome reveals a novel iron assimilation mechanism linked to nitric oxide stress during early infection

    PubMed Central

    2013-01-01

    Background The initial interaction between host cell and pathogen sets the stage for the ensuing infection and ultimately determine the course of disease. However, there is limited knowledge of the transcripts utilized by host and pathogen and how they may impact one another during this critical step. The purpose of this study was to create a host-Mycobacterium avium subsp. paratuberculosis (MAP) interactome for early infection in an epithelium-macrophage co-culture system using RNA-seq. Results Establishment of the host-MAP interactome revealed a novel iron assimilation system for carboxymycobactin. Iron assimilation is linked to nitric oxide synthase-2 production by the host and subsequent nitric oxide buildup. Iron limitation as well as nitric oxide is a prompt for MAP to enter into an iron sequestration program. This new iron sequestration program provides an explanation for mycobactin independence in some MAP strains grown in vitro as well as during infection within the host cell. Utilization of such a pathway is likely to aid MAP establishment and long-term survival within the host. Conclusions The host-MAP interactome identified a number of metabolic, DNA repair and virulence genes worthy for consideration as novel drug targets as well as future pathogenesis studies. Reported interactome data may also be utilized to conduct focused, hypothesis-driven research. Co-culture of uninfected bovine epithelial cells (MAC-T) and primary bovine macrophages creates a tolerant genotype as demonstrated by downregulation of inflammatory pathways. This co-culture system may serve as a model to investigate other bovine enteric pathogens. PMID:24112552

  13. System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max).

    PubMed

    Xu, Yungang; Guo, Maozu; Zou, Quan; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

    2014-01-01

    Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome

  14. Spatiotemporal features for asynchronous event-based data

    PubMed Central

    Lagorce, Xavier; Ieng, Sio-Hoi; Clady, Xavier; Pfeiffer, Michael; Benosman, Ryad B.

    2015-01-01

    Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the reliable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time. PMID:25759637

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

    PubMed

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

    2016-07-10

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

  16. Bayesian spatio-temporal analysis and geospatial risk factors of human monocytic ehrlichiosis.

    PubMed

    Raghavan, Ram K; Neises, Daniel; Goodin, Douglas G; Andresen, Daniel A; Ganta, Roman R

    2014-01-01

    Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005-2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005-2012, and identified poverty status, relative humidity, and an interactive factor, 'diurnal temperature range x mixed forest area' as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases.

  17. Mercury Toolset for Spatiotemporal Metadata

    NASA Astrophysics Data System (ADS)

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

    2010-06-01

    Mercury (http://mercury.ornl.gov) 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.

  18. Mercury Toolset for Spatiotemporal Metadata

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

    Mercury (http://mercury.ornl.gov) 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.

  19. Network analysis and protein function prediction with the PRODISTIN Web site.

    PubMed

    Baudot, Anaïs; Souiai, Ouissem; Brun, Christine

    2012-01-01

    Interactions between macromolecules are deciphered to gain information about biological processes and protein function. This information is hidden in large interaction networks, yet very complicated to dissect. In this context, the PRODISTIN Web site is dedicated to the clustering of network proteins according to the identity of their interaction partners, and to the subsequent functional annotation of these clusters. It allows analysing functionally networks and eventually leads to the prediction of function for uncharacterized protein based on their belonging to protein clusters. PRODISTIN analyses also provide an overview of the different biological processes existing in a given interactome. Here, we present a step-by-step procedure to analyse interaction networks using the PRODISTIN Web site. The protocol is illustrated by an application to the Campylobacter jejuni interactome.

  20. Spatiotemporal testing and modeling of catfish retinal neurons.

    PubMed Central

    Krausz, H I; Naka, K

    1980-01-01

    The responses of retinal neurons depend on the interaction of both temporal and spatial aspects of a light stimulus. We developed a linear spatiotemporal model of receptor and horizontal cell layers in the catfish retina based on reciprocal interactions between both layers and coupling within each. Horizontal cell transfer properties were measured experimentally using white-noise intensity modulated light spots of different diameters and were compared with analytical predictions based on the model. Good agreement was obtained with a reasonable choice of model space-constants and feedback parameters. Furthermore, the same set of parameter values determined from spot experiments enabled accurate prediction of experimental horizontal cell responses to traveling gratings. The proposed feedback connections from horizontal cells to receptors quicken the time-course of responses in both layers and sharpen receptive fields. PMID:7260243

  1. Neural method of spatiotemporal filter design

    NASA Astrophysics Data System (ADS)

    Szostakowski, Jaroslaw

    1997-10-01

    There is a lot of applications in medical imaging, computer vision, and the communications, where the video processing is critical. Although many techniques have been successfully developed for the filtering of the still-images, significantly fewer techniques have been proposed for the filtering of noisy image sequences. In this paper the novel approach to spatio- temporal filtering design is proposed. The multilayer perceptrons and functional-link nets are used for the 3D filtering. The spatio-temporal patterns are creating from real motion video images. The neural networks learn these patterns. The perceptrons with different number of layers and neurons in each layer are tested. Also, the different input functions in functional- link net are searched. The practical examples of the filtering are shown and compared with traditional (non-neural) spatio-temporal methods. The results are very interesting and the neural spatio-temporal filters seems to be very efficient tool for video noise reduction.

  2. Termites promote resistance of decomposition to spatiotemporal variability in rainfall.

    PubMed

    Veldhuis, Michiel P; Laso, Francisco J; Olff, Han; Berg, Matty P

    2017-02-01

    The ecological impact of rapid environmental change will depend on the resistance of key ecosystems processes, which may be promoted by species that exert strong control over local environmental conditions. Recent theoretical work suggests that macrodetritivores increase the resistance of African savanna ecosystems to changing climatic conditions, but experimental evidence is lacking. We examined the effect of large fungus-growing termites and other non-fungus-growing macrodetritivores on decomposition rates empirically with strong spatiotemporal variability in rainfall and temperature. Non-fungus-growing larger macrodetritivores (earthworms, woodlice, millipedes) promoted decomposition rates relative to microbes and small soil fauna (+34%) but both groups reduced their activities with decreasing rainfall. However, fungus-growing termites increased decomposition rates strongest (+123%) under the most water-limited conditions, making overall decomposition rates mostly independent from rainfall. We conclude that fungus-growing termites are of special importance in decoupling decomposition rates from spatiotemporal variability in rainfall due to the buffered environment they create within their extended phenotype (mounds), that allows decomposition to continue when abiotic conditions outside are less favorable. This points at a wider class of possibly important ecological processes, where soil-plant-animal interactions decouple ecosystem processes from large-scale climatic gradients. This may strongly alter predictions from current climate change models.

  3. A Modified Consumer Inkjet for Spatiotemporal Control of Gene Expression

    PubMed Central

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

    2009-01-01

    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. PMID:19763256

  4. Spatiotemporal resource distribution and foraging strategies of ants (Hymenoptera: Formicidae)

    PubMed Central

    Lanan, Michele

    2014-01-01

    The distribution of food resources in space and time is likely to be an important factor governing the type of foraging strategy used by ants. However, no previous systematic attempt has been made to determine whether spatiotemporal resource distribution is in fact correlated with foraging strategy across the ants. In this analysis, I present data compiled from the literature on the foraging strategy and food resource use of 402 species of ants from across the phylogenetic tree. By categorizing the distribution of resources reported in these studies in terms of size relative to colony size, spatial distribution relative to colony foraging range, frequency of occurrence in time relative to worker life span, and depletability (i.e., whether the colony can cause a change in resource frequency), I demonstrate that different foraging strategies are indeed associated with specific spatiotemporal resource attributes. The general patterns I describe here can therefore be used as a framework to inform predictions in future studies of ant foraging behavior. No differences were found between resources collected via short-term recruitment strategies (group recruitment, short-term trails, and volatile recruitment), whereas different resource distributions were associated with solitary foraging, trunk trails, long-term trail networks, group raiding, and raiding. In many cases, ant species use a combination of different foraging strategies to collect diverse resources. It is useful to consider these foraging strategies not as separate options but as modular parts of the total foraging effort of a colony. PMID:25525497

  5. Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts

    USGS Publications Warehouse

    Conn, Paul B.; Johnson, Devin S.; Ver Hoef, Jay M.; Hooten, Mevin B.; London, Joshua M.; Boveng, Peter L.

    2015-01-01

    Ecologists often fit models to survey data to estimate and explain variation in animal abundance. Such models typically require that animal density remains constant across the landscape where sampling is being conducted, a potentially problematic assumption for animals inhabiting dynamic landscapes or otherwise exhibiting considerable spatiotemporal variation in density. We review several concepts from the burgeoning literature on spatiotemporal statistical models, including the nature of the temporal structure (i.e., descriptive or dynamical) and strategies for dimension reduction to promote computational tractability. We also review several features as they specifically relate to abundance estimation, including boundary conditions, population closure, choice of link function, and extrapolation of predicted relationships to unsampled areas. We then compare a suite of novel and existing spatiotemporal hierarchical models for animal count data that permit animal density to vary over space and time, including formulations motivated by resource selection and allowing for closed populations. We gauge the relative performance (bias, precision, computational demands) of alternative spatiotemporal models when confronted with simulated and real data sets from dynamic animal populations. For the latter, we analyze spotted seal (Phoca largha) counts from an aerial survey of the Bering Sea where the quantity and quality of suitable habitat (sea ice) changed dramatically while surveys were being conducted. Simulation analyses suggested that multiple types of spatiotemporal models provide reasonable inference (low positive bias, high precision) about animal abundance, but have potential for overestimating precision. Analysis of spotted seal data indicated that several model formulations, including those based on a log-Gaussian Cox process, had a tendency to overestimate abundance. By contrast, a model that included a population closure assumption and a scale prior on total

  6. Magnetic nanoparticles to recover cellular organelles and study the time resolved nanoparticle-cell interactome throughout uptake.

    PubMed

    Bertoli, Filippo; Davies, Gemma-Louise; Monopoli, Marco P; Moloney, Micheal; Gun'ko, Yurii K; Salvati, Anna; Dawson, Kenneth A

    2014-08-27

    Nanoparticles in contact with cells and living organisms generate quite novel interactions at the interface between the nanoparticle surface and the surrounding biological environment. However, a detailed time resolved molecular level description of the evolving interactions as nanoparticles are internalized and trafficked within the cellular environment is still missing and will certainly be required for the emerging arena of nanoparticle-cell interactions to mature. In this paper promising methodologies to map out the time resolved nanoparticle-cell interactome for nanoparticle uptake are discussed. Thus silica coated magnetite nanoparticles are presented to cells and their magnetic properties used to isolate, in a time resolved manner, the organelles containing the nanoparticles. Characterization of the recovered fractions shows that different cell compartments are isolated at different times, in agreement with imaging results on nanoparticle intracellular location. Subsequently the internalized nanoparticles can be further isolated from the recovered organelles, allowing the study of the most tightly nanoparticle-bound biomolecules, analogous to the 'hard corona' that so far has mostly been characterized in extracellular environments. Preliminary data on the recovered nanoparticles suggest that significant portion of the original corona (derived from the serum in which particles are presented to the cells) is preserved as nanoparticles are trafficked through the cells.

  7. Differences of AMPA and kainate receptor interactomes identify a novel AMPA receptor auxiliary subunit, GSG1L

    PubMed Central

    Shanks, Natalie F.; Savas, Jeffrey N.; Maruo, Tomohiko; Cais, Ondrej; Hirao, Atsushi; Oe, Souichi; Ghosh, Anirvan; Noda, Yasuko; Greger, Ingo H.; Yates, John R.; Nakagawa, Terunaga

    2012-01-01

    AMPA receptor (AMPA-R) complexes consist of channel forming subunits, GluA1–4 and auxiliary proteins including TARPs, CNIHs, synDIG1, and CKAMP44, which can modulate AMPA-R function in specific ways. Combinatorial effects of four GluA subunits binding to various auxiliary subunits amplify the functional diversity of AMPA-Rs. The significance and magnitude of molecular diversity, however, remain elusive. To gain insight into the molecular complexity of AMPA and kainate receptors (KA-Rs), we compared the proteins that co-purify with each receptor type in rat brain. This interactome study identified the majority of known interacting proteins and more importantly, provides novel candidates for further studies. We validate the claudin homologue GSG1L as a novel binding protein and unique modulator of AMPA-R gating, as determined by detailed molecular, cellular, electrophysiological, and biochemical experiments. GSG1L extends the functional variety of AMPA-R complexes and further investigation of other candidates may reveal additional complexity of ionotropic glutamate receptor function. PMID:22813734

  8. A non-synonymous single-nucleotide polymorphism associated with multiple sclerosis risk affects the EVI5 interactome

    PubMed Central

    Didonna, Alessandro; Isobe, Noriko; Caillier, Stacy J.; Li, Kathy H.; Burlingame, Alma L.; Hauser, Stephen L.; Baranzini, Sergio E.; Patsopoulos, Nikolaos A.; Oksenberg, Jorge R.

    2015-01-01

    Despite recent progress in the characterization of genetic loci associated with multiple sclerosis (MS) risk, the ubiquitous linkage disequilibrium operating across the genome has stalled efforts to distinguish causative variants from proxy single-nucleotide polymorphisms (SNPs). Here, we have identified through fine mapping and meta-analysis EVI5 as the most plausible disease risk gene within the 1p22.1 locus. We further show that an exonic SNP associated with risk induces changes in superficial hydrophobicity patterns of the coiled-coil domain of EVI5, which, in turns, affects the EVI5 interactome. Immunoprecipitation of wild-type and mutated EVI5 followed by mass spectrometry generated a roster of disease-specific interactors functionally linked to lipid metabolism. Among the exclusive binding partners of the risk variant, we describe the novel interaction with sphingosine 1-phosphate lyase (SGPL1)—a key enzyme for the creation of the sphingosine-1 phosphate gradient, which is relevant to the pathogenic process and therapeutic management of MS. PMID:26433934

  9. “Stop Ne(c)king around”: How interactomics contributes to functionally characterize Nek family kinases

    PubMed Central

    Meirelles, Gabriela Vaz; Perez, Arina Marina; de Souza, Edmárcia Elisa; Basei, Fernanda Luisa; Papa, Priscila Ferreira; Melo Hanchuk, Talita Diniz; Cardoso, Vanessa Bomfim; Kobarg, Jörg

    2014-01-01

    Aside from Polo and Aurora, a third but less studied kinase family involved in mitosis regulation is the never in mitosis-gene A (NIMA)-related kinases (Neks). The founding member of this family is the sole member NIMA of Aspergillus nidulans, which is crucial for the initiation of mitosis in that organism. All 11 human Neks have been functionally assigned to one of the three core functions established for this family in mammals: (1) centrioles/mitosis; (2) primary ciliary function/ciliopathies; and (3) DNA damage response (DDR). Recent findings, especially on Nek 1 and 8, showed however, that several Neks participate in parallel in at least two of these contexts: primary ciliary function and DDR. In the core section of this in-depth review, we report the current detailed functional knowledge on each of the 11 Neks. In the discussion, we return to the cross-connections among Neks and point out how our and other groups’ functional and interactomics studies revealed that most Neks interact with protein partners associated with two if not all three of the functional contexts. We then raise the hypothesis that Neks may be the connecting regulatory elements that allow the cell to fine tune and synchronize the cellular events associated with these three core functions. The new and exciting findings on the Nek family open new perspectives and should allow the Neks to finally claim the attention they deserve in the field of kinases and cell cycle biology. PMID:24921005

  10. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma.

    PubMed

    Sharma, Amitabh; Menche, Jörg; Huang, C Chris; Ort, Tatiana; Zhou, Xiaobo; Kitsak, Maksim; Sahni, Nidhi; Thibault, Derek; Voung, Linh; Guo, Feng; Ghiassian, Susan Dina; Gulbahce, Natali; Baribaud, Frédéric; Tocker, Joel; Dobrin, Radu; Barnathan, Elliot; Liu, Hao; Panettieri, Reynold A; Tantisira, Kelan G; Qiu, Weiliang; Raby, Benjamin A; Silverman, Edwin K; Vidal, Marc; Weiss, Scott T; Barabási, Albert-László

    2015-06-01

    Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.

  11. A Spatio-temporal Model of African Animal Trypanosomosis Risk

    PubMed Central

    Dicko, Ahmadou H.; Percoma, Lassane; Sow, Adama; Adam, Yahaya; Mahama, Charles; Sidibé, Issa; Dayo, Guiguigbaza-Kossigan; Thévenon, Sophie; Fonta, William; Sanfo, Safietou; Djiteye, Aligui; Salou, Ernest; Djohan, Vincent; Cecchi, Giuliano; Bouyer, Jérémy

    2015-01-01

    Background African animal trypanosomosis (AAT) is a major constraint to sustainable development of cattle farming in sub-Saharan Africa. The habitat of the tsetse fly vector is increasingly fragmented owing to demographic pressure and shifts in climate, which leads to heterogeneous risk of cyclical transmission both in space and time. In Burkina Faso and Ghana, the most important vectors are riverine species, namely Glossina palpalis gambiensis and G. tachinoides, which are more resilient to human-induced changes than the savannah and forest species. Although many authors studied the distribution of AAT risk both in space and time, spatio-temporal models allowing predictions of it are lacking. Methodology/Principal Findings We used datasets generated by various projects, including two baseline surveys conducted in Burkina Faso and Ghana within PATTEC (Pan African Tsetse and Trypanosomosis Eradication Campaign) national initiatives. We computed the entomological inoculation rate (EIR) or tsetse challenge using a range of environmental data. The tsetse apparent density and their infection rate were separately estimated and subsequently combined to derive the EIR using a “one layer-one model” approach. The estimated EIR was then projected into suitable habitat. This risk index was finally validated against data on bovine trypanosomosis. It allowed a good prediction of the parasitological status (r2 = 67%), showed a positive correlation but less predictive power with serological status (r2 = 22%) aggregated at the village level but was not related to the illness status (r2 = 2%). Conclusions/Significance The presented spatio-temporal model provides a fine-scale picture of the dynamics of AAT risk in sub-humid areas of West Africa. The estimated EIR was high in the proximity of rivers during the dry season and more widespread during the rainy season. The present analysis is a first step in a broader framework for an efficient risk management of climate

  12. Spatiotemporal chaos in Easter Island ecology.

    PubMed

    Sprott, J C

    2012-10-01

    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.

  13. Analysis of spatiotemporal signals of complex systems

    NASA Astrophysics Data System (ADS)

    Uhl, C.; Friedrich, R.; Haken, H.

    1995-05-01

    We present a method of analyzing spatiotemporal signals emerging from nonequilibrium self-organizing systems that are close to instability. The algorithm aims at an identification of spatial modes and corresponding order-parameter equations. We discuss and demonstrate the method by examples of simulated codimension I and II instabilities, including a numerically integrated partial differential equation leading to ``blinking states.''

  14. Systems biology approaches and tools for analysis of interactomes and multi-target drugs.

    PubMed

    Schrattenholz, André; Groebe, Karlfried; Soskic, Vukic

    2010-01-01

    Systems biology is essentially a proteomic and epigenetic exercise because the relatively condensed information of genomes unfolds on the level of proteins. The flexibility of cellular architectures is not only mediated by a dazzling number of proteinaceous species but moreover by the kinetics of their molecular changes: The time scales of posttranslational modifications range from milliseconds to years. The genetic framework of an organism only provides the blue print of protein embodiments which are constantly shaped by external input. Indeed, posttranslational modifications of proteins represent the scope and velocity of these inputs and fulfil the requirements of integration of external spatiotemporal signal transduction inside an organism. The optimization of biochemical networks for this type of information processing and storage results in chemically extremely fine tuned molecular entities. The huge dynamic range of concentrations, the chemical diversity and the necessity of synchronisation of complex protein expression patterns pose the major challenge of systemic analysis of biological models. One further message is that many of the key reactions in living systems are essentially based on interactions of moderate affinities and moderate selectivities. This principle is responsible for the enormous flexibility and redundancy of cellular circuitries. In complex disorders such as cancer or neurodegenerative diseases, which initially appear to be rooted in relatively subtle dysfunctions of multimodal physiologic pathways, drug discovery programs based on the concept of high affinity/high specificity compounds ("one-target, one-disease"), which has been dominating the pharmaceutical industry for a long time, increasingly turn out to be unsuccessful. Despite improvements in rational drug design and high throughput screening methods, the number of novel, single-target drugs fell much behind expectations during the past decade, and the treatment of "complex

  15. Annotating spatio-temporal datasets for meaningful analysis in the Web

    NASA Astrophysics Data System (ADS)

    Stasch, Christoph; Pebesma, Edzer; Scheider, Simon

    2014-05-01

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

  16. The spatiotemporal transfer function of the Limulus lateral eye

    PubMed Central

    1978-01-01

    The dynamics of the Limulus retina may be well described by the spatiotemporal transfer function, which measures the response of the eye to moving sinusoidal gratings. We consider a model for this system, which incorporates an excitatory generator potential, and self- and lateral inhibitory processes. Procedures are described which allow estimation of parameters for the model consistent with the empirical transfer function data. Transfer functions calculated from the model show good agreement with laboratory measurements, and may be used to predict accurately the response of the eye to arbitrary moving stimuli. The model allows convenient interpretation of the transfer function measurements in terms of physiological processes which underly the response of the Limulus retina. PMID:211177

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

    PubMed Central

    Tanaka, Naoaki; Hämäläinen, 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.

    2009-01-01

    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. PMID:20006721

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

    PubMed

    Tanaka, Naoaki; Hämäläinen, 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

    2010-03-01

    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.

  19. Spatiotemporal dynamics and optical vortices in a photorefractive phase-conjugate resonator

    NASA Technical Reports Server (NTRS)

    Liu, Siuying Raymond; Indebetouw, Guy

    1992-01-01

    A truncated modal expansion approach is used to study the spatiotemporal dynamics of a phase-conjugate resonator as a function of Bragg detuning. The numerical results reveal a rich variety of behaviors. Emphasis is given to the spatial distribution of optical vortices, their trajectories and their relationship to the beam's spatial coherence. The limitations of the model are discussed and experimental results are presented for comparison with the model's predictions and assessment of its soundness.

  20. An Interactome-Centered Protein Discovery Approach Reveals Novel Components Involved in Mitosome Function and Homeostasis in Giardia lamblia.

    PubMed

    Rout, Samuel; Zumthor, Jon Paulin; Schraner, Elisabeth M; Faso, Carmen; Hehl, Adrian B

    2016-12-01

    Protozoan parasites of the genus Giardia are highly prevalent globally, and infect a wide range of vertebrate hosts including humans, with proliferation and pathology restricted to the small intestine. This narrow ecological specialization entailed extensive structural and functional adaptations during host-parasite co-evolution. An example is the streamlined mitosomal proteome with iron-sulphur protein maturation as the only biochemical pathway clearly associated with this organelle. Here, we applied techniques in microscopy and protein biochemistry to investigate the mitosomal membrane proteome in association to mitosome homeostasis. Live cell imaging revealed a highly immobilized array of 30-40 physically distinct mitosome organelles in trophozoites. We provide direct evidence for the single giardial dynamin-related protein as a contributor to mitosomal morphogenesis and homeostasis. To overcome inherent limitations that have hitherto severely hampered the characterization of these unique organelles we applied a novel interaction-based proteome discovery strategy using forward and reverse protein co-immunoprecipitation. This allowed generation of organelle proteome data strictly in a protein-protein interaction context. We built an initial Tom40-centered outer membrane interactome by co-immunoprecipitation experiments, identifying small GTPases, factors with dual mitosome and endoplasmic reticulum (ER) distribution, as well as novel matrix proteins. Through iterative expansion of this protein-protein interaction network, we were able to i) significantly extend this interaction-based mitosomal proteome to include other membrane-associated proteins with possible roles in mitosome morphogenesis and connection to other subcellular compartments, and ii) identify novel matrix proteins which may shed light on mitosome-associated metabolic functions other than Fe-S cluster biogenesis. Functional analysis also revealed conceptual conservation of protein translocation

  1. An Interactome-Centered Protein Discovery Approach Reveals Novel Components Involved in Mitosome Function and Homeostasis in Giardia lamblia

    PubMed Central

    Rout, Samuel; Zumthor, Jon Paulin; Schraner, Elisabeth M.

    2016-01-01

    Protozoan parasites of the genus Giardia are highly prevalent globally, and infect a wide range of vertebrate hosts including humans, with proliferation and pathology restricted to the small intestine. This narrow ecological specialization entailed extensive structural and functional adaptations during host-parasite co-evolution. An example is the streamlined mitosomal proteome with iron-sulphur protein maturation as the only biochemical pathway clearly associated with this organelle. Here, we applied techniques in microscopy and protein biochemistry to investigate the mitosomal membrane proteome in association to mitosome homeostasis. Live cell imaging revealed a highly immobilized array of 30–40 physically distinct mitosome organelles in trophozoites. We provide direct evidence for the single giardial dynamin-related protein as a contributor to mitosomal morphogenesis and homeostasis. To overcome inherent limitations that have hitherto severely hampered the characterization of these unique organelles we applied a novel interaction-based proteome discovery strategy using forward and reverse protein co-immunoprecipitation. This allowed generation of organelle proteome data strictly in a protein-protein interaction context. We built an initial Tom40-centered outer membrane interactome by co-immunoprecipitation experiments, identifying small GTPases, factors with dual mitosome and endoplasmic reticulum (ER) distribution, as well as novel matrix proteins. Through iterative expansion of this protein-protein interaction network, we were able to i) significantly extend this interaction-based mitosomal proteome to include other membrane-associated proteins with possible roles in mitosome morphogenesis and connection to other subcellular compartments, and ii) identify novel matrix proteins which may shed light on mitosome-associated metabolic functions other than Fe-S cluster biogenesis. Functional analysis also revealed conceptual conservation of protein translocation

  2. A SILAC-based proteomics elicits the molecular interactome of alisertib (MLN8237) in human erythroleukemia K562 cells

    PubMed Central

    Shu, Li-Ping; Zhou, Zhi-Wei; Zi, Dan; He, Zhi-Xu; Zhou, Shu-Feng

    2015-01-01

    Alisertib (MLN8237, ALS), an Aurora kinase A (AURKA) inhibitor, exerts potent anti-tumor effects in the treatment of solid tumor and hematologic malignancies in preclinical and clinical studies. However, the fully spectrum of molecular targets of ALS and its anticancer effect in the treatment of chronic myeloid leukemia (CML) are not clear. This study aimed to examine the proteomic responses to ALS treatment and unveil the molecular interactome and possible mechanisms for its anticancer effect in K562 cells using stable-isotope labeling by amino acids in cell culture (SILAC) approach. The proteomic data identified that ALS treatment modulated the expression of 1541 protein molecules (570 up; 971 down). The pathway analysis showed that 299 signaling pathways and 459 cellular functional proteins directly responded to ALS treatment in K562 cells. These targeted molecules and signaling pathways were mainly involved in cell growth and proliferation, cell metabolism, and cell survival and death. Subsequently, the effects of ALS on cell cycle distribution, apoptosis, and autophagy were verified. The flow cytometric analysis showed that ALS significantly induced G2/M phase arrest and the Western blotting assays showed that ALS induced apoptosis via mitochondria-dependent pathway and promoted autophagy with the involvement of PI3K/Akt/mTOR, p38 MAPK, and AMPK signaling pathways in K562 cells. Collectively, this study provides a clue to quantitatively evaluate the proteomic responses to ALS and assists in globally identifying the potential molecular targets and elucidating the underlying mechanisms of ALS for CML treatment, which may help develop new efficacious and safe therapies for CML treatment. PMID:26807190

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

    PubMed Central

    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.

