Fragmentation of Bose-Einstein Condensates

We present the theory of bosonic systems with multiple condensates, unifying disparate models which are found in the literature, and discuss how degeneracies, interactions, and symmetries conspire to give rise to this unusual behavior. We show that as degeneracies multiply, so do the types of fragmentation, eventually leading to strongly correlated states with no trace of condensation.Comment: 16 pages, 1 figure, revtex

Optics and Quantum Electronics

Contains reports on eleven research projects.Joint Services Electronics Program (Contract DAAG29-83-K-0003)National Science Foundation (Grant ECS83-05448)National Science Foundation (Grant ECS83-10718)National Science Foundation (Grant ECS82-11650)National Science Foundation (Grant ECS84-06290)U.S. Air Force - Office of Scientific Research (Contract AFOSR-85-0213)National Institutes of Health (Grant 1 RO1 GM35459

Optics and Quantum Electronics

Contains reports on ten research projects.Joint Services Electronics Program (Contract DAALO3-86-K-0002)National Science Foundation (Grant ECS 83-05448)National Science Foundation (Grant ECS 83-10718)National Science Foundation (Grant ECS 82-11650)National Science Foundation (Grant ECS 84-13178)National Science Foundation (Grant ECS 85-52701)US Air Force - Office of Scientific Research (Contract AFOSR-85-0213)National Institutes of Health (Contract 5-RO1-GM35459)U.S. Navy - Office of Naval Research (Contract N00014-86-K-0117

Genomics of Divergence along a Continuum of Parapatric Population Differentiation

MM received funding from the Max Planck innovation funds for this project. PGDF was supported by a Marie Curie European Reintegration Grant (proposal nr 270891). CE was supported by German Science Foundation grants (DFG, EI 841/4-1 and EI 841/6-1)

Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma.

Purpose: Develop an integrated intra-site and inter-site radiomics-clinical-genomic marker of high grade serous ovarian cancer (HGSOC) outcomes and explore the biological basis of radiomics with respect to molecular signaling pathways and the tumor microenvironment (TME). Method: Seventy-five stage III-IV HGSOC patients from internal (N = 40) and external factors via the Cancer Imaging Archive (TCGA) (N = 35) with pre-operative contrast enhanced CT, attempted primary cytoreduction, at least two disease sites, and molecular analysis performed within TCGA were retrospectively analyzed. An intra-site and inter-site radiomics (cluDiss) measure was combined with clinical-genomic variables (iRCG) and compared against conventional (volume and number of sites) and average radiomics (N = 75) for prognosticating progression-free survival (PFS) and platinum resistance. Correlation with molecular signaling and TME derived using a single sample gene set enrichment that was measured. Results: The iRCG model had the best platinum resistance classification accuracy (AUROC of 0.78 [95% CI 0.77 to 0.80]). CluDiss was associated with PFS (HR 1.03 [95% CI: 1.01 to 1.05], p = 0.002), negatively correlated with Wnt signaling, and positively to immune TME. Conclusions: CluDiss and the iRCG prognosticated HGSOC outcomes better than conventional and average radiomic measures and could better stratify patient outcomes if validated on larger multi-center trials

Extensive Copy-Number Variation of Young Genes across Stickleback Populations

MM received funding from the Max Planck innovation funds for this project. PGDF was supported by a Marie Curie European Reintegration Grant (proposal nr 270891). CE was supported by German Science Foundation grants (DFG, EI 841/4-1 and EI 841/6-1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Integrating Factor Analysis and a Transgenic Mouse Model to Reveal a Peripheral Blood Predictor of Breast Tumors

