Measurement of inclusive jet charged-particle fragmentation functions in Pb+Pb collisions at √sNN = 2.76 TeV with the ATLAS detector

Measurements of charged-particle fragmentation functions of jets produced in ultra-relativistic nuclear collisions can provide insight into the modification of parton showers in the hot, dense medium created in the collisions. ATLAS has measured jets in √sNN=2.76 TeV Pb+Pb collisions at the LHC using a data set recorded in 2011 with an integrated luminosity of 0.14 nb−1. Jets were reconstructed using the anti-kt algorithm with distance parameter values R = 0.2, 0.3, and 0.4. Distributions of charged-particle transverse momentum and longitudinal momentum fraction are reported for seven bins in collision centrality for R=0.4 jets with pjetT>100 GeV. Commensurate minimum pT values are used for the other radii. Ratios of fragment distributions in each centrality bin to those measured in the most peripheral bin are presented. These ratios show a reduction of fragment yield in central collisions relative to peripheral collisions at intermediate z values, 0.04≲z≲0.2 and an enhancement in fragment yield for z≲0.04. A smaller, less significant enhancement is observed at large z and large pT in central collisions.

The Ste20-like kinase misshapen functions together with Bicaudal-D and dynein in driving nuclear migration in the developing drosophila eye.

Nuclear translocation, driven by the motility apparatus consisting of the cytoplasmic dynein motor and microtubules, is essential for cell migration during embryonic development. Bicaudal-D (Bic-D), an evolutionarily conserved dynein-interacting protein, is required for developmental control of nuclear migration in Drosophila. Nothing is known about the signaling events that coordinate the function of Bic-D and dynein during development. Here, we show that Misshapen (Msn), the fly homolog of the vertebrate Nck-interacting kinase is a component of a novel signaling pathway that regulates photoreceptor (R-cell) nuclear migration in the developing Drosophila compound eye. Msn, like Bic-D, is required for the apical migration of differentiating R-cell precursor nuclei. msn displays strong genetic interaction with Bic-D. Biochemical studies demonstrate that Msn increases the phosphorylation of Bic-D, which appears to be necessary for the apical accumulation of both Bic-D and dynein in developing R-cell precursor cells. We propose that Msn functions together with Bic-D to regulate the apical localization of dynein in generating directed nuclear migration within differentiating R-cell precursor cells.

Melanoma Cell-Intrinsic PD-1 Receptor Functions Promote Tumor Growth.

Therapeutic antibodies targeting programmed cell death 1 (PD-1) activate tumor-specific immunity and have shown remarkable efficacy in the treatment of melanoma. Yet, little is known about tumor cell-intrinsic PD-1 pathway effects. Here, we show that murine and human melanomas contain PD-1-expressing cancer subpopulations and demonstrate that melanoma cell-intrinsic PD-1 promotes tumorigenesis, even in mice lacking adaptive immunity. PD-1 inhibition on melanoma cells by RNAi, blocking antibodies, or mutagenesis of melanoma-PD-1 signaling motifs suppresses tumor growth in immunocompetent, immunocompromised, and PD-1-deficient tumor graft recipient mice. Conversely, melanoma-specific PD-1 overexpression enhances tumorigenicity, as does engagement of melanoma-PD-1 by its ligand, PD-L1, whereas melanoma-PD-L1 inhibition or knockout of host-PD-L1 attenuate growth of PD-1-positive melanomas. Mechanistically, the melanoma-PD-1 receptor modulates downstream effectors of mTOR signaling. Our results identify melanoma cell-intrinsic functions of the PD-1:PD-L1 axis in tumor growth and suggest that blocking melanoma-PD-1 might contribute to the striking clinical efficacy of anti-PD-1 therapy.

MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().