    2010-01-01

    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 (www.toxodb.org). 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. PMID:20403389

  4. Spatiotemporal video segmentation based on graphical models.

    PubMed

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

    2005-07-01

    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.

  5. What Is Spatio-Temporal Data Warehousing?

    NASA Astrophysics Data System (ADS)

    Vaisman, Alejandro; Zimányi, Esteban

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

  6. Nonlinear Analysis of Spatiotemporal Heart Data

    NASA Astrophysics Data System (ADS)

    Simonotto, Jennifer; Spano, Mark; Ditto, William; Kavanagh, Katherine; Harrison, Robert G.

    2002-03-01

    Studying the nonlinear dynamics of ventricular fibrillation (VF) and ventricular tachycardia (VT) is necessary for the development of better models of and treatments for VF/VT. Through the use of voltage sensitive dyes and new high speed video cameras, we can now obtain optical mapping data that exhibit signal to noise ratios high enough to tackle (in 2D) the acquisition and analysis of spatiotemporal dynamics of VF/VT. It is now possible to effectively apply conventional statistical analyses (spatial correlation, coherence, signal decomposition), as well as techniques based upon wavefront motion (phase maps, propagation velocity vectors, waveform dynamics) of the electrical waves under study. Yet it is unclear if these measures alone will suffice to describe the complexity of the system. Thus we combine statistical and optical analysis with nonlinear analysis (entropy measures, symbolic dynamics, unstable periodic orbits (UPO) statistics) in order to extract the most information of the spatiotemporal behavior of VF/VT.

  7. Toward Full Spatiotemporal Control on the Nanoscale

    NASA Astrophysics Data System (ADS)

    Durach, Maxim; Rusina, Anastasia; Stockman, Mark I.; Nelson, Keith

    2007-10-01

    We introduce an approach to implement full coherent control on nanometer length scales. It is based on spatio-temporal modulation of the surface plasmon polariton (SPP) fields at the thick edge of a nanowedge. The SPP wavepackets propagating toward the sharp edge of this nanowedge are compressed and adiabatically concentrated at a nanofocus, forming an ultrashort pulse of local fields. The one-dimensional spatial profile and temporal waveform of this pulse are completely coherently controlled.

  8. Toward full spatiotemporal control on the nanoscale.

    PubMed

    Durach, Maxim; Rusina, Anastasia; Stockman, Mark I; Nelson, Keith

    2007-10-01

    We introduce an approach to implement full coherent control on nanometer length scales. It is based on spatiotemporal modulation of the surface plasmon polariton (SPP) fields at the thick edge of a nanowedge. The SPP wavepackets propagating toward the sharp edge of this nanowedge are compressed and adiabatically concentrated at a nanofocus, forming an ultrashort pulse of local fields. The profile of the focused waveform as a function of time and one spatial dimension is completely coherently controlled.

  9. A Hierarchical Bayesian Approach for Learning Sparse Spatio-Temporal Decomposition of Multichannel EEG

    PubMed Central

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

    2011-01-01

    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 spatiotemporal decomposition 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 spatiotemporal patterns and the evolution 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

  10. Transcriptomic Analysis of the Claudin Interactome in Malignant Pleural Mesothelioma: Evaluation of the Effect of Disease Phenotype, Asbestos Exposure, and CDKN2A Deletion Status

    PubMed Central

    Rouka, Erasmia; Vavougios, Georgios D.; Solenov, Evgeniy I.; Gourgoulianis, Konstantinos I.; Hatzoglou, Chrissi; Zarogiannis, Sotirios G.

    2017-01-01

    Malignant pleural mesothelioma (MPM) is a highly aggressive tumor primarily associated with asbestos exposure. Early detection of MPM is restricted by the long latency period until clinical presentation, the ineffectiveness of imaging techniques in early stage detection and the lack of non-invasive biomarkers with high sensitivity and specificity. In this study we used transcriptome data mining in order to determine which CLAUDIN (CLDN) genes are differentially expressed in MPM as compared to controls. Using the same approach we identified the interactome of the differentially expressed CLDN genes and assessed their expression profile. Subsequently, we evaluated the effect of tumor histology, asbestos exposure, CDKN2A deletion status, and gender on the gene expression level of the claudin interactome. We found that 5 out of 15 studied CLDNs (4, 5, 8, 10, 15) and 4 out of 27 available interactors (S100B, SHBG, CDH5, CXCL8) were differentially expressed in MPM specimens vs. healthy tissues. The genes encoding the CLDN-15 and S100B proteins present differences in their expression profile between the three histological subtypes of MPM. Moreover, CLDN-15 is significantly under-expressed in the cohort of patients with previous history of asbestos exposure. CLDN-15 was also found significantly underexpressed in patients lacking the CDKN2A gene. These results warrant the detailed in vitro investigation of the role of CDLN-15 in the pathobiology of MPM. PMID:28377727

  11. Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases

    SciTech Connect

    Smedley, Damian; Kohler, Sebastian; Czeschik, Johanna Christina; Amberger, Joanna; Bocchini, Carol; Hamosh, Ada; Veldboer, Julian; Zemojtel, Tomasz; Robinson, Peter N.

    2014-07-30

    Here, whole-exome sequencing (WES) has opened up previously unheard of possibilities for identifying novel disease genes in Mendelian disorders, only about half of which have been elucidated to date. However, interpretation of WES data remains challenging. As a result, we analyze protein–protein association (PPA) networks to identify candidate genes in the vicinity of genes previously implicated in a disease. The analysis, using a random-walk with restart (RWR) method, is adapted to the setting of WES by developing a composite variant-gene relevance score based on the rarity, location and predicted pathogenicity of variants and the RWR evaluation of genes harboring the variants. Benchmarking using known disease variants from 88 disease-gene families reveals that the correct gene is ranked among the top 10 candidates in ≥50% of cases, a figure which we confirmed using a prospective study of disease genes identified in 2012 and PPA data produced before that date. In conclusion, we implement our method in a freely available Web server, ExomeWalker, that displays a ranked list of candidates together with information on PPAs, frequency and predicted pathogenicity of the variants to allow quick and effective searches for candidates that are likely to reward closer investigation.

  12. Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases

    DOE PAGES

    Smedley, Damian; Kohler, Sebastian; Czeschik, Johanna Christina; ...

    2014-07-30

    Here, whole-exome sequencing (WES) has opened up previously unheard of possibilities for identifying novel disease genes in Mendelian disorders, only about half of which have been elucidated to date. However, interpretation of WES data remains challenging. As a result, we analyze protein–protein association (PPA) networks to identify candidate genes in the vicinity of genes previously implicated in a disease. The analysis, using a random-walk with restart (RWR) method, is adapted to the setting of WES by developing a composite variant-gene relevance score based on the rarity, location and predicted pathogenicity of variants and the RWR evaluation of genes harboring themore » variants. Benchmarking using known disease variants from 88 disease-gene families reveals that the correct gene is ranked among the top 10 candidates in ≥50% of cases, a figure which we confirmed using a prospective study of disease genes identified in 2012 and PPA data produced before that date. In conclusion, we implement our method in a freely available Web server, ExomeWalker, that displays a ranked list of candidates together with information on PPAs, frequency and predicted pathogenicity of the variants to allow quick and effective searches for candidates that are likely to reward closer investigation.« less

  13. Spatio-Temporal Patterns of Schistosomiasis Japonica in Lake and Marshland Areas in China: The Effect of Snail Habitats

    PubMed Central

    Hu, Yi; Gao, Jie; Chi, Meina; Luo, Can; Lynn, Henry; Sun, Liqian; Tao, Bo; Wang, Decheng; Zhang, Zhijie; Jiang, Qingwu

    2014-01-01

    The progress of the integrated control policy for schistosomiasis implemented since 2005 in China, which is aiming at reducing the roles of bovines and humans as infection sources, may be challenged by persistent presence of infected snails in lake and marshland areas. Based on annual parasitologic data for schistosomiasis during 2004–2011 in Xingzi County, a spatio-temporal kriging model was used to investigate the spatio-temporal pattern of schistosomiasis risk. Results showed that environmental factors related to snail habitats can explain the spatio-temporal variation of schistosomiasis. Predictive maps of schistosomiasis risk illustrated that clusters of the disease fluctuated during 2004–2008; there was an extensive outbreak in 2008 and attenuated disease occurrences afterwards. An area with an annually constant cluster of schistosomiasis was identified. Our study suggests that targeting snail habitats located within high-risk areas for schistosomiasis would be an economic and sustainable way of schistosomiasis control in the future. PMID:24980498

  14. Controlling spatiotemporal chaos in active dissipative-dispersive nonlinear systems

    NASA Astrophysics Data System (ADS)

    Gomes, S. N.; Pradas, M.; Kalliadasis, S.; Papageorgiou, D. T.; Pavliotis, G. A.

    2015-08-01

    We present an alternative methodology for the stabilization and control of infinite-dimensional dynamical systems exhibiting low-dimensional spatiotemporal chaos. We show that with an appropriate choice of time-dependent controls we are able to stabilize and/or control all stable or unstable solutions, including steady solutions, traveling waves (single and multipulse ones or bound states), and spatiotemporal chaos. We exemplify our methodology with the generalized Kuramoto-Sivashinsky equation, a paradigmatic model of spatiotemporal chaos, which is known to exhibit a rich spectrum of wave forms and wave transitions and a rich variety of spatiotemporal structures.

  15. A network medicine approach to quantify distance between hereditary disease modules on the interactome

    NASA Astrophysics Data System (ADS)

    Caniza, Horacio; Romero, Alfonso E.; Paccanaro, Alberto

    2015-12-01

    We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature. We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure.

  16. A network medicine approach to quantify distance between hereditary disease modules on the interactome

    PubMed Central

    Caniza, Horacio; Romero, Alfonso E.; Paccanaro, Alberto

    2015-01-01

    We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature. We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure. PMID:26631976

  17. A network medicine approach to quantify distance between hereditary disease modules on the interactome.

    PubMed

    Caniza, Horacio; Romero, Alfonso E; Paccanaro, Alberto

    2015-12-03

    We introduce a MeSH-based method that accurately quantifies similarity between heritable diseases at molecular level. This method effectively brings together the existing information about diseases that is scattered across the vast corpus of biomedical literature. We prove that sets of MeSH terms provide a highly descriptive representation of heritable disease and that the structure of MeSH provides a natural way of combining individual MeSH vocabularies. We show that our measure can be used effectively in the prediction of candidate disease genes. We developed a web application to query more than 28.5 million relationships between 7,574 hereditary diseases (96% of OMIM) based on our similarity measure.

  18. Integrated interactions database: tissue-specific view of the human and model organism interactomes.

    PubMed

    Kotlyar, Max; Pastrello, Chiara; Sheahan, Nicholas; Jurisica, Igor

    2016-01-04

    IID (Integrated Interactions Database) is the first database providing tissue-specific protein-protein interactions (PPIs) for model organisms and human. IID covers six species (S. cerevisiae (yeast), C. elegans (worm), D. melonogaster (fly), R. norvegicus (rat), M. musculus (mouse) and H. sapiens (human)) and up to 30 tissues per species. Users query IID by providing a set of proteins or PPIs from any of these organisms, and specifying species and tissues where IID should search for interactions. If query proteins are not from the selected species, IID enables searches across species and tissues automatically by using their orthologs; for example, retrieving interactions in a given tissue, conserved in human and mouse. Interaction data in IID comprises three types of PPI networks: experimentally detected PPIs from major databases, orthologous PPIs and high-confidence computationally predicted PPIs. Interactions are assigned to tissues where their proteins pairs or encoding genes are expressed. IID is a major replacement of the I2D interaction database, with larger PPI networks (a total of 1,566,043 PPIs among 68,831 proteins), tissue annotations for interactions, and new query, analysis and data visualization capabilities. IID is available at http://ophid.utoronto.ca/iid.

  19. Mitochondrial protein interactome elucidated by chemical cross-linking mass spectrometry.

    PubMed

    Schweppe, Devin K; Chavez, Juan D; Lee, Chi Fung; Caudal, Arianne; Kruse, Shane E; Stuppard, Rudy; Marcinek, David J; Shadel, Gerald S; Tian, Rong; Bruce, James E

    2017-02-14

    Mitochondrial protein interactions and complexes facilitate mitochondrial function. These complexes range from simple dimers to the respirasome supercomplex consisting of oxidative phosphorylation complexes I, III, and IV. To improve understanding of mitochondrial function, we used chemical cross-linking mass spectrometry to identify 2,427 cross-linked peptide pairs from 327 mitochondrial proteins in whole, respiring murine mitochondria. In situ interactions were observed in proteins throughout the electron transport chain membrane complexes, ATP synthase, and the mitochondrial contact site and cristae organizing system (MICOS) complex. Cross-linked sites showed excellent agreement with empirical protein structures and delivered complementary constraints for in silico protein docking. These data established direct physical evidence of the assembly of the complex I-III respirasome and enabled prediction of in situ interfacial regions of the complexes. Finally, we established a database and tools to harness the cross-linked interactions we observed as molecular probes, allowing quantification of conformation-dependent protein interfaces and dynamic protein complex assembly.

  20. Spatiotemporal dynamics of random stimuli account for trial-to-trial variability in perceptual decision making.

    PubMed

    Park, Hame; Lueckmann, Jan-Matthis; von Kriegstein, Katharina; Bitzer, Sebastian; Kiebel, Stefan J

    2016-01-11

    Decisions in everyday life are prone to error. Standard models typically assume that errors during perceptual decisions are due to noise. However, it is unclear how noise in the sensory input affects the decision. Here we show that there are experimental tasks for which one can analyse the exact spatio-temporal details of a dynamic sensory noise and better understand variability in human perceptual decisions. Using a new experimental visual tracking task and a novel Bayesian decision making model, we found that the spatio-temporal noise fluctuations in the input of single trials explain a significant part of the observed responses. Our results show that modelling the precise internal representations of human participants helps predict when perceptual decisions go wrong. Furthermore, by modelling precisely the stimuli at the single-trial level, we were able to identify the underlying mechanism of perceptual decision making in more detail than standard models.

  1. Transition effects characterization on spatiotemporal images

    NASA Astrophysics Data System (ADS)

    Ruiloba, Rosa I.; Joly, Philippe

    2000-10-01

    This article presents the results of a study on spatio-temporal images to evaluate their performances for video-to-shots segmentation purposes. Some shots segmentation methods involve spatio-temporal images that are computed by a projection of successive video frames over the X or Y-axis. On these projections, transition effects and motion are supposed to have different characteristics. Whereas cuts can be easily recognized, the main problem remains in determining a measure that discriminates motions from gradual transition effects. In this article, the quality of transition detections based on line similarity of spatio-temporal images is studied. The probability functions of several measures are estimated to determine which one produce the lowest detection error rate. These distributions are computed on four classes of events: intra shot sequences without motion, sequences with cuts, sequences with fades and sequences with motion. A line matching is performed, based on correlation estimations between projection lines. To separate these classes, we estimate first the density probability functions of the correlation between consecutive lines for each class. For different line segment sizes, the experimental results prove that the class separation can not be clearly produced. To take into account the evolution of the correlation and because we try to detect some particular types of boundaries, we then consider ratios between statistic moments. There are computed over a subset of correlation values. The results show that used measures, based on the matching of projection lines, can not discriminate between motion and fade. Only a subset of motions will be differentiated from gradual transitions. Therefore previous measures should be combined with methods that produce complementary results. Such a method could be a similar measure based on correlation between spatial-shifted segments.

  2. Spatiotemporal evolution of bacterial biofilm colonies

    NASA Astrophysics Data System (ADS)

    Wilking, James; Koehler, Stephan; Sinha, Naveen; Seminara, Agnese; Brenner, Michael; Weitz, David

    2014-03-01

    Many bacteria on earth live in surface-attached communities known as biofilms. Gene expression in a biofilm is typically varied, resulting in a variety of phenotypes within a single film. These phenotypes play a critical role in biofilm physiology and development. We use time-resolved, wide-field fluorescence microscopy to image triple-labeled fluorescent Bacillus Subtilis colonies grown on agar to determine in a non-invasive fashion the evolving phenotypes. We infer their transition rates from the resulting spatiotemporal maps of gene expression. Moreover, we correlate these transition rates with local measurements of nutrient concentration to determine the influence of extracellular signals on gene expression.

  3. Spatiotemporal rogue events in femtosecond filamentation

    SciTech Connect

    Majus, D.; Jukna, V.; Valiulis, G.; Dubietis, A.; Faccio, D.

    2011-02-15

    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.

  4. Spatio-temporal population estimates for risk management

    NASA Astrophysics Data System (ADS)

    Cockings, Samantha; Martin, David; Smith, Alan; Martin, Rebecca

    2013-04-01

    Accurate estimation of population at risk from hazards and effective emergency management of events require not just appropriate spatio-temporal modelling of hazards but also of population. While much recent effort has been focused on improving the modelling and predictions of hazards (both natural and anthropogenic), there has been little parallel advance in the measurement or modelling of population statistics. Different hazard types occur over diverse temporal cycles, are of varying duration and differ significantly in their spatial extent. Even events of the same hazard type, such as flood events, vary markedly in their spatial and temporal characteristics. Conceptually and pragmatically then, population estimates should also be available for similarly varying spatio-temporal scales. Routine population statistics derived from traditional censuses or surveys are usually static representations in both space and time, recording people at their place of usual residence on census/survey night and presenting data for administratively defined areas. Such representations effectively fix the scale of population estimates in both space and time, which is unhelpful for meaningful risk management. Over recent years, the Pop24/7 programme of research, based at the University of Southampton (UK), has developed a framework for spatio-temporal modelling of population, based on gridded population surfaces. Based on a data model which is fully flexible in terms of space and time, the framework allows population estimates to be produced for any time slice relevant to the data contained in the model. It is based around a set of origin and destination centroids, which have capacities, spatial extents and catchment areas, all of which can vary temporally, such as by time of day, day of week, season. A background layer, containing information on features such as transport networks and landuse, provides information on the likelihood of people being in certain places at specific times

  5. Differential Regulation of Cryptic Genetic Variation Shapes the Genetic Interactome Underlying Complex Traits

    PubMed Central

    Yadav, Anupama; Dhole, Kaustubh

    2016-01-01

    Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets. PMID:28172852

  6. The quantum basis of spatiotemporality in perception and consciousness.

    PubMed

    Igamberdiev, Abir U; Shklovskiy-Kordi, Nikita E

    2017-02-21

    Living systems inhabit the area of the world which is shaped by the predictable space-time of physical objects and forces that can be incorporated into their perception pattern. The process of selecting a "habitable" space-time is the internal quantum measurement in which living systems become embedded into the environment that supports their living state. This means that living organisms choose a coordinate system in which the influence of measurement is minimal. We discuss specific roles of biological macromolecules, in particular of the cytoskeleton, in shaping perception patterns formed in the internal measurement process. Operation of neuron is based on the transmission of signals via cytoskeleton where the digital output is generated that can be decoded through a reflective action of the perceiving agent. It is concluded that the principle of optimality in biology as formulated by Liberman et al. (BioSystems 22, 135-154, 1989) is related to the establishment of spatiotemporal patterns that are maximally predictable and can hold the living state for a prolonged time. This is achieved by the selection of a habitable space approximated to the conditions described by classical physics.

  7. Noise can prevent onset of chaos in spatiotemporal population dynamics

    NASA Astrophysics Data System (ADS)

    Petrovskii, S.; Morozov, A.; Malchow, H.; Sieber, M.

    2010-11-01

    Many theoretical approaches predict the dynamics of interacting populations to be chaotic but that has very rarely been observed in ecological data. It has therefore risen a question about factors that can prevent the onset of chaos by, for instance, making the population fluctuations synchronized over the whole habitat. One such factor is stochasticity. The so-called Moran effect predicts that a spatially correlated noise can synchronize the local population dynamics in a spatially discrete system, thus preventing the onset of spatiotemporal chaos. On the whole, however, the issue of noise has remained controversial and insufficiently understood. In particular, a well-built nonspatial theory infers that noise enhances chaos by making the system more sensitive to the initial conditions. In this paper, we address the problem of the interplay between deterministic dynamics and noise by considering a spatially explicit predator-prey system where some parameters are affected by noise. Our findings are rather counter-intuitive. We show that a small noise (i.e. preserving the deterministic skeleton) can indeed synchronize the population oscillations throughout space and hence keep the dynamics regular, but the dependence of the chaos prevention probability on the noise intensity is of resonance type. Once chaos has developed, it appears to be stable with respect to a small noise but it can be suppressed by a large noise. Finally, we show that our results are in a good qualitative agreement with some available field data.

  8. Workload induced spatio-temporal distortions and safety of flight

    SciTech Connect

    Barrett, C.L.; Weisgerber, S.A.; Naval Weapons Center, China Lake, CA )

    1989-01-01

    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.

  9. Asynchronous Visualization of Spatiotemporal Information for Multiple Moving Targets

    ERIC Educational Resources Information Center

    Wang, Huadong

    2013-01-01

    In the modern information age, the quantity and complexity of spatiotemporal data is increasing both rapidly and continuously. Sensor systems with multiple feeds that gather multidimensional spatiotemporal data will result in information clusters and overload, as well as a high cognitive load for users of these systems. To meet future…

  10. Spatiotemporal Data Mining, Analysis, and Visualization of Human Activity Data

    ERIC Educational Resources Information Center

    Li, Xun

    2012-01-01

    This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data…

  11. Faster P300 Classifier Training Using Spatiotemporal Beamforming.

    PubMed

    Wittevrongel, Benjamin; Van Hulle, Marc M

    2016-05-01

    The linearly-constrained minimum-variance (LCMV) beamformer is traditionally used as a spatial filter for source localization, but here we consider its spatiotemporal extension for P300 classification. We compare two variants and show that the spatiotemporal LCMV beamformer is at par with state-of-the-art P300 classifiers, but several orders of magnitude faster in training the classifier.

  12. Characterization of the Cardiac Overexpression of HSPB2 Reveals Mitochondrial and Myogenic Roles Supported by a Cardiac HspB2 Interactome

    PubMed Central

    Grose, Julianne H.; Langston, Kelsey; Wang, Xiaohui; Squires, Shayne; Mustafi, Soumyajit Banerjee; Hayes, Whitney; Neubert, Jonathan; Fischer, Susan K.; Fasano, Matthew; Saunders, Gina Moore; Dai, Qiang; Christians, Elisabeth; Lewandowski, E. Douglas; Ping, Peipei; Benjamin, Ivor J.

    2015-01-01

    Small Heat Shock Proteins (sHSPs) are molecular chaperones that transiently interact with other proteins, thereby assisting with quality control of proper protein folding and/or degradation. They are also recruited to protect cells from a variety of stresses in response to extreme heat, heavy metals, and oxidative-reductive stress. Although ten human sHSPs have been identified, their likely diverse biological functions remain an enigma in health and disease, and much less is known about non-redundant roles in selective cells and tissues. Herein, we set out to comprehensively characterize the cardiac-restricted Heat Shock Protein B-2 (HspB2), which exhibited ischemic cardioprotection in transgenic overexpressing mice including reduced infarct size and maintenance of ATP levels. Global yeast two-hybrid analysis using HspB2 (bait) and a human cardiac library (prey) coupled with co-immunoprecipitation studies for mitochondrial target validation revealed the first HspB2 “cardiac interactome” to contain many myofibril and mitochondrial-binding partners consistent with the overexpression phenotype. This interactome has been submitted to the Biological General Repository for Interaction Datasets (BioGRID). A related sHSP chaperone HspB5 had only partially overlapping binding partners, supporting specificity of the interactome as well as non-redundant roles reported for these sHSPs. Evidence that the cardiac yeast two-hybrid HspB2 interactome targets resident mitochondrial client proteins is consistent with the role of HspB2 in maintaining ATP levels and suggests new chaperone-dependent functions for metabolic homeostasis. One of the HspB2 targets, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), has reported roles in HspB2 associated phenotypes including cardiac ATP production, mitochondrial function, and apoptosis, and was validated as a potential client protein of HspB2 through chaperone assays. From the clientele and phenotypes identified herein, it is tempting to

  13. Spatiotemporal-atlas-based dynamic speech imaging

    NASA Astrophysics Data System (ADS)

    Fu, Maojing; Woo, Jonghye; Liang, Zhi-Pei; Sutton, Bradley P.

