1,296 research outputs found
Gravitational Higgs Mechanism
We discuss the gravitational Higgs mechanism in domain wall background
solutions that arise in the theory of 5-dimensional Einstein-Hilbert gravity
coupled to a scalar field with a non-trivial potential. The scalar fluctuations
in such backgrounds can be completely gauged away, and so can be the
graviphoton fluctuations. On the other hand, we show that the graviscalar
fluctuations do not have normalizable modes. As to the 4-dimensional graviton
fluctuations, in the case where the volume of the transverse dimension is
finite the massive modes are plane-wave normalizable, while the zero mode is
quadratically normalizable. We then discuss the coupling of domain wall gravity
to localized 4-dimensional matter. In particular, we point out that this
coupling is consistent only if the matter is conformal. This is different from
the Randall-Sundrum case as there is a discontinuity in the delta-function-like
limit of such a smooth domain wall - the latter breaks diffeomorphisms only
spontaneously, while the Randall-Sundrum brane breaks diffeomorphisms
explicitly. Finally, at the quantum level both the domain wall as well as the
Randall-Sundrum setups suffer from inconsistencies in the coupling between
gravity and localized matter, as well as the fact that gravity is generically
expected to be delocalized in such backgrounds due to higher curvature terms.Comment: 16 pages, revtex; a minor correctio
In-Plane and Out-of-Plane MEMS Motion Sensors Based on Fringe Capacitances
Abstract New MEMS motion sensors have been developed. These prototypes are based on a sensing technique that exploits the fringe capacitance between two co-planar electrodes designed over a thin oxide layer covering a grounded wafer substrate. A relevant fraction of the electric-field streamlines, generated by the readout voltage applied between the electrodes, develops in the air (or vacuum) volume over the electrodes. A grounded suspended mass moving within this volume modifies the streamlines configuration, causing relative changes in the capacitance between the electrodes as large as the ∼80% of the initial value. Two types of devices based on the described concept have been designed and built in an industrial surface micromachining process, to sense acceleration in the direction both parallel and orthogonal to the substrate surface. The realized devices have been tested and a sensitivity of ∼0.9 fF/g and ∼0.2 fF/g has been obtained for the in plane and for the out-of-plane structures respectively
Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice.
To gain insight into how mutant huntingtin (mHtt) CAG repeat length modifies Huntington's disease (HD) pathogenesis, we profiled mRNA in over 600 brain and peripheral tissue samples from HD knock-in mice with increasing CAG repeat lengths. We found repeat length-dependent transcriptional signatures to be prominent in the striatum, less so in cortex, and minimal in the liver. Coexpression network analyses revealed 13 striatal and 5 cortical modules that correlated highly with CAG length and age, and that were preserved in HD models and sometimes in patients. Top striatal modules implicated mHtt CAG length and age in graded impairment in the expression of identity genes for striatal medium spiny neurons and in dysregulation of cyclic AMP signaling, cell death and protocadherin genes. We used proteomics to confirm 790 genes and 5 striatal modules with CAG length-dependent dysregulation at the protein level, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo
Identifying Pathway Proteins in Networks using Convergence
One of the key goals of systems biology concerns the analysis of experimental biological data available to the scientific public. New technologies are rapidly developed to observe and report whole-scale biological phenomena; however, few methods exist with the ability to produce specific, testable hypotheses from this noisy ‘big’ data. In this work, we propose an approach that combines the power of data-driven network theory along with knowledge-based ontology to tackle this problem. Network models are especially powerful due to their ability to display elements of interest and their relationships as internetwork structures. Additionally, ontological data actually supplements the confidence of relationships within the model without clouding critical structure identification. As such, we postulate that given a (gene/protein) marker set of interest, we can systematically identify the core of their interactions (if they are indeed working together toward a biological function), via elimination of original markers and addition of additional necessary markers. This concept, which we refer to as “convergence,” harnesses the idea of “guilt-by-association” and recursion to identify whether a core of relationships exists between markers. In this study, we test graph theoretic concepts such as shortest-path, k-Nearest- Neighbor and clustering) to identify cores iteratively in data- and knowledge-based networks in the canonical yeast Pheromone Mating Response pathway. Additionally, we provide results for convergence application in virus infection, hearing loss, and Parkinson’s disease. Our results indicate that if a marker set has common discrete function, this approach is able to identify that function, its interacting markers, and any new elements necessary to complete the structural core of that function. The result below find that the shortest path function is the best approach of those used, finding small target sets that contain a majority or all of the markers in the gold standard pathway. The power of this approach lies in its ability to be used in investigative studies to inform decisions concerning target selection
Is human blood a good surrogate for brain tissue in transcriptional studies?
