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Search Results: 1 - 10 of 390992 matches for " T. J. Christian "
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Clustering of genes into regulons using integrated modeling-COGRIM
Guang Chen, Shane T Jensen, Christian J Stoeckert
Genome Biology , 2007, DOI: 10.1186/gb-2007-8-1-r4
Abstract: The interactions of transcriptional regulators of gene expression with each other and their target genes are often summarized in the form of regulatory modules and networks, which can be used as a basis for understanding cellular processes. The computational procedures that are employed to identify gene regulatory modules and networks have traditionally used information from expression data, binding motifs, or genome-wide location analysis of DNA-binding regulators [1]. A typical approach has been to first use clustering algorithms on expression data to find sets of co-expressed and potentially co-regulated genes, and then the upstream regulatory regions of the genes in each cluster are analyzed for common cis-regulatory elements (motifs) or modules of several cis-regulatory elements located in close proximity to each other [2]. These cis-regulatory elements are the potential binding sites of transcription factor (TF) proteins, which bind directly to the DNA sequence in order to increase or decrease transcription of specific target genes. This computational strategy can also be employed using chromatin immunoprecipitation (ChIP) technology, which identifies genomic sequences that are enriched for physical binding of a particular TF [3]. Although such approaches have proven to be useful, their power is inherently limited by the fact that each data source provides only partial information: expression data provides only indirect evidence of regulation, upstream regulatory region searches provide only potential binding sites that may not be bound by TFs, and ChIP binding data provides only physical binding information that may not be functional in terms of controlling gene expression.There has been substantial recent research into the integration of biological data sources for the discovery of regulatory networks. Different approaches taken have included heuristic algorithms [4,5], linear models [6-12], and probabilistic models [13,14]. The GRAM algorithm [4] employed e
Teukolsky Master Equation: De Rham wave equation for the gravitational and electromagnetic fields in vacuum
Donato Bini,Christian Cherubini,Robert T Jantzen,Remo J. Ruffini
Physics , 2002, DOI: 10.1143/PTP.107.967
Abstract: A new version of the Teukolksy Master Equation, describing any massless field of different spin $s=1/2,1,3/2,2$ in the Kerr black hole, is presented here in the form of a wave equation containing additional curvature terms. These results suggest a relation between curvature perturbation theory in general relativity and the exact wave equations satisfied by the Weyl and the Maxwell tensors, known in the literature as the de Rham-Lichnerowicz Laplacian equations. We discuss these Laplacians both in the Newman-Penrose formalism and in the Geroch-Held-Penrose variant for an arbitrary vacuum spacetime. Perturbative expansion of these wave equations results in a recursive scheme valid for higher orders. This approach, apart from the obvious implications for the gravitational and electromagnetic wave propagation on a curved spacetime, explains and extends the results in the literature for perturbative analysis by clarifying their true origins in the exact theory.
Primordial black holes as a tool for constraining non-Gaussianity
Christian T. Byrnes,Edmund J. Copeland,Anne M. Green
Physics , 2012, DOI: 10.1103/PhysRevD.86.043512
Abstract: Primordial Black Holes (PBH's) can form in the early Universe from the collapse of large density fluctuations. Tight observational limits on their abundance constrain the amplitude of the primordial fluctuations on very small scales which can not otherwise be constrained, with PBH's only forming from the extremely rare large fluctuations. The number of PBH's formed is therefore sensitive to small changes in the shape of the tail of the fluctuation distribution, which itself depends on the amount of non-Gaussianity present. We study, for the first time, how quadratic and cubic local non-Gaussianity of arbitrary size (parameterised by f_nl and g_nl respectively) affects the PBH abundance and the resulting constraints on the amplitude of the fluctuations on very small scales. Intriguingly we find that even non-linearity parameters of order unity have a significant impact on the PBH abundance. The sign of the non-Gaussianity is particularly important, with the constraint on the allowed fluctuation amplitude tightening by an order of magnitude as f_nl changes from just -0.5 to 0.5. We find that if PBH's are observed in the future, then regardless of the amplitude of the fluctuations, non-negligible negative f_nl would be ruled out. Finally we show that g_nl can have an even larger effect on the number of PBH's formed than f_nl.
