Observables in Neutrino Mass Spectroscopy Using Atoms

The process of collective de-excitation of atoms in a metastable level into emission mode of a single photon plus a neutrino pair, called radiative emission of neutrino pair (RENP), is sensitive to the absolute neutrino mass scale, to the neutrino mass hierarchy and to the nature (Dirac or Majorana) of massive neutrinos. We investigate how the indicated neutrino mass and mixing observables can be determined from the measurement of the corresponding continuous photon spectrum taking the example of a transition between specific levels of the Yb atom. The possibility of determining the nature of massive neutrinos and, if neutrinos are Majorana fermions, of obtaining information about the Majorana phases in the neutrino mixing matrix, is analyzed in the cases of normal hierarchical, inverted hierarchical and quasi-degenerate types of neutrino mass spectrum. We find, in particular, that the sensitivity to the nature of massive neutrinos depends critically on the atomic level energy difference relevant in the RENP. \ua9 2013 Elsevier B.V

The seesaw mechanism at TeV scale in the 3-3-1 model with right-handed neutrinos

We implement the seesaw mechanism in the 3-3-1 model with right-handed neutrinos. This is accomplished by the introduction of a scalar sextet into the model and the spontaneous violation of the lepton number. We identify the Majoron as a singlet under $SU_L(2)\otimes U_Y(1)$ symmetry, which makes it safe under the current bounds imposed by electroweak data. The main result of this work is that the seesaw mechanism works already at TeV scale with the outcome that the right-handed neutrino masses lie in the electroweak scale, in the range from MeV to tens of GeV. This window provides a great opportunity to test their appearance at current detectors, though when we contrast our results with some previous analysis concerning detection sensitivity at LHC, we conclude that further work is needed in order to validate this search.Comment: about 13 pages, no figure

Enhanced Ca(2+) signaling, mild primary aldosteronism, and hypertension in a familial hyperaldosteronism mouse model (Cacna1h(M1560V/+))

Gain-of-function mutations in the CACNA1H gene (encoding the T-type calcium channel Ca(V)3.2) cause autosomal-dominant familial hyperaldosteronism type IV (FH-IV) and early-onset hypertension in humans. We used CRISPR/Cas9 to generate Cacna1h(M1560V/+) knockin mice as a model of the most common FH-IV mutation, along with corresponding knockout mice (Cacna1h(-/-)). Adrenal morphology of both Cacna1h(M1560V/+) and Cacna1h(-/-) mice was normal. Cacna1h(M1560V/+) mice had elevated aldosterone:renin ratios (a screening parameter for primary aldosteronism). Their adrenal Cyp11b2 (aldosterone synthase) expression was increased and remained elevated on a high-salt diet (relative autonomy, characteristic of primary aldosteronism), but plasma aldosterone was only elevated in male animals. The systolic blood pressure of Cacna1h(M1560V/+) mice was 8 mmHg higher than in wild-type littermates and remained elevated on a high-salt diet. Cacna1h(-/-) mice had elevated renal Ren1 (renin-1) expression but normal adrenal Cyp11b2 levels, suggesting that in the absence of Ca(V)3.2, stimulation of the renin-angiotensin system activates alternative calcium entry pathways to maintain normal aldosterone production. On a cellular level, Cacna1h(M1560V/+) adrenal slices showed increased baseline and peak intracellular calcium concentrations in the zona glomerulosa compared to controls, but the frequency of calcium spikes did not rise. We conclude that FH-IV, on a molecular level, is caused by elevated intracellular Ca(2+) concentrations as a signal for aldosterone production in adrenal glomerulosa cells. We demonstrate that a germline Cacna1h gain-of-function mutation is sufficient to cause mild primary aldosteronism, whereas loss of Ca(V)3.2 channel function can be compensated for in a chronic setting

Direct Numerical Simulation of Interfacial Flows: Implicit Sharp-Interface Method (I-SIM)

In recent work (Nourgaliev, Liou, Theofanous, JCP in press) we demonstrated that numerical simulations of interfacial flows in the presence of strong shear must be cast in dynamically sharp terms (sharp interface treatment or SIM), and that moreover they must meet stringent resolution requirements (i.e., resolving the critical layer). The present work is an outgrowth of that work aiming to overcome consequent limitations on the temporal treatment, which become still more severe in the presence of phase change. The key is to avoid operator splitting between interface motion, fluid convection, viscous/heat diffusion and reactions; instead treating all these non-linear operators fully-coupled within a Newton iteration scheme. To this end, the SIM’s cut-cell meshing is combined with the high-orderaccurate implicit Runge-Kutta and the “recovery” Discontinuous Galerkin methods along with a Jacobian-free, Krylov subspace iteration algorithm and its physics-based preconditioning. In particular, the interfacial geometry (i.e., marker’s positions and volumes of cut cells) is a part of the Newton-Krylov solution vector, so that the interface dynamics and fluid motions are fully-(non-linearly)-coupled. We show that our method is: (a) robust (L-stable) and efficient, allowing to step over stability time steps at will while maintaining high-(up to the 5th)-order temporal accuracy; (b) fully conservative, even near multimaterial contacts, without any adverse consequences (pressure/velocity oscillations); and (c) highorder-accurate in spatial discretization (demonstrated here up to the 12th-order for smoothin-the-bulk-fluid flows), capturing interfacial jumps sharply, within one cell. Performance is illustrated with a variety of test problems, including low-Mach-number “manufactured” solutions, shock dynamics/tracking with slow dynamic time scales, and multi-fluid, highspeed shock-tube problems. We briefly discuss preconditioning, and we introduce two physics-based preconditioners – “Block-Diagonal” and “Internal energy-Pressure-Velocity Partially Decoupled”, demonstrating the ability to efficiently solve all-speed flows with strong effects from viscous dissipation and heat conduction

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel

A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved

Track A Basic Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138319/1/jia218438.pd

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis