05). PA-specific gamma interferon (IFN-␥) and interleukin-4 (IL-4) CD4؉ cell frequencies and T cell stimulation indices were sustained through 50.5 months (the last time point measured). PA-specific memory B cell frequencies were highly variable but, in general, were detectable in peripheral blood mononuclear cells (PBMC) by 2 months, were significantly above control levels by 7 months, and remained detectable in the HuAVA and 1:5 and 1:20 AVA groups through 42 months (the last time point measured). HuAVA and diluted AVA elicited a combined Th1/Th2 response and robust immunological priming, with sustained production of high-avidity PA-specific functional antibody, long-term immune cell competence, and immunological memory (30 months for 1:20 AVA and 52 months for 1:10 AVA). Vaccinated animals surviving inhalation anthrax developed high-magnitude anamnestic anti-PA IgG and TNA responses.
Anthrax toxin (ATx) is composed of the binary exotoxins lethal toxin (LTx) and edema toxin (ETx).Results showed that macaque anti-AVA sera neutralized LTx in vitro, even when PA was prebound to cells. Neutralization titers in surviving versus nonsurviving animals and between prechallenge and postchallenge activities were highly correlated. These data demonstrate that AVA stimulates a myriad of antibodies that recognize multiple neutralizing epitopes and confirm that change, loss, or occlusion of epitopes after PA is processed from PA83 to PA63 at the cell surface does not significantly affect in vitro neutralizing efficacy. Furthermore, these data support the idea that the full-length PA83 monomer is an appropriate immunogen for inclusion in next-generation anthrax vaccines.
A data-driven approach to characterizing the risk of cyanobacteria-based harmful algal blooms (cyanoHABs) was undertaken for the Ohio River. Twenty-five years of river discharge data were used to develop Bayesian regression models that are currently applicable to 20 sites spread-out along the entire 1579 km of the river’s length. Two site-level prediction models were developed based on the antecedent flow conditions of the two blooms that occurred on the river in 2015 and 2019: one predicts if the current year will have a bloom (the occurrence model), and another predicts bloom persistence (the persistence model). Predictors for both models were based on time-lagged average flow exceedances and a site’s characteristic residence time under low flow conditions. Model results are presented in terms of probabilities of occurrence or persistence with uncertainty. Although the occurrence of the 2019 bloom was well predicted with the modeling approach, the limited number of events constrained formal model validation. However, as a measure of performance, leave-one-out cross validation returned low misclassification rates, suggesting that future years with flow time series like the previous bloom years will be correctly predicted and characterized for persistence potential. The prediction probabilities are served in real time as a component of a risk characterization tool/web application. In addition to presenting the model’s results, the tool was designed with visualization options for studying water quality trends among eight river sites currently collecting data that could be associated with or indicative of bloom conditions. The tool is made accessible to river water quality professionals to support risk communication to stakeholders, as well as serving as a real-time water data monitoring utility.
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