    2016-03-01

    Dynamic magnetic resonance imaging (DS-MRI) has been recognized as a promising method for visualizing articulatory motion of speech in scientific research and clinical applications. However, characterization of the gestural and acoustical properties of the vocal tract remains a challenging task for DS-MRI because it requires: 1) reconstructing high-quality spatiotemporal images by incorporating stronger prior knowledge; and 2) quantitatively interpreting the reconstructed images that contain great motion variability. This work presents a novel imaging method that simultaneously meets both requirements by integrating a spatiotemporal atlas into a Partial Separability (PS) model-based imaging framework. Through the use of an atlas-driven sparsity constraint, this method is capable of capturing high-quality articulatory dynamics at an imaging speed of 102 frames per second and a spatial resolution of 2.2 × 2.2 mm2. Moreover, the proposed method enables quantitative characterization of variability of speech motion, compared to the generic motion pattern across all subjects, through the spatial residual components.

  14. Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas.

    PubMed

    Pirani, Monica; Gulliver, John; Fuller, Gary W; Blangiardo, Marta

    2014-01-01

    This paper describes a Bayesian hierarchical approach to predict short-term concentrations of particle pollution in an urban environment, with application to inhalable particulate matter (PM10) in Greater London. We developed and compared several spatiotemporal models that differently accounted for factors affecting the spatiotemporal properties of particle concentrations. We considered two main source contributions to ambient measurements: (i) the long-range transport of the secondary fraction of particles, which temporal variability was described by a latent variable derived from rural concentrations; and (ii) the local primary component of particles (traffic- and non-traffic-related) captured by the output of the dispersion model ADMS-Urban, which site-specific effect was described by a Bayesian kriging. We also assessed the effect of spatiotemporal covariates, including type of site, daily temperature to describe the seasonal changes in chemical processes affecting local PM10 concentrations that are not considered in local-scale dispersion models and day of the week to account for time-varying emission rates not available in emissions inventories. The evaluation of the predictive ability of the models, obtained via a cross-validation approach, revealed that concentration estimates in urban areas benefit from combining the city-scale particle component and the long-range transport component with covariates that account for the residual spatiotemporal variation in the pollution process.

  15. Spatiotemporal temperature profiling of corneal surface during LTK

    NASA Astrophysics Data System (ADS)

    Papaioannou, Thanassis; Maguen, Ezra I.; Grundfest, Warren S.

    2002-06-01

    Accurate prediction of LTK treatments requires refined thermal corneal models which necessitate precise input parameters. The overall objective of this study was to provide detailed information on the spatiotemporal temperature profile of the corneal surface, during in-vitro thermal keratoplasty. LTK was performed in-vitro on freshly harvested porcine eyes (N equals 16) with the Sunrise Technologies corneal shaping system (Model SUN 1000). Spatiotemporal thermal imaging of the irradiated corneas were obtained with a short wave Inframetrics thermal camera (Model PM290). Images were obtained at 8-bits resolution, with ~100 microns spatial and ~17 msec temporal resolution respectively. Treatment pattern consisted of eight spots at 6 mm zone, while lasing was conducted at settings of either 100 mJ and 15 pulses (N equals 8), or 260 mJ and 7 pulses (N equals 8). Temporal and spatial variation of the corneal surface temperatures were calculated at locations of importance to LTK. At the laser spot, temperature profiles consisted of transients coinciding approximately with the laser pulses. Maximum transient temperatures observed were 98.0+/- 4.6 degree(s)C for the high and 56.3+/- 2.6 degree(s)C for the low energy respectively. These temperature transients were superimposed on an envelope of lower-slowly varying temperatures. The maximum temperatures observed for this temperature envelope, were 51.8+/- 3.4 degree(s)C for the high and 35.4+/- 3.4 degree(s)C for the low energy respectively. The evolution of either the maximum temperature transients or the lower temperature envelope, followed exponential growth of the form: T equals A * exp(B*t). Maximum temperatures at locations 0.5 mm and 1 mm away from the laser spot, reached 25.7 degree(s)C and 23.3 degree(s)C for the low energy, and 34 degree(s)C and 25.6 degree(s)C for the high energy settings respectively. Temperature decay constants were approximately 2 to 3 sec, while the spatial temperature profile at the laser

  16. Analysis of the Rab GTPase Interactome in Dendritic Cells Reveals Anti-microbial Functions of the Rab32 Complex in Bacterial Containment.

    PubMed

    Li, Yuanyuan; Wang, Yu; Zou, Liyun; Tang, Xiangyu; Yang, Yi; Ma, Li; Jia, Qingzhu; Ni, Qingshan; Liu, Siqi; Tang, Lizhang; Lin, Regina; Wong, Elizabeth; Sun, Wei; Wang, Liting; Wei, Quanfang; Ran, Haiying; Zhang, Liqun; Lian, Hengning; Huang, Wei; Wu, Yuzhang; Li, Qi-Jing; Wan, Ying

    2016-02-16

    Dendritic cells (DCs) orchestrate complex membrane trafficking through an interconnected transportation network linked together by Rab GTPases. Through a tandem affinity purification strategy and mass spectrometry, we depicted an interactomic landscape of major members of the mammalian Rab GTPase family. When complemented with imaging tools, this proteomic analysis provided a global view of intracellular membrane organization. Driven by this analysis, we investigated dynamic changes to the Rab32 subnetwork in DCs induced by L. monocytogenes infection and uncovered an essential role of this subnetwork in controlling the intracellular proliferation of L. monocytogenes. Mechanistically, Rab32 formed a persistent complex with two interacting proteins, PHB and PHB2, to encompass bacteria both during early phagosome formation and after L. monocytogenes escaped the original containment vacuole. Collectively, we have provided a functional compartmentalization overview and an organizational framework of intracellular Rab-mediated vesicle trafficking that can serve as a resource for future investigations.

  17. A Comprehensive Membrane Interactome Mapping of Sho1p Reveals Fps1p as a Novel Key Player in the Regulation of the HOG Pathway in S. cerevisiae

    PubMed Central

    Lam, Mandy Hiu Yi; Snider, Jamie; Rehal, Monique; Wong, Victoria; Aboualizadeh, Farzaneh; Drecun, Luka; Wong, Olivia; Jubran, Bellal; Li, Meirui; Ali, Mehrab; Jessulat, Matthew; Deineko, Viktor; Miller, Rachel; Lee, Mid eum; Park, Hay-Oak; Davidson, Alan; Babu, Mohan; Stagljar, Igor

    2017-01-01

    Sho1p, an integral membrane protein, plays a vital role in the high-osmolarity glycerol (HOG) mitogen-activated protein kinase pathway in the yeast Saccharomyces cerevisiae. Activated under conditions of high osmotic stress, it interacts with other HOG pathway proteins to mediate cell signaling events, ensuring that yeast cells can adapt and remain viable. In an attempt to further understand how the function of Sho1p is regulated through its protein–protein interactions (PPIs), we identified 49 unique Sho1p PPIs through the use of membrane yeast two-hybrid (MYTH), an assay specifically suited to identify PPIs of full-length integral membrane proteins in their native membrane environment. Secondary validation by literature search, or two complementary PPI assays, confirmed 80% of these interactions, resulting in a high-quality Sho1p interactome. This set of putative PPIs included both previously characterized interactors, along with a large subset of interactors that have not been previously identified as binding to Sho1p. The SH3 domain of Sho1p was found to be important for binding to many of these interactors. One particular novel interactor of interest is the glycerol transporter Fps1p, which was shown to require the SH3 domain of Sho1p for binding via its N-terminal soluble regulatory domain. Furthermore, we found that Fps1p is involved in the positive regulation of Sho1p function and plays a role in the phosphorylation of the downstream kinase Hog1p. This study represents the largest membrane interactome analysis of Sho1p to date and complements past studies on the HOG pathway by increasing our understanding of Sho1p regulation. PMID:25644660

  18. Spatiotemporal Stochastic Resonance:Theory and Experiment

    NASA Astrophysics Data System (ADS)

    Peter, Jung

    1996-03-01

    The amplification of weak periodic signals in bistable or excitable systems via stochastic resonance has been studied intensively over the last years. We are going one step further and ask: Can noise enhance spatiotemporal patterns in excitable media and can this effect be observed in nature? To this end, we are looking at large, two dimensional arrays of coupled excitable elements. Due to the coupling, excitation can propagate through the array in form of nonlinear waves. We observe target waves, rotating spiral waves and other wave forms. If the coupling between the elements is below a critical threshold, any excitational pattern will die out in the absence of noise. Below this threshold, large scale rotating spiral waves - as they are observed above threshold - can be maintained by a proper level of the noise[1]. Furthermore, their geometric features, such as the curvature can be controlled by the homogeneous noise level[2]. If the noise level is too large, break up of spiral waves and collisions with spontaneously nucleated waves yields spiral turbulence. Driving our array with a spatiotemporal pattern, e.g. a rotating spiral wave, we show that for weak coupling the excitational response of the array shows stochastic resonance - an effect we have termed spatiotemporal stochastic resonance. In the last part of the talk I'll make contact with calcium waves, observed in astrocyte cultures and hippocampus slices[3]. A. Cornell-Bell and collaborators[3] have pointed out the role of calcium waves for long-range glial signaling. We demonstrate the similarity of calcium waves with nonlinear waves in noisy excitable media. The noise level in the tissue is characterized by spontaneous activity and can be controlled by applying neuro-transmitter substances[3]. Noise effects in our model are compared with the effect of neuro-transmitters on calcium waves. [1]P. Jung and G. Mayer-Kress, CHAOS 5, 458 (1995). [2]P. Jung and G. Mayer-Kress, Phys. Rev. Lett.62, 2682 (1995). [3

  19. Bayesian spatiotemporal interpolation of rainfall in the Central Chilean Andes

    NASA Astrophysics Data System (ADS)

    Ossa-Moreno, Juan; Keir, Greg; McIntyre, Neil

    2016-04-01

    Water availability in the populous and economically significant Central Chilean region is governed by complex interactions between precipitation, temperature, snow and glacier melt, and streamflow. Streamflow prediction at daily time scales depends strongly on accurate estimations of precipitation in this predominantly dry region, particularly during the winter period. This can be difficult as gauged rainfall records are scarce, especially in the higher elevation regions of the Chilean Andes, and topographic influences on rainfall are not well understood. Remotely sensed precipitation and topographic products can be used to construct spatiotemporal multivariate regression models to estimate rainfall at ungauged locations. However, classical estimation methods such as kriging cannot easily accommodate the complicated statistical features of the data, including many 'no rainfall' observations, as well as non-normality, non-stationarity, and temporal autocorrelation. We use a separable space-time model to predict rainfall using the R-INLA package for computationally efficient Bayesian inference, using the gridded CHIRPS satellite-based rainfall dataset and digital elevation models as covariates. We jointly model both the probability of rainfall occurrence on a given day (using a binomial likelihood) as well as amount (using a gamma likelihood or similar). Correlation in space and time is modelled using a Gaussian Markov Random Field (GMRF) with a Matérn spatial covariance function which can evolve over time according to an autoregressive model if desired. It is possible to evaluate the GMRF at relatively coarse temporal resolution to speed up computations, but still produce daily rainfall predictions. We describe the process of model selection and inference using an information criterion approach, which we use to objectively select from competing models with various combinations of temporal smoothing, likelihoods, and autoregressive model orders.

  20. The Role of the Beetle Hypocryphalus mangiferae (Coleoptera: Curculionidae) in the Spatiotemporal Dynamics of Mango Wilt.

    PubMed

    Galdino, Tarcísio Visintin da Silva; Ferreira, Dalton de Oliveira; Santana Júnior, Paulo Antônio; Arcanjo, Lucas de Paulo; Queiroz, Elenir Aparecida; Sarmento, Renato Almeida; Picanço, Marcelo Coutinho

    2017-03-03

    The knowledge of the spatiotemporal dynamics of pathogens and their vectors is an important step in determining the pathogen dispersion pattern and the role of vectors in disease dynamics. However, in the case of mango wilt little is known about its spatiotemporal dynamics and the relationship of its vector [the beetle Hypocryphalus mangiferae (Stebbing 1914)] to these dynamics. The aim of this work was to determine the spatial-seasonal dynamic of H. mangiferae attacks and mango wilt in mango orchards and to verify the importance of H. mangiferae in the spatiotemporal dynamics of the disease. Two mango orchards were monitored during a period of 3 yr. The plants in these orchards were georeferenced and inspected monthly to quantify the number of plants attacked by beetles and the fungus. In these orchards, the percentage of mango trees attacked by beetles was always higher than the percentage infected by the fungus. The colonization of mango trees by beetles and the fungus occurred by colonization of trees both distant and proximal to previously attacked trees. The new plants attacked by the fungus emerged in places where the beetles had previously begun their attack. This phenomenon led to a large overlap in sites of beetle and fungal occurrence, indicating that establishment by the beetle was followed by establishment by the fungus. This information can be used by farmers to predict disease infection, and to control bark beetle infestation in mango orchards.

  1. Spatiotemporal multifractal characteristics of electromagnetic radiation in response to deep coal rock bursts

    NASA Astrophysics Data System (ADS)

    Hu, S.; Wang, E.; Liu, X.

    2014-08-01

    Dynamic collapses of deeply mined coal rocks are severe threats to miners; in order to predict collapses more accurately using electromagnetic radiation (EMR), we investigate the spatiotemporal multifractal characteristics and formation mechanism of EMR induced by underground coal mining. Coal rock in the burst-prone zone often exchanges materials (gas, water and coal) and energy with its environment and gradually transitions from its original stable equilibrium structure to a nonequilibrium dissipative structure with implicit spatiotemporal complexity or multifractal structures, resulting in temporal variation in multifractal EMR. The inherent law of EMR time series during damage evolution was analyzed by using time-varying multifractal theory. Results show that the time-varying multifractal characteristics of EMR are determined by damage evolution processes. Moreover, the dissipated energy caused by the damage evolutions, such as crack propagation, fractal sliding and shearing, can be regarded as the fingerprint of various EMR micro-mechanics. The dynamic spatiotemporal multifractal spectrum of EMR considers both spatial (multiple fractures) and temporal (dynamic evolution) characteristics of coal rocks and records the dynamic evolution processes of rock bursts. Thus, it can be used to evaluate the coal deformation and fracture process. The study is of significance for us to understand the EMR mechanism in detail and to increase the accuracy of the EMR method in forecasting dynamic disasters.

  2. Spatiotemporal multifractal characteristics of electromagnetic radiation in response to deep coal rock bursts

    NASA Astrophysics Data System (ADS)

    Shaobin, H.; Enyuan, W.; Xiaofei, L.

    2014-04-01

    Dynamic collapses of deeply mined coal rocks are severe threats to miners, in order to predict the collapses more accurately using electromagnetic radiation (EMR), we investigate the spatiotemporal multifractal characteristics and formation mechanism of EMR induced by underground coal mining. Coal rock in the burst-prone zone often exchanges materials and energy with its environment and gradually transits from its original stable equilibrium structure to a non-equilibrium dissipative structure with implicit spatiotemporal complexity or multifractal structures, resulting in temporal variation in multifractal EMR. The inherent law of EMR time series during damage evolution was analyzed by using time-varying multifractal theory. Results show that the time-varying multifractal characteristics of EMR are determined by damage evolutions process, the dissipated energy caused by damage evolutions such as crack propagation, fractal sliding and shearing can be regarded as the fingerprint of various EMR micro-mechanics. Dynamic spatiotemporal multifractal spectrum of EMR considers both spatial (multiple fractures) and temporal (dynamic evolution) characteristics of coal rocks, and records the dynamic evolution processes of rock bursts. Thus, it can be used to evaluate the coal deformation and fracture process. The study is of significance for us to in-depth understand EMR mechanism and to increase the accuracy of applying the EMR method to forecast dynamic disasters.

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

    NASA Astrophysics Data System (ADS)

    Bhattacharjee, S.; Ghosh, S. K.

    2015-07-01

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

  4. Spatiotemporal Co-existence of Female Thyroid and Breast Cancers in Hangzhou, China

    PubMed Central

    Fei, Xufeng; Christakos, George; Lou, Zhaohan; Ren, Yanjun; Liu, Qingmin; Wu, Jiaping

    2016-01-01

    Thyroid and breast cancers (TC, BC) are common female malignant tumors worldwide. Studies suggest that TC patients have a higher BC risk, and vice versa. However, it has not been investigated quantitatively if there is an association between the space-time TC and BC incidence distributions at the population level. This work aims to answer this question. 5358 TC and 8784 BC (female) cases were diagnosed in Hangzhou (China, 2008–2012). Pearson and Spearman rank correlation coefficients of the TC and BC incidences were high, and their patterns were geographically similar. The spatiotemporal co-existence of TC and BC distributions was investigated using the integrative disease predictability (IDP) criterion: if TC-BC association is part of the disease mapping knowledge bases, it should yield improved space-time incidence predictions. Improved TC (BC) incidence predictions were generated when integrating both TC and BC data than when using only TC (BC) data. IDP consistently demonstrated the spatiotemporal co-existence of TC and BC distributions throughout Hangzhou (2008–2012), which means that when the population experiences high incidences of one kind of cancer attention should be paid to the other kind of cancer too. The strength of TC-BC association was measured by the IDP coefficients and incidence prediction accuracy. PMID:27341638

  5. Spatiotemporal Co-existence of Female Thyroid and Breast Cancers in Hangzhou, China.

    PubMed

    Fei, Xufeng; Christakos, George; Lou, Zhaohan; Ren, Yanjun; Liu, Qingmin; Wu, Jiaping

    2016-06-24

    Thyroid and breast cancers (TC, BC) are common female malignant tumors worldwide. Studies suggest that TC patients have a higher BC risk, and vice versa. However, it has not been investigated quantitatively if there is an association between the space-time TC and BC incidence distributions at the population level. This work aims to answer this question. 5358 TC and 8784 BC (female) cases were diagnosed in Hangzhou (China, 2008-2012). Pearson and Spearman rank correlation coefficients of the TC and BC incidences were high, and their patterns were geographically similar. The spatiotemporal co-existence of TC and BC distributions was investigated using the integrative disease predictability (IDP) criterion: if TC-BC association is part of the disease mapping knowledge bases, it should yield improved space-time incidence predictions. Improved TC (BC) incidence predictions were generated when integrating both TC and BC data than when using only TC (BC) data. IDP consistently demonstrated the spatiotemporal co-existence of TC and BC distributions throughout Hangzhou (2008-2012), which means that when the population experiences high incidences of one kind of cancer attention should be paid to the other kind of cancer too. The strength of TC-BC association was measured by the IDP coefficients and incidence prediction accuracy.

  6. Spatiotemporal Co-existence of Female Thyroid and Breast Cancers in Hangzhou, China

    NASA Astrophysics Data System (ADS)

    Fei, Xufeng; Christakos, George; Lou, Zhaohan; Ren, Yanjun; Liu, Qingmin; Wu, Jiaping

    2016-06-01

    Thyroid and breast cancers (TC, BC) are common female malignant tumors worldwide. Studies suggest that TC patients have a higher BC risk, and vice versa. However, it has not been investigated quantitatively if there is an association between the space-time TC and BC incidence distributions at the population level. This work aims to answer this question. 5358 TC and 8784 BC (female) cases were diagnosed in Hangzhou (China, 2008–2012). Pearson and Spearman rank correlation coefficients of the TC and BC incidences were high, and their patterns were geographically similar. The spatiotemporal co-existence of TC and BC distributions was investigated using the integrative disease predictability (IDP) criterion: if TC-BC association is part of the disease mapping knowledge bases, it should yield improved space-time incidence predictions. Improved TC (BC) incidence predictions were generated when integrating both TC and BC data than when using only TC (BC) data. IDP consistently demonstrated the spatiotemporal co-existence of TC and BC distributions throughout Hangzhou (2008–2012), which means that when the population experiences high incidences of one kind of cancer attention should be paid to the other kind of cancer too. The strength of TC-BC association was measured by the IDP coefficients and incidence prediction accuracy.

  7. Optimal spatiotemporal reduced order modeling for nonlinear dynamical systems

    NASA Astrophysics Data System (ADS)

    LaBryer, Allen

    Proposed in this dissertation is a novel reduced order modeling (ROM) framework called optimal spatiotemporal reduced order modeling (OPSTROM) for nonlinear dynamical systems. The OPSTROM approach is a data-driven methodology for the synthesis of multiscale reduced order models (ROMs) which can be used to enhance the efficiency and reliability of under-resolved simulations for nonlinear dynamical systems. In the context of nonlinear continuum dynamics, the OPSTROM approach relies on the concept of embedding subgrid-scale models into the governing equations in order to account for the effects due to unresolved spatial and temporal scales. Traditional ROMs neglect these effects, whereas most other multiscale ROMs account for these effects in ways that are inconsistent with the underlying spatiotemporal statistical structure of the nonlinear dynamical system. The OPSTROM framework presented in this dissertation begins with a general system of partial differential equations, which are modified for an under-resolved simulation in space and time with an arbitrary discretization scheme. Basic filtering concepts are used to demonstrate the manner in which residual terms, representing subgrid-scale dynamics, arise with a coarse computational grid. Models for these residual terms are then developed by accounting for the underlying spatiotemporal statistical structure in a consistent manner. These subgrid-scale models are designed to provide closure by accounting for the dynamic interactions between spatiotemporal macroscales and microscales which are otherwise neglected in a ROM. For a given resolution, the predictions obtained with the modified system of equations are optimal (in a mean-square sense) as the subgrid-scale models are based upon principles of mean-square error minimization, conditional expectations and stochastic estimation. Methods are suggested for efficient model construction, appraisal, error measure, and implementation with a couple of well-known time

  8. Spatiotemporal dynamics of counterpropagating Airy beams

    PubMed Central

    Wiersma, Noémi; Marsal, Nicolas; Sciamanna, Marc; Wolfersberger, Delphine

    2015-01-01

    We analyse theoretically the spatiotemporal dynamics of two incoherent counterpropagating Airy beams interacting in a photorefractive crystal under focusing conditions. For a large enough nonlinearity strength the interaction between the two Airy beams leads to light-induced waveguiding. The stability of the waveguide is determined by the crystal length, the nonlinearity strength and the beam’s intensities and is improved when comparing to the situation using Gaussian beams. We further identify the threshold above which the waveguide is no longer static but evolves dynamically either time-periodically or even chaotically. Above the stability threshold, each Airy-soliton moves erratically between privileged output positions that correspond to the spatial positions of the lobes of the counterpropagating Airy beam. These results suggest new ways of creating dynamically varying waveguides, optical logic gates and chaos-based computing. PMID:26315530

  9. Nonlinear Synchronization Analysis of Spatiotemporal Heart Data

    NASA Astrophysics Data System (ADS)

    Simonotto, Jennifer D.; Furman, Michael D.; Spano, Mark L.; Ditto, William L.; Liu, Gang; Kavanagh, Katherine M.

    2003-08-01

    A high-speed video camera and voltage-sensitive dyes were used to acquire high resolution (80×80 pixels) and high-speed (500 μs/frame) optical signals of ventricular fibrillation in a Langendorff-perfused porcine heart. The resulting spatiotemporal dynamics were recorded before and after the application of a defibrillation shock in order to study the mechanism of defibrillation failure. We calculate nonlinear synchronization index measures to qualify the evolution of different types of activity on the heart surface (focal, reentry). We observe changes with time in the spatial distribution of the first Fourier mode, showing that two main types of activity compete on the heart surface during a failed defibrillation.

  10. Secure communication based on spatiotemporal chaos

    NASA Astrophysics Data System (ADS)

    Ren, Hai-Peng; Bai, Chao

    2015-08-01

    In this paper, we propose a novel approach to secure communication based on spatiotemporal chaos. At the transmitter end, the state variables of the coupled map lattice system are divided into two groups: one is used as the key to encrypt the plaintext in the N-shift encryption function, and the other is used to mix with the output of the N-shift function to further confuse the information to transmit. At the receiver end, the receiver lattices are driven by the received signal to synchronize with the transmitter lattices and an inverse procedure of the encoding is conducted to decode the information. Numerical simulation and experiment based on the TI TMS320C6713 Digital Signal Processor (DSP) show the feasibility and the validity of the proposed scheme. Project supported by the National Natural Science Foundation of China (Grant No. 61172070) and the Funds from the Science and Technology Innovation Team of Shaanxi Province, China (Grant No. 2013CKT-04).

  11. Cortical spatiotemporal dimensionality reduction for visual grouping.

    PubMed

    Cocci, Giacomo; Barbieri, Davide; Citti, Giovanna; Sarti, Alessandro

    2015-06-01

    The visual systems of many mammals, including humans, are able to integrate the geometric information of visual stimuli and perform cognitive tasks at the first stages of the cortical processing. This is thought to be the result of a combination of mechanisms, which include feature extraction at the single cell level and geometric processing by means of cell connectivity. We present a geometric model of such connectivities in the space of detected features associated with spatiotemporal visual stimuli and show how they can be used to obtain low-level object segmentation. The main idea is to define a spectral clustering procedure with anisotropic affinities over data sets consisting of embeddings of the visual stimuli into higher-dimensional spaces. Neural plausibility of the proposed arguments will be discussed.

  12. Concerning immune synapses: a spatiotemporal timeline

    PubMed Central

    Ortega-Carrion, Alvaro; Vicente-Manzanares, Miguel

    2016-01-01

    The term “immune synapse” was originally coined to highlight the similarities between the synaptic contacts between neurons in the central nervous system and the cognate, antigen-dependent interactions between T cells and antigen-presenting cells. Here, instead of offering a comprehensive molecular catalogue of molecules involved in the establishment, stabilization, function, and resolution of the immune synapse, we follow a spatiotemporal timeline that begins at the initiation of exploratory contacts between the T cell and the antigen-presenting cell and ends with the termination of the contact. We focus on specific aspects that distinguish synapses established by cytotoxic and T helper cells as well as unresolved issues and controversies regarding the formation of this intercellular structure. PMID:27092248

  13. Attention Modulates Spatio-temporal Grouping

    PubMed Central

    Aydın, Murat; Herzog, Michael H.; Öğmen, Haluk

    2011-01-01

    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. PMID:21266181

  14. Changes in measured spatiotemporal patterns of hydrological response after partial deforestation in a headwater catchment

    NASA Astrophysics Data System (ADS)

    Wiekenkamp, I.; Huisman, J. A.; Bogena, H. R.; Graf, A.; Lin, H. S.; Drüe, C.; Vereecken, H.

    2016-11-01

    Although the hydrological effects of land use change have been studied extensively, only few datasets are available to accurately describe, model, and predict detailed changes in spatiotemporal patterns of hydrological fluxes and states due to land use change. The Wüstebach catchment within the TERENO (TERrestrial Environmental Observatories) network in Germany provides a unique monitoring setup to measure the major components of the water balance (evapotranspiration, discharge, precipitation) and the spatiotemporal distribution of soil moisture before and after a partial deforestation. Here, we present five years of measured hydrological data, including all major water budget components three years before and two years after a partial deforestation. A data-driven approach was used to understand changes and related feedback mechanisms in spatiotemporal hydrological response patterns. As expected from earlier studies, the partial deforestation caused a decrease in evapotranspiration and an increase in discharge. A closer look at the high resolution datasets revealed new insights in the intra-annual variability and relationship between the water balance components. The overall decrease in evapotranspiration caused a large increase in soil water storage in the deforested region, especially during the summer period, which in turn caused an increase in the frequency of high discharge in the same period. Although the evapotranspiration in the forested region was larger on average, the deforested region showed a higher evapotranspiration during part of the summer period. This could be related to wetter conditions in the deforested area, accompanied with the emergence of grass vegetation. At the same time, wetter soil moisture conditions in the deforested area increased the spatial variance of soil moisture in the summer and therewith altered the relationship between spatial mean and variance. Altogether, this study illustrates that detailed spatiotemporal monitoring can

  15. Spatio-temporal Dependency of Extremes: Some Results and a Study on Indian Summer Precipitation Data

    NASA Astrophysics Data System (ADS)

    Chatterjee, S.

    2015-12-01

    Observations over spatial, temporal and spatiotemporal grids are expected to have statistical dependencies, and modeling to account for such dependencies adds complexity, but not necessarily value, in many cases of climate and other data analyses. Sometimes however, there may not be any additional dependency beyond what can be accounted for using covariates and fixed and random effects. Additionally, dependency patterns may not besimilar for different quantities of interest, for example, the dependency properties while modeling for a mean may not be the same as those obtained while modeling an extreme quantile. Thus, there is a need to quantify the degree of dependency, and test for different hypotheses including that of a lack of dependency, or structured patterns of dependency. We study a classical Space-Time Index measure suggested for testing spatio-temporal associations and show that it has low power in hypothesis tests and is generally unreliable. Newer, computation-driven statistical and machine learning methods for quantifying and testing dependency patterns are presented, and some of their statistical properties are studied in detail. We conduct a study of spatio-temporal dependency in Indiansummer precipitation data, and particularly the properties of extreme precipitation during Indian monsoons. As expected, we find that the nature of extreme precipitations depend on some global and some local climate features. However, the spatio-temporal relationship between extreme precipitation events depends additionally on the threshold used to define an extreme event. This may be leveraged for a more precise modeling of extreme events, and for reducing uncertainty in predicting such events.

  16. Characterizing configurations of fire ignition points through spatiotemporal point processes

    NASA Astrophysics Data System (ADS)

    Comas, C.; Costafreda-Aumedes, S.; Vega-Garcia, C.

    2014-04-01

    Human-caused forest fires are usually regarded as unpredictable but often exhibit trends towards clustering in certain locations and periods. Characterizing such configurations is crucial for understanding spatiotemporal fire dynamics and implementing preventive actions. Our objectives were to analyse the spatiotemporal point configuration and to test for spatiotemporal interaction. We characterized the spatiotemporal structure of 984 fire ignition points in a study area of Galicia, Spain, during 2007-2011 by the K-Ripley's function. Our results suggest the presence of spatiotemporal structures for time lags of less than two years and ignition point distances in the range 0-12 km. Ignition centre points at time lags of less than 100 days are aggregated for any inter-event distance. This cluster structure loses strength as the time lag increases, and at time lags of more than 365 days this cluster structure is not significant for any lag distance. Our results also suggest spatiotemporal interdependencies at time lags of less than 100 days and inter-event distances of less than 10 km. At time lags of up to 365 days spatiotemporal components are independent for any point distance. These results suggest that risk conditions occur locally and are short-lived in this study area.

  17. Spatio-temporal properties of letter crowding

    PubMed Central

    Chung, Susana T. L.

    2016-01-01

    Crowding between adjacent letters has been investigated primarily as a spatial effect. The purpose of this study was to investigate the spatio-temporal properties of letter crowding. Specifically, we examined the systematic changes in the degradation effects in letter identification performance when adjacent letters were presented with a temporal asynchrony, as a function of letter separation and between the fovea and the periphery. We measured proportion-correct performance for identifying the middle target letter in strings of three lowercase letters at the fovea and 10° in the inferior visual field, for a range of center-to-center letter separations and a range of stimulus onset asynchronies (SOA) between the target and flanking letters (positive SOAs: target preceded flankers). As expected, the accuracy for identifying the target letters reduces with decreases in letter separation. This crowding effect shows a strong dependency on SOAs, such that crowding is maximal between 0 and ∼100 ms (depending on conditions) and diminishes for larger SOAs (positive or negative). Maximal crowding does not require the target and flanking letters to physically coexist for the entire presentation duration. Most importantly, crowding can be minimized even for closely spaced letters if there is a large temporal asynchrony between the target and flankers. The reliance of letter identification performance on SOAs and how it changes with letter separations imply that the crowding effect can be traded between space and time. Our findings are consistent with the notion that crowding should be considered as a spatio-temporal, and not simply a spatial, effect. PMID:27088895

  18. Spatio-temporal properties of letter crowding.

    PubMed

    Chung, Susana T L

    2016-01-01

    Crowding between adjacent letters has been investigated primarily as a spatial effect. The purpose of this study was to investigate the spatio-temporal properties of letter crowding. Specifically, we examined the systematic changes in the degradation effects in letter identification performance when adjacent letters were presented with a temporal asynchrony, as a function of letter separation and between the fovea and the periphery. We measured proportion-correct performance for identifying the middle target letter in strings of three lowercase letters at the fovea and 10° in the inferior visual field, for a range of center-to-center letter separations and a range of stimulus onset asynchronies (SOA) between the target and flanking letters (positive SOAs: target preceded flankers). As expected, the accuracy for identifying the target letters reduces with decreases in letter separation. This crowding effect shows a strong dependency on SOAs, such that crowding is maximal between 0 and ∼100 ms (depending on conditions) and diminishes for larger SOAs (positive or negative). Maximal crowding does not require the target and flanking letters to physically coexist for the entire presentation duration. Most importantly, crowding can be minimized even for closely spaced letters if there is a large temporal asynchrony between the target and flankers. The reliance of letter identification performance on SOAs and how it changes with letter separations imply that the crowding effect can be traded between space and time. Our findings are consistent with the notion that crowding should be considered as a spatio-temporal, and not simply a spatial, effect.

  19. Spatiotemporal characteristics of soil temperature memory in China from observation

    NASA Astrophysics Data System (ADS)

    Yang, Kai; Zhang, Jingyong

    2016-11-01

    Similar to soil moisture, soil temperature has a memory of atmospheric anomalies. However, soil temperature memory over China is still largely unclear, especially in observation. In this study, we investigate the spatiotemporal characteristics of soil temperature memory over China using subsurface (10-80 cm) and deep (160-320 cm) soil temperature data for 626 stations during the period of 1981 to 2005. The red noise method is adopted to estimate soil temperature memory. Results show that the soil temperature memory differs spatially and varies with soil depth and season. Influenced by climate regimes, soil temperature memory at all six layers (with depths of 10, 20, 40, 80, 160, and 320 cm) shows a similar spatial pattern dominated by a northwest to southeast gradient, with relatively high values over arid and semiarid areas of northwestern part of China and relatively low values over humid and semihumid areas of southeastern part of China. During all four seasons, memory lengths increase with soil depth. The average memory of subsurface soil over China can last several months, and for soil at 320 cm, it can be 1 year or more. We also find that seasonal and regional differences of soil temperature memory are stronger in deep soil layers than those in subsurface soil layers. Our findings suggest that soil temperature memory can offer potential for improving seasonal climate prediction over northwestern China. In the meanwhile, the limitations of the methods used in this study should be recognized.

  20. Systemic risk and spatiotemporal dynamics of the US housing market

    NASA Astrophysics Data System (ADS)

    Meng, Hao; Xie, Wen-Jie; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene

    2014-01-01

    Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets and find that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. By looking at the evolution of different quantities such as eigenvalues and eigenvectors, we find that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. We find that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles.

  1. Spatiotemporal dynamics of auditory attention synchronize with speech

    PubMed Central

    Wöstmann, Malte; Herrmann, Björn; Maess, Burkhard

    2016-01-01

    Attention plays a fundamental role in selectively processing stimuli in our environment despite distraction. Spatial attention induces increasing and decreasing power of neural alpha oscillations (8–12 Hz) in brain regions ipsilateral and contralateral to the locus of attention, respectively. This study tested whether the hemispheric lateralization of alpha power codes not just the spatial location but also the temporal structure of the stimulus. Participants attended to spoken digits presented to one ear and ignored tightly synchronized distracting digits presented to the other ear. In the magnetoencephalogram, spatial attention induced lateralization of alpha power in parietal, but notably also in auditory cortical regions. This alpha power lateralization was not maintained steadily but fluctuated in synchrony with the speech rate and lagged the time course of low-frequency (1–5 Hz) sensory synchronization. Higher amplitude of alpha power modulation at the speech rate was predictive of a listener’s enhanced performance of stream-specific speech comprehension. Our findings demonstrate that alpha power lateralization is modulated in tune with the sensory input and acts as a spatiotemporal filter controlling the read-out of sensory content. PMID:27001861

  2. Systemic risk and spatiotemporal dynamics of the US housing market.

    PubMed

    Meng, Hao; Xie, Wen-Jie; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H Eugene

    2014-01-13

    Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets and find that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. By looking at the evolution of different quantities such as eigenvalues and eigenvectors, we find that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. We find that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles.

  3. Systemic risk and spatiotemporal dynamics of the US housing market

    PubMed Central

    Meng, Hao; Xie, Wen-Jie; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene

    2014-01-01

    Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975–2011) at the state level based on the Random Matrix Theory (RMT). We identify richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets and find that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. By looking at the evolution of different quantities such as eigenvalues and eigenvectors, we find that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. We find that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles. PMID:24413626

  4. The Mammalian Septin Interactome

    PubMed Central

    Neubauer, Katharina; Zieger, Barbara

    2017-01-01

    Septins are GTP-binding and membrane-interacting proteins with a highly conserved domain structure involved in various cellular processes, including cytoskeleton organization, cytokinesis, and membrane dynamics. To date, 13 different septin genes have been identified in mammals (SEPT1 to SEPT12 and SEPT14), which can be classified into four distinct subgroups based on the sequence homology of their domain structure (SEPT2, SEPT3, SEPT6, and SEPT7 subgroup). The family members of these subgroups have a strong affinity for other septins and form apolar tri-, hexa-, or octameric complexes consisting of multiple septin polypeptides. The first characterized core complex is the hetero-trimer SEPT2-6-7. Within these complexes single septins can be exchanged in a subgroup-specific manner. Hexamers contain SEPT2 and SEPT6 subgroup members and SEPT7 in two copies each whereas the octamers additionally comprise two SEPT9 subgroup septins. The various isoforms seem to determine the function and regulation of the septin complex. Septins self-assemble into higher-order structures, including filaments and rings in orders, which are typical for different cell types. Misregulation of septins leads to human diseases such as neurodegenerative and bleeding disorders. In non-dividing cells such as neuronal tissue and platelets septins have been associated with exocytosis. However, many mechanistic details and roles attributed to septins are poorly understood. We describe here some important mammalian septin interactions with a special focus on the clinically relevant septin interactions. PMID:28224124

  5. Ambient Air Pollution and Preeclampsia: A Spatiotemporal Analysis

    PubMed Central

    Figueras, Francesc; Basagaña, Xavier; Beelen, Rob; Martinez, David; Cirach, Marta; Schembari, Anna; Hoek, Gerard; Brunekreef, Bert; Nieuwenhuijsen, Mark J

    2013-01-01

    Background: Available evidence concerning the association between air pollution and preeclampsia is limited, and specific associations with early- and late-onset preeclampsia have not been assessed. Objectives: We investigated the association, if any, between preeclampsia (all, early-, and late-onset) and exposure to nitrogen dioxide, nitrogen oxides, particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5; fine particles), ≤ 10 μm, and 2.5–10 μm, and PM2.5 light absorption (a proxy for elemental carbon) during the entire pregnancy and during the first, second, and third trimesters. Methods: This study was based on 8,398 pregnancies (including 103 cases of preeclampsia) among women residing in Barcelona, Spain (2000–2005). We applied a spatiotemporal exposure assessment framework using land use regression models to predict ambient pollutant levels during each week of pregnancy at the geocoded residence address of each woman at the time of birth. Logistic and conditional logistic regression models were used to estimate unadjusted and adjusted associations. Results: We found positive associations for most of our evaluated outcome–exposure pairs, with the strongest associations observed for preeclampsia and late-onset preeclampsia in relation to the third-trimester exposure to fine particulate pollutants, and for early-onset preeclampsia in relation to the first-trimester exposure to fine particulate pollutants. Among our investigated associations, those of first- and third-trimester exposures to PM2.5 and third-trimester exposure to PM2.5 absorbance and all preeclampsia, and third-trimester PM2.5 exposure and late-onset preeclampsia attained statistical significance. Conclusion: We observed increased risk of preeclampsia associated with exposure to fine particulate air pollution. Our findings, in combination with previous evidence suggesting distinct pathogenic mechanisms for early- and late-onset preeclampsia, support additional research on this

  6. Assessing the impact of a movement network on the spatiotemporal spread of infectious diseases.

    PubMed

    Schrödle, Birgit; Held, Leonhard; Rue, Håvard

    2012-09-01

    Linking information on a movement network with space-time data on disease incidence is one of the key challenges in infectious disease epidemiology. In this article, we propose and compare two statistical frameworks for this purpose, namely, parameter-driven (PD) and observation-driven (OD) models. Bayesian inference in PD models is done using integrated nested Laplace approximations, while OD models can be easily fitted with existing software using maximum likelihood. The predictive performance of both formulations is assessed using proper scoring rules. As a case study, the impact of cattle trade on the spatiotemporal spread of Coxiellosis in Swiss cows, 2004-2009, is finally investigated.

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  8. Using atmospheric observations to evaluate the spatiotemporal variability of CO2 fluxes simulated by terrestrial biospheric models

    NASA Astrophysics Data System (ADS)

    Fang, Y.; Michalak, A. M.; Shiga, Y. P.; Yadav, V.

    2014-12-01

    Terrestrial biospheric models (TBMs) are used to extrapolate local observations and process-level understanding of land-atmosphere carbon exchange to larger regions, and serve as predictive tools for examining carbon-climate interactions. Understanding the performance of TBMs is thus crucial to the carbon cycle and climate science communities. In this study, we present and assess an approach to evaluating the spatiotemporal patterns, rather than aggregated magnitudes, of net ecosystem exchange (NEE) simulated by TBMs using atmospheric CO2 measurements. The approach is based on statistical model selection implemented within a high-resolution atmospheric inverse model. Using synthetic data experiments, we find that current atmospheric observations are sensitive to the underlying spatiotemporal flux variability at sub-biome scales for a large portion of North America, and that atmospheric observations can therefore be used to evaluate simulated spatiotemporal flux patterns as well as to differentiate between multiple competing TBMs. Experiments using real atmospheric observations and four prototypical TBMs further confirm the applicability of the method, and demonstrate that the performance of TBMs in simulating the spatiotemporal patterns of NEE varies substantially across seasons, with best performance during the growing season and more limited skill during transition seasons. This result is consistent with previous work showing that the ability of TBMs to model flux magnitudes is also seasonally-dependent. Overall, the proposed approach provides a new avenue for evaluating TBM performance based on sub-biome-scale flux patterns, presenting an opportunity for assessing and informing model development using atmospheric observations.

  9. Spatiotemporal Gait Parameters as Predictors of Lower-Limb Overuse Injuries in Military Training

    PubMed Central

    Gottlieb, Uri; Lozin, Mariya

    2016-01-01

    The study objective was to determine whether spatiotemporal gait parameters could predict lower-limb overuse injuries in cohort of combat soldiers during first year of military service. Newly recruited infantry soldiers walked on a treadmill at a 15° incline with a fixed speed of 1.67 m/sec while wearing a standard military vest with a 10 kg load. Stride time variability, stride length variability, step length asymmetry, and the duration of the loading response phase of the gait cycle were measured. Injury data on 76 soldiers who did not report musculoskeletal complaints at initial screening were collected one year after recruitment. Multiple logistic regression analyses were conducted to determine the predictive effect of the gait parameters on lower-limb injuries. Twenty-four soldiers (31.6%) had overuse injuries during the first year after recruitment. Duration of the loading response was a significant predictor of general lower-limb injury (p < 0.05), as well as of foot/ankle and knee injuries (p < 0.05, p < 0.01, resp.). A cutoff value of less than 12.15% for loading response duration predicted knee injuries with 83% sensitivity and 67% specificity. This study demonstrates the utility of spatiotemporal gait evaluation, a simple screening tool before military training, which may help to identify individuals at risk of lower-limb overuse injuries. PMID:27478864

  10. Finding equilibrium in the spatiotemporal chaos of the complex Ginzburg-Landau equation

    NASA Astrophysics Data System (ADS)

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

    2016-11-01

    Equilibrium statistical mechanics allows the prediction of collective behaviors of large numbers of interacting objects from just a few system-wide properties; however, a similar theory does not exist for far-from-equilibrium systems exhibiting complex spatial and temporal behavior. We propose a method for predicting behaviors in a broad class of such systems and apply these ideas to an archetypal example, the spatiotemporal chaotic 1D complex Ginzburg-Landau equation in the defect chaos regime. Building on the ideas of Ruelle and of Cross and Hohenberg that a spatiotemporal chaotic system can be considered a collection of weakly interacting dynamical units of a characteristic size, the chaotic length scale, we identify underlying, mesoscale, chaotic units and effective interaction potentials between them. We find that the resulting equilibrium Takahashi model accurately predicts distributions of particle numbers. These results suggest the intriguing possibility that a class of far-from-equilibrium systems may be well described at coarse-grained scales by the well-established theory of equilibrium statistical mechanics.

  11. Finding equilibrium in the spatiotemporal chaos of the complex Ginzburg-Landau equation.

    PubMed

    Ballard, Christopher C; Esty, C Clark; Egolf, David A

    2016-11-01

    Equilibrium statistical mechanics allows the prediction of collective behaviors of large numbers of interacting objects from just a few system-wide properties; however, a similar theory does not exist for far-from-equilibrium systems exhibiting complex spatial and temporal behavior. We propose a method for predicting behaviors in a broad class of such systems and apply these ideas to an archetypal example, the spatiotemporal chaotic 1D complex Ginzburg-Landau equation in the defect chaos regime. Building on the ideas of Ruelle and of Cross and Hohenberg that a spatiotemporal chaotic system can be considered a collection of weakly interacting dynamical units of a characteristic size, the chaotic length scale, we identify underlying, mesoscale, chaotic units and effective interaction potentials between them. We find that the resulting equilibrium Takahashi model accurately predicts distributions of particle numbers. These results suggest the intriguing possibility that a class of far-from-equilibrium systems may be well described at coarse-grained scales by the well-established theory of equilibrium statistical mechanics.

  12. Transient Spatiotemporal Chaos in a Synaptically Coupled Neural Network

    NASA Astrophysics Data System (ADS)

    Lafranceschina, Jacopo; Wackerbauer, Renate

    2014-03-01

    Spatiotemporal chaos is transient in a diffusively coupled Morris-Lecar neural network. This study shows that the addition of synaptic coupling in the ring network reduces the average lifetime of spatiotemporal chaos for small to intermediate coupling strength and almost all numbers of synapses. For large coupling strength, close to the threshold of excitation, the average lifetime increases beyond the value for only diffusive coupling, and the collapse to the rest state dominates over the collapse to a traveling pulse state. The regime of spatiotemporal chaos is characterized by a slightly increasing Lyaponov exponent and degree of phase coherence as the number of synaptic links increases. The presence of transient spatiotemporal chaos in a network of coupled neurons and the associated chaotic saddle provides a possibility for switching between metastable states observed in information processing and brain function. This research is supported by the University of Alaska Fairbanks.

  13. Transition of spatiotemporal patterns in neuronal networks with chemical synapses

    NASA Astrophysics Data System (ADS)

    Wang, Rong; Li, Jiajia; Du, Mengmeng; Lei, Jinzhi; Wu, Ying

    2016-11-01

    In mammalian neocortex plane waves, spiral and irregular waves appear alternately. In this paper, we study the transition of spatiotemporal patterns in neuronal networks in which neurons are coupled via two types of chemical synapses: fast excitatory synapse and fast inhibitory synapse. Our results indicate that the fast excitatory synapse connection is easier to induce regular spatiotemporal patterns than fast inhibitory synapse connection, and the mechanism is discussed through bifurcation analysis of a single neuron. We introduce the permutation entropy as a measure of network firing complexity to study the mechanisms of formation and transition of spatiotemporal patterns. Our calculations show that the spatiotemporal pattern transitions are closely connected to a sudden decrease in the firing complexity of neuronal networks, and the neuronal networks with fast excitatory synapses have higher firing complexity than those with fast inhibitory synapses.

  14. 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

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

    2012-01-01

    primary resistance to HER2-targeted therapies by transcriptionally screening the autophagy interactome. The degree of predictive accuracy warrants further investigation in the clinical situation. PMID:23307622

  15. Spatiotemporal localized modes in PT-symmetric optical media

    NASA Astrophysics Data System (ADS)

    Wang, Yue-Yue; Dai, Chao-Qing; Wang, Xiao-Gang

    2014-09-01

    We firstly obtain spatiotemporal localized mode solutions of a (3+1)-dimensional nonlinear Schrödinger equation in PT-symmetric potentials, and then discuss the linear stability of LMs, which are also tested by means of direct simulations. Moreover, phase switches and transverse power-flow density associated with these localized modes have also been examined. At last, we investigate the dynamical behaviors of spatiotemporal LMs in three kinds of inhomogeneous media.

  16. Spatiotemporal binary interaction and designer quasi-particle condensates

    NASA Astrophysics Data System (ADS)

    Ramaswamy, Radha; Pattu Sakthi, Vinayagam; Hyun Jong, Shin; Kuppuswamy, Porsezian

    2014-03-01

    We introduce a new integrable model to investigate the dynamics of two component quasi-particle condensates with spatiotemporal interaction strengths. We derive the associated Lax pair of the coupled Gross—Pitaevskii (GP) equation and construct matter wave solitons. We show that the spatiotemporal binary interaction strengths not only facilitate the stabilization of the condensates, but also enables one to fabricate condensates with desirable densities, geometries, and properties, leading to the so-called “designer quasi-particle condensates”.

  17. Manipulation of spatiotemporal photon distribution via chromatic aberration.

    PubMed

    Li, Yuelin; Chemerisov, Sergey

    2008-09-01

    We demonstrate a spatiotemporal laser-pulse-shaping scheme that exploits the chromatic aberration in a dispersive lens. This normally harmful effect transforms the phase modulation into a beam-size modulation at the focal plane. In combination with the intricate diffraction effect via beam apodization, this method provides a spatiotemporal control of photon distribution with an accuracy of diffraction limit on a time scale of femtoseconds.

  18. Spatiotemporal characteristics of flood protection level

    NASA Astrophysics Data System (ADS)

    Tanoue, M.; Ikeuchi, H.; Hirabayashi, Y.

    2015-12-01

    Flooding is the most frequent natural hazard and its global impacts will be rising associated with climate change and socioeconomic growth. So, the understanding of the physical and spatial-temporal patterns of risk drivers (exposure, damage, and flood protection level) are required to conduct effective adaptation and reduce the negative impacts of flooding. Although the understanding of exposure and damage has greatly improved using a combination of numerical model simulation and spatiotemporal distributions of population and asset, that of flood protection level is still lacking in particular spatial patterns. Previous research clarifies its temporal variation and relationship with per-capita income, however they do not consider its spatial variation. Flood protection level was associated with geographical characteristics (e.g., soil type and tectonic zone etc). This study tried to estimate spatiotemporal of flood protection level at country level and discuss about relationship between its spatial patterns and geographical characteristics. Mortality rate (percentage of fatalities in modeled exposed population) and loss rate (percentage of losses in modeled exposed GDP) to fluvial river flooding across the world suggested by Jongmann et al. (2015) were estimated from modeled flood exposure and damage statistics taken from the International Emergency Disasters Database. The result indicated that mortality rate reduced across the world from 1990 to 2005. The degree of its reduction decreased with increasing per-capita income level. On the other hand, loss rate at high income and middle low income levels reduced, while that at middle high income and low income levels drastically increased between 1995 and 2000 due to growth economic and occurrence of serious fluvial river flooding. Spatial distribution of mortality and loss rates were high in East Asia, the western part of South America, and the eastern part of Europe. These regions seem to be corresponded to the

  19. Origin and Spatiotemporal Control of Cuspate Forelands

    NASA Astrophysics Data System (ADS)

    McNinch, J. E.

