In-medium effective chiral lagrangians and the pion mass in nuclear matter

We argue that the effective pion mass in nuclear matter obtained from chiral effective lagrangians is unique and does not depend on off-mass-shell extensions of the pion fields as e.g. the PCAC choice. The effective pion mass in isospin symmetric nuclear matter is predicted to increase slightly with increasing nuclear density, whereas the effective time-like pion decay constant and the magnitude of the density-dependent quark condensate decrease appreciably. The in-medium Gell-Mann-Oakes-Renner relation as well as other in-medium identities are studied in addition. Finally, several constraints on effective lagrangians for the description of the pion propagation in isospin symmetric, isotropic and homogenous nuclear matter are discussed. (Talk presented at the workshop ``Hirschegg '95: Hadrons in Nuclear Matter'', Hirschegg, Kleinwalsertal, Austria, January 16-21, 1995)Comment: 14 pages, LaTeX, some typographical errors correcte

S-wave Meson-Nucleon Interactions and the Meson Mass in Nuclear Matter from Chiral Effective Lagrangians

Chiral effective lagrangians may differ in their prediction of meson-nucleon scattering amplitudes off-meson-mass-shell, but must yield identical S-matrix elements. We argue that the effective meson mass in nuclear matter obtained from chiral effective lagrangians is also unique. Off-mass-shell amplitudes obtained using the PCAC choice of pion field must therefore not be viewed as fundamental constraints on the dynamics, the determination of the effective meson mass in nuclear matter or the possible existence of meson condensates in the ground state of nuclear matter. This hypothesis is borne out by a calculation of the effective mass in two commonly employed formulations of chiral perturbation theory which yield different meson-nucleon scattering amplitudes off-meson-mass-shell.Comment: 23 pages, LaTeX, 2 Postscript figures (fig1.ps, fig2.ps

The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory interactions, and apply the method to predict a large portion of the regulatory network of the archaeon Halobacterium NRC-1. The Inferelator uses regression and variable selection to identify transcriptional influences on genes based on the integration of genome annotation and expression data. The learned network successfully predicted Halobacterium's global expression under novel perturbations with predictive power similar to that seen over training data. Several specific regulatory predictions were experimentally tested and verified

From Kaon-Nuclear Interactions to Kaon Condensation

An effective chiral Lagrangian in heavy-fermion formalism whose parameters are constrained by kaon-nucleon and kaon-nuclear interactions next to the leading order in chiral expansion is used to describe kaon condensation in dense ``neutron star" matter. The critical density is found to be robust with respect to the parameters of the chiral Lagrangian and comes out to be $\rho_c\sim (3 - 4)\rho_0$. Once kaon condensation sets in, the system is no longer composed of neutron matter but of nuclear matter. Possible consequences on stellar collapse with the formation of compact ``nuclear stars" or light-mass black holes are pointed out.Comment: 20 pages, LaTeX, NORDITA-93/30 N, SUNY-NTG-93-

Prediction of phenotype and gene expression for combinations of mutations

Molecular interactions provide paths for information flows. Genetic interactions reveal active information flows and reflect their functional consequences. We integrated these complementary data types to model the transcription network controlling cell differentiation in yeast. Genetic interactions were inferred from linear decomposition of gene expression data and were used to direct the construction of a molecular interaction network mediating these genetic effects. This network included both known and novel regulatory influences, and predicted genetic interactions. For corresponding combinations of mutations, the network model predicted quantitative gene expression profiles and precise phenotypic effects. Multiple predictions were tested and verified

Derivation of genetic interaction networks from quantitative phenotype data

We have generalized the derivation of genetic-interaction networks from quantitative phenotype data. Familiar and unfamiliar modes of genetic interaction were identified and defined. A network was derived from agar-invasion phenotypes of mutant yeast. Mutations showed specific modes of genetic interaction with specific biological processes. Mutations formed cliques of significant mutual information in their large-scale patterns of genetic interaction. These local and global interaction patterns reflect the effects of gene perturbations on biological processes and pathways

Kaon Energies in Dense Matter

We discuss the role of kaon-nucleon and nucleon-nucleon correlations in kaon condensation in dense matter. Correlations raise the threshold density for kaon condensation, possibly to densities higher than those encountered in stable neutron stars.Comment: RevTeX, 11 pages, 2 PostScript figures; manuscript also available, in PostScript form, at http://www.nordita.dk/locinfo/preprints.htm

Validation and calibration of next-generation sequencing to identify Epstein-Barr virus-positive gastric cancer in The Cancer Genome Atlas

The Epstein-Barr virus (EBV)-positive subtype of gastric adenocarcinoma is conventionally identified by in situ hybridization (ISH) for viral nucleic acids, but next-generation sequencing represents a potential alternative. We therefore determined normalized EBV read counts by whole genome, whole exome, mRNA and miRNA sequencing for 295 fresh-frozen gastric tumor samples. Formalin-fixed, paraffin-embedded tissue sections were retrieved for ISH confirmation of 13 high-EBV and 11 low-EBV cases. In pairwise comparisons, individual samples were either concordantly high or concordantly low by all genomic methods for which data were available. Empiric cut-offs of sequencing counts identified 26 (9%) tumors as EBV-positive. EBV-positivity or negativity by molecular testing was confirmed by EBER-ISH in all but one tumor evaluated by both approaches (kappa=0.91). EBV-positive gastric tumors may be accurately identified by quantifying viral sequences in genomic data. Simultaneous analyses of human and viral DNA, mRNA and miRNA could streamline tumor profiling for clinical care and research