A Cross Entropy Algorithm for Classification with δ\delta−Patterns

International audienceA classification strategy based on $\delta$-patterns is developed via a combinatorial optimization problem related with the maximal clique generation problem on a graph. The proposed solution uses the cross entropy method and has the advantage to be particularly suitable for large datasets. This study is tailored for the particularities of the genomic data

Proof of Jacobi identity in generalized quantum dynamics

We prove that the Jacobi identity for the generalized Poisson bracket is satisfied in the generalization of Heisenberg picture quantum mechanics recently proposed by one of us (SLA). The identity holds for any combination of fermionic and bosonic fields, and requires no assumptions about their mutual commutativity.Comment: 9 pages, plain tex file, IASSNS-HEP-93/4

Specific Heat Exponent for the 3-d Ising Model from a 24-th Order High Temperature Series

We compute high temperature expansions of the 3-d Ising model using a recursive transfer-matrix algorithm and extend the expansion of the free energy to 24th order. Using ID-Pade and ratio methods, we extract the critical exponent of the specific heat to be alpha=0.104(4).Comment: 10 pages, LaTeX with 5 eps-figures using epsf.sty, IASSNS-93/83 and WUB-93-4

Series expansions without diagrams

We discuss the use of recursive enumeration schemes to obtain low and high temperature series expansions for discrete statistical systems. Using linear combinations of generalized helical lattices, the method is competitive with diagramatic approaches and is easily generalizable. We illustrate the approach using the Ising model and generate low temperature series in up to five dimensions and high temperature series in three dimensions. The method is general and can be applied to any discrete model. We describe how it would work for Potts models.Comment: 24 pages, IASSNS-HEP-93/1

Low temperature expansion for the 3-d Ising Model

We compute the weak coupling expansion for the energy of the three dimensional Ising model through 48 excited bonds. We also compute the magnetization through 40 excited bonds. This was achieved via a recursive enumeration of states of fixed energy on a set of finite lattices. We use a linear combination of lattices with a generalization of helical boundary conditions to eliminate finite volume effects.Comment: 10 pages, IASSNS-HEP-92/42, BNL-4767

Glueball spectrum in a (1+1)-dimensional model for QCD

We consider (1+1)-dimensional QCD coupled to scalars in the adjoint representation of the gauge group SU($N$). This model results from dimensional reduction of the (2+1)-dimensional pure glue theory. In the large-N limit we study the spectrum of glueballs numerically, using the discretized \lcq. We find a discrete spectrum of bound states, with the density of levels growing approximately exponentially with the mass. A few low-lying states are very close to being eigenstates of the parton number, and their masses can be accurately calculated by truncated diagonalizations.Comment: 17 pages, uses phyzzx and table.tex, 5 figures available upon request from [email protected]

Yeast Growth Plasticity Is Regulated by Environment-Specific Multi-QTL Interactions

For a unicellular, non-motile organism like Saccharomyces cerevisiae, carbon sources act both as nutrients and as signaling molecules and consequently affect various fitness parameters including growth. It is therefore advantageous for yeast strains to adapt their growth to carbon source variation. The ability of a given genotype to manifest different phenotypes in varying environments is known as phenotypic plasticity. To identify quantitative trait loci (QTL) that drive plasticity in growth, two growth parameters (growth rate and biomass) were measured in a published dataset from meiotic recombinants of two genetically divergent yeast strains grown in different carbon sources. To identify QTL contributing to plasticity across pairs of environments, gene-environment interaction mapping was performed, which identified several QTL that have a differential effect across environments, some of which act antagonistically across pairs of environments. Multi-QTL analysis identified loci interacting with previously known growth affecting QTL as well as novel two-QTL interactions that affect growth. A QTL that had no significant independent effect was found to alter growth rate and biomass for several carbon sources through two-QTL interactions. Our study demonstrates that environment-specific epistatic interactions contribute to the growth plasticity in yeast. We propose that a targeted scan for epistatic interactions, such as the one described here, can help unravel mechanisms regulating phenotypic plasticity

ERBB2 overexpression suppresses stress-induced autophagy and renders ERBB2-induced mammary tumorigenesis independent of monoallelicBecn1loss

