Optimization of ground and excited state wavefunctions and van der Waals clusters

A quantum Monte Carlo method is introduced to optimize excited state trial wavefunctions. The method is applied in a correlation function Monte Carlo calculation to compute ground and excited state energies of bosonic van der Waals clusters of upto seven particles. The calculations are performed using trial wavefunctions with general three-body correlations

Localized helium excitations in 4He_N-benzene clusters

We compute ground and excited state properties of small helium clusters 4He_N containing a single benzene impurity molecule. Ground-state structures and energies are obtained for N=1,2,3,14 from importance-sampled, rigid-body diffusion Monte Carlo (DMC). Excited state energies due to helium vibrational motion near the molecule surface are evaluated using the projection operator, imaginary time spectral evolution (POITSE) method. We find excitation energies of up to ~23 K above the ground state. These states all possess vibrational character of helium atoms in a highly anisotropic potential due to the aromatic molecule, and can be categorized in terms of localized and collective vibrational modes. These results appear to provide precursors for a transition from localized to collective helium excitations at molecular nanosubstrates of increasing size. We discuss the implications of these results for analysis of anomalous spectral features in recent spectroscopic studies of large aromatic molecules in helium clusters.Comment: 15 pages, 5 figures, submitted to Phys. Rev.

Heterologous Tissue Culture Expression Signature Predicts Human Breast Cancer Prognosis

BACKGROUND: Cancer patients have highly variable clinical outcomes owing to many factors, among which are genes that determine the likelihood of invasion and metastasis. This predisposition can be reflected in the gene expression pattern of the primary tumor, which may predict outcomes and guide the choice of treatment better than other clinical predictors. METHODOLOGY/PRINCIPAL FINDINGS: We developed an mRNA expression-based model that can predict prognosis/outcomes of human breast cancer patients regardless of microarray platform and patient group. Our model was developed using genes differentially expressed in mouse plasma cell tumors growing in vivo versus those growing in vitro. The prediction system was validated using published data from three cohorts of patients for whom microarray and clinical data had been compiled. The model stratified patients into four independent survival groups (BEST, GOOD, BAD, and WORST: log-rank test p = 1.7×10(−8)). CONCLUSIONS: Our model significantly improved the survival prediction over other expression-based models and permitted recognition of patients with different prognoses within the estrogen receptor-positive group and within a single pathological tumor class. Basing our predictor on a dataset that originated in a different species and a different cell type may have rendered it less sensitive to proliferation differences and endowed it with wide applicability. SIGNIFICANCE: Prognosis prediction for patients with breast cancer is currently based on histopathological typing and estrogen receptor positivity. Yet both assays define groups that are heterogeneous in survival. Gene expression profiling allows subdivision of these groups and recognition of patients whose tumors are very unlikely to be lethal and those with much grimmer outlooks, which can augment the predictive power of conventional tumor analysis and aid the clinician in choosing relaxed vs. aggressive therapy

Gene expression profiling reveals different pathways related to Abl and other genes that cooperate with c-Myc in a model of plasma cell neoplasia

<p>Abstract</p> <p>Background</p> <p>To elucidate the genes involved in the neoplastic transformation of B cells, global gene expression profiles were generated using Affymetrix U74Av2 microarrays, containing 12,488 genes, for four different groups of mouse B-cell lymphomas and six subtypes of pristane-induced mouse plasma cell tumors, three of which developed much earlier than the others.</p> <p>Results</p> <p>Unsupervised hierarchical cluster analysis exhibited two main sub-clusters of samples: a B-cell lymphoma cluster and a plasma cell tumor cluster with subclusters reflecting mechanism of induction. This report represents the first step in using global gene expression to investigate molecular signatures related to the role of cooperating oncogenes in a model of Myc-induced carcinogenesis. Within a single subgroup, e.g., ABPCs, plasma cell tumors that contained typical T(12;15) chromosomal translocations did not display gene expression patterns distinct from those with variant T(6;15) translocations, in which the breakpoint was in the <it>Pvt-1 </it>locus, 230 kb 3' of c-<it>Myc</it>, suggesting that c-<it>Myc </it>activation was the initiating factor in both. When integrated with previously published Affymetrix array data from human multiple myelomas, the IL-6-transgenic subset of mouse plasma cell tumors clustered more closely with MM1 subsets of human myelomas, slow-appearing plasma cell tumors clustered together with MM2, while plasma cell tumors accelerated by v-Abl clustered with the more aggressive MM3-MM4 myeloma subsets. Slow-appearing plasma cell tumors expressed <it>Socs1 </it>and <it>Socs2 </it>but v-<it>Abl</it>-accelerated plasma cell tumors expressed 4–5 times as much. Both v-<it>Abl</it>-accelerated and non-v-<it>Ab</it>l-associated tumors exhibited phosphorylated STAT 1 and 3, but only v-Abl-accelerated plasma cell tumors lost viability and STAT 1 and 3 phosphorylation when cultured in the presence of the v-Abl kinase inhibitor, STI-571. These data suggest that the Jak/Stat pathway was critical in the transformation acceleration by v-Abl and that v-Abl activity remained essential throughout the life of the tumors, not just in their acceleration. A different pathway appears to predominate in the more slowly arising plasma cell tumors.</p> <p>Conclusion</p> <p>Gene expression profiling differentiates not only B-cell lymphomas from plasma cell tumors but also distinguishes slow from accelerated plasma cell tumors. These data and those obtained from the sensitivity of v-Abl-accelerated plasma cell tumors and their phosphorylated STAT proteins indicate that these similar tumors utilize different signaling pathways but share a common initiating genetic lesion, a c-<it>Myc</it>-activating chromosome translocation.</p