Sustained CTL activation by murine pulmonary epithelial cells promotes the development of COPD-like disease

Chronic obstructive pulmonary disease (COPD) is a lethal progressive lung disease culminating in permanent airway obstruction and alveolar enlargement. Previous studies suggest CTL involvement in COPD progression; however, their precise role remains unknown. Here, we investigated whether the CTL activation receptor NK cell group 2D (NKG2D) contributes to the development of COPD. Using primary murine lung epithelium isolated from mice chronically exposed to cigarette smoke and cultured epithelial cells exposed to cigarette smoke extract in vitro, we demonstrated induced expression of the NKG2D ligand retinoic acid early tran - script 1 (RAET1)as well as NKG2D-mediated cytotoxicity. Furthermore, a genetic model of inducible RAET1 expression on mouse pulmonary epithelial cells yielded a severe emphysematous phenotype characterized by epithelial apoptosis and increased CTL activation, which was reversed by blocking NKG2D activation. We also assessed whether NKG2D ligand expression corresponded with pulmonary disease in human patients by staining airway and peripheral lung tissues from never smokers, smokers with normal lung function, and current and former smokers with COPD. NKG2D ligand expression was independent of NKG2D receptor expression in COPD patients, demonstrating that ligand expression is the limiting factor in CTL activation. These results demonstrate that aberrant, persistent NKG2D ligand expression in the pulmonary epithelium contributes to the development of COPD pathologies.

Climate change impacts on lakes: an integrated ecological perspective based on a multi-faceted approach, with special focus on shallow lakes

Freshwater ecosystems and their biodiversity are presently seriously threatened by global development and population growth, leading to increases in nutrient inputs and intensification of eutrophication-induced problems in receiving fresh waters, particularly in lakes. Climate change constitutes another threat exacerbating the symptoms of eutrophication and species migration and loss. Unequivocal evidence of climate change impacts is still highly fragmented despite the intensive research, in part due to the variety and uncertainty of climate models and underlying emission scenarios but also due to the different approaches applied to study its effects. We first describe the strengths and weaknesses of the multi-faceted approaches that are presently available for elucidating the effects of climate change in lakes, including space-for-time substitution, time series, experiments, palaeoecology and modelling. Reviewing combined results from studies based on the various approaches, we describe the likely effects of climate changes on biological communities, trophic dynamics and the ecological state of lakes. We further discuss potential mitigation and adaptation measures to counteract the effects of climate change on lakes and, finally, we highlight some of the future challenges that we face to improve our capacity for successful prediction.

Indirect likelihood inference (revised)

Standard indirect Inference (II) estimators take a given finite-dimensional statistic, Z_{n} , and then estimate the parameters by matching the sample statistic with the model-implied population moment. We here propose a novel estimation method that utilizes all available information contained in the distribution of Z_{n} , not just its first moment. This is done by computing the likelihood of Z_{n}, and then estimating the parameters by either maximizing the likelihood or computing the posterior mean for a given prior of the parameters. These are referred to as the maximum indirect likelihood (MIL) and Bayesian Indirect Likelihood (BIL) estimators, respectively. We show that the IL estimators are first-order equivalent to the corresponding moment-based II estimator that employs the optimal weighting matrix. However, due to higher-order features of Z_{n} , the IL estimators are higher order efficient relative to the standard II estimator. The likelihood of Z_{n} will in general be unknown and so simulated versions of IL estimators are developed. Monte Carlo results for a structural auction model and a DSGE model show that the proposed estimators indeed have attractive finite sample properties.