U(1) Gauge Theory as Quantum Hydrodynamics

It is shown that gauge theories are most naturally studied via a polar decomposition of the field variable. Gauge transformations may be viewed as those that leave the density invariant but change the phase variable by additive amounts. The path integral approach is used to compute the partition function. When gauge fields are included, the constraint brought about by gauge invariance simply means an appropriate linear combination of the gradients of the phase variable and the gauge field is invariant. No gauge fixing is needed in this approach that is closest to the spirit of the gauge principle. We derive an exact formula for the condensate fraction and in case it is zero, an exact formula for the anomalous exponent. We also derive a formula for the vortex strength which involves computing radiation corrections.Comment: 15 pages, Plain LaTeX, final published versio

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset

Single-Particle Green Functions in Exactly Solvable Models of Bose and Fermi Liquids

Based on a class of exactly solvable models of interacting bose and fermi liquids, we compute the single-particle propagators of these systems exactly for all wavelengths and energies and in any number of spatial dimensions. The field operators are expressed in terms of bose fields that correspond to displacements of the condensate in the bose case and displacements of the fermi sea in the fermi case. Unlike some of the previous attempts, the present attempt reduces the answer for the spectral function in any dimension in both fermi and bose systems to quadratures. It is shown that when only the lowest order sea-displacement terms are included, the random phase approximation in its many guises is recovered in the fermi case, and Bogoliubov's theory in the bose case. The momentum distribution is evaluated using two different approaches, exact diagonalisation and the equation of motion approach. The novelty being of course, the exact computation of single-particle properties including short wavelength behaviour.Comment: Latest version to be published in Phys. Rev. B. enlarged to around 40 page

The Role of Nonequilibrium Dynamical Screening in Carrier Thermalization

We investigate the role played by nonequilibrium dynamical screening in the thermalization of carriers in a simplified two-component two-band model of a semiconductor. The main feature of our approach is the theoretically sound treatment of collisions. We abandon Fermi's Golden rule in favor of a nonequilibrium field theoretic formalism as the former is applicable only in the long-time regime. We also introduce the concept of nonequilibrium dynamical screening. The dephasing of excitonic quantum beats as a result of carrier-carrier scattering is brought out. At low densities it is found that the dephasing times due to carrier-carrier scattering is in picoseconds and not femtoseconds, in agreement with experiments. The polarization dephasing rates are computed as a function of the excited carrier density and it is found that the dephasing rate for carrier-carrier scattering is proportional to the carrier density at ultralow densities. The scaling relation is sublinear at higher densities, which enables a comparison with experiment.Comment: Revised version with additional refs. 12 pages, figs. available upon request; Submitted to Phys. Rev.

TMPRSS2-ERG -specific transcriptional modulation is associated with prostate cancer biomarkers and TGF-β signaling

<p>Abstract</p> <p>Background</p> <p><it>TMPRSS2-ERG </it>gene fusions occur in about 50% of all prostate cancer cases and represent promising markers for molecular subtyping. Although <it>TMPRSS2-ERG </it>fusion seems to be a critical event in prostate cancer, the precise functional role in cancer development and progression is still unclear.</p> <p>Methods</p> <p>We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon 1.0 ST microarrays.</p> <p>Results</p> <p>Comparison of gene expression levels among <it>TMPRSS2-ERG </it>fusion-positive and negative tumors as well as benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known biomarkers for prostate cancer detection like <it>CRISP3 </it>were found to be associated with the gene fusion status. WNT and TGF-β/BMP signaling pathways were significantly associated with genes upregulated in <it>TMPRSS2-ERG </it>fusion-positive tumors.</p> <p>Conclusions</p> <p>The <it>TMPRSS2-ERG </it>gene fusion results in the modulation of transcriptional patterns and cellular pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and targeted therapy.</p

A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses

Network Clustering Revealed the Systemic Alterations of Mitochondrial Protein Expression

The mitochondrial protein repertoire varies depending on the cellular state. Protein component modifications caused by mitochondrial DNA (mtDNA) depletion are related to a wide range of human diseases; however, little is known about how nuclear-encoded mitochondrial proteins (mt proteome) changes under such dysfunctional states. In this study, we investigated the systemic alterations of mtDNA-depleted (ρ0) mitochondria by using network analysis of gene expression data. By modularizing the quantified proteomics data into protein functional networks, systemic properties of mitochondrial dysfunction were analyzed. We discovered that up-regulated and down-regulated proteins were organized into two predominant subnetworks that exhibited distinct biological processes. The down-regulated network modules are involved in typical mitochondrial functions, while up-regulated proteins are responsible for mtDNA repair and regulation of mt protein expression and transport. Furthermore, comparisons of proteome and transcriptome data revealed that ρ0 cells attempted to compensate for mtDNA depletion by modulating the coordinated expression/transport of mt proteins. Our results demonstrate that mt protein composition changed to remodel the functional organization of mitochondrial protein networks in response to dysfunctional cellular states. Human mt protein functional networks provide a framework for understanding how cells respond to mitochondrial dysfunctions