Abstract Background Since human brain tissue is often unavailable for transcriptional profiling studies, blood expression data is frequently used as a substitute. The underlying hypothesis in such studies is that genes expressed in brain tissue leave a transcriptional footprint in blood. We tested this hypothesis by relating three human brain expression data sets (from cortex, cerebellum and caudate nucleus) to two large human blood expression data sets (comprised of 1463 individuals). Results We found mean expression levels were weakly correlated between the brain and blood data (r range: [0.24,0.32]). Further, we tested whether co-expression relationships were preserved between the three brain regions and blood. Only a handful of brain co-expression modules showed strong evidence of preservation and these modules could be combined into a single large blood module. We also identified highly connected intramodular "hub" genes inside preserved modules. These preserved intramodular hub genes had the following properties: first, their expression levels tended to be significantly more heritable than those from non-preserved intramodular hub genes (p < 10-90); second, they had highly significant positive correlations with the following cluster of differentiation genes: CD58, CD47, CD48, CD53 and CD164; third, a significant number of them were known to be involved in infection mechanisms, post-transcriptional and post-translational modification and other basic processes. Conclusions Overall, we find transcriptome organization is poorly preserved between brain and blood. However, the subset of preserved co-expression relationships characterized here may aid future efforts to identify blood biomarkers for neurological and neuropsychiatric diseases when brain tissue samples are unavailable
Energy Transfer between Throats from a 10d Perspective
Strongly warped regions, also known as throats, are a common feature of the
type IIB string theory landscape. If one of the throats is heated during
cosmological evolution, the energy is subsequently transferred to other throats
or to massless fields in the unwarped bulk of the Calabi-Yau orientifold. This
energy transfer proceeds either by Hawking radiation from the black hole
horizon in the heated throat or, at later times, by the decay of
throat-localized Kaluza-Klein states. In both cases, we calculate in a 10d
setup the energy transfer rate (respectively decay rate) as a function of the
AdS scales of the throats and of their relative distance. Compared to existing
results based on 5d models, we find a significant suppression of the energy
transfer rates if the size of the embedding Calabi-Yau orientifold is much
larger than the AdS radii of the throats. This effect can be partially
compensated by a small distance between the throats. These results are
relevant, e.g., for the analysis of reheating after brane inflation. Our
calculation employs the dual gauge theory picture in which each throat is
described by a strongly coupled 4d gauge theory, the degrees of freedom of
which are localized at a certain position in the compact space.Comment: 25 pages; a comment adde
A Remark on Non-conformal Non-supersymmetric Theories with Vanishing Vacuum Energy Density
We discuss non-conformal non-supersymmetric large N gauge theories with
vanishing vacuum energy density to all orders in perturbation theory. These
gauge theories can be obtained via a field theory limit of Type IIB D3-branes
embedded in orbifolded space-times. We also discuss gravity in this setup.Comment: 13 pages, revtex; a minor change in wordin
Warped Spectroscopy: Localization of Frozen Bulk Modes
We study the 10D equation of motion of dilaton-axion fluctuations in type IIB
string compactifications with three-form flux, taking warping into account.
Using simplified models with physics comparable to actual compactifications, we
argue that the lightest mode localizes in long warped throats and takes a mass
of order the warped string scale. Also, Gukov-Vafa-Witten superpotential is
valid for the lightest mass mode; however, the mass is similar to the
Kaluza-Klein scale, so the dilaton-axion should be integrated out of the
effective theory in this long throat regime (leaving a constant
superpotential). On the other hand, there is a large hierarchy between
flux-induced and KK mass scales for moderate or weak warping. This hierarchy
agrees with arguments given for trivial warping. Along the way, we also
estimate the effect of the other 10D supergravity equations of motion on the
dilaton-axion fluctuation, since these equations act as constraints. We argue
that they give negligible corrections to the simplest approximation.Comment: 24pp + appendices, 6 figs, JHEP3 class; v2. corrected reference; v3.
added clarifications; v4. corrected typo
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
figure
Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms
Motivation :Reconstructing the topology of a gene regulatory network is one
of the key tasks in systems biology. Despite of the wide variety of proposed
methods, very little work has been dedicated to the assessment of their
stability properties. Here we present a methodical comparison of the
performance of a novel method (RegnANN) for gene network inference based on
multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER),
focussing our analysis on the prediction variability induced by both the
network intrinsic structure and the available data.
Results: The extensive evaluation on both synthetic data and a selection of
gene modules of "Escherichia coli" indicates that all the algorithms suffer of
instability and variability issues with regards to the reconstruction of the
topology of the network. This instability makes objectively very hard the task
of establishing which method performs best. Nevertheless, RegnANN shows MCC
scores that compare very favorably with all the other inference methods tested.
Availability: The software for the RegnANN inference algorithm is distributed
under GPL3 and it is available at the corresponding author home page
(http://mpba.fbk.eu/grimaldi/regnann-supmat
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