Mongolian tree rings indicate that recent moisture extremes, although unusual, are not unprecedented in the last 2060 years.

Oxidative stress is elevated in numerous environmental exposures and diseases. Millions of dollars have been spent to try to ameliorate this damaging process using anti-oxidant therapies. Currently, the best accepted biomarker of oxidative stress is the lipid oxidation product 8-iso-prostaglandin F2α (8-iso-PGF2α), which has been measured in over a thousand human and animal studies. 8-iso-PGF2α generation has been exclusively attributed to nonenzymatic chemical lipid peroxidation (CLP). However, 8-iso-PGF2α can also be produced enzymatically by prostaglandin-endoperoxide synthases (PGHS) in vivo. When failing to account for PGHS-dependent generation, 8-iso-PGF2α cannot be interpreted as a selective biomarker of oxidative stress. We investigated the formation of 8-iso-PGF2α in rats exposed to carbon tetrachloride (CCl4) or lipopolysaccharide (LPS) using the 8-iso-PGF2α/PGF2α ratio to quantitatively determine the source(s) of 8-iso-PGF2α. Upon exposure to a 120mg/kg dose of CCl4, the contribution of CLP accounted for only 55.6±19.4% of measured 8-iso-PGF2α, whereas in the 1200mg/kg dose, CLP was the predominant source of 8-iso-PGF2α (86.6±8.0% of total). In contrast to CCl4, exposure to 0.5mg/kg LPS was characterized by a significant increase in both the contribution of PGHS (59.5±7.0) and CLP (40.5±14.0%). In conclusion, significant generation of 8-iso-PGF2α occurs through enzymatic as well as chemical lipid peroxidation. The distribution of the contribution is dependent on the exposure agent as well as the dose. The 8-iso-PGF2α/PGF2α ratio accurately determines the source of 8-iso-PGF2α and provides an absolute measure of oxidative stress in vivo.

In many applications researchers are typically interested in testing for inequality constraints in the context of linear fixed effects and mixed effects models. Although there exists a large body of literature for performing statistical inference under inequality constraints, user friendly statistical software implementing such methods is lacking, especially in the context of linear fixed and mixed effects models. In this article we introduce CLME, a package in the R language that can be used for testing a broad collection of inequality constraints. It uses residual bootstrap based methodology which is reasonably robust to non-normality as well as heteroscedasticity. The package is illustrated using two data sets. The package also contains a graphical user interface built using the shiny package.

This document contains supplementary materials for Davidov, Jelsema, and Peddada (2017) (the "main text"). This supplement contains three primary sections: Section S1 contains all of the proofs of theorems presented in the main text. Section S2 contains full results from the simulation described and presented in section 4 of the main text. Section S3 contains full results from the simulation described and presented in section 5.1 of the main text. Theorem and equation references throughout are consistent with the main text. For additional clarity, all references originating from this supplement are preceded by an 'S' (e.g.; theorem (1.2) first appears in the main text, while equation (S1.1) is first shown in this supplement).

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