    2006-12-01

    Cuspate shoreline forms are ubiquitous throughout shorelines of the world and span a wide range in size from sub-meter to 102 kilometers; in grain size from fine to coarse; and in energetic settings from small lakes and lagoons to open ocean beaches with virtually unlimited fetch. Cuspate forelands, with wavelengths ranging from 1-120 km, are found worldwide and are the largest of the rhythmic coastal forms. The origin of cuspate forelands has been debated for well over a century but no theory has emerged that fully explains both the origin and the processes controlling the size and time necessary for development. Despite this uncertainty of spatiotemporal controls, large-scale rhythmic morphology of cuspate forelands strongly suggests that scaling and linkages between physical and sedimentary processes may span many orders of magnitude. The same hydrodynamic forces driving sediment transport at the granular scale, such as wave orbital motion and mean currents, likely play a role in shaping the much larger cuspate shorelines. A survey of passive margin, microtidal shorelines from the US and select locations around the world reveals that cuspate forelands are only found in regions where the predominant wind direction is shore-parallel. Modeling and observations of waves, currents, and sediment accumulation from Cape Lookout, North Carolina indicate that water and sediment are transported well seaward of cuspate foreland promontories by wind-driven and tidal residual currents leading to: 1) the rapid growth of spits and sub-aqueous shoals, 2) elimination of sediment exchange between adjacent littoral cells, and 3) control of the wave diffraction point around the promontories. These results and earlier modeling work related to high-angle waves suggest that wind leads to feedback between waves, currents, and promontory-shoal bathymetry and ultimately defines the size and development time of cuspate forelands. Lastly, a linear relationship between the size of cuspate

  20. Bioimage informatics for understanding spatiotemporal dynamics of cellular processes.

    PubMed

    Yang, Ge

    2013-01-01

    The inner environment of the cell is highly dynamic and heterogeneous yet exquisitely organized. Successful completion of cellular processes within this environment depends on the right molecules or molecular complexes to function at the right place at the right time. Understanding spatiotemporal behaviors of cellular processes is therefore essential to understanding their molecular mechanisms at the systems level. These behaviors are usually visualized and recorded using imaging techniques. However, to infer from them systems-level molecular mechanisms, computational analysis and understanding of recorded image data is crucial, not only for acquiring quantitative behavior measurements but also for comprehending complex interactions among the molecules or molecular complexes involved. The technology of computational analysis and understanding of biological images is often referred to simply as bioimage informatics. This article introduces fundamentals of bioimage informatics for understanding spatiotemporal dynamics of cellular processes and reviews recent advances on this topic. Basic bioimage informatics concepts and techniques for characterizing spatiotemporal cell dynamics are introduced first. Studies on specific cellular processes such as cell migration and signal transduction are then used as examples to analyze and summarize recent advances, with the focus on transforming quantitative measurements of spatiotemporal cellular behaviors into knowledge of underlying molecular mechanisms. Despite the advances made, substantial technological challenges remain, especially in representation of spatiotemporal cellular behaviors and inference of systems-level molecular mechanisms. These challenges are briefly discussed. Overall, understanding spatiotemporal cell dynamics will provide critical insights into how specific cellular processes as well as the entire inner cellular environment are dynamically organized and regulated.

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

    PubMed Central

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

    2010-01-01

    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. PMID:20387828

  2. Spatio-temporal availability of soft mast in clearcuts in the Southern Appalachians

    USGS Publications Warehouse

    Reynolds-Hogland, M. J.; Mitchell, M.S.; Powell, R.A.

    2006-01-01

    Soft mast is an important resource for many wild populations in the Southern Appalachians, yet the way clear-cutting affects availability of soft mast though time is not fully understood. We tested a theoretical model of temporal availability of soft mast in clearcuts using empirical data on percent cover and berry production of Gaylussacia, Vaccinium, and Rubus spp. plants in 100 stands that were clearcut (0-122 years old) in the Southern Appalachian Mountains. We modeled the relationship between soft mast availability and stand age, evaluated the effects of topography and forest type on soft mast, developed statistical models for predicting the spatio-temporal distribution of soft mast, and tested the hypothesis that percent cover of berry plants and berry production provided similar information about soft mast availability. We found temporal dynamics explained berry production better than it predicted percent plant cover, whereas topographic variables influenced percent plant cover more than they influenced berry production. Berry production and percent plant cover were highest in ???2-9-year-old stands. Percent plant cover was lowest in 10-69-year-old stands and intermediate in 70+-year-old stands. Three of our spatio-temporal models performed well during model testing and they were not biased by the training data, indicating the inferences about spatio-temporal availability of soft mast extended beyond our sample data. The methods we used to estimate the distribution of soft mast may be useful for modeling distributions of other resources. ?? 2006 Elsevier B.V. All rights reserved.

  3. Spatiotemporal gait parameters and recurrent falls in community-dwelling elderly women: a prospective study

    PubMed Central

    Moreira, Bruno S.; Sampaio, Rosana F.; Kirkwood, Renata N.

    2015-01-01

    BACKGROUND: Falling is a common but devastating and costly problem of aging. There is no consensus in the literature on whether the spatial and temporal gait parameters could identify elderly people at risk of recurrent falls. OBJECTIVE: To determine whether spatiotemporal gait parameters could predict recurrent falls in elderly women. METHOD: One hundred and forty-eight elderly women (65-85 years) participated in this study. Seven spatiotemporal gait parameters were collected with the GAITRite(r) system. Falls were recorded prospectively during 12 months through biweekly phone contacts. Elderly women who reported two or more falls throughout the follow-up period were considered as recurrent fallers. Principal component analysis (PCA) and discriminant analysis followed by biplot graph interpretation were applied to the gait parameters. RESULTS: After 12 months, 23 elderly women fell twice or more and comprised the recurrent fallers group and 110 with one or no falls comprised the non-recurrent fallers group. PCA resulted in three components that explained 88.3% of data variance. Discriminant analysis showed that none of the components could significantly discriminate the groups. However, visual inspection of the biplot showed a trend towards group separation in relation to gait velocity and stance time. PC1 represented gait rhythm and showed that recurrent fallers tend to walk with lower velocity and cadence and increased stance time in relation to non-recurrent fallers. CONCLUSIONS: The analyzed spatiotemporal gait parameters failed to predict recurrent falls in this sample. The PCA-biplot technique highlighted important trends or red flags that should be considered when evaluating recurrent falls in elderly females. PMID:25714603

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

    NASA Astrophysics Data System (ADS)

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

    2013-02-01

    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.

  5. Automatic spatiotemporal matching of detected pleural thickenings

    NASA Astrophysics Data System (ADS)

    Chaisaowong, Kraisorn; Keller, Simon Kai; Kraus, Thomas

    2014-01-01

    Pleural thickenings can be found in asbestos exposed patient's lung. Non-invasive diagnosis including CT imaging can detect aggressive malignant pleural mesothelioma in its early stage. In order to create a quantitative documentation of automatic detected pleural thickenings over time, the differences in volume and thickness of the detected thickenings have to be calculated. Physicians usually estimate the change of each thickening via visual comparison which provides neither quantitative nor qualitative measures. In this work, automatic spatiotemporal matching techniques of the detected pleural thickenings at two points of time based on the semi-automatic registration have been developed, implemented, and tested so that the same thickening can be compared fully automatically. As result, the application of the mapping technique using the principal components analysis turns out to be advantageous than the feature-based mapping using centroid and mean Hounsfield Units of each thickening, since the resulting sensitivity was improved to 98.46% from 42.19%, while the accuracy of feature-based mapping is only slightly higher (84.38% to 76.19%).

  6. Contextualized trajectory parsing with spatiotemporal graph.

    PubMed

    Liu, Xiaobai; Lin, Liang; Jin, Hai

    2013-12-01

    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.

  7. Network Analysis Using Spatio-Temporal Patterns

    NASA Astrophysics Data System (ADS)

    Miranda, Gisele H. B.; Machicao, Jeaneth; Bruno, Odemir M.

    2016-08-01

    Different network models have been proposed along the last years inspired by real-world topologies. The characterization of these models implies the understanding of the underlying network phenomena, which accounts structural and dynamic properties. Several mathematical tools can be employed to characterize such properties as Cellular Automata (CA), which can be defined as dynamical systems of discrete nature composed by spatially distributed units governed by deterministic rules. In this paper, we proposed a method based on the modeling of one specific CA over distinct network topologies in order to perform the classification of the network model. The proposed methodology consists in the modeling of a binary totalistic CA over a network. The transition function that governs each CA cell is based on the density of living neighbors. Secondly, the distribution of the Shannon entropy is obtained from the evolved spatio-temporal pattern of the referred CA and used as a network descriptor. The experiments were performed using a dataset composed of four different types of networks: random, small-world, scale-free and geographical. We also used cross-validation for training purposes. We evaluated the accuracy of classification as a function of the initial number of living neighbors, and, also, as a function of a threshold parameter related to the density of living neighbors. The results show high accuracy values in distinguishing among the network models which demonstrates the feasibility of the proposed method.

  8. Spatiotemporal directional analysis of 4D echocardiography

    NASA Astrophysics Data System (ADS)

    Angelini-Casadevall, Elsa D.; Laine, Andrew F.; Takuma, Shin; Homma, Shunichi

    2000-12-01

    Speckle noise corrupts ultrasonic data by introducing sharp changes in an echocardiographic image intensity profile, while attenuation alters the intensity of equally significant cardiac structures. These properties introduce inhomogeneity in the spatial domain and suggests that measures based on phase information rather than intensity are more appropriate for denoising and cardiac border detection. The present analysis method relies on the expansion of temporal ultrasonic volume data on complex exponential wavelet-like basis functions called Brushlets. These basis functions decompose a signal into distinct patterns of oriented textures. Projected coefficients are associated with distinct 'brush strokes' of a particular size and orientation. 4D overcomplete brushlet analysis is applied to temporal echocardiographic values. We show that adding the time dimension in the analysis dramatically improves the quality and robustness of the method without adding complexity in the design of a segmentation tool. We have investigated mathematical and empirical methods for identifying the most 'efficient' brush stroke sizes and orientations for decomposition and reconstruction on both phantom and clinical data. In order to determine the 'best tiling' or equivalently, the 'best brushlet basis', we use an entorpy-based information cost metric function. Quantitative validation and clinical applications of this new spatio-temporal analysis tool are reported for balloon phantoms and clinical data sets.

  9. Multisensory control of hippocampal spatiotemporal selectivity.

    PubMed

    Ravassard, Pascal; Kees, Ashley; Willers, Bernard; Ho, David; Aharoni, Daniel; Cushman, Jesse; Aghajan, Zahra M; Mehta, Mayank R

    2013-06-14

    The hippocampal cognitive map is thought to be driven by distal visual cues and self-motion cues. However, other sensory cues also influence place cells. Hence, we measured rat hippocampal activity in virtual reality (VR), where only distal visual and nonvestibular self-motion cues provided spatial information, and in the real world (RW). In VR, place cells showed robust spatial selectivity; however, only 20% were track active, compared with 45% in the RW. This indicates that distal visual and nonvestibular self-motion cues are sufficient to provide selectivity, but vestibular and other sensory cues present in RW are necessary to fully activate the place-cell population. In addition, bidirectional cells preferentially encoded distance along the track in VR, while encoding absolute position in RW. Taken together, these results suggest the differential contributions of these sensory cues in shaping the hippocampal population code. Theta frequency was reduced, and its speed dependence was abolished in VR, but phase precession was unaffected, constraining mechanisms governing both hippocampal theta oscillations and temporal coding. These results reveal cooperative and competitive interactions between sensory cues for control over hippocampal spatiotemporal selectivity and theta rhythm.

  10. The Activation-Induced Assembly of an RNA/Protein Interactome Centered on the Splicing Factor U2AF2 Regulates Gene Expression in Human CD4 T Cells.

    PubMed

    Whisenant, Thomas C; Peralta, Eigen R; Aarreberg, Lauren D; Gao, Nina J; Head, Steven R; Ordoukhanian, Phillip; Williamson, Jamie R; Salomon, Daniel R

    2015-01-01

    Activation of CD4 T cells is a reaction to challenges such as microbial pathogens, cancer and toxins that defines adaptive immune responses. The roles of T cell receptor crosslinking, intracellular signaling, and transcription factor activation are well described, but the importance of post-transcriptional regulation by RNA-binding proteins (RBPs) has not been considered in depth. We describe a new model expanding and activating primary human CD4 T cells and applied this to characterizing activation-induced assembly of splicing factors centered on U2AF2. We immunoprecipitated U2AF2 to identify what mRNA transcripts were bound as a function of activation by TCR crosslinking and costimulation. In parallel, mass spectrometry revealed the proteins incorporated into the U2AF2-centered RNA/protein interactome. Molecules that retained interaction with the U2AF2 complex after RNAse treatment were designated as "central" interactome members (CIMs). Mass spectrometry also identified a second class of activation-induced proteins, "peripheral" interactome members (PIMs), that bound to the same transcripts but were not in physical association with U2AF2 or its partners. siRNA knockdown of two CIMs and two PIMs caused changes in activation marker expression, cytokine secretion, and gene expression that were unique to each protein and mapped to pathways associated with key aspects of T cell activation. While knocking down the PIM, SYNCRIP, impacts a limited but immunologically important set of U2AF2-bound transcripts, knockdown of U2AF1 significantly impairs assembly of the majority of protein and mRNA components in the activation-induced interactome. These results demonstrated that CIMs and PIMs, either directly or indirectly through RNA, assembled into activation-induced U2AF2 complexes and play roles in post-transcriptional regulation of genes related to cytokine secretion. These data suggest an additional layer of regulation mediated by the activation-induced assembly of RNA

  11. The Activation-Induced Assembly of an RNA/Protein Interactome Centered on the Splicing Factor U2AF2 Regulates Gene Expression in Human CD4 T Cells

    PubMed Central

    Aarreberg, Lauren D.; Gao, Nina J.; Head, Steven R.; Ordoukhanian, Phillip; Williamson, Jamie R.; Salomon, Daniel R.

    2015-01-01

    Activation of CD4 T cells is a reaction to challenges such as microbial pathogens, cancer and toxins that defines adaptive immune responses. The roles of T cell receptor crosslinking, intracellular signaling, and transcription factor activation are well described, but the importance of post-transcriptional regulation by RNA-binding proteins (RBPs) has not been considered in depth. We describe a new model expanding and activating primary human CD4 T cells and applied this to characterizing activation-induced assembly of splicing factors centered on U2AF2. We immunoprecipitated U2AF2 to identify what mRNA transcripts were bound as a function of activation by TCR crosslinking and costimulation. In parallel, mass spectrometry revealed the proteins incorporated into the U2AF2-centered RNA/protein interactome. Molecules that retained interaction with the U2AF2 complex after RNAse treatment were designated as “central” interactome members (CIMs). Mass spectrometry also identified a second class of activation-induced proteins, “peripheral” interactome members (PIMs), that bound to the same transcripts but were not in physical association with U2AF2 or its partners. siRNA knockdown of two CIMs and two PIMs caused changes in activation marker expression, cytokine secretion, and gene expression that were unique to each protein and mapped to pathways associated with key aspects of T cell activation. While knocking down the PIM, SYNCRIP, impacts a limited but immunologically important set of U2AF2-bound transcripts, knockdown of U2AF1 significantly impairs assembly of the majority of protein and mRNA components in the activation-induced interactome. These results demonstrated that CIMs and PIMs, either directly or indirectly through RNA, assembled into activation-induced U2AF2 complexes and play roles in post-transcriptional regulation of genes related to cytokine secretion. These data suggest an additional layer of regulation mediated by the activation-induced assembly

  12. MaxiK Channel Interactome Reveals its Interaction with GABA Transporter 3 and Heat Shock Protein 60 in the Mammalian Brain

    PubMed Central

    Singh, Harpreet; Li, Min; Hall, Lyra; Chen, Scarlett; Sukur, Sowmya; Lu, Rong; Caputo, Anna; Meredith, Andrea L.; Stefani, Enrico; Toro, Ligia

    2016-01-01

    Large conductance voltage and calcium-activated potassium (MaxiK) channels are activated by membrane depolarization and elevated cytosolic Ca2+. In the brain, they localize to neurons and astrocytes, where they play roles such as resetting the membrane potential during an action potential, neurotransmitter release, and neurovascular coupling. MaxiK channels are known to associate with several modulatory proteins and accessory subunits, and each of these interactions can have distinct physiological consequences. To uncover new players in MaxiK channel brain physiology, we applied a directed proteomic approach and obtained MaxiK channel pore-forming α subunit brain interactome using specific antibodies. Controls included immunoprecipitations with rabbit IgG and with anti-MaxiK antibodies in wild type and MaxiK channel knockout mice (Kcnma1−/−), respectively. We have found known and unreported interactive partners that localize to the plasma membrane, extracellular space, cytosol and intracellular organelles including mitochondria, nucleus, endoplasmic reticulum and Golgi apparatus. Localization of MaxiK channel to mitochondria was further confirmed using purified brain mitochondria colabeled with MitoTracker. Independent proof of MaxiK channel interaction with previously unidentified partners is given for GABA transporter 3 (GAT3) and heat shock protein 60 (HSP60). In HEK293T cells, both GAT3 and HSP60 coimmunoprecipitated and colocalized with MaxiK channel; colabeling was observed mainly at the cell periphery with GAT3 and intracellularly with HSP60 with protein proximity indices of ~0.6 and ~0.4, respectively. In rat primary hippocampal neurons, colocalization index was identical for GAT3 (~0.6) and slightly higher for HSP60 (~0.5) association with MaxiK channel. The results of this study provide a complete interactome of MaxiK channel the mouse brain, further establish the localization of MaxiK channel in the mouse brain mitochondria and demonstrate the

  13. MaxiK channel interactome reveals its interaction with GABA transporter 3 and heat shock protein 60 in the mammalian brain.

    PubMed

    Singh, H; Li, M; Hall, L; Chen, S; Sukur, S; Lu, R; Caputo, A; Meredith, A L; Stefani, E; Toro, L

    2016-03-11

    Large conductance voltage and calcium-activated potassium (MaxiK) channels are activated by membrane depolarization and elevated cytosolic Ca(2+). In the brain, they localize to neurons and astrocytes, where they play roles such as resetting the membrane potential during an action potential, neurotransmitter release, and neurovascular coupling. MaxiK channels are known to associate with several modulatory proteins and accessory subunits, and each of these interactions can have distinct physiological consequences. To uncover new players in MaxiK channel brain physiology, we applied a directed proteomic approach and obtained MaxiK channel pore-forming α subunit brain interactome using specific antibodies. Controls included immunoprecipitations with rabbit immunoglobulin G (IgG) and with anti-MaxiK antibodies in wild type and MaxiK channel knockout mice (Kcnma1(-/-)), respectively. We have found known and unreported interactive partners that localize to the plasma membrane, extracellular space, cytosol and intracellular organelles including mitochondria, nucleus, endoplasmic reticulum and Golgi apparatus. Localization of MaxiK channel to mitochondria was further confirmed using purified brain mitochondria colabeled with MitoTracker. Independent proof of MaxiK channel interaction with previously unidentified partners is given for GABA transporter 3 (GAT3) and heat shock protein 60 (HSP60). In human embryonic kidney 293 cells containing SV40 T-antigen (HEK293T) cells, both GAT3 and HSP60 coimmunoprecipitated and colocalized with MaxiK channel; colabeling was observed mainly at the cell periphery with GAT3 and intracellularly with HSP60 with protein proximity indices of ∼ 0.6 and ∼ 0.4, respectively. In rat primary hippocampal neurons, colocalization index was identical for GAT3 (∼ 0.6) and slightly higher for HSP60 (∼ 0.5) association with MaxiK channel. The results of this study provide a complete interactome of MaxiK channel the mouse brain, further establish

  14. Identification of the hypoxia-inducible factor 2α nuclear interactome in melanoma cells reveals master proteins involved in melanoma development.

    PubMed

    Steunou, Anne-Lise; Ducoux-Petit, Manuelle; Lazar, Ikrame; Monsarrat, Bernard; Erard, Monique; Muller, Catherine; Clottes, Eric; Burlet-Schiltz, Odile; Nieto, Laurence

    2013-03-01

    Hypoxia-inducible factors (HIFs) are heterodimeric transcription factors that play a key role in cellular adaptation to hypoxia. HIF proteins are composed of an α subunit regulated by oxygen pressure (essentially HIF1α or HIF2α) and a constitutively expressed β subunit. These proteins are often overexpressed in cancer cells, and HIF overexpression frequently correlates with poor prognosis, making HIF proteins promising therapeutic targets. HIF proteins are involved in melanoma initiation and progression; however, the specific function of HIF2 in melanoma has not yet been studied comprehensively. Identifying protein complexes is a valuable way to uncover protein function, and affinity purification coupled with mass spectrometry and label-free quantification is a reliable method for this approach. We therefore applied quantitative interaction proteomics to identify exhaustively the nuclear complexes containing HIF2α in a human melanoma cell line, 501mel. We report, for the first time, a high-throughput analysis of the interactome of an HIF subunit. Seventy proteins were identified that interact with HIF2α, including some well-known HIF partners and some new interactors. The new HIF2α partners microphthalmia-associated transcription factor, SOX10, and AP2α, which are master actors of melanoma development, were confirmed via co-immunoprecipitation experiments. Their ability to bind to HIF1α was also tested: microphthalmia-associated transcription factor and SOX10 were confirmed as HIF1α partners, but the transcription factor AP2α was not. AP2α expression correlates with low invasive capacities. Interestingly, we demonstrated that when HIF2α was overexpressed, only cells expressing large amounts of AP2α exhibited decreased invasive capacities in hypoxia relative to normoxia. The simultaneous presence of both transcription factors therefore reduces cells' invasive properties. Knowledge of the HIF2α interactome is thus a useful resource for investigating

  15. Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes.

    PubMed

    Kratz, Louis; Nishino, Ko

    2012-05-01

    Tracking pedestrians is a vital component of many computer vision applications, including surveillance, scene understanding, and behavior analysis. Videos of crowded scenes present significant challenges to tracking due to the large number of pedestrians and the frequent partial occlusions that they produce. The movement of each pedestrian, however, contributes to the overall crowd motion (i.e., the collective motions of the scene's constituents over the entire video) that exhibits an underlying spatially and temporally varying structured pattern. In this paper, we present a novel Bayesian framework for tracking pedestrians in videos of crowded scenes using a space-time model of the crowd motion. We represent the crowd motion with a collection of hidden Markov models trained on local spatio-temporal motion patterns, i.e., the motion patterns exhibited by pedestrians as they move through local space-time regions of the video. Using this unique representation, we predict the next local spatio-temporal motion pattern a tracked pedestrian will exhibit based on the observed frames of the video. We then use this prediction as a prior for tracking the movement of an individual in videos of extremely crowded scenes. We show that our approach of leveraging the crowd motion enables tracking in videos of complex scenes that present unique difficulty to other approaches.

  16. Resonant spatiotemporal learning in large random recurrent networks.

    PubMed

    Daucé, Emmanuel; Quoy, Mathias; Doyon, Bernard

    2002-09-01

    Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives stimulating spatiotemporal signals, and the secondary layer is a fully connected random recurrent network. This secondary layer spontaneously displays complex chaotic dynamics. All connections have a constant time delay. We use for our experiments a Hebbian (covariance) learning rule. This rule slowly modifies the weights under the influence of a periodic stimulus. The effect of learning is twofold: (i) it simplifies the secondary-layer dynamics, which eventually stabilizes to a periodic orbit; and (ii) it connects the secondary layer to the primary layer, and realizes a feedback from the secondary to the primary layer. This feedback signal is added to the incoming signal, and matches it (i.e., the secondary layer performs a one-step prediction of the forthcoming stimulus). After learning, a resonant behavior can be observed: the system resonates with familiar stimuli, which activates a feedback signal. In particular, this resonance allows the recognition and retrieval of partial signals, and dynamic maintenance of the memory of past stimuli. This resonance is highly sensitive to the temporal relationships and to the periodicity of the presented stimuli. When we present stimuli which do not match in time or space, the feedback remains silent. The number of different stimuli for which resonant behavior can be learned is analyzed. As with Hopfield networks, the capacity is proportional to the size of the second, recurrent layer. Moreover, the high capacity displayed allows the implementation of our model on real-time systems interacting with their environment. Such an implementation is reported in the case of a simple behavior-based recognition task on a mobile robot. Finally, we present some functional analogies with biological systems in terms of autonomy and dynamic binding, and present

  17. Spatio-temporal coupling of EEG signals in epilepsy

    NASA Astrophysics Data System (ADS)

    Senger, Vanessa; Müller, Jens; Tetzlaff, Ronald

    2011-05-01

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

  18. Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5.

    PubMed

    Wang, Meng; Sampson, Paul D; Hu, Jianlin; Kleeman, Michael; Keller, Joshua P; Olives, Casey; Szpiro, Adam A; Vedal, Sverre; Kaufman, Joel D

    2016-05-17

    Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 μm (PM2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over 9 years' data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root-mean-square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O3 (RMSE [ppb] for CTM, 6.6; LUR, 4.6; composite, 3.6) than for PM2.5 (RMSE [μg/m(3)] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatiotemporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatiotemporal modeling framework.

  19. Spatio-temporal variability in ventricular fibrillation

    NASA Astrophysics Data System (ADS)

    Hastings, Harold M.; Evans, Steven J.; Fenton, Flavio H.; Garfinkel, Alan

    2001-03-01

    It is widely believed that reentrant ventricular tachycardia arises when a spiral wave of activation takes over and drives the ventricle at a rate significantly faster than sinus rhythm, and that ventricular fibrillation (VF), a spatio-temporally disorganized form of cardiac activity leading to sudden cardiac death, arises when this spiral breaks down into multiple offspring. Many authors have found that VF displays significant spatial and temporal organization. The purpose of this research is to quantify time scales and temporal and spatial variability in VF. Surface electrograms were obtained from a stable canine model of VF (cf. Nwasokwa and Bodenheimer, Am. J. Physiol. 253, H643 (1987)). These electrograms were analyzed to identify activation times to an accuracy of 1 ms (cf. Garfinkel et al., J. Clin. Invest. 99, 305 (1997)), yielded eighteen usable series, each containing over 1024 intervactivation intervals, two or three from widely spaced sites per episode of VF, 7 total episodes in 4 animals. Spatial and long-term (60 - 120 sec) temporal variability were analyzed and compared by ANOVA techniques (Evans et al., Proc. Royal Soc. B265, 2167 (1998)). In 6 of 7 episodes, spatial variability among sites was statistically more significant than variability between the first and second halves of each series. More recently, Fourier analysis of these series found three distinct scaling regions, with power law dynamics in each and break points of ca. 1 sec and 4 sec. Finally, there was significant variability in the fraction of "short" interactivation intervals (lasting < 60 of 125 ms) among sites. Together these results suggest variability in physiological properties among sites and consequent variability in spiral wave dynamics among sites.