Bayesian variable selection and data integration for biological regulatory networks
Shane T. Jensen,Guang Chen,Christian J. Stoeckert, Jr
Mathematics , 2006, DOI: 10.1214/07-AOAS130
Abstract: A substantial focus of research in molecular biology are gene regulatory networks: the set of transcription factors and target genes which control the involvement of different biological processes in living cells. Previous statistical approaches for identifying gene regulatory networks have used gene expression data, ChIP binding data or promoter sequence data, but each of these resources provides only partial information. We present a Bayesian hierarchical model that integrates all three data types in a principled variable selection framework. The gene expression data are modeled as a function of the unknown gene regulatory network which has an informed prior distribution based upon both ChIP binding and promoter sequence data. We also present a variable weighting methodology for the principled balancing of multiple sources of prior information. We apply our procedure to the discovery of gene regulatory relationships in Saccharomyces cerevisiae (Yeast) for which we can use several external sources of information to validate our results. Our inferred relationships show greater biological relevance on the external validation measures than previous data integration methods. Our model also estimates synergistic and antagonistic interactions between transcription factors, many of which are validated by previous studies. We also evaluate the results from our procedure for the weighting for multiple sources of prior information. Finally, we discuss our methodology in the context of previous approaches to data integration and Bayesian variable selection.
Experiences with efficient methodologies for teaching computer programming to geoscientists
Christian T. Jacobs,Gerard J. Gorman,Lorraine Craig
Computer Science , 2015,
Abstract: Computer programming was once thought of as a skill required only by professional software developers. But today, given the ubiquitous nature of computation and data science it is quickly becoming necessary for all scientists and engineers to have at least a basic knowledge of how to program. Teaching how to program, particularly to those students with little or no computing background, is well-known to be a difficult task. However, there is also a wealth of evidence-based teaching practices for teaching programming skills which can be applied to greatly improve learning outcomes and the student experience. Adopting these practices naturally gives rise to greater learning efficiency - this is critical if programming is to be integrated into an already busy geoscience curriculum. This paper considers an undergraduate computer programming course, run during the last 5 years in the Department of Earth Science and Engineering at Imperial College London. The teaching methodologies that were used each year are discussed alongside the challenges that were encountered, and how the methodologies affected student performance. Anonymised student marks and feedback are used to highlight this, and also how the adjustments made to the course eventually resulted in a highly effective learning environment.
Patterns of Coupled Theta Activity in Amygdala-Hippocampal-Prefrontal Cortical Circuits during Fear Extinction
J?rg Lesting, Rajeevan T. Narayanan, Christian Kluge, Susan Sangha, Thomas Seidenbecher, Hans-Christian Pape
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0021714
Abstract: Signals related to fear memory and extinction are processed within brain pathways involving the lateral amygdala (LA) for formation of aversive stimulus associations, the CA1 area of the hippocampus for context-dependent modulation of these associations, and the infralimbic region of the medial prefrontal cortex (mPFC) for extinction processes. While many studies have addressed the contribution of each of these modules individually, little is known about their interactions and how they function as an integrated system. Here we show, by combining multiple site local field potential (LFP) and unit recordings in freely behaving mice in a fear conditioning paradigm, that theta oscillations may provide a means for temporally and functionally connecting these modules. Theta oscillations occurred with high specificity in the CA1-LA-mPFC network. Theta coupling increased between all areas during retrieval of conditioned fear, and declined during extinction learning. During extinction recall, theta coupling partly rebounded in LA-mPFC and CA1-mPFC, and remained at a low level in CA1-LA. Interfering with theta coupling through local electrical microstimulation in CA1-LA affected conditioned fear and extinction recall depending on theta phase. These results support the hypothesis that theta coupling provides a means for inter-areal coordination in conditioned behavioral responsiveness. More specifically, theta oscillations seem to contribute to a population code indicating conditioned stimuli during recall of fear memory before and after extinction.
Progress towards an accurate determination of the Boltzmann constant by Doppler spectroscopy
Cyril Lemarchand,Meriam Triki,Beno?t Darquié,Christian J. Bordé,Christian Chardonnet,Christophe Daussy
Physics , 2010, DOI: 10.1088/1367-2630/13/7/073028
Abstract: In this paper, we present significant progress performed on an experiment dedicated to the determination of the Boltzmann constant, k, by accurately measuring the Doppler absorption profile of a line in a gas of ammonia at thermal equilibrium. This optical method based on the first principles of statistical mechanics is an alternative to the acoustical method which has led to the unique determination of k published by the CODATA with a relative accuracy of 1.7 ppm. We report on the first measurement of the Boltzmann constant by laser spectroscopy with a statistical uncertainty below 10 ppm, more specifically 6.4 ppm. This progress results from improvements in the detection method and in the statistical treatment of the data. In addition, we have recorded the hyperfine structure of the probed saQ(6,3) rovibrational line of ammonia by saturation spectroscopy and thus determine very precisely the induced 4.36 (2) ppm broadening of the absorption linewidth. We also show that, in our well chosen experimental conditions, saturation effects have a negligible impact on the linewidth. Finally, we draw the route to future developments for an absolute determination of with an accuracy of a few ppm.
The Tropical Forest and Fire Emissions Experiment: method evaluation of volatile organic compound emissions measured by PTR-MS, FTIR, and GC from tropical biomass burning
T. G. Karl, T. J. Christian, R. J. Yokelson, P. Artaxo, W. M. Hao,A. Guenther
Atmospheric Chemistry and Physics (ACP) & Discussions (ACPD) , 2007,
Abstract: Volatile Organic Compound (VOC) emissions from fires in tropical forest fuels were quantified using Proton-Transfer-Reaction Mass Spectrometry (PTRMS), Fourier Transform Infrared Spectroscopy (FTIR) and gas chromatography (GC) coupled to PTRMS (GC-PTR-MS). We investigated VOC emissions from 19 controlled laboratory fires at the USFS (United States Forest Service) Fire Sciences Laboratory and 16 fires during an intensive airborne field campaign during the peak of the burning season in Brazil in 2004. The VOC emissions were dominated by oxygenated VOCs (OVOC) (OVOC/NMHC ~4:1, NMHC: non-methane hydrocarbons) The specificity of the PTR-MS instrument, which measures the mass to charge ratio of VOCs ionized by H3O+ ions, was validated by gas chromatography and by intercomparing in-situ measurements with those obtained from an open path FTIR instrument. Emission ratios for methyl vinyl ketone, methacrolein, crotonaldehyde, acrylonitrile and pyrrole were measured in the field for the first time. Our measurements show a higher contribution of OVOCs than previously assumed for modeling purposes. Comparison of fresh (<15 min) and aged (>1 h–1 d) smoke suggests altered emission ratios due to gas phase chemistry for acetone but not for acetaldehyde and methanol. Emission ratios for numerous, important, reactive VOCs with respect to acetonitrile (a biomass burning tracer) are presented.
Corrigendum to "The tropical forest and fire emissions experiment: laboratory fire measurements and synthesis of campaign data" published in Atmos. Chem. Phys., 8, 3509–3527, 2008
R. J. Yokelson, T. J. Christian, T. G. Karl,A. Guenther
Atmospheric Chemistry and Physics (ACP) & Discussions (ACPD) , 2008,
Abstract: No abstract available.
The tropical forest and fire emissions experiment: laboratory fire measurements and synthesis of campaign data
R. J. Yokelson, T. J. Christian, T. G. Karl,A. Guenther
Atmospheric Chemistry and Physics (ACP) & Discussions (ACPD) , 2008,
Abstract: As part of the Tropical Forest and Fire Emissions Experiment (TROFFEE), tropical forest fuels were burned in a large, biomass-fire simulation facility and the smoke was characterized with open-path Fourier transform infrared spectroscopy (FTIR), proton-transfer reaction mass spectrometry (PTR-MS), gas chromatography (GC), GC/PTR-MS, and filter sampling of the particles. In most cases, about one-third of the fuel chlorine ended up in the particles and about one-half remained in the ash. About 50% of the mass of non-methane organic compounds (NMOC) emitted by these fires could be identified with the available instrumentation. The lab fire emission factors (EF, g compound emitted per kg dry fuel burned) were coupled with EF obtained during the TROFFEE airborne and ground-based field campaigns. This revealed several types of EF dependence on parameters such as the ratio of flaming to smoldering combustion and fuel characteristics. The synthesis of data from the different TROFFEE platforms was also used to derive EF for all the measured species for both primary deforestation fires and pasture maintenance fires – the two main types of biomass burning in the Amazon. Many of the EF are larger than those in widely-used earlier work. This is mostly due to the inclusion of newly-available, large EF for the initially-unlofted smoldering emissions from residual logs in pastures and the assumption that these emissions make a significant contribution (~40%) to the total emissions from pasture fires. The TROFFEE EF for particles with aerodynamic diameter <2.5 microns (EFPM2.5) is 14.8 g/kg for primary deforestation fires and 18.7 g/kg for pasture maintenance fires. These EFPM2.5 are significantly larger than a previous recommendation (9.1 g/kg) and lead to an estimated pyrogenic primary PM2.5 source for the Amazon that is 84% larger. New regional budgets for biogenic and pyrogenic emissions were roughly estimated. Coupled with an estimate of secondary aerosol formation in the Amazon and source apportionment studies, the regional budgets suggest that ~5% of the total mass of the regionally generated NMOC end up as secondary organic aerosol within the Amazonian boundary layer within 1–3 days. New global budgets confirm that biogenic emissions and biomass burning are the two largest global sources of NMOC with an estimated production of approximately 1000 (770–1400) and 500 (250–630) Tg/yr, respectively. It follows that plants and fires may also be the two main global sources of secondary organic aerosol. A limited set of emission ratios (ER) is given for sugar cane burning, which may help estimate the air quality impacts of burning this major crop, which is often grown in densely populated areas.
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