A novel approach to detect hot-spots in large-scale multivariate data

Background: Progressive advances in the measurement of complex multifactorial components of biological processes involving both spatial and temporal domains have made it difficult to identify the variables (genes, proteins, neurons etc.) significantly changed activities in response to a stimulus within large data sets using conventional statistical approaches. The set of all changed variables is termed hot-spots. The detection of such hot spots is considered to be an NP hard problem, but by first establishing its theoretical foundation we have been able to develop an algorithm that provides a solution. Results: Our results show that a first-order phase transition is observable whose critical point separates the hot-spot set from the remaining variables. Its application is also found to be more successful than existing approaches in identifying statistically significant hot-spots both with simulated data sets and in real large-scale multivariate data sets from gene arrays, electrophysiological recording and functional magnetic resonance imaging experiments. Conclusion: In summary, this new statistical algorithm should provide a powerful new analytical tool to extract the maximum information from complex biological multivariate data

Uncovering interactions in the frequency domain

Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multielectrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs recorded from distributed sites in the right hemisphere of a macaque monkey

Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning

Training task-completion dialogue agents with reinforcement learning usually requires a large number of real user experiences. The Dyna-Q algorithm extends Q-learning by integrating a world model, and thus can effectively boost training efficiency using simulated experiences generated by the world model. The effectiveness of Dyna-Q, however, depends on the quality of the world model - or implicitly, the pre-specified ratio of real vs. simulated experiences used for Q-learning. To this end, we extend the recently proposed Deep Dyna-Q (DDQ) framework by integrating a switcher that automatically determines whether to use a real or simulated experience for Q-learning. Furthermore, we explore the use of active learning for improving sample efficiency, by encouraging the world model to generate simulated experiences in the state-action space where the agent has not (fully) explored. Our results show that by combining switcher and active learning, the new framework named as Switch-based Active Deep Dyna-Q (Switch-DDQ), leads to significant improvement over DDQ and Q-learning baselines in both simulation and human evaluations.Comment: 8 pages, 9 figures, AAAI 201

Impact of environmental inputs on reverse-engineering approach to network structures

Background: Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. Results: With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. Conclusion: We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations

dATP/ATP, a Multifunctional Nucleotide, Stimulates Bacterial Cell Lysis, Extracellular DNA Release and Biofilm Development

Background: Signaling by extracellular adenosine 59-triphosphase (eATP) is very common for cell-to-cell communication in many basic patho-physiological development processes. Rapid release of ATP into the extracellular environment from distressed or injured eukaryotic cells due to pathogens or other etiological factors can serve as a ‘‘danger signal’’, activating host innate immunity. However, little is known about how or whether pathogenic bacteria respond to this ‘‘danger signal’’. Methods and Principal Findings: Here we report that extracellular dATP/ATP can stimulate bacterial adhesion and biofilm formation via increased cell lysis and extracellular DNA (eDNA) release. We demonstrate that extracellular dATP/ATP also stimulates bacterial adherence in vitro to human bronchial epithelial cells. Conclusions and Significance: These data suggest that bacteria may sense extracellular dATP/ATP as a signal of ‘‘danger’’ and form biofilms to protect them from host innate immunity. This study reveals a very important and unrecognized phenomenon that both bacteria and host cells could respond to a common important signal molecule in a race to adapt to the presence of one another. We propose that extracellular dATP/ATP functions as an ‘‘inter-domain’ ’ warning signal that serves to induce protective measures in both Bacterial and Eukaryotic cells