Viscoelastic Phase Separation in Shear Flow

We numerically investigate viscoelastic phase separation in polymer solutions under shear using a time-dependent Ginzburg-Landau model. The gross variables in our model are the polymer volume fraction and a conformation tensor. The latter represents chain deformations and relaxes slowly on the rheological time giving rise to a large viscoelastic stress. The polymer and the solvent obey two-fluid dynamics in which the viscoelastic stress acts asymmetrically on the polymer and, as a result, the stress and the diffusion are dynamically coupled. Below the coexistence curve, interfaces appear with increasing the quench depth and the solvent regions act as a lubricant. In these cases the composition heterogeneity causes more enhanced viscoelastic heterogeneity and the macroscopic stress is decreased at fixed applied shear rate. We find steady two-phase states composed of the polymer-rich and solvent-rich regions, where the characteristic domain size is inversely proportional to the average shear stress for various shear rates. The deviatoric stress components exhibit large temporal fluctuations. The normal stress difference can take negative values transiently at weak shear.Comment: 16pages, 16figures, to be published in Phys.Rev.

Length of the weaning period affects postweaning growth, health, and carcass merit of ranch-direct beef calves weaned during the fall

Bovine respiratory disease (BRD) is the most economically devastating feedlot disease. Risk factors associated with incidence of BRD include (1) stress associated with maternal separation, (2) stress associated with introduction to an unfamiliar environment, (3) poor intake associated with introduction of novel feedstuffs into the animal\u27s diet, (4) exposure to novel pathogens upon transport to a feeding facility and commingling with unfamiliar cattle, (5) inappropriately administered respiratory disease vaccination programs, and (6) poor response to respiratory disease vaccination programs. Management practices that are collectively referred to as preconditioning are thought to minimize damage to the beef carcass from the BRD complex. Preconditioning management reduces the aforementioned risk factors for respiratory disease by (1) using a relatively long ranch-of-origin weaning period following maternal separation, (2) exposing calves to concentrate-type feedstuffs, and (3) producing heightened resistance to respiratory disease-causing organisms through a preweaning vaccination program. The effectiveness of such programs for preserving animal performance is highly touted by certain segments of the beef industry. Ranch-of-origin weaning periods of up to 60 days are suggested for preconditioning beef calves prior to sale; however, optimal length of the ranch-of-origin weaning period has not been determined experimentally. The objective of this study was to test the validity of beef industry assumptions about appropriate length of ranch-of-origin weaning periods for calves aged 160 to 220 days and weaned during the fall

Observational consequences of the Standard Model Higgs inflation variants

We consider the possibility to observationally differentiate the Standard Model (SM) Higgs driven inflation with non-minimal couplingto gravity from other variants of SM Higgs inflation based on the scalar field theories with non-canonical kinetic term such as Galileon-like kinetic term and kinetic term with non-minimal derivative coupling to the Einstein tensor. In order to ensure consistent results, we study the SM Higgs inflation variants by using the same method, computing the full dynamics of the background and perturbations of the Higgs field during inflation at quantum level. Assuming that all the SM Higgs inflation variants are consistent theories, we use the MCMC technique to derive constraints on the inflationnoary parameters and the Higgs boson mass from their fit to WMAP7+SN+BAO data set. We conclude that a combination of a Higgs mass measurement by the LHC and accurate determination by the PLANCK satellite of the spectral index of curvature perturbations and tensor-to-scalar ratio will enable to distinguish among these models. We also show that the consistency relations of the SM Higgs inflation variants are distinct enough to differentiate the models.Comment: 22 pages, 4 figure

Race and smoking status associated with paclitaxel drug response in patient-derived lymphoblastoid cell lines

The use of ex-vivo model systems to provide a level of forecasting for in-vivo characteristics remains an important need for cancer therapeutics. The use of lymphoblastoid cell lines (LCLs) is an attractive approach for pharmacogenomics and toxicogenomics, due to their scalability, efficiency, and cost-effectiveness. There is little data on the impact of demographic or clinical covariates on LCL response to chemotherapy. Paclitaxel sensitivity was determined in LCLs from 93 breast cancer patients from the University of North Carolina Lineberger Comprehensive Cancer Center Breast Cancer Database to test for potential associations and/or confounders in paclitaxel dose-response assays. Measures of paclitaxel cell viability were associated with patient data included treatment regimens, cancer status, demographic and environmental variables, and clinical outcomes. We used multivariate analysis of variance to identify the in-vivo variables associated with ex-vivo dose-response. In this unique dataset that includes both in-vivo and ex-vivo data from breast cancer patients, race (P = 0.0049) and smoking status (P = 0.0050) were found to be significantly associated with ex-vivo dose-response in LCLs. Racial differences in clinical dose-response have been previously described, but the smoking association has not been reported. Our results indicate that in-vivo smoking status can influence ex-vivo dose-response in LCLs, and more precise measures of covariates may allow for more precise forecasting of clinical effect. In addition, understanding the mechanism by which exposure to smoking in-vivo effects ex-vivo dose-response in LCLs may open up new avenues in the quest for better therapeutic prediction

Stellar populations of classical and pseudo-bulges for a sample of isolated spiral galaxies