  20. Spatiotemporal modeling of node temperatures in supercomputers

    DOE PAGES

    Storlie, Curtis Byron; Reich, Brian James; Rust, William Newton; ...

    2016-06-10

    Los Alamos National Laboratory (LANL) is home to many large supercomputing clusters. These clusters require an enormous amount of power (~500-2000 kW each), and most of this energy is converted into heat. Thus, cooling the components of the supercomputer becomes a critical and expensive endeavor. Recently a project was initiated to investigate the effect that changes to the cooling system in a machine room had on three large machines that were housed there. Coupled with this goal was the aim to develop a general good-practice for characterizing the effect of cooling changes and monitoring machine node temperatures in this andmore » other machine rooms. This paper focuses on the statistical approach used to quantify the effect that several cooling changes to the room had on the temperatures of the individual nodes of the computers. The largest cluster in the room has 1,600 nodes that run a variety of jobs during general use. Since extremes temperatures are important, a Normal distribution plus generalized Pareto distribution for the upper tail is used to model the marginal distribution, along with a Gaussian process copula to account for spatio-temporal dependence. A Gaussian Markov random field (GMRF) model is used to model the spatial effects on the node temperatures as the cooling changes take place. This model is then used to assess the condition of the node temperatures after each change to the room. The analysis approach was used to uncover the cause of a problematic episode of overheating nodes on one of the supercomputing clusters. Lastly, this same approach can easily be applied to monitor and investigate cooling systems at other data centers, as well.« less

  1. Spatiotemporal modeling of node temperatures in supercomputers

    SciTech Connect

    Storlie, Curtis Byron; Reich, Brian James; Rust, William Newton; Ticknor, Lawrence O.; Bonnie, Amanda Marie; Montoya, Andrew J.; Michalak, Sarah E.

    2016-06-10

    Los Alamos National Laboratory (LANL) is home to many large supercomputing clusters. These clusters require an enormous amount of power (~500-2000 kW each), and most of this energy is converted into heat. Thus, cooling the components of the supercomputer becomes a critical and expensive endeavor. Recently a project was initiated to investigate the effect that changes to the cooling system in a machine room had on three large machines that were housed there. Coupled with this goal was the aim to develop a general good-practice for characterizing the effect of cooling changes and monitoring machine node temperatures in this and other machine rooms. This paper focuses on the statistical approach used to quantify the effect that several cooling changes to the room had on the temperatures of the individual nodes of the computers. The largest cluster in the room has 1,600 nodes that run a variety of jobs during general use. Since extremes temperatures are important, a Normal distribution plus generalized Pareto distribution for the upper tail is used to model the marginal distribution, along with a Gaussian process copula to account for spatio-temporal dependence. A Gaussian Markov random field (GMRF) model is used to model the spatial effects on the node temperatures as the cooling changes take place. This model is then used to assess the condition of the node temperatures after each change to the room. The analysis approach was used to uncover the cause of a problematic episode of overheating nodes on one of the supercomputing clusters. Lastly, this same approach can easily be applied to monitor and investigate cooling systems at other data centers, as well.

  2. Spatio-temporal networks: reachability, centrality and robustness

    PubMed Central

    Musolesi, Mirco

    2016-01-01

    Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks. PMID:27429776

  3. Proteomic Analysis of the EWS-Fli-1 Interactome Reveals the Role of the Lysosome in EWS-Fli-1 Turnover

    PubMed Central

    2015-01-01

    Ewing sarcoma is a cancer of bone and soft tissue in children that is characterized by a chromosomal translocation involving EWS and an Ets family transcription factor, most commonly Fli-1. EWS-Fli-1 fusion accounts for 85% of cases. The growth and survival of Ewing sarcoma cells are critically dependent on EWS-Fli-1. A large body of evidence has established that EWS-Fli-1 functions as a DNA-binding transcription factor that regulates the expression of a number of genes important for cell proliferation and transformation. However, little is known about the biochemical properties of the EWS-Fli-1 protein. We undertook a series of proteomic analyses to dissect the EWS-Fli-1 interactome. Employing a proximity-dependent biotinylation technique, BioID, we identified cation-independent mannose 6-phosphate receptor (CIMPR) as a protein located in the vicinity of EWS-Fli-1 within a cell. CIMPR is a cargo that mediates the delivery of lysosomal hydrolases from the trans-Golgi network to the endosome, which are subsequently transferred to the lysosomes. Further molecular cell biological analyses uncovered a role for lysosomes in the turnover of the EWS-Fli-1 protein. We demonstrate that an mTORC1 active-site inhibitor, torin 1, which stimulates the TFEB-lysosome pathway, can induce the degradation of EWS-Fli-1, suggesting a potential therapeutic approach to target EWS-Fli-1 for degradation. PMID:24999758

  4. Interactome analysis of AMP-activated protein kinase (AMPK)-α1 and -β1 in INS-1 pancreatic beta-cells by affinity purification-mass spectrometry.

    PubMed

    Moon, Sungyoon; Han, Dohyun; Kim, Yikwon; Jin, Jonghwa; Ho, Won-Kyung; Kim, Youngsoo

    2014-03-14

    The heterotrimeric enzyme AMP-activated protein kinase (AMPK) is a major metabolic factor that regulates the homeostasis of cellular energy. In particular, AMPK mediates the insulin resistance that is associated with type 2 diabetes. Generally, cellular processes require tight regulation of protein kinases, which is effected through their formation of complex with other proteins and substrates. Despite their critical function in regulation and pathogenesis, there are limited data on the interaction of protein kinases. To identify proteins that interact with AMPK, we performed large-scale affinity purification (AP)-mass spectrometry (MS) of the AMPK-α1 and -β1 subunits. Through a comprehensive analysis, using a combination of immunoprecipitaion and ion trap mass spectrometry, we identified 381 unique proteins in the AMPKα/β interactomes: 325 partners of AMPK-α1 and 243 for AMPK-β1. Further, we identified 196 novel protein-protein interactions with AMPK-α1 and AMPK-β1. Notably, in our bioinformatics analysis, the novel interaction partners mediated functions that are related to the regulation of actin organization. Specifically, several such proteins were linked to pancreatic beta-cell functions, including glucose-stimulated insulin secretion, beta-cell development, beta-cell differentiation, and cell-cell communication.

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

    PubMed Central

    2012-01-01

    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

  6. Elucidation of epithelial-mesenchymal transition-related pathways in a triple-negative breast cancer cell line model by multi-omics interactome analysis.

    PubMed

    Pauling, Josch K; Christensen, Anne G; Batra, Richa; Alcaraz, Nicolas; Barbosa, Eudes; Larsen, Martin R; Beck, Hans C; Leth-Larsen, Rikke; Azevedo, Vasco; Ditzel, Henrik J; Baumbach, Jan

    2014-11-01

    In life sciences, and particularly biomedical research, linking aberrant pathways exhibiting phenotype-specific alterations to the underlying physical condition or disease is an ongoing challenge. Computationally, a key approach for pathway identification is data enrichment, combined with generation of biological networks. This allows identification of intrinsic patterns in the data and their linkage to a specific context such as cellular compartments, diseases or functions. Identification of aberrant pathways by traditional approaches is often limited to biological networks based on either gene expression, protein expression or post-translational modifications. To overcome single omics analysis, we developed a set of computational methods that allow a combined analysis of data collections from multiple omics fields utilizing hybrid interactome networks. We apply these methods to data obtained from a triple-negative breast cancer cell line model, combining data sets of gene and protein expression as well as protein phosphorylation. We focus on alterations associated with the phenotypical differences arising from epithelial-mesenchymal transition in two breast cancer cell lines exhibiting epithelial-like and mesenchymal-like morphology, respectively. Here we identified altered protein signaling activity in a complex biologically relevant network, related to focal adhesion and migration of breast cancer cells. We found dysregulated functional network modules revealing altered phosphorylation-dependent activity in concordance with the phenotypic traits and migrating potential of the tested model. In addition, we identified Ser267 on zyxin, a protein coupled to actin filament polymerization, as a potential in vivo phosphorylation target of cyclin-dependent kinase 1.

  7. Interactomic analysis of REST/NRSF and implications of its functional links with the transcription suppressor TRIM28 during neuronal differentiation

    PubMed Central

    Lee, Namgyu; Park, Sung Jin; Haddad, Ghazal; Kim, Dae-Kyum; Park, Seon-Min; Park, Sang Ki; Choi, Kwan Yong

    2016-01-01

    RE-1 silencing transcription factor (REST) is a transcriptional repressor that regulates gene expression by binding to repressor element 1. However, despite its critical function in physiology, little is known about its interaction proteins. Here we identified 204 REST-interacting proteins using affinity purification and mass spectrometry. The interactome included proteins associated with mRNA processing/splicing, chromatin organization, and transcription. The interactions of these REST-interacting proteins, which included TRIM28, were confirmed by co-immunoprecipitation and immunocytochemistry, respectively. Gene Ontology (GO) analysis revealed that neuronal differentiation-related GO terms were enriched among target genes that were co-regulated by REST and TRIM28, while the level of CTNND2 was increased by the knockdown of REST and TRIM28. Consistently, the level of CTNND2 increased while those of REST and TRIM28 decreased during neuronal differentiation in the primary neurons, suggesting that CTNND2 expression may be co-regulated by both. Furthermore, neurite outgrowth was increased by depletion of REST or TRIM28, implying that reduction of both REST and TRIM28 could promote neuronal differentiation via induction of CTNND2 expression. In conclusion, our study of REST reveals novel interacting proteins which could be a valuable resource for investigating unidentified functions of REST and also suggested functional links between REST and TRIM28 during neuronal development. PMID:27976729

  8. Proteomic Analysis of the EWS-Fli-1 Interactome Reveals the Role of the Lysosome in EWS-Fli-1 Turnover.

    PubMed

    Elzi, David J; Song, Meihua; Hakala, Kevin; Weintraub, Susan T; Shiio, Yuzuru

    2014-08-01

    Ewing sarcoma is a cancer of bone and soft tissue in children that is characterized by a chromosomal translocation involving EWS and an Ets family transcription factor, most commonly Fli-1. EWS-Fli-1 fusion accounts for 85% of cases. The growth and survival of Ewing sarcoma cells are critically dependent on EWS-Fli-1. A large body of evidence has established that EWS-Fli-1 functions as a DNA-binding transcription factor that regulates the expression of a number of genes important for cell proliferation and transformation. However, little is known about the biochemical properties of the EWS-Fli-1 protein. We undertook a series of proteomic analyses to dissect the EWS-Fli-1 interactome. Employing a proximity-dependent biotinylation technique, BioID, we identified cation-independent mannose 6-phosphate receptor (CIMPR) as a protein located in the vicinity of EWS-Fli-1 within a cell. CIMPR is a cargo that mediates the delivery of lysosomal hydrolases from the trans-Golgi network to the endosome, which are subsequently transferred to the lysosomes. Further molecular cell biological analyses uncovered a role for lysosomes in the turnover of the EWS-Fli-1 protein. We demonstrate that an mTORC1 active-site inhibitor, torin 1, which stimulates the TFEB-lysosome pathway, can induce the degradation of EWS-Fli-1, suggesting a potential therapeutic approach to target EWS-Fli-1 for degradation.

  9. Spatio-temporal propagation of cascading overload failures in spatially embedded networks.

    PubMed

    Zhao, Jichang; Li, Daqing; Sanhedrai, Hillel; Cohen, Reuven; Havlin, Shlomo

    2016-01-12

    Different from the direct contact in epidemics spread, overload failures propagate through hidden functional dependencies. Many studies focused on the critical conditions and catastrophic consequences of cascading failures. However, to understand the network vulnerability and mitigate the cascading overload failures, the knowledge of how the failures propagate in time and space is essential but still missing. Here we study the spatio-temporal propagation behaviour of cascading overload failures analytically and numerically on spatially embedded networks. The cascading overload failures are found to spread radially from the centre of the initial failure with an approximately constant velocity. The propagation velocity decreases with increasing tolerance, and can be well predicted by our theoretical framework with one single correction for all the tolerance values. This propagation velocity is found similar in various model networks and real network structures. Our findings may help to predict the dynamics of cascading overload failures in realistic systems.

  10. Spatio-temporal propagation of cascading overload failures in spatially embedded networks

    PubMed Central

    Zhao, Jichang; Li, Daqing; Sanhedrai, Hillel; Cohen, Reuven; Havlin, Shlomo

    2016-01-01

    Different from the direct contact in epidemics spread, overload failures propagate through hidden functional dependencies. Many studies focused on the critical conditions and catastrophic consequences of cascading failures. However, to understand the network vulnerability and mitigate the cascading overload failures, the knowledge of how the failures propagate in time and space is essential but still missing. Here we study the spatio-temporal propagation behaviour of cascading overload failures analytically and numerically on spatially embedded networks. The cascading overload failures are found to spread radially from the centre of the initial failure with an approximately constant velocity. The propagation velocity decreases with increasing tolerance, and can be well predicted by our theoretical framework with one single correction for all the tolerance values. This propagation velocity is found similar in various model networks and real network structures. Our findings may help to predict the dynamics of cascading overload failures in realistic systems. PMID:26754065

  11. Predicting the fission yeast protein interaction network.

    PubMed

    Pancaldi, Vera; Saraç, Omer S; Rallis, Charalampos; McLean, Janel R; Převorovský, Martin; Gould, Kathleen; Beyer, Andreas; Bähler, Jürg

    2012-04-01

    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 (www.bahlerlab.info/PInt).

  12. Predicting the Fission Yeast Protein Interaction Network

    PubMed Central

    Pancaldi, Vera; Saraç, Ömer S.; Rallis, Charalampos; McLean, Janel R.; Převorovský, Martin; Gould, Kathleen; Beyer, Andreas; Bähler, Jürg

    2012-01-01

    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 (www.bahlerlab.info/PInt). PMID:22540037

  13. Field scale spatio-temporal soil moisture variability for trafficability and crop water availability

    NASA Astrophysics Data System (ADS)

    Carranza, Coleen; van der Ploeg, Martine; Ritsema, Coen

    2016-04-01

    Spatio-temporal patterns of soil moisture have been studied mostly for inputs in land surface models for weather and climate predictions. Remote sensing techniques for estimation of soil moisture have been explored because of the good spatial coverage at different scales. Current available satellite data provide surface soil moisture as microwave systems only measure soil moisture content up to 5cm soil depth. The OWAS1S project will focus on estimation of soil moisture from freely available Sentinel-1 datasets for operational water management in agricultural areas. As part of the project, it is essential to develop spatio-temporal methods to estimate root zone soil moisture from surface soil moisture. This will be used for crop water availability and trafficability in selected agricultural fields in the Netherlands. A network of single capacitance sensors installed per field will provide continuous measurements of soil moisture in the study area. Ground penetrating radar will be used to measure soil moisture variability within a single field for different time periods. During wetter months, optimal conditions for traffic will be assessed using simultaneous soil strength and soil moisture measurements. Towards water deficit periods, focus is on the relation (or the lack thereof) between surface soil moisture and root zone soil moisture to determine the amount of water for crops. Spatio-temporal distribution will determine important physical controls for surface and root zone soil moisture and provide insights for root-zone soil moisture. Existing models for field scale soil-water balance and data assimilation methods (e.g. Kalman filter) will be combined to estimate root zone soil moisture. Furthermore, effects of root development on soil structure and soil hydraulic properties and subsequent effects on trafficability and crop water availability will be investigated. This research project has recently started, therefore we want to present methods and framework of

  14. Spatiotemporal dynamics of optogenetically induced and spontaneous seizure transitions in primary generalized epilepsy

    PubMed Central

    Truccolo, Wilson; Wang, Jing; Nurmikko, Arto V.

    2014-01-01

    Transitions into primary generalized epileptic seizures occur abruptly and synchronously across the brain. Their potential triggers remain unknown. We used optogenetics to causally test the hypothesis that rhythmic population bursting of excitatory neurons in a local neocortical region can rapidly trigger absence seizures. Most previous studies have been purely correlational, and it remains unclear whether epileptiform events induced by rhythmic stimulation (e.g., sensory/electrical) mimic actual spontaneous seizures, especially regarding their spatiotemporal dynamics. In this study, we used a novel combination of intracortical optogenetic stimulation and microelectrode array recordings in freely moving WAG/Rij rats, a model of absence epilepsy with a cortical focus in the somatosensory cortex (SI). We report three main findings: 1) Brief rhythmic bursting, evoked by optical stimulation of neocortical excitatory neurons at frequencies around 10 Hz, induced seizures consisting of self-sustained spike-wave discharges (SWDs) for about 10% of stimulation trials. The probability of inducing seizures was frequency-dependent, reaching a maximum at 10 Hz. 2) Local field potential power before stimulation and response amplitudes during stimulation both predicted seizure induction, demonstrating a modulatory effect of brain states and neural excitation levels. 3) Evoked responses during stimulation propagated as cortical waves, likely reaching the cortical focus, which in turn generated self-sustained SWDs after stimulation was terminated. Importantly, SWDs during induced and spontaneous seizures propagated with the same spatiotemporal dynamics. Our findings demonstrate that local rhythmic bursting of excitatory neurons in neocortex at particular frequencies, under susceptible ongoing brain states, is sufficient to trigger primary generalized seizures with stereotypical spatiotemporal dynamics. PMID:25552645

  15. Experimental study of spatiotemporally localized surface gravity water waves.

    PubMed

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

    2012-07-01

    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.

  16. Spatiotemporal Regulation of Synaptic Vesicle Fusion Sites in Central Synapses.

    PubMed

    Maschi, Dario; Klyachko, Vitaly A

    2017-04-05

    The number and availability of vesicle release sites at the synaptic active zone (AZ) are critical factors governing neurotransmitter release; yet, these fundamental synaptic parameters have remained undetermined. Moreover, how neural activity regulates the spatiotemporal properties of the release sites within individual central synapses is unknown. Here, we combined a nanoscale imaging approach with advanced image analysis to detect individual vesicle fusion events with ∼27 nm localization precision at single hippocampal synapses under physiological conditions. Our results revealed the presence of multiple distinct release sites within individual hippocampal synapses. Release sites were distributed throughout the AZ and underwent repeated reuse. Furthermore, the spatiotemporal properties of the release sites were activity dependent with a reduction in reuse frequency and a shift in location toward the AZ periphery during high-frequency stimulation. These findings have revealed fundamental spatiotemporal properties of individual release sites in small central synapses and their activity-dependent modulation.

  17. Spiking neural network for recognizing spatiotemporal sequences of spikes

    NASA Astrophysics Data System (ADS)

    Jin, Dezhe Z.

    2004-02-01

    Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons.

  18. Predictability of Conversation Partners

    NASA Astrophysics Data System (ADS)

    Takaguchi, Taro; Nakamura, Mitsuhiro; Sato, Nobuo; Yano, Kazuo; Masuda, Naoki

    2011-08-01

    Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information-theoretic method to the spatiotemporal data of cell-phone locations, [C. Song , ScienceSCIEAS0036-8075 327, 1018 (2010)] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one’s conversation partners is defined as the degree to which one’s next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to a certain extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by long-tailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual’s predictability is correlated with the position of the individual in the static social network derived from the data. Individuals confined in a community—in the sense of an abundance of surrounding triangles—tend to have low predictability, and those bridging different communities tend to have high predictability.

  19. Routes to spatiotemporal chaos in Kerr optical frequency combs

    SciTech Connect

    Coillet, Aurélien; Chembo, Yanne K.

    2014-03-15

    We investigate the various routes to spatiotemporal chaos in Kerr optical frequency combs, obtained through pumping an ultra-high Q-factor whispering-gallery mode resonator with a continuous-wave laser. The Lugiato–Lefever model is used to build bifurcation diagrams with regards to the parameters that are externally controllable, namely, the frequency and the power of the pumping laser. We show that the spatiotemporal chaos emerging from Turing patterns and solitons display distinctive dynamical features. Experimental spectra of chaotic Kerr combs are also presented for both cases, in excellent agreement with theoretical spectra.

  20. Classification with spatio-temporal interpixel class dependency contexts

    NASA Technical Reports Server (NTRS)

    Jeon, Byeungwoo; Landgrebe, David A.

    1992-01-01

    A contextual classifier which can utilize both spatial and temporal interpixel dependency contexts is investigated. After spatial and temporal neighbors are defined, a general form of maximum a posterior spatiotemporal contextual classifier is derived. This contextual classifier is simplified under several assumptions. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to allow a computationally efficient contextual classification. Experimental results with bitemporal TM data show significant improvement of classification accuracy over noncontextual pixelwise classifiers. This spatiotemporal contextual classifier should find use in many applications of remote sensing, especially when the classification accuracy is important.

  1. Tensor analysis methods for activity characterization in spatiotemporal data

    SciTech Connect

    Haass, Michael Joseph; Van Benthem, Mark Hilary; Ochoa, Edward M.

    2014-03-01

    Tensor (multiway array) factorization and decomposition offers unique advantages for activity characterization in spatio-temporal datasets because these methods are compatible with sparse matrices and maintain multiway structure that is otherwise lost in collapsing for regular matrix factorization. This report describes our research as part of the PANTHER LDRD Grand Challenge to develop a foundational basis of mathematical techniques and visualizations that enable unsophisticated users (e.g. users who are not steeped in the mathematical details of matrix algebra and mulitway computations) to discover hidden patterns in large spatiotemporal data sets.

  2. Spatiotemporal focusing dynamics in plasmas at X-ray wavelength

    SciTech Connect

    Sharma, A. Tibai, Z.; Hebling, J.; Mishra, S. K.

    2014-03-15

    Using a finite curvature beam, we investigate here the spatiotemporal focusing dynamics of a laser pulse in plasmas at X-ray wavelength. We trace the dependence of curvature parameter on the focusing of laser pulse and recognize that the self-focusing in plasma is more intense for the X-ray laser pulse with curved wavefront than with flat wavefront. The simulation results demonstrate that spatiotemporal focusing dynamics in plasmas can be controlled with the appropriate choice of beam-plasma parameters to explore the high intensity effects in X-ray regime.

  3. Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS

    PubMed Central

    2013-01-01

    Background The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms. Results In this study we demonstrate a random-forest based technique, ENTS, for the computational prediction of protein-protein interactions based only on primary sequence data. Our approach is able to efficiently predict interactions on a whole-genome scale for any eukaryotic organism, using pairwise combinations of conserved domains and predicted subcellular localization of proteins as input features. We present the first predicted interactome for the forest tree Populus trichocarpa in addition to the predicted interactomes for Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Arabidopsis thaliana. Comparing our approach to other PPI predictors, we find that ENTS performs comparably to or better than a number of existing approaches, including several that utilize a variety of functional information for their predictions. We also find that the predicted interactions are biologically meaningful, as indicated by similarity in functional annotations and enrichment of co-expressed genes in public microarray datasets. Furthermore, we demonstrate some of the biological insights that can be gained from these predicted interaction networks. We show that the predicted interactions yield informative groupings of P. trichocarpa metabolic pathways, literature-supported associations among human disease states, and theory-supported insight into the evolutionary dynamics of duplicated genes in paleopolyploid plants. Conclusion We conclude that the ENTS classifier will be a valuable tool for the de novo annotation of genome

  4. A general science-based framework for dynamical spatio-temporal models

    USGS Publications Warehouse

    Wikle, C.K.; Hooten, M.B.

    2010-01-01

    Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic

  5. Spatiotemporal monthly rainfall forecasting for south-eastern and eastern Australia using climatic indices

    NASA Astrophysics Data System (ADS)

    Montazerolghaem, Maryam; Vervoort, Willem; Minasny, Budiman; McBratney, Alex

    2014-05-01

    Knowledge about future rainfall would significantly benefit land, water resources and agriculture management, as this assists with planning and management decisions. Forecasting spatiotemporal monthly rainfall is difficult, especially in Australia where there is a complex interaction between topography and the effect of Indian and Pacific Ocean. This study describes a method for spatiotemporal monthly rainfall forecasting in south-eastern and eastern part of Australia using climatic and non-climatic variables. Rainfall data were obtained from Bureau of Meteorology (BoM) from 136 high quality weather stations from the south-eastern and eastern part of Australia with monthly rainfall records from 1879 to 2012. To reduce spatial complexity of the area and improve model accuracy, spatial classification (regionalization) was considered as first step. Significant predictors for each sub-region among lagged climatic input variables were selected using Fuzzy Ranking Algorithm (FRA). Climate classification: 1) discovered homogenous sub-regions with a similar rainfall patterns and investigated spatiotemporal rainfall variations in the area, 2) allowed selection of significant predictors with a fine resolution for each area, 3) improved the prediction model and increased model accuracy. PCA was used to reduce the dimensions of the dataset and to remove the rainfall time series correlation. K-means clustering was used on the loadings of PCs describing 93% of long-term monthly rainfall variations. The analysis was repeated for different numbers of sub-regions (3 - 8) to identify the best number of clusters to improve the forecast model performance. Subsequently, a Fuzzy Ranking Algorithm (FRA) was applied to the lagged climatic predictors and monthly rainfall in each sub-region to identify the best predictors. After these two stages of pre-processing, a Neural Network model was developed and optimized for each of the sub-regions as well as for the entire area. It is concluded

  6. Spatio-temporal topological relationships between land parcels in cadastral database

    NASA Astrophysics Data System (ADS)

    Song, W.; Zhang, F.

    2014-04-01

    There are complex spatio-temporal relationships among cadastral entities. Cadastral spatio-temporal data model should not only describe the data structure of cadastral objects, but also express cadastral spatio-temporal relationships between cadastral objects. In the past, many experts and scholars have proposed a variety of cadastral spatio-temporal data models, but few of them concentrated on the representation of spatiotemporal relationships and few of them make systematic studies on spatiotemporal relationships between cadastral objects. The studies on spatio-temporal topological relationships are not abundant. In the paper, we initially review current approaches to the studies of spatio-temporal topological relationships, and argue that spatio-temporal topological relation is the combination of temporal topology on the time dimension and spatial topology on the spatial dimension. Subsequently, we discuss and develop an integrated representation of spatio-temporal topological relationships within a 3-dimensional temporal space. In the end, based on the semantics of spatiotemporal changes between land parcels, we conclude the possible spatio-temporal topological relations between land parcels, which provide the theoretical basis for creating, updating and maintaining of land parcels in the cadastral database.

  7. Intrinsic Islet Heterogeneity and Gap Junction Coupling Determine Spatiotemporal Ca2+ Wave Dynamics

    PubMed Central

    Benninger, Richard K.P.; Hutchens, Troy; Head, W. Steven; McCaughey, Michael J.; Zhang, Min; Le Marchand, Sylvain J.; Satin, Leslie S.; Piston, David W.

    2014-01-01

    Insulin is released from the islets of Langerhans in discrete pulses that are linked to synchronized oscillations of intracellular free calcium ([Ca2+]i). Associated with each synchronized oscillation is a propagating calcium wave mediated by Connexin36 (Cx36) gap junctions. A computational islet model predicted that waves emerge due to heterogeneity in β-cell function throughout the islet. To test this, we applied defined patterns of glucose stimulation across the islet using a microfluidic device and measured how these perturbations affect calcium wave propagation. We further investigated how gap junction coupling regulates spatiotemporal [Ca2+]i dynamics in the face of heterogeneous glucose stimulation. Calcium waves were found to originate in regions of the islet having elevated excitability, and this heterogeneity is an intrinsic property of islet β-cells. The extent of [Ca2+]i elevation across the islet in the presence of heterogeneity is gap-junction dependent, which reveals a glucose dependence of gap junction coupling. To better describe these observations, we had to modify the computational islet model to consider the electrochemical gradient between neighboring β-cells. These results reveal how the spatiotemporal [Ca2+]i dynamics of the islet depend on β-cell heterogeneity and cell-cell coupling, and are important for understanding the regulation of coordinated insulin release across the islet. PMID:25468351

  8. Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems.

    PubMed

    Marwan, Norbert; Kurths, Jürgen

    2015-09-01

    We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development of complex systems analysis. First, we discuss the transforming of a time series from such systems to a complex network. The natural approach is to calculate the recurrence matrix and interpret such as the adjacency matrix of an associated complex network, called recurrence network. Using complex network measures, such as transitivity coefficient, we demonstrate that this approach is very efficient for identifying qualitative transitions in observational data, e.g., when analyzing paleoclimate regime transitions. Second, we demonstrate the use of directed spatial networks constructed from spatio-temporal measurements of such systems that can be derived from the synchronized-in-time occurrence of extreme events in different spatial regions. Although there are many possibilities to investigate such spatial networks, we present here the new measure of network divergence and how it can be used to develop a prediction scheme of extreme rainfall events.

  9. Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.

    PubMed

    Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen

    2016-07-27

    Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.

  10. Functional Principal Component Analysis of Spatio-Temporal Point Processes with Applications in Disease Surveillance

    PubMed Central

    Li, Yehua; Guan, Yongtao

    2014-01-01

    In disease surveillance applications, the disease events are modeled by spatio-temporal point processes. We propose a new class of semiparametric generalized linear mixed model for such data, where the event rate is related to some known risk factors and some unknown latent random effects. We model the latent spatio-temporal process as spatially correlated functional data, and propose Poisson maximum likelihood and composite likelihood methods based on spline approximations to estimate the mean and covariance functions of the latent process. By performing functional principal component analysis to the latent process, we can better understand the correlation structure in the point process. We also propose an empirical Bayes method to predict the latent spatial random effects, which can help highlight hot areas with unusually high event rates. Under an increasing domain and increasing knots asymptotic framework, we establish the asymptotic distribution for the parametric components in the model and the asymptotic convergence rates for the functional principal component estimators. We illustrate the methodology through a simulation study and an application to the Connecticut Tumor Registry data. PMID:25368436

  11. Spatio-temporal evolution of biogeochemical processes at a landfill site

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    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, δ18O, δ2H, 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.

  12. The role of climate and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria

    NASA Astrophysics Data System (ADS)

    Abdussalam, Auwal; Thornes, John; Leckebusch, Gregor

    2015-04-01

    Nigeria has a number of climate-sensitive infectious diseases; one of the most important of these diseases that remains a threat to public health is cholera. This study investigates the influences of both meteorological and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria. A stepwise multiple regression models are used to estimate the influence of the year-to-year variations of cholera cases and deaths for individual states in the country and as well for three groups of states that are classified based on annual rainfall amount. Specifically, seasonal mean maximum and minimum temperatures and annual rainfall totals were analysed with annual aggregate count of cholera cases and deaths, taking into account of the socioeconomic factors that are potentially enhancing vulnerability such as: absolute poverty, adult literacy, access to pipe borne water and population density. Result reveals that the most important explanatory meteorological and socioeconomic variables in explaining the spatiotemporal variability of the disease are rainfall totals, seasonal mean maximum temperature, absolute poverty, and accessibility to pipe borne water. The influences of socioeconomic factors appeared to be more pronounced in the northern part of the country, and vice-versa in the case of meteorological factors. Also, cross validated models output suggests a strong possibility of disease prediction, which will help authorities to put effective control measures in place which depend on prevention, and or efficient response.

  13. Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market

    NASA Astrophysics Data System (ADS)

    Gong, Pu; Weng, Yingliang

    2016-01-01

    This paper generalizes a recently proposed spatial autoregressive model and introduces a spatiotemporal model for forecasting stock returns. We support the view that stock returns are affected not only by the absolute values of factors such as firm size, book-to-market ratio and momentum but also by the relative values of factors like trading volume ranking and market capitalization ranking in each period. This article studies a new method for constructing stocks' reference groups; the method is called quartile method. Applying the method empirically to the Shanghai Stock Exchange 50 Index, we compare the daily volatility forecasting performance and the out-of-sample forecasting performance of Value-at-Risk (VaR) estimated by different models. The empirical results show that the spatiotemporal model performs surprisingly well in terms of capturing spatial dependences among individual stocks, and it produces more accurate VaR forecasts than the other three models introduced in the previous literature. Moreover, the findings indicate that both allowing for serial correlation in the disturbances and using time-varying spatial weight matrices can greatly improve the predictive accuracy of a spatial autoregressive model.

  14. Proteomics profiling of interactome dynamics by colocalisation analysis (COLA)† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6mb00701e Click here for additional data file. Click here for additional data file.

    PubMed Central

    Sailem, Heba Z.; Kümper, Sandra; Tape, Christopher J.; McCully, Ryan R.; Paul, Angela; Anjomani-Virmouni, Sara; Jørgensen, Claus; Poulogiannis, George; Marshall, Christopher J.

    2017-01-01

    Localisation and protein function are intimately linked in eukaryotes, as proteins are localised to specific compartments where they come into proximity of other functionally relevant proteins. Significant co-localisation of two proteins can therefore be indicative of their functional association. We here present COLA, a proteomics based strategy coupled with a bioinformatics framework to detect protein–protein co-localisations on a global scale. COLA reveals functional interactions by matching proteins with significant similarity in their subcellular localisation signatures. The rapid nature of COLA allows mapping of interactome dynamics across different conditions or treatments with high precision. PMID:27824369

  15. Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesn’t Fit All

    PubMed Central

    Brommesson, Peter; Wennergren, Uno; Lindström, Tom

    2016-01-01

    The structure of contacts that mediate transmission has a pronounced effect on the outbreak dynamics of infectious disease and simulation models are powerful tools to inform policy decisions. Most simulation models of livestock disease spread rely to some degree on predictions of animal movement between holdings. Typically, movements are more common between nearby farms than between those located far away from each other. Here, we assessed spatiotemporal variation in such distance dependence of animal movement contacts from an epidemiological perspective. We evaluated and compared nine statistical models, applied to Swedish movement data from 2008. The models differed in at what level (if at all), they accounted for regional and/or seasonal heterogeneities in the distance dependence of the contacts. Using a kernel approach to describe how probability of contacts between farms changes with distance, we developed a hierarchical Bayesian framework and estimated parameters by using Markov Chain Monte Carlo techniques. We evaluated models by three different approaches of model selection. First, we used Deviance Information Criterion to evaluate their performance relative to each other. Secondly, we estimated the log predictive posterior distribution, this was also used to evaluate their relative performance. Thirdly, we performed posterior predictive checks by simulating movements with each of the parameterized models and evaluated their ability to recapture relevant summary statistics. Independent of selection criteria, we found that accounting for regional heterogeneity improved model accuracy. We also found that accounting for seasonal heterogeneity was beneficial, in terms of model accuracy, according to two of three methods used for model selection. Our results have important implications for livestock disease spread models where movement is an important risk factor for between farm transmission. We argue that modelers should refrain from using methods to simulate

  16. Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesn't Fit All.

    PubMed

    Brommesson, Peter; Wennergren, Uno; Lindström, Tom

    2016-01-01

    The structure of contacts that mediate transmission has a pronounced effect on the outbreak dynamics of infectious disease and simulation models are powerful tools to inform policy decisions. Most simulation models of livestock disease spread rely to some degree on predictions of animal movement between holdings. Typically, movements are more common between nearby farms than between those located far away from each other. Here, we assessed spatiotemporal variation in such distance dependence of animal movement contacts from an epidemiological perspective. We evaluated and compared nine statistical models, applied to Swedish movement data from 2008. The models differed in at what level (if at all), they accounted for regional and/or seasonal heterogeneities in the distance dependence of the contacts. Using a kernel approach to describe how probability of contacts between farms changes with distance, we developed a hierarchical Bayesian framework and estimated parameters by using Markov Chain Monte Carlo techniques. We evaluated models by three different approaches of model selection. First, we used Deviance Information Criterion to evaluate their performance relative to each other. Secondly, we estimated the log predictive posterior distribution, this was also used to evaluate their relative performance. Thirdly, we performed posterior predictive checks by simulating movements with each of the parameterized models and evaluated their ability to recapture relevant summary statistics. Independent of selection criteria, we found that accounting for regional heterogeneity improved model accuracy. We also found that accounting for seasonal heterogeneity was beneficial, in terms of model accuracy, according to two of three methods used for model selection. Our results have important implications for livestock disease spread models where movement is an important risk factor for between farm transmission. We argue that modelers should refrain from using methods to simulate

  17. Integrated Spatio-Temporal Ecological Modeling System

    DTIC Science & Technology

    1998-07-01

    11 Hierarchy Theory The predictability of ecological systems is inherently limited and is dependent on the scales (May 1986; Levin 1989; Vasconcelos ...and associates (1987) advocate a hierarchical paradigm to better understand the patterns in landscape ecology. Vasconcelos , Zeigler, and associates...modeling system is hierarchical and includes individuals, patch, and the whole landscape (Perestrello de Vasconcelos , Zeigler, et al. 1993). These are but

  18. Spatio-temporal evaluation matrices for geospatial data

    NASA Astrophysics Data System (ADS)

    Triglav, Joc; Petrovič, Dušan; Stopar, Bojan

    2011-02-01

    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.

  19. Spatiotemporal Coupling of the Tongue in Amyotrophic Lateral Sclerosis

    ERIC Educational Resources Information Center

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

    2012-01-01

    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…

  20. Spatio-temporal saliency perception via hypercomplex frequency spectral contrast.

    PubMed

    Li, Ce; Xue, Jianru; Zheng, Nanning; Lan, Xuguang; Tian, Zhiqiang

    2013-03-12

    Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor. In this paper, we propose a novel spatio-temporal saliency perception method based on spatio-temporal hypercomplex spectral contrast (HSC). Firstly, the proposed HSC algorithm represent the features in the HSV (hue, saturation and value) color space and features of motion by a hypercomplex number. Secondly, the spatio-temporal salient objects are efficiently detected by hypercomplex Fourier spectral contrast in parallel. Finally, our saliency perception model also incorporates with the non-uniform sampling, which is a common phenomenon of human vision that directs visual attention to the logarithmic center of the image/video in natural scenes. The experimental results on the public saliency perception datasets demonstrate the effectiveness of the proposed approach compared to eleven state-of-the-art approaches. In addition, we extend the proposed model to moving object extraction in dynamic scenes, and the proposed algorithm is superior to the traditional algorithms.

  1. Spatio-Temporal Dynamics of Cross Polarized Wave Generation

    NASA Astrophysics Data System (ADS)

    Adams, Daniel; Squier, Jeff; Durfee, Charles

    2009-10-01

    We use time-domain Spatially and Spectrally Resolved Interferometry (SSRI) to investigate cross-polarized wave (XPW) generation in barium fluoride. We find that the XPW pulse is √3 smaller than the input in the spatiotemporal domain regardless of the input chirp. Additionally, we calculate a temporally dependent focal length resulting from the nonlinear interaction, and discuss its implications.

  2. Finding Spatio-Temporal Patterns in Large Sensor Datasets

    ERIC Educational Resources Information Center

    McGuire, Michael Patrick

    2010-01-01

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

  3. 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 ...

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

    ERIC Educational Resources Information Center

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

    2007-01-01

    We investigated infants' sensitivity to spatiotemporal structure. In Experiment 1, circles appeared in a statistically 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;…

  5. Spatiotemporal Phase Synchronization in a Large Array of Convective Oscillators

    NASA Astrophysics Data System (ADS)

    Miranda, Montserrat A.; Burguete, Javier

    In a quasi-1D thermal convective system consisting of a large array of nonlinearly coupled oscillators, clustering is the way to achieve a regime of mostly antiphase synchronized oscillators. This regime is characterized by a spatiotemporal doubling of traveling modes. As the dynamics is explored beyond a spatiotemporal chaos regime with weak coupling, new interacting modes emerge through a supercritical bifurcation. In this new regime, the system exhibits coherent subsystems of antiphase synchronized oscillators, which are stationary clusters following a spatiotemporal beating phenomena. This regime is the result of a stronger coupling. We show from a phase mismatch model applied to each oscillator, that these phase coherent domains undergo a global phase instability meanwhile the interactions between oscillators become nonlocal. For each value of the control parameter we find out the time-varying topology (link matrix) from the contact interactions between oscillators. The new characteristic spatiotemporal scales are extracted from the antiphase correlations at the time intervals defined by the link matrix. The interpretation of these experimental results contributes to widen the understanding of other complex systems exhibiting similar phase chaotic dynamics in 2D and 3D.

  6. Water shortage risk assessment using spatiotemporal flow simulation

    NASA Astrophysics Data System (ADS)

    Hsieh, Hsin-I.; Su, Ming-Daw; Wu, Yii-Chen; Cheng, Ke-Sheng

    2016-12-01

    Paddy irrigation practices in Taiwan utilize complicated water conveyance networks which draw streamflows from different tributaries. Characterizing and simulating streamflow series is thus an essential task for irrigation risk assessment and planning mitigation measures. It generally involves modeling the temporal variation and spatial correlation of streamflow data at different sites. Like many other environmental variables, streamflows are asymmetric and non-Gaussian. Such properties exacerbate the difficulties in spatiotemporal modeling of streamflow data. A stochastic spatiotemporal simulation approach capable of generating non-Gaussian ten-day period streamflow data series at different sites is presented in this paper. Historical flow data from different flow stations in southern Taiwan were used to exemplify the application of the proposed model. Simulated realizations of the spatiotemporal anisotropic multivariate Pearson type III distribution were validated by comparing parameters and spatiotemporal correlation characteristics of the simulated data and the observed streamflow data. Risks of irrigation water shortage were estimated and the effect of mitigation measures was assessed using the simulated data.

  7. Cubic map algebra functions for spatio-temporal analysis

    USGS Publications Warehouse

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

    2005-01-01

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

  8. Fast Spatio-Temporal Data Mining from Large Geophysical Datasets

    NASA Technical Reports Server (NTRS)

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

    1995-01-01

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

  9. Spatiotemporal response of crystals in x-ray Bragg diffraction

    NASA Astrophysics Data System (ADS)

    Shvyd'ko, Yuri; Lindberg, Ryan

    2012-10-01

    The spatiotemporal response of crystals in x-ray Bragg diffraction resulting from excitation by an ultrashort, laterally confined x-ray pulse is studied theoretically. The theory presents an extension of the analysis in symmetric reflection geometry [R. R. Lindberg and Y. V. Shvyd’ko, Phys. Rev. ST Accel. Beams 15, 050706 (2012)PRABFM1098-440210.1103/PhysRevSTAB.15.050706] to the generic case, which includes Bragg diffraction both in reflection (Bragg) and transmission (Laue) asymmetric scattering geometries. The spatiotemporal response is presented as a product of a crystal-intrinsic plane-wave spatiotemporal response function and an envelope function defined by the crystal-independent transverse profile of the incident beam and the scattering geometry. The diffracted wave fields exhibit amplitude modulation perpendicular to the propagation direction due to both angular dispersion and the dispersion due to Bragg’s law. The characteristic measure of the spatiotemporal response is expressed in terms of a few parameters: the extinction length, crystal thickness, Bragg angle, asymmetry angle, and the speed of light. Applications to self-seeding of hard x-ray free-electron lasers are discussed, with particular emphasis on the relative advantages of using either the Bragg or Laue scattering geometries. Intensity front inclination in asymmetric diffraction can be used to make snapshots of ultrafast processes with femtosecond resolution.

  10. Supporting user-defined granularities in a spatiotemporal conceptual model

    USGS Publications Warehouse

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

    2002-01-01

    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.

  11. Analysis of spatiotemporal signals: A method based on perturbation theory

    NASA Astrophysics Data System (ADS)

    Hutt, A.; Uhl, C.; Friedrich, R.

    1999-08-01

    We present a method of analyzing spatiotemporal signals with respect to its underlying dynamics. The algorithm aims at the determination of spatial modes and a criterion for the number of interacting modes. Simultaneously, a way of filtering of nonorthogonal noise is shown. The method is discussed by examples of simulated stable fixpoints and the Lorenz attractor.

  12. Spatio-temporal Laplacian pyramid coding for action recognition.

    PubMed

    Shao, Ling; Zhen, Xiantong; Tao, Dacheng; Li, Xuelong

    2014-06-01

    We present a novel descriptor, called spatio-temporal Laplacian pyramid coding (STLPC), for holistic representation of human actions. In contrast to sparse representations based on detected local interest points, STLPC regards a video sequence as a whole with spatio-temporal features directly extracted from it, which prevents the loss of information in sparse representations. Through decomposing each sequence into a set of band-pass-filtered components, the proposed pyramid model localizes features residing at different scales, and therefore is able to effectively encode the motion information of actions. To make features further invariant and resistant to distortions as well as noise, a bank of 3-D Gabor filters is applied to each level of the Laplacian pyramid, followed by max pooling within filter bands and over spatio-temporal neighborhoods. Since the convolving and pooling are performed spatio-temporally, the coding model can capture structural and motion information simultaneously and provide an informative representation of actions. The proposed method achieves superb recognition rates on the KTH, the multiview IXMAS, the challenging UCF Sports, and the newly released HMDB51 datasets. It outperforms state of the art methods showing its great potential on action recognition.

  13. Robust visual tracking with dual spatio-temporal context trackers

    NASA Astrophysics Data System (ADS)

    Sun, Shiyan; Zhang, Hong; Yuan, Ding

    2015-12-01

    Visual tracking is a challenging problem in computer vision. Recent years, significant numbers of trackers have been proposed. Among these trackers, tracking with dense spatio-temporal context has been proved to be an efficient and accurate method. Other than trackers with online trained classifier that struggle to meet the requirement of real-time tracking task, a tracker with spatio-temporal context can run at hundreds of frames per second with Fast Fourier Transform (FFT). Nevertheless, the performance of the tracker with Spatio-temporal context relies heavily on the learning rate of the context, which restricts the robustness of the tracker. In this paper, we proposed a tracking method with dual spatio-temporal context trackers that hold different learning rate during tracking. The tracker with high learning rate could track the target smoothly when the appearance of target changes, while the tracker with low learning rate could percepts the occlusion occurring and continues to track when the target starts to emerge again. To find the target among the candidates from these two trackers, we adopt Normalized Correlation Coefficient (NCC) to evaluate the confidence of each sample. Experimental results show that the proposed algorithm performs robustly against several state-of-the-art tracking methods.

  14. 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...

  15. Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.

    PubMed

    Lu, Jun; Xie, Kan; McFarland, Dennis J

    2014-07-01

    Movement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.

  16. Forecasting Hotspots-A Predictive Analytics Approach.

    PubMed

    Maciejewski, R; Hafen, R; Rudolph, S; Larew, S G; Mitchell, M A; Cleveland, W S; Ebert, D S

    2011-04-01

    Current visual analytics systems provide users with the means to explore trends in their data. Linked views and interactive displays provide insight into correlations among people, events, and places in space and time. Analysts search for events of interest through statistical tools linked to visual displays, drill down into the data, and form hypotheses based upon the available information. However, current systems stop short of predicting events. In spatiotemporal data, analysts are searching for regions of space and time with unusually high incidences of events (hotspots). In the cases where hotspots are found, analysts would like to predict how these regions may grow in order to plan resource allocation and preventative measures. Furthermore, analysts would also like to predict where future hotspots may occur. To facilitate such forecasting, we have created a predictive visual analytics toolkit that provides analysts with linked spatiotemporal and statistical analytic views. Our system models spatiotemporal events through the combination of kernel density estimation for event distribution and seasonal trend decomposition by loess smoothing for temporal predictions. We provide analysts with estimates of error in our modeling, along with spatial and temporal alerts to indicate the occurrence of statistically significant hotspots. Spatial data are distributed based on a modeling of previous event locations, thereby maintaining a temporal coherence with past events. Such tools allow analysts to perform real-time hypothesis testing, plan intervention strategies, and allocate resources to correspond to perceived threats.

  17. Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI.

    PubMed

    Eichele, Tom; Specht, Karsten; Moosmann, Matthias; Jongsma, Marijtje L A; Quiroga, Rodrigo Quian; Nordby, Helge; Hugdahl, Kenneth

    2005-12-06

    The brain acts as an integrated information processing system, which methods in cognitive neuroscience have so far depicted in a fragmented fashion. Here, we propose a simple and robust way to integrate functional MRI (fMRI) with single trial event-related potentials (ERP) to provide a more complete spatiotemporal characterization of evoked responses in the human brain. The idea behind the approach is to find brain regions whose fMRI responses can be predicted by paradigm-induced amplitude modulations of simultaneously acquired single trial ERPs. The method was used to study a variant of a two-stimulus auditory target detection (odd-ball) paradigm that manipulated predictability through alternations of stimulus sequences with random or regular target-to-target intervals. In addition to electrophysiologic and hemodynamic evoked responses to auditory targets per se, single-trial modulations were expressed during the latencies of the P2 (170-ms), N2 (200-ms), and P3 (320-ms) components and predicted spatially separated fMRI activation patterns. These spatiotemporal matches, i.e., the prediction of hemodynamic activation by time-variant information from single trial ERPs, permit inferences about regional responses using fMRI with the temporal resolution provided by electrophysiology.

  18. Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes.

    PubMed

    Guney, Emre; Oliva, Baldo

    2014-01-01

    Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.

  19. Identification of the dichotomous role of age-related LCK in calorie restriction revealed by integrative analysis of cDNA microarray and interactome.

    PubMed

    Park, Daeui; Lee, Eun Kyeong; Jang, Eun Jee; Jeong, Hyoung Oh; Kim, Byoung-Chul; Ha, Young Mi; Hong, Seong Eui; Yu, Byung Pal; Chung, Hae Young

    2013-08-01

    Among the many experimental paradigms used for the investigation of aging, the calorie restriction (CR) model has been proven to be the most useful in gerontological research. Exploration of the mechanisms underlying CR has produced a wealth of data. To identify key molecules controlled by aging and CR, we integrated data from 84 mouse and rat cDNA microarrays with a protein-protein interaction network. On the basis of this integrative analysis, we selected three genes that are upregulated in aging but downregulated by CR and two genes that are downregulated in aging but upregulated by CR. One of these key molecules is lymphocyte-specific protein tyrosine kinase (LCK). To further confirm this result on LCK, we performed a series of experiments in vitro and in vivo using kidneys obtained from aged ad libitum-fed and CR rats. Our major significant findings are as follows: (1) identification of LCK as a key molecule using integrative analysis; (2) confirmation that the age-related increase in LCK was modulated by CR and that protein tyrosine kinase activity was decreased using a LCK-specific inhibitor; and (3) upregulation of LCK leads to NF-κB activation in a ONOO(-) generation-dependent manner, which is modulated by CR. These results indicate that LCK could be considered a target attenuated by the anti-aging effects of CR. Integrative analysis of cDNA microarray and interactome data are powerful tools for identifying target molecules that are involved in the aging process and modulated by CR.

  20. Spatiotemporal Dynamics of Vibrio spp. within the Sydney Harbour Estuary

    PubMed Central

    Siboni, Nachshon; Balaraju, Varunan; Carney, Richard; Labbate, Maurizio; Seymour, Justin R.

    2016-01-01

    Vibrio are a genus of marine bacteria that have substantial environmental and human health importance, and there is evidence that their impact may be increasing as a consequence of changing environmental conditions. We investigated the abundance and composition of the Vibrio community within the Sydney Harbour estuary, one of the most densely populated coastal areas in Australia, and a region currently experiencing rapidly changing environmental conditions. Using quantitative PCR (qPCR) and Vibrio-specific 16S rRNA amplicon sequencing approaches we observed significant spatial and seasonal variation in the abundance and composition of the Vibrio community. Total Vibrio spp. abundance, derived from qPCR analysis, was higher during the late summer than winter and within locations with mid-range salinity (5–26 ppt). In addition we targeted three clinically important pathogens: Vibrio cholerae, V. Vulnificus, and V. parahaemolyticus. While toxigenic strains of V. cholerae were not detected in any samples, non-toxigenic strains were detected in 71% of samples, spanning a salinity range of 0–37 ppt and were observed during both late summer and winter. In contrast, pathogenic V. vulnificus was only detected in 14% of samples, with its occurrence restricted to the late summer and a salinity range of 5–26 ppt. V. parahaemolyticus was not observed at any site or time point. A Vibrio-specific 16S rRNA amplicon sequencing approach revealed clear shifts in Vibrio community composition across sites and between seasons, with several Vibrio operational taxonomic units (OTUs) displaying marked spatial patterns and seasonal trends. Shifts in the composition of the Vibrio community between seasons were primarily driven by changes in temperature, salinity and NO2, while a range of factors including pH, salinity, dissolved oxygen (DO) and NOx (Nitrogen Oxides) explained the observed spatial variation. Our evidence for the presence of a spatiotemporally dynamic Vibrio community

  1. A High Performance Bayesian Computing Framework for Spatiotemporal Uncertainty Modeling

    NASA Astrophysics Data System (ADS)

    Cao, G.

    2015-12-01

    All types of spatiotemporal measurements are subject to uncertainty. With spatiotemporal data becomes increasingly involved in scientific research and decision making, it is important to appropriately model the impact of uncertainty. Quantitatively modeling spatiotemporal uncertainty, however, is a challenging problem considering the complex dependence and dataheterogeneities.State-space models provide a unifying and intuitive framework for dynamic systems modeling. In this paper, we aim to extend the conventional state-space models for uncertainty modeling in space-time contexts while accounting for spatiotemporal effects and data heterogeneities. Gaussian Markov Random Field (GMRF) models, also known as conditional autoregressive models, are arguably the most commonly used methods for modeling of spatially dependent data. GMRF models basically assume that a geo-referenced variable primarily depends on its neighborhood (Markov property), and the spatial dependence structure is described via a precision matrix. Recent study has shown that GMRFs are efficient approximation to the commonly used Gaussian fields (e.g., Kriging), and compared with Gaussian fields, GMRFs enjoy a series of appealing features, such as fast computation and easily accounting for heterogeneities in spatial data (e.g, point and areal). This paper represents each spatial dataset as a GMRF and integrates them into a state-space form to statistically model the temporal dynamics. Different types of spatial measurements (e.g., categorical, count or continuous), can be accounted for by according link functions. A fast alternative to MCMC framework, so-called Integrated Nested Laplace Approximation (INLA), was adopted for model inference.Preliminary case studies will be conducted to showcase the advantages of the described framework. In the first case, we apply the proposed method for modeling the water table elevation of Ogallala aquifer over the past decades. In the second case, we analyze the

  2. Prediction model of potential hepatocarcinogenicity of rat hepatocarcinogens using a large-scale toxicogenomics database

    SciTech Connect

    Uehara, Takeki; Minowa, Yohsuke; Morikawa, Yuji; Kondo, Chiaki; Maruyama, Toshiyuki; Kato, Ikuo; Nakatsu, Noriyuki; Igarashi, Yoshinobu; Ono, Atsushi; Hayashi, Hitomi; Mitsumori, Kunitoshi; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro

    2011-09-15

    The present study was performed to develop a robust gene-based prediction model for early assessment of potential hepatocarcinogenicity of chemicals in rats by using our toxicogenomics database, TG-GATEs (Genomics-Assisted Toxicity Evaluation System developed by the Toxicogenomics Project in Japan). The positive training set consisted of high- or middle-dose groups that received 6 different non-genotoxic hepatocarcinogens during a 28-day period. The negative training set consisted of high- or middle-dose groups of 54 non-carcinogens. Support vector machine combined with wrapper-type gene selection algorithms was used for modeling. Consequently, our best classifier yielded prediction accuracies for hepatocarcinogenicity of 99% sensitivity and 97% specificity in the training data set, and false positive prediction was almost completely eliminated. Pathway analysis of feature genes revealed that the mitogen-activated protein kinase p38- and phosphatidylinositol-3-kinase-centered interactome and the v-myc myelocytomatosis viral oncogene homolog-centered interactome were the 2 most significant networks. The usefulness and robustness of our predictor were further confirmed in an independent validation data set obtained from the public database. Interestingly, similar positive predictions were obtained in several genotoxic hepatocarcinogens as well as non-genotoxic hepatocarcinogens. These results indicate that the expression profiles of our newly selected candidate biomarker genes might be common characteristics in the early stage of carcinogenesis for both genotoxic and non-genotoxic carcinogens in the rat liver. Our toxicogenomic model might be useful for the prospective screening of hepatocarcinogenicity of compounds and prioritization of compounds for carcinogenicity testing. - Highlights: >We developed a toxicogenomic model to predict hepatocarcinogenicity of chemicals. >The optimized model consisting of 9 probes had 99% sensitivity and 97% specificity. >This model

  3. Using atmospheric observations to evaluate the spatiotemporal variability of CO2 fluxes simulated by terrestrial biospheric models

    NASA Astrophysics Data System (ADS)

    Fang, Y.; Michalak, A. M.; Shiga, Y. P.; Yadav, V.

    2014-06-01

    Terrestrial biospheric models (TBMs) are used to extrapolate local observations and process-level understanding of land-atmosphere carbon exchange to larger regions, and serve as a predictive tool for examining carbon-climate interactions. Understanding the performance of TBMs is thus crucial to the carbon cycle and climate science. In this study, we propose a statistical model selection approach for evaluating the spatiotemporal patterns of net ecosystem exchange (NEE) simulated by TBMs using atmospheric CO2 measurements. We find that current atmospheric observations are sensitive to the underlying spatiotemporal flux variability at sub-biome scales for a large portion of the North American continent, and that atmospheric observations can therefore be used to evaluate simulated spatiotemporal flux patterns, rather than focusing solely on flux magnitudes at aggregated scales. Results show that the proposed approach can be used to assess whether a TBM represents a substantial portion of the underlying flux variability as well as to differentiate among multiple competing TBMs. When applying the proposed approach to four prototypical TBMs, we find that the performance of TBMs varies substantially across seasons, with best performance during the growing season and limited skill during transition seasons. This seasonal difference in the ability of TBMs to represent the spatiotemporal flux variability may reflect the models' capability to represent the seasonally-varying influence of environmental drivers on fluxes. While none of the TBMs consistently outperforms the others, differences among the examined models are at least partially attributable to their internal structures. Overall, the proposed approach provides a new avenue for evaluating TBM performance based on sub-biome scale flux patterns, presenting an opportunity for assessing and informing model development using atmospheric observations.

  4. Spatio-Temporally Restricted Expression of Cell Adhesion Molecules during Chicken Embryonic Development

    PubMed Central

    Roy, Priti; Bandyopadhyay, Amitabha

    2014-01-01

    Differential cell adhesive properties are known to regulate important developmental events like cell sorting and cell migration. Cadherins and protocadherins are known to mediate these cellular properties. Though a large number of such molecules have been predicted, their characterization in terms of interactive properties and cellular roles is far from being comprehensive. To narrow down the tissue context and collect correlative evidence for tissue specific roles of these molecules, we have carried out whole-mount in situ hybridization based RNA expression study for seven cadherins and four protocadherins. In developing chicken embryos (HH stages 18, 22, 26 and 28) cadherins and protocadherins are expressed in tissue restricted manner. This expression study elucidates precise expression domains of cell adhesion molecules in the context of developing embryos. These expression domains provide spatio-temporal context in which the function of these genes can be further explored. PMID:24806091

  5. Spatiotemporal solitons in birefringent media near the zero-dispersion point

    NASA Astrophysics Data System (ADS)

    Fitrakis, E. P.; Nistazakis, H. E.; Malomed, B. A.; Frantzeskakis, D. J.; Kevrekidis, P. G.

    2006-09-01

    We present a new species of spatiotemporal solitons (STSs, alias light bullets) that may be formed through the interaction of two waves with different polarizations in a birefringent Kerr medium near the point at which the second-order group-velocity dispersion (GVD) vanishes in the presence of a uniform cw background in one component. The analysis is based on an asymptotic reduction of the two cross-phase, modulation-coupled (2+1)-dimensional nonlinear Schrödinger equations, incorporating the third-order GVD to the Davey-Stewartson (DS) systems of type I or II for the self-defocusing and focusing Kerr nonlinearity, respectively. The STSs are then predicted following the pattern of the dromion solutions of the DS-I system and lump solitons of the DS-II system. Strictly speaking, the solitons are unstable, but they are shown to be stable objects on experimentally relevant scales of the propagation distance.

  6. Spatiotemporal modeling with temporal-invariant variogram subgroups to estimate fine particulate matter PM2.5 concentrations

    NASA Astrophysics Data System (ADS)

    Chen, Chu-Chih; Wu, Chang-Fu; Yu, Hwa-Lung; Chan, Chang-Chuan; Cheng, Tsun-Jen

    2012-07-01

    Short-term exposure estimation of daily air pollution levels incorporating geographic information system (GIS) into spatiotemporal modeling remains a great challenge for assessing corresponding acute adverse health effects. Due to daily meteorological effects on the dispersion of pollutants, explanatory spatial covariables and their coefficients may not be the same as in classical land-use regression (LUR) modeling for long-term exposure. In this paper, we propose a two-stage spatiotemporal model for daily fine particulate matter (PM2.5) concentration prediction: first, daily nonlinear temporal trends are estimated through a generalized additive model, and second, GIS covariates are used to predict spatial variation in the temporal trend-removed residuals. To account for spatial dependence on meteorological conditions, the dates of the study period are divided by the sill of the daily empirical variogram into approximately temporal-invariant subgroups. Within each subgroup, daily PM2.5 estimations are obtained by combining the temporal and spatial parts of the estimations from the two stages. The proposed method is applied to the modeling of spatiotemporal PM2.5 concentrations observed at 18 ambient air monitoring stations in Taipei metropolitan area during 2006-2008. The results showed that the PM2.5 concentrations decreased whereas the relative humidity and wind speed increased with the sill subgroups, which may be due to the effects of daily meteorological conditions on the dispersions of the particles. Also, the covariates and their coefficients of the LUR models varied with subgroups and had in general higher adjusted R-squares and smaller root mean square errors in prediction than those of a single overall LUR model.

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

    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.

  8. Geophysical Factor Resolving of Rainfall Mechanism for Super Typhoons by Using Multiple Spatiotemporal Components Analysis

    NASA Astrophysics Data System (ADS)

    Huang, Chien-Lin; Hsu, Nien-Sheng

    2016-04-01

    This study develops a novel methodology to resolve the geophysical cause of typhoon-induced rainfall considering diverse dynamic co-evolution at multiple spatiotemporal components. The multi-order hidden patterns of complex hydrological process in chaos are detected to understand the fundamental laws of rainfall mechanism. The discovered spatiotemporal features are utilized to develop a state-of-the-art descriptive statistical model for mechanism validation, modeling and further prediction during typhoons. The time series of hourly typhoon precipitation from different types of moving track, atmospheric field and landforms are respectively precede the signal analytical process to qualify each type of rainfall cause and to quantify the corresponding affected degree based on the measured geophysical atmospheric-hydrological variables. This study applies the developed methodology in Taiwan Island which is constituted by complex diverse landform formation. The identified driving-causes include: (1) cloud height to ground surface; (2) co-movement effect induced by typhoon wind field with monsoon; (3) stem capacity; (4) interaction between typhoon rain band and terrain; (5) structural intensity variance of typhoon; and (6) integrated cloudy density of rain band. Results show that: (1) for the central maximum wind speed exceeding 51 m/sec, Causes (1) and (3) are the primary ones to generate rainfall; (2) for the typhoon moving toward the direction of 155° to 175°, Cause (2) is the primary one; (3) for the direction of 90° to 155°, Cause (4) is the primary one; (4) for the typhoon passing through mountain chain which above 3500 m, Cause (5) is the primary one; and (5) for the moving speed lower than 18 km/hr, Cause (6) is the primary one. Besides, the multiple geophysical component-based precipitation modeling can achieve 81% of average accuracy and 0.732 of average correlation coefficient (CC) within average 46 hours of duration, that improve their predictability.

  9. Quantifying Uncertainty in Spatio-temporal Forest Composition Changes Inferred from Fossil Pollen Records

    NASA Astrophysics Data System (ADS)

    Dawson, A.; Paciorek, C. J.; McLachlan, J. S.; Goring, S. J.; Williams, J. W.; Jackson, S. T.

    2014-12-01

    Understanding past compositional changes in vegetation provides insight about ecosystem dynamics in response to changing environments. Past vegetation reconstructions rely predominantly on fossil pollen data from sedimentary lake cores, which acts as a proxy record for the surrounding vegetation. Stratigraphic changes in these pollen records allow us to infer changes in composition and species distributions. Pollen records collected from a network of sites allow us to make inference about the spatio-temporal changes in vegetation over thousands of years. However, the complexity of the relationship between pollen deposits and surrounding vegetation, as well as the spatially sparse set of fossil pollen sites are important sources of uncertainty. In addition, uncertainty arises from the carbon dating and age-depth modelling processes. To reconstruct vegetation composition including uncertainty for the Upper Midwestern USA, we build a Bayesian hierarchical model that links vegetation composition to fossil pollen data via a dispersal model. In the calibration phase, we estimate the relationship between vegetation and pollen for the settlement era using Public Land Survey data and a network of pollen records. In the prediction phase, parameter estimates obtained during the calibration phase are used to estimate latent species distributions and relative abundances over the last 2500 years. We account for additional uncertainty in the pollen records by: allowing expert palynologists to identify pre-settlement pollen samples to be included in our calibration data, and through the incorporation of age uncertainty obtained from the Bayesian age-depth model BACON in our prediction data. Resulting spatio-temporal composition and abundance estimates will be used to improve forecasting capabilities of ecosystem models.

  10. Effective and efficient analysis of spatio-temporal data

    NASA Astrophysics Data System (ADS)

    Zhang, Zhongnan

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

  11. Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction

    PubMed Central

    2016-01-01

    De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/. PMID:27893735

  12. Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution.

    PubMed

    Friberg, Mariel D; Zhai, Xinxin; Holmes, Heather A; Chang, Howard H; Strickland, Matthew J; Sarnat, Stefanie Ebelt; Tolbert, Paige E; Russell, Armistead G; Mulholland, James A

    2016-04-05

    Investigations of ambient air pollution health effects rely on complete and accurate spatiotemporal air pollutant estimates. Three methods are developed for fusing ambient monitor measurements and 12 km resolution chemical transport model (CMAQ) simulations to estimate daily air pollutant concentrations across Georgia. Temporal variance is determined by observations in one method, with the annual mean CMAQ field providing spatial structure. A second method involves scaling daily CMAQ simulated fields using mean observations to reduce bias. Finally, a weighted average of these results based on prediction of temporal variance provides optimized daily estimates for each 12 × 12 km grid. These methods were applied to daily metrics of 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) over the state of Georgia for a seven-year period (2002-2008). Cross-validation demonstrates a wide range in optimized model performance across pollutants, with SO2 predicted most poorly due to limitations in coal combustion plume monitoring and modeling. For the other pollutants studied, 54-88% of the spatiotemporal variance (Pearson R(2) from cross-validation) was captured, with ozone and PM2.5 predicted best. The optimized fusion approach developed provides daily spatial field estimates of air pollutant concentrations and uncertainties that are consistent with observations, emissions, and meteorology.

  13. Spatiotemporal fuzzy based climate forecasting for Australia

    NASA Astrophysics Data System (ADS)

    Montazerolghaem, M.; Vervoort, R. W.; Minasny, B.; McBratney, A.

    2012-12-01

    Variation in weather and climate events impacts agriculture production processes, and profits across years. Therefore, seasonal rainfall prediction is an important factor for strategic and tactical decision making in agricultural, land and water resource management. This study aims to apply optimal data-driven techniques for fine resolution climate classification and forecasting over South-eastern Australia. Data were used in this study were included daily precipitation, maximum and minimum temperature data collected over 40 years from 107 weather stations in Southeast Australia acquired from the Bureau of Meteorology (BOM). Fuzzy-k means clustering techniques (FKM) were applied on one year weekly time series. Cluster centroids and memberships of rainfall and temperature weekly time series for one year period provide meaningful and insight into weather variability in time and space over the study. Stations are grouped based on their memberships in rainfall and temperature classes. The result showed that FKM is a useful method for trend analysis and pattern discovery in space and time. Outcomes indicate improvement in the climate classification of the area at the station level. An associate project is gathering higher spatial density on-farm data. This high-resolution climate data collected at the farm scale will be analyzed similarly in the future to improve spatial resolution of our classification. The second stage of this study consists of development of a fine-resolution forecasting model for predicting rainfall. FKM was applied on a metrics which included input and output time series to extract rules and relationships between them. After classification, rules were extracted within each class based on forecasting time, space and extreme climate events followed by effective sea surface temperature anomalies. These rules and a lookup table of input and output centroids were used for rainfall prediction in the form of weekly time series for the next six months. One

  14. Sniffing and spatiotemporal coding in olfaction.

    PubMed

    Scott, John W

    2006-02-01

    The act of sniffing increases the air velocity and changes the duration of airflow in the nose. It is not yet clear how these changes interact with the intrinsic timing within the olfactory bulb, but this is a matter of current research activity. An action of sniffing in generating a high velocity that alters the sorption of odorants onto the lining of the nasal cavity is expected from the established work on odorant properties and sorption in the frog nose. Recent work indicates that the receptor properties in the olfactory epithelium and olfactory bulb are correlated with the receptor gene expression zones. The responses in both the epithelium and the olfactory bulb are predictable to a considerable extent by the hydrophobicity of odorants. Furthermore, receptor expression in both rodent and salamander nose interacts with the shapes of the nasal cavity to place the receptor sensitivity to odorants in optimal places according to the aerodynamic properties of the nose.

  15. Ordering spatiotemporal chaos in complex thermosensitive neuron networks

    NASA Astrophysics Data System (ADS)

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

    2006-04-01

    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.

  16. Spatiotemporal analysis of ERP data in emotional processing

    NASA Astrophysics Data System (ADS)

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

    2006-03-01

    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. A modified fuzzy k-mean clustering method based on shape similarity is applied to the grand mean ERPs and the statistical analysis is performed to define the significance of each segmentation map. In the results, positive and negative conditions showed different spatiotemporal patterns of ERP. The results were in accord with other emotional study by fMRI or PET.

  17. Visualization and detection of spatio-temporal hot-spot and cluster for dengue in Petaling district, Malaysia

    NASA Astrophysics Data System (ADS)

    Rizwan, M.; Dass, Sarat C.; Sagayan, Vijanth

    2016-11-01

    Hot-spot and cluster detection is a part of disease surveillance to find out which regions are effected most by the disease. Analysis of these clusters of disease used for longer periods of time can lead to future prediction of disease outbreaks. Dengue is one of the most important arboviral disease in Malaysia which needs proper surveillance and control strategies. Our current study presents hot-spot and cluster detection of dengue outbreaks in the district of Petaling, Selangor in Malaysia during the year 2014 using the registered cases of dengue in the district. Detected hot-spot from this spatio-temporal analysis of registered dengue could provide a trend for the future dengue outbreak predictions. Use of dengue registered cases for the future prediction could be more effective then using land use variables and climate data. R software is used for the hot-spots detection. Custom clustering definition is considered for the analysis.

  18. Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count.

    PubMed

    Nowosad, Jakub; Stach, Alfred; Kasprzyk, Idalia; Weryszko-Chmielewska, Elżbieta; Piotrowska-Weryszko, Krystyna; Puc, Małgorzata; Grewling, Łukasz; Pędziszewska, Anna; Uruska, Agnieszka; Myszkowska, Dorota; Chłopek, Kazimiera; Majkowska-Wojciechowska, Barbara

    The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of Corylus, Alnus, and Betula using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during exposure. The dataset was divided into a training set and a test set, using a stratified random split. For each taxon and city, the model was built using a random forest method. Corylus models performed poorly. However, the study revealed the possibility of predicting with substantial accuracy the occurrence of days with high pollen concentrations of Alnus and Betula using past pollen count data from monitoring sites. These results can be used for building (1) simpler models, which require data only from aerobiological monitoring sites, and (2) combined meteorological and aerobiological models for predicting high levels of pollen concentration.

  19. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

    NASA Astrophysics Data System (ADS)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  20. Spatiotemporal causal modeling for the management of Dengue Fever

    NASA Astrophysics Data System (ADS)

    Yu, Hwa-Lung; Huang, Tailin; Lee, Chieh-Han

    2015-04-01

    Increasing climatic extremes have caused growing concerns about the health effects and disease outbreaks. The association between climate variation and the occurrence of epidemic diseases play an important role on a country's public health systems. Part of the impacts are direct casualties associated with the increasing frequency and intensity of typhoons, the proliferation of disease vectors and the short-term increase of clinic visits on gastro-intestinal discomforts, diarrhea, dermatosis, or psychological trauma. Other impacts come indirectly from the influence of disasters on the ecological and socio-economic systems, including the changes of air/water quality, living environment and employment condition. Previous risk assessment studies on dengue fever focus mostly on climatic and non-climatic factors and their association with vectors' reproducing pattern. The public-health implication may appear simple. Considering the seasonal changes and regional differences, however, the causality of the impacts is full of uncertainties. Without further investigation, the underlying dengue fever risk dynamics may not be assessed accurately. The objective of this study is to develop an epistemic framework for assessing dynamic dengue fever risk across space and time. The proposed framework integrates cross-departmental data, including public-health databases, precipitation data over time and various socio-economic data. We explore public-health issues induced by typhoon through literature review and spatiotemporal analytic techniques on public health databases. From those data, we identify relevant variables and possible causal relationships, and their spatiotemporal patterns derived from our proposed spatiotemporal techniques. Eventually, we create a spatiotemporal causal network and a framework for modeling dynamic dengue fever risk.

  1. Local Spatio-Temporal Analysis in Vision Systems

    DTIC Science & Technology

    1994-07-31

    evaluation, purchase and installation of a MasPar parallel computer. 14. SUBJECT TERMS 15. NUMBER OF PAGES 30 •ic QuaLlY INSPECTED 3 16. PRICE CODE 17...called "energy" models of spatio-temporal v!ion), and also in engineering analysis of digitized images (viz: the Fast Fourier Transform , which decomposes...nonstationary image data, and (when coupled with multiband/ wavelet decompositions) for the computation of symbolic descriptions of space-varying

  2. Ultrabroadband Dispersive Radiation by Spatiotemporal Oscillation of Multimode Waves.

    PubMed

    Wright, Logan G; Wabnitz, Stefan; Christodoulides, Demetrios N; Wise, Frank W

    2015-11-27

    In nonlinear dynamical systems, qualitatively distinct phenomena occur depending continuously on the size of the bounded domain containing the system. For nonlinear waves, a multimode waveguide is a bounded three-dimensional domain, allowing observation of dynamics impossible in open settings. Here we study radiation emitted by bounded nonlinear waves: the spatiotemporal oscillations of solitons in multimode fiber generate multimode dispersive waves over an ultrabroadband spectral range. This work suggests routes to sources of coherent electromagnetic waves with unprecedented spectral range.

  3. Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data.

    PubMed

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

    2005-10-15

    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.

  4. Delay driven spatiotemporal chaos in single species population dynamics models.

    PubMed

    Jankovic, Masha; Petrovskii, Sergei; Banerjee, Malay

    2016-08-01

    Questions surrounding the prevalence of complex population dynamics form one of the central themes in ecology. Limit cycles and spatiotemporal chaos are examples that have been widely recognised theoretically, although their importance and applicability to natural populations remains debatable. The ecological processes underlying such dynamics are thought to be numerous, though there seems to be consent as to delayed density dependence being one of the main driving forces. Indeed, time delay is a common feature of many ecological systems and can significantly influence population dynamics. In general, time delays may arise from inter- and intra-specific trophic interactions or population structure, however in the context of single species populations they are linked to more intrinsic biological phenomena such as gestation or resource regeneration. In this paper, we consider theoretically the spatiotemporal dynamics of a single species population using two different mathematical formulations. Firstly, we revisit the diffusive logistic equation in which the per capita growth is a function of some specified delayed argument. We then modify the model by incorporating a spatial convolution which results in a biologically more viable integro-differential model. Using the combination of analytical and numerical techniques, we investigate the effect of time delay on pattern formation. In particular, we show that for sufficiently large values of time delay the system's dynamics are indicative to spatiotemporal chaos. The chaotic dynamics arising in the wake of a travelling population front can be preceded by either a plateau corresponding to dynamical stabilisation of the unstable equilibrium or by periodic oscillations.

  5. The spatiotemporal properties of visual completion measured by response classification.

    PubMed

    Gold, Jason M; Shubel, Erin

    2006-03-03

    A constant problem faced by the visual system is the identification of partly occluded objects within the visual scene. Recent experiments have demonstrated that the visual system engages in a process of visual completion, where the hidden parts of objects are filled into the visual representation. Recent experiments have also suggested that there may be a time course to this completion process. Here, we examined the spatiotemporal properties of visual completion by having observers classify figures defined by either luminance-defined or illusory contours and then correlating their decisions with externally added spatiotemporal visual noise. This "response classification" technique allowed us to derive a spatiotemporal correlation map (a "classification movie") that revealed the locations used by observers at each point in space and time during the stimulus presentation. We found that observers gradually became more influenced by noise at locations corresponding to illusory contours across the first 175 ms of stimulus present