Characterization of the role of Samsn1 loss in multiple myeloma development

First published: 04 July 2020The protein SAMSN1 was recently identified as a putative tumor suppressor in multiple myeloma, with re-expression of Samsn1 in the 5TGM1/KaLwRij murine model of myeloma leading to a near complete abrogation of intramedullary tumor growth. Here, we sought to clarify the mechanism underlying this finding. Intratibial administration of 5TGM1 myeloma cells into KaLwRij mice revealed that Samsn1 had no effect on primary tumor growth, but that its expression significantly inhibited the metastasis of these primary tumors. Notably, neither in vitro nor in vivo migration was affected by Samsn1 expression. Both knocking-out SAMSN1 in the RPMI-8226 and JJN3 human myeloma cell lines, and retrovirally expressing SAMSN1 in the LP-1 and OPM2 human myeloma cell lines had no effect on either cell proliferation or migration in vitro. Altering SAMSN1 expression in these human myeloma cells did not affect the capacity of the cells to establish either primary or metastatic intramedullary tumors when administered intratibially into immune deficient NSG mice. Unexpectedly, the tumor suppressive and anti-metastatic activity of Samsn1 in 5TGM1 cells were not evidenced following cell administration either intratibially or intravenously to NSG mice. Crucially, the growth of Samsn1-expressing 5TGM1 cells was limited in C57BL/6/Samsn1-/- mice but not in C57BL/6 Samsn1+/+ mice. We conclude that the reported potent in vivo tumor suppressor activity of Samsn1 can be attributed, in large part, to graft-rejection from Samsn1-/- recipient mice. This has broad implications for the design and interpretation of experiments that utilize cancer cells and knockout mice that are mismatched for expression of specific proteins.Natasha L. Friend, Duncan R. Hewett, Vasilios Panagopoulos, Jacqueline E. Noll, Kate Vandyke, Krzysztof M. Mrozik, Stephen Fitter, Andrew C.W. Zannettin

The Theory of Brown Dwarfs and Extrasolar Giant Planets

Straddling the traditional realms of the planets and the stars, objects below the edge of the main sequence have such unique properties, and are being discovered in such quantities, that one can rightly claim that a new field at the interface of planetary science and and astronomy is being born. In this review, we explore the essential elements of the theory of brown dwarfs and giant planets, as well as of the new spectroscopic classes L and T. To this end, we describe their evolution, spectra, atmospheric compositions, chemistry, physics, and nuclear phases and explain the basic systematics of substellar-mass objects across three orders of magnitude in both mass and age and a factor of 30 in effective temperature. Moreover, we discuss the distinctive features of those extrasolar giant planets that are irradiated by a central primary, in particular their reflection spectra, albedos, and transits. Aspects of the latest theory of Jupiter and Saturn are also presented. Throughout, we highlight the effects of condensates, clouds, molecular abundances, and molecular/atomic opacities in brown dwarf and giant planet atmospheres and summarize the resulting spectral diagnostics. Where possible, the theory is put in its current observational context.Comment: 67 pages (including 36 figures), RMP RevTeX LaTeX, accepted for publication in the Reviews of Modern Physics. 30 figures are color. Most of the figures are in GIF format to reduce the overall size. The full version with figures can also be found at: http://jupiter.as.arizona.edu/~burrows/papers/rm

Feasibility studies for the measurement of time-like proton electromagnetic form factors from p¯ p→ μ+μ- at P ¯ ANDA at FAIR

This paper reports on Monte Carlo simulation results for future measurements of the moduli of time-like proton electromagnetic form factors, | GE| and | GM| , using the p¯ p→ μ+μ- reaction at P ¯ ANDA (FAIR). The electromagnetic form factors are fundamental quantities parameterizing the electric and magnetic structure of hadrons. This work estimates the statistical and total accuracy with which the form factors can be measured at P ¯ ANDA , using an analysis of simulated data within the PandaRoot software framework. The most crucial background channel is p¯ p→ π+π-, due to the very similar behavior of muons and pions in the detector. The suppression factors are evaluated for this and all other relevant background channels at different values of antiproton beam momentum. The signal/background separation is based on a multivariate analysis, using the Boosted Decision Trees method. An expected background subtraction is included in this study, based on realistic angular distributions of the background contribution. Systematic uncertainties are considered and the relative total uncertainties of the form factor measurements are presented

Pileup mitigation at CMS in 13 TeV data

With increasing instantaneous luminosity at the LHC come additional reconstruction challenges. At high luminosity, many collisions occur simultaneously within one proton-proton bunch crossing. The isolation of an interesting collision from the additional "pileup" collisions is needed for effective physics performance. In the CMS Collaboration, several techniques capable of mitigating the impact of these pileup collisions have been developed. Such methods include charged-hadron subtraction, pileup jet identification, isospin-based neutral particle "δβ" correction, and, most recently, pileup per particle identification. This paper surveys the performance of these techniques for jet and missing transverse momentum reconstruction, as well as muon isolation. The analysis makes use of data corresponding to 35.9 fb$^{-1}$ collected with the CMS experiment in 2016 at a center-of-mass energy of 13 TeV. The performance of each algorithm is discussed for up to 70 simultaneous collisions per bunch crossing. Significant improvements are found in the identification of pileup jets, the jet energy, mass, and angular resolution, missing transverse momentum resolution, and muon isolation when using pileup per particle identification

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at √s = 13TeV, corresponding to an integrated luminosity of 35.9 fb−1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency

A measurement of the Higgs boson mass in the diphoton decay channel

A measurement of the mass of the Higgs boson in the diphoton decay channel is presented. This analysis is based on 35.9fb$^{-1}$ of proton-proton collision data collected during the 2016 LHC running period, with the CMS detector at a centre-of-mass energy of 13TeV. A refined detector calibration and new analysis techniques have been used to improve the precision of this measurement. The Higgs boson mass is measured to be m$_{H}$=125.78 ±0.26 GeV. This is combined with a measurement of mHalready performed in the H→ZZ→4${l}$ decay channel using the same data set, giving m$_{H}$=125.46 ±0.16 GeV. This result, when further combined with an earlier measurement of mHusing data collected in 2011 and 2012 with the CMS detector, gives a value for the Higgs boson mass of m$_{H}$=125.38 ±0.14 GeV. This is currently the most precise measurement of the mass of the Higgs boson