In this paper we present the stellar population synthesis results for a sample of 75 bulges in isolated spiral Sb-Sc galaxies, using the spectroscopic data from the Sloan Digital Sky Survey and the STARLIGHT code. We find that both pseudo-bulges and classical bulges in our sample are predominantly composed of old stellar populations, with mean mass-weighted stellar age around 10 Gyr. While the stellar population of pseudo-bulges is, in general, younger than that of classical bulges, the difference is not significant, which indicates that it is hard to distinguish pseudo-bulges from classical bulges, at least for these isolated galaxies, only based on their stellar populations. Pseudo-bulges have star formation activities with relatively longer timescale than classical bulges, indicating that secular evolution is more important in this kind of systems. Our results also show that pseudo-bulges have a lower stellar velocity dispersion than their classical counterparts, which suggests that classical bulges are more dispersion-supported than pseudo-bulges.Comment: 10 pages, 8 figures. Accepted for publication in Astrophysics & Space Scienc

Cluster Density and the IMF

Observed variations in the IMF are reviewed with an emphasis on environmental density. The remote field IMF studied in the LMC by several authors is clearly steeper than most cluster IMFs, which have slopes close to the Salpeter value. Local field regions of star formation, like Taurus, may have relatively steep IMFs too. Very dense and massive clusters, like super star clusters, could have flatter IMFs, or inner-truncated IMFs. We propose that these variations are the result of three distinct processes during star formation that affect the mass function in different ways depending on mass range. At solar to intermediate stellar masses, gas processes involving thermal pressure and supersonic turbulence determine the basic scale for stellar mass, starting with the observed pre-stellar condensations, and they define the mass function from several tenths to several solar masses. Brown dwarfs require extraordinarily high pressures for fragmentation from the gas, and presumably form inside the pre-stellar condensations during mutual collisions, secondary fragmentations, or in disks. High mass stars form in excess of the numbers expected from pure turbulent fragmentation as pre-stellar condensations coalesce and accrete with an enhanced gravitational cross section. Variations in the interaction rate, interaction strength, and accretion rate among the primary fragments formed by turbulence lead to variations in the relative proportions of brown dwarfs, solar to intermediate mass stars, and high mass stars.Comment: 14 pages, 3 figures, to be published in ``IMF@50: A Fest-Colloquium in honor of Edwin E. Salpeter,'' held at Abbazia di Spineto, Siena, Italy, May 16-20, 2004. Kluwer Academic Publishers; edited by E. Corbelli, F. Palla, and H. Zinnecke

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

Fitting the integrated Spectral Energy Distributions of Galaxies

Fitting the spectral energy distributions (SEDs) of galaxies is an almost universally used technique that has matured significantly in the last decade. Model predictions and fitting procedures have improved significantly over this time, attempting to keep up with the vastly increased volume and quality of available data. We review here the field of SED fitting, describing the modelling of ultraviolet to infrared galaxy SEDs, the creation of multiwavelength data sets, and the methods used to fit model SEDs to observed galaxy data sets. We touch upon the achievements and challenges in the major ingredients of SED fitting, with a special emphasis on describing the interplay between the quality of the available data, the quality of the available models, and the best fitting technique to use in order to obtain a realistic measurement as well as realistic uncertainties. We conclude that SED fitting can be used effectively to derive a range of physical properties of galaxies, such as redshift, stellar masses, star formation rates, dust masses, and metallicities, with care taken not to over-interpret the available data. Yet there still exist many issues such as estimating the age of the oldest stars in a galaxy, finer details ofdust properties and dust-star geometry, and the influences of poorly understood, luminous stellar types and phases. The challenge for the coming years will be to improve both the models and the observational data sets to resolve these uncertainties. The present review will be made available on an interactive, moderated web page (sedfitting.org), where the community can access and change the text. The intention is to expand the text and keep it up to date over the coming years.Comment: 54 pages, 26 figures, Accepted for publication in Astrophysics & Space Scienc

A selective ATP-binding cassette subfamily G member 2 efflux inhibitor revealed via high-throughput flow cytometry

Chemotherapeutics tumor resistance is a principal reason for treatment failure, and clinical and experimental data indicate that multidrug transporters such as ATP-binding cassette (ABC) B1 and ABCG2 play a leading role by preventing cytotoxic intracellular drug concentrations. Functional efflux inhibition of existing chemotherapeutics by these pumps continues to present a promising approach for treatment. A contributing factor to the failure of existing inhibitors in clinical applications is limited understanding of specific substrate/inhibitor/pump interactions. We have identified selective efflux inhibitors by profiling multiple ABC transporters against a library of small molecules to find molecular probes to further explore such interactions. In our primary screening protocol using JC-1 as a dual-pump fluorescent reporter substrate, we identified a piperazine-substituted pyrazolo[1,5-a]pyrimidine substructure with promise for selective efflux inhibition. As a result of a focused structure-activity relationship (SAR)-driven chemistry effort, we describe compound 1 (CID44640177), an efflux inhibitor with selectivity toward ABCG2 over ABCB1. Compound 1 is also shown to potentiate the activity of mitoxantrone in vitro as well as preliminarily in vivo in an ABCG2-overexpressing tumor model. At least two analogues significantly reduce tumor size in combination with the chemotherapeutic topotecan. To our knowledge, low nanomolar chemoreversal activity coupled with direct evidence of efflux inhibition for ABCG2 is unprecedented