Model-free measurement of the excited-state fraction in a Rb-85 magneto-optical trap

Citation: Veshapidze, G., Bang, J. Y., Fehrenbach, C. W., Nguyen, H., & DePaola, B. D. (2015). Model-free measurement of the excited-state fraction in a Rb-85 magneto-optical trap. Physical Review A, 91(5), 5. doi:10.1103/PhysRevA.91.053423In many experiments involving magneto-optical traps (MOTs), it is imperative to know the fraction of atoms left in an excited state by the cooling and trapping lasers. In most cases, researchers have used formulas that were derived for simple two-level systems interacting with a single beam of light having a well-defined polarization, and in the absence of magnetic or electric fields. However, a MOT environment is much more complex than this. Here we directly measure the excited fraction in a MOT of Rb-85 atoms in a model-independent manner for a wide range of trapping conditions. We then fit our measured fractions to an ansatz based on a simple model. Knowing only the trapping laser's total intensity and detuning from resonance, one can then use this ansatz to accurately predict the excited fraction. The work is a companion piece to similar measurements on a MOT of Rb-87

A Medium-Resolution Near-Infrared Spectral Library of Late Type Stars: I

We present an empirical infrared spectral library of medium resolution (R~2000-3000) H (1.6 micron) and K (2.2 micron) band spectra of 218 red stars, spanning a range of [Fe/H] from ~-2.2 to ~+0.3. The sample includes Galactic disk stars, bulge stars from Baade's window, and red giants from Galactic globular clusters. We report the values of 19 indices covering 12 spectral features measured from the spectra in the library. Finally, we derive calibrations to estimate the effective temperature, and diagnostic relationships to determine the luminosity classes of individual stars from near-infrared spectra. This paper is part of a larger effort aimed at building a near-IR spectral library to be incorporated in population synthesis models, as well as, at testing synthetic stellar spectra.Comment: 34 pages, 12 figures; accepted for publication at ApJS; the spectra are available from the authors upon reques

Synthetic High-Resolution Line Spectra of Star-Forming Galaxies Below 1200A

We have generated a set of far-ultraviolet stellar libraries using spectra of OB and Wolf-Rayet stars in the Galaxy and the Large and Small Magellanic Cloud. The spectra were collected with the Far Ultraviolet Spectroscopic Explorer and cover a wavelength range from 1003.1 to 1182.7A at a resolution of 0.127A. The libraries extend from the earliest O- to late-O and early-B stars for the Magellanic Cloud and Galactic libraries, respectively. Attention is paid to the complex blending of stellar and interstellar lines, which can be significant, especially in models using Galactic stars. The most severe contamination is due to molecular hydrogen. Using a simple model for the H$_2$ line strength, we were able to remove the molecular hydrogen lines in a subset of Magellanic Cloud stars. Variations of the photospheric and wind features of CIII 1176, OVI 1032, 1038, PV 1118, 1128, and SIV 1063, 1073, 1074 are discussed as a function of temperature and luminosity class. The spectral libraries were implemented into the LavalSB and Starburst99 packages and used to compute a standard set of synthetic spectra of star-forming galaxies. Representative spectra are presented for various initial mass functions and star formation histories. The valid parameter space is confined to the youngest ages of less than 10 Myr for an instantaneous burst, prior to the age when incompleteness of spectral types in the libraries sets in. For a continuous burst at solar metallicity, the parameter space is not limited. The suite of models is useful for interpreting the restframe far-ultraviolet in local and high-redshift galaxies.Comment: 33 pages including 13 figures, accepted for publication in Ap

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation