Measles immune suppression:lessons from the Macaque Model

Measles remains a significant childhood disease, and is associated with a transient immune suppression. Paradoxically, measles virus (MV) infection also induces robust MV-specific immune responses. Current hypotheses for the mechanism underlying measles immune suppression focus on functional impairment of lymphocytes or antigen-presenting cells, caused by infection with or exposure to MV. We have generated stable recombinant MVs that express enhanced green fluorescent protein, and remain virulent in non-human primates. By performing a comprehensive study of virological, immunological, hematological and histopathological observations made in animals euthanized at different time points after MV infection, we developed a model explaining measles immune suppression which fits with the "measles paradox". Here we show that MV preferentially infects CD45RA - memory T-lymphocytes and follicular B-lymphocytes, resulting in high infection levels in these populations. After the peak of viremia MV-infected lymphocytes were cleared within days, followed by immune activation and lymph node enlargement. During this period tuberculin-specific T-lymphocyte responses disappeared, whilst strong MV-specific T-lymphocyte responses emerged. Histopathological analysis of lymphoid tissues showed lymphocyte depletion in the B- and T-cell areas in the absence of apoptotic cells, paralleled by infiltration of T-lymphocytes into B-cell follicles and reappearance of proliferating cells. Our findings indicate an immune-mediated clearance of MV-infected CD45RA - memory T-lymphocytes and follicular B-lymphocytes, which causes temporary immunological amnesia. The rapid oligoclonal expansion of MV-specific lymphocytes and bystander cells masks this depletion, explaining the short duration of measles lymphopenia yet long duration of immune suppression. © 2012 de Vries et al.

Interpretable semantic vectors from a joint model of brain-and text-based meaning

Vector space models (VSMs) represent word meanings as points in a high dimensional space. VSMs are typically created using a large text corpora, and so represent word semantics as observed in text. We present a new algorithm (JNNSE) that can incorporate a measure of semantics not previously used to create VSMs: brain activation data recorded while people read words. The resulting model takes advantage of the complementary strengths and weaknesses of corpus and brain activation data to give a more complete representation of semantics. Evaluations show that the model 1) matches a behavioral measure of semantics more closely, 2) can be used to predict corpus data for unseen words and 3) has predictive power that generalizes across brain imaging technologies and across subjects. We believe that the model is thus a more faithful representation of mental vocabularies.