Abstract Background Transgenic mouse tumor models have the advantage of facilitating controlled in vivo oncogenic perturbations in a common genetic background. This provides an idealized context for generating transcriptome-based diagnostic models while minimizing the inherent noisiness of high-throughput technologies. However, the question remains whether models developed in such a setting are suitable prototypes for useful human diagnostics. We show that latent factor modeling of the peripheral blood transcriptome in a mouse model of breast cancer provides the basis for using computational methods to link a mouse model to a prototype human diagnostic based on a common underlying biological response to the presence of a tumor. Methods We used gene expression data from mouse peripheral blood cell (PBC) samples to identify significantly differentially expressed genes using supervised classification and sparse ANOVA. We employed these transcriptome data as the starting point for developing a breast tumor predictor from human peripheral blood mononuclear cells (PBMCs) by using a factor modeling approach. Results The predictor distinguished breast cancer patients from healthy individuals in a cohort of patients independent from that used to build the factors and train the model with 89% sensitivity, 100% specificity and an area under the curve (AUC) of 0.97 using Youden's J-statistic to objectively select the model's classification threshold. Both permutation testing of the model and evaluating the model strategy by swapping the training and validation sets highlight its stability. Conclusions We describe a human breast tumor predictor based on the gene expression of mouse PBCs. This strategy overcomes many of the limitations of earlier studies by using the model system to reduce noise and identify transcripts associated with the presence of a breast tumor over other potentially confounding factors. Our results serve as a proof-of-concept for using an animal model to develop a blood-based diagnostic, and it establishes an experimental framework for identifying predictors of solid tumors, not only in the context of breast cancer, but also in other types of cancer.</p

Statistical techniques to construct assays for identifying likely responders to a treatment under evaluation from cell line genomic data

<p>Abstract</p> <p>Background</p> <p>Developing the right drugs for the right patients has become a mantra of drug development. In practice, it is very difficult to identify subsets of patients who will respond to a drug under evaluation. Most of the time, no single diagnostic will be available, and more complex decision rules will be required to define a sensitive population, using, for instance, mRNA expression, protein expression or DNA copy number. Moreover, diagnostic development will often begin with in-vitro cell-line data and a high-dimensional exploratory platform, only later to be transferred to a diagnostic assay for use with patient samples. In this manuscript, we present a novel approach to developing robust genomic predictors that are not only capable of generalizing from in-vitro to patient, but are also amenable to clinically validated assays such as qRT-PCR.</p> <p>Methods</p> <p>Using our approach, we constructed a predictor of sensitivity to dacetuzumab, an investigational drug for CD40-expressing malignancies such as lymphoma using genomic measurements of cell lines treated with dacetuzumab. Additionally, we evaluated several state-of-the-art prediction methods by independently pairing the feature selection and classification components of the predictor. In this way, we constructed several predictors that we validated on an independent DLBCL patient dataset. Similar analyses were performed on genomic measurements of breast cancer cell lines and patients to construct a predictor of estrogen receptor (ER) status.</p> <p>Results</p> <p>The best dacetuzumab sensitivity predictors involved ten or fewer genes and accurately classified lymphoma patients by their survival and known prognostic subtypes. The best ER status classifiers involved one or two genes and led to accurate ER status predictions more than 85% of the time. The novel method we proposed performed as well or better than other methods evaluated.</p> <p>Conclusions</p> <p>We demonstrated the feasibility of combining feature selection techniques with classification methods to develop assays using cell line genomic measurements that performed well in patient data. In both case studies, we constructed parsimonious models that generalized well from cell lines to patients.</p

Optics and Quantum Electronics

Contains table of contents for Section 2 and reports on twenty research projects.Charles S. Draper Laboratory Contract DL-H-404179Joint Services Electronics Program Contract DAALO3-89-C-0001National Sciences Foundation Grant EET 87-00474National Science Foundation Grant EET 88-15834U.S. Air Force - Office of Scientific Research Contract F49620-88-C-0089National Science Foundation Grant ECS 85-52701International Business Machines CorporationMassachusetts General Hospital Contract N00014-86K-0117National Institutes of Health Grant 2-RO1-GM35459U.S. Department of Energy Grant DE-FG02-89-ER14012Lawrence Livermore National Laboratory Subcontract B04870

Optics and Quantum Electronics

Contains table of contents for Section 2 and reports on eighteen research projects.National Science Foundation (Grant EET 87-00474)Joint Services Electronics Program (Contract DAAL03-86-K-0002)Joint Services Electronics Program (Contract DAALO3-89-C-0001)Charles Stark Draper Laboratory (Grant DL-H-285408)Charles Stark Draper Laboratory (Grant DL-H-2854018)National Science Foundation (Grant EET 87-03404)National Science Foundation (Grant ECS 84-06290)U.S. Air Force - Office of Scientific Research (Contract F49620-88-C-0089)AT&T Bell FoundationNational Science Foundation (Grant ECS 85-52701)National Institutes of Health (Grant 5-RO1-GM35459)Massachusetts General Hospital (Office of Naval Research Contract N00014-86-K-0117)Lawrence Livermore National Laboratory (Subcontract B048704