Peptide immunotherapy in allergic asthma generates IL-10-dependent immunological tolerance associated with linked epitope suppression

Treatment of patients with allergic asthma using low doses of peptides containing T cell epitopes from Fel d 1, the major cat allergen, reduces allergic sensitization and improves surrogate markers of disease. Here, we demonstrate a key immunological mechanism, linked epitope suppression, associated with this therapeutic effect. Treatment with selected epitopes from a single allergen resulted in suppression of responses to other ("linked") epitopes within the same molecule. This phenomenon was induced after peptide immunotherapy in human asthmatic subjects and in a novel HLA-DR1 transgenic mouse model of asthma. Tracking of allergen-specific T cells using DR1 tetramers determined that suppression was associated with the induction of interleukin (IL)-10(+) T cells that were more abundant than T cells specific for the single-treatment peptide and was reversed by anti -IL-10 receptor administration. Resolution of airway pathophysiology in this model was associated with reduced recruitment, proliferation, and effector function of allergen-specific Th2 cells. Our results provide, for the first time, in vivo evidence of linked epitope suppression and IL-10 induction in both human allergic disease and a mouse model designed to closely mimic peptide therapy in humans

In praise of organizational forgetting

This article reviews and evaluates the concept of organizational forgetting. Drawing on established literature in the field of organizational learning, the authors analyze forgetting from three perspectives—cognitive, behavioral, and social. They argue a counterintuitive line that forgetting, in the right circumstances, can be beneficial for companies and demonstrate how the advantages and disadvantages vary according to the perspective adopted. The authors conclude with some practical suggestions about how companies can increase their ability to forget and also offer suggestions about the academic research agenda

Ecological prediction with nonlinear multivariate time-frequency functional data models

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River