Defective autophagy has been implicated in mammary tumorigenesis, as the gene encoding the essential autophagy regulator BECN1 is deleted in human breast cancers and Becn1+/− mice develop mammary hyperplasias. In agreement with a recent study, which reports concurrent allelic BECN1 loss and ERBB2 amplification in a small number of human breast tumors, we found that low BECN1 mRNA correlates with ERBB2-overexpression in breast cancers, suggesting that BECN1 loss and ERBB2 overexpression may functionally interact in mammary tumorigenesis. We now report that ERBB2 overexpression suppressed autophagic response to stress in mouse mammary and human breast cancer cells. ERBB2-overexpressing Becn1+/+ and Becn1+/− immortalized mouse mammary epithelial cells (iMMECs) formed mammary tumors in nude mice with similar kinetics, and monoallelic Becn1 loss did not alter ERBB2- and PyMT-driven mammary tumorigenesis. In human breast cancer databases, ERBB2-expressing tumors exhibit a low autophagy gene signature, independent of BECN1 mRNA expression, and have similar gene expression profiles with non-ERBB2-expressing breast tumors with low BECN1 levels. We also found that ERBB2-expressing BT474 breast cancer cells, despite being partially autophagy-deficient under stress, can be sensitized to the anti-ERBB2 antibody trastuzumab (tzb) by further pharmacological or genetic autophagy inhibition. Our results indicate that ERBB2-driven mammary tumorigenesis is associated with functional autophagy suppression and ERBB2-positive breast cancers are partially autophagy-deficient even in a wild-type BECN1 background. Furthermore and extending earlier findings using tzb-resistant cells, exogenously imposed autophagy inhibition increases the anticancer effect of trastuzumab on tzb-sensitive ERBB2-expressing breast tumor cells, indicating that pharmacological autophagy suppression has a wider role in the treatment of ERBB2-positive breast cancer

Identifying mRNA targets of microRNA dysregulated in cancer: with application to clear cell Renal Cell Carcinoma

BACKGROUND. MicroRNA regulate mRNA levels in a tissue specific way, either by inducing degradation of the transcript or by inhibiting translation or transcription. Putative mRNA targets of microRNA identified from seed sequence matches are available in many databases. However, such matches have a high false positive rate and cannot identify tissue specificity of regulation. RESULTS. We describe a simple method to identify direct mRNA targets of microRNA dysregulated in cancers from expression level measurements in patient matched tumor/normal samples. The word "direct" is used here in a strict sense to: a) represent mRNA which have an exact seed sequence match to the microRNA in their 3'UTR, b) the seed sequence match is strictly conserved across mouse, human, rat and dog genomes, c) the mRNA and microRNA expression levels can distinguish tumor from normal with high significance and d) the microRNA/mRNA expression levels are strongly and significantly anti-correlated in tumor and/or normal samples. We apply and validate the method using clear cell Renal Cell Carcinoma (ccRCC) and matched normal kidney samples, limiting our analysis to mRNA targets which undergo degradation of the mRNA transcript because of a perfect seed sequence match. Dysregulated microRNA and mRNA are first identified by comparing their expression levels in tumor vs normal samples. Putative dysregulated microRNA/mRNA pairs are identified from these using seed sequence matches, requiring that the seed sequence be conserved in human/dog/rat/mouse genomes. These are further pruned by requiring a strong anti-correlation signature in tumor and/or normal samples. The method revealed many new regulations in ccRCC. For instance, loss of miR-149, miR-200c and mir-141 causes gain of function of oncogenes (KCNMA1, LOX), VEGFA and SEMA6A respectively and increased levels of miR-142-3p, miR-185, mir-34a, miR-224, miR-21 cause loss of function of tumor suppressors LRRC2, PTPN13, SFRP1, ERBB4, and (SLC12A1, TCF21) respectively. We also found strong anti-correlation between VEGFA and the miR-200 family of microRNA: miR-200a*, 200b, 200c and miR-141. Several identified microRNA/mRNA pairs were validated on an independent set of matched ccRCC/normal samples. The regulation of SEMA6A by miR-141 was verified by a transfection assay. CONCLUSIONS. We describe a simple and reliable method to identify direct gene targets of microRNA in any cancer. The constraints we impose (strong dysregulation signature for microRNA and mRNA levels between tumor/normal samples, evolutionary conservation of seed sequence and strong anti-correlation of expression levels) remove spurious matches and identify a subset of robust, tissue specific, functional mRNA targets of dysregulated microRNA.Cancer Institute of New Jersy; New Jersey Commission for Cacner Research; Lineberger Comprehensive Cancer Center Tissue Procurement and Genomics Core Facility; Crawford Fun