Vision and Objectives

The purpose of Industry Day is to exchange information with industry to increase understanding of the Government's current vision and objectives for the xEVA Production and Services Contract. The presentation provides industry with the opportunity to provide input into the xEVAPS procurement strategy and encourage offerors to submit questions and comments. A technical overview of the xEVA System serves as the foundation for the content related to draft requirements in the SOW

Oxidative stress via hydrogen peroxide and menadione does not induce the secretion of IGFBP-5 in primary rat hepatocytes

Conference abstract describing how oxidative stress via hydrogen peroxide and menadione does not induce the secretion of IGFBP-5 in primary rat hepatocytes. Presented at the 2010 annual congress of the british toxicology societ

Muscling In on PGC-1α for Improved Quality of Life in ALS

Impaired activity of peroxisome proliferator-activated receptor (PPAR)-γ coactivator (PGC)-1α has been implicated in the pathophysiology of several neurodegenerative disorders. In this issue, Da Cruz et al. (2012) show improved muscle function, but not survival, with increased PGC-1α activity in muscle in a mouse model of amyotrophic lateral sclerosis

Molecular insights into Parkinson's disease

Parkinson’s disease is a neurodegenerative movement disorder characterized by loss of midbrain dopaminergic neurons leading to motor abnormalities and autonomic dysfunctions. Despite intensive research, the etiology of Parkinson’s disease remains poorly understood leaving us with no effective therapeutic options. However, the recent identification of genes linked to heritable forms of Parkinson’s disease has revolutionized research in the field and has begun to provide new clues to disease pathogenesis. Here we discuss these recent genetic advances and highlight their significance in our quest to better understand common underlying disease mechanisms that will help us identify innovative neuroprotective therapies for Parkinson’s disease

Minimizing Bias in Biomass Allometry: Model Selection and Log‐Transformation of Data

Nonlinear regression is increasingly used to develop allometric equations for forest biomass estimation (i.e., as opposed to the traditional approach of log‐transformation followed by linear regression). Most statistical software packages, however, assume additive errors by default, violating a key assumption of allometric theory and possibly producing spurious models. Here, we show that such models may bias stand‐level biomass estimates by up to 100 percent in young forests, and we present an alternative nonlinear fitting approach that conforms with allometric theory