Cortactin and phagocytosis in isolated Sertoli cells

BACKGROUND: Cortactin, an actin binding protein, has been associated with Sertoli cell ectoplasmic specializations in vivo, based on its immunolocalization around the heads of elongated spermatids, but not previously identified in isolated Sertoli cells. In an in vitro model of Sertoli cell-spermatid binding, cortactin was identified around debris and dead germ cells. Based on this observation, we hypothesized that this actin binding protein may be associated with a non-junction-related physiological function, such as phagocytosis. The purpose of this study was to identify the presence and distribution of cortactin in isolated rat Sertoli cells active in phagocytic activity following the addition of 0.8 μm latex beads. RESULTS: Sertoli cell monocultures were incubated with or without follicle stimulating hormone (FSH; 0.1 μg/ml) in the presence or absence of cytochalasin D (2 μM), as an actin disrupter. Cortactin was identified by standard immunostaining with anti-cortactin, clone 4F11 (Upstate) after incubation times of 15 min, 2 hr, and 24 hr with or without beads. Cells exposed to no hormone and no beads appeared to have a ubiquitous distribution of cortactin throughout the cytoplasm. In the presence of cytochalasin D, cortactin immunostaining was punctate and distributed in a pattern similar to that reported for actin in cells exposed to cytochalasin D. Sertoli cells not exposed to FSH, but activated with beads, did not show cortactin immunostaining around the phagocytized beads at any of the time periods. FSH exposure did not alter the distribution of cortactin within Sertoli cells, even when phagocytic activity was upregulated by the presence of beads. CONCLUSION: Results of this study suggest cortactin is not associated with peripheralized actin at junctional or phagocytic sites. Further studies are necessary to clarify the role of cortactin in Sertoli cells

Ψ′/Ψ\Psi'/\Psi ratio in Nucleus-Nucleus Collisions : a Measure for the Chiral Symmetry Restoration Temperature ?

We argue that a decrease of the chiral scalar meson mass is responsible for re-creation of $\Psi'$ from $J/\Psi$ in ultrarelativistic nucleus-nucleus collisions. This causes the charmonium yields to freeze out at temperatures close to the chiral symmetry restoration temperature $T_c$. As a result $\Psi'/\Psi$ may serve as a thermometer for $T_c$ itself. Results in a detailed reaction model support the conjecture. They show good agreement with recent data of NA38 and NA50 for $J/\Psi$ and $\Psi'$ production in S on U and Pb on Pb collisions.Comment: 4 pages revtex including 3 postscript figure

Toward the AdS/CFT dual of the "Little Bang"

This (rather subjective) review sums up few years of work devoted to explain various aspects of high energy heavy ion collisions using the AdS/CFT correspondence. The central issue of is is formation of the trapped surface (black hole) phenomenon, seen by a distant observer as the entropy production. We end up discussing an issue of classical gravitational radiation by an ultrarelativistic falling body and the so called breaking self-force related to it.Comment: a review to appear in topical volume of reviews collected by editors, S.Bass and G.Casaladerrey-Solan

Race, Slavery, and the Expression of Sexual Violence in Louisa Picquet, The Octoroon

Historically, victims of sexual violence have rarely left written accounts of their abuse, so while sexual violence has long been associated with slavery in the United States, historians have few accounts from formerly enslaved people who experienced it first-hand. Through a close reading of the narrative of Louisa Picquet, a survivor of sexual violence in Georgia and Louisiana, this article reflects on the recovery of evidence of sexual violence under slavery through amanuensis-recorded testimony, the unintended evidence of survival within the violent archive of female slavery, and the expression of “race” as an authorial device through which to demonstrate the multigenerational nature of sexual victimhood

Risk-taking in disorders of natural and drug rewards: neural correlates and effects of probability, valence, and magnitude.

Pathological behaviors toward drugs and food rewards have underlying commonalities. Risk-taking has a fourfold pattern varying as a function of probability and valence leading to the nonlinearity of probability weighting with overweighting of small probabilities and underweighting of large probabilities. Here we assess these influences on risk-taking in patients with pathological behaviors toward drug and food rewards and examine structural neural correlates of nonlinearity of probability weighting in healthy volunteers. In the anticipation of rewards, subjects with binge eating disorder show greater risk-taking, similar to substance-use disorders. Methamphetamine-dependent subjects had greater nonlinearity of probability weighting along with impaired subjective discrimination of probability and reward magnitude. Ex-smokers also had lower risk-taking to rewards compared with non-smokers. In the anticipation of losses, obesity without binge eating had a similar pattern to other substance-use disorders. Obese subjects with binge eating also have impaired discrimination of subjective value similar to that of the methamphetamine-dependent subjects. Nonlinearity of probability weighting was associated with lower gray matter volume in dorsolateral and ventromedial prefrontal cortex and orbitofrontal cortex in healthy volunteers. Our findings support a distinct subtype of binge eating disorder in obesity with similarities in risk-taking in the reward domain to substance use disorders. The results dovetail with the current approach of defining mechanistically based dimensional approaches rather than categorical approaches to psychiatric disorders. The relationship to risk probability and valence may underlie the propensity toward pathological behaviors toward different types of rewards.This is the final version. It was first published by NPG at http://www.nature.com/npp/journal/v40/n4/full/npp2014242a.htm

In Support of a Patient-Driven Initiative and Petition to Lower the High Price of Cancer Drugs

Comment in Lowering the High Cost of Cancer Drugs--III. [Mayo Clin Proc. 2016] Lowering the High Cost of Cancer Drugs--I. [Mayo Clin Proc. 2016] Lowering the High Cost of Cancer Drugs--IV. [Mayo Clin Proc. 2016] In Reply--Lowering the High Cost of Cancer Drugs. [Mayo Clin Proc. 2016] US oncologists call for government regulation to curb drug price rises. [BMJ. 2015

Rational Extension of the Ribosome Biogenesis Pathway Using Network-Guided Genetics

Gene networks are an efficient route for associating candidate genes with biological processes. Here, networks are used to discover more than 15 new genes for ribosomal subunit maturation, rRNA processing, and ribosomal export from the nucleus

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe