Using peptide nanoparticle technology, we have designed two novel vaccine constructs representing M2e in monomeric (Mono-M2e) and tetrameric (Tetra-M2e) forms. Groups of specific pathogen free (SPF) chickens were immunized intramuscularly with Mono-M2e or Tetra-M2e with and without an adjuvant. Two weeks after the second boost, chickens were challenged with 107.2 EID50 of H5N2 low pathogenicity avian influenza (LPAI) virus. M2e-specific antibody responses to each of the vaccine constructs were tested by ELISA. Vaccinated chickens exhibited increased M2e-specific IgG responses for each of the constructs as compared to a non-vaccinated group. However, the vaccine construct Tetra-M2e elicited a significantly higher antibody response when it was used with an adjuvant. On the other hand, virus neutralization assays indicated that immune protection is not by way of neutralizing antibodies. The level of protection was evaluated using quantitative real time PCR at 4, 6, and 8 days post-challenge with H5N2 LPAI by measuring virus shedding from trachea and cloaca. The Tetra-M2e with adjuvant offered statistically significant (P < 0.05) protection against subtype H5N2 LPAI by reduction of the AI virus shedding. The results suggest that the self-assembling polypeptide nanoparticle shows promise as a potential platform for a development of a vaccine against AI.
Recent studies have found that the El Niño–Southern Oscillation (ENSO) has statistically significant influences on extreme precipitation. A limitation of most existing work is that a separate generalized extreme value (GEV) distribution is fitted for each individual site. Such models cannot address important questions that involve events jointly defined across multiple sites; for instance, what is the probability that the 50 year return levels of three sites in the vicinity of a city occur in the same season? With the latest statistical methodology for spatial extremes, we fit max‐stable process models to winter maximum daily precipitation of 192 sites in California over 55 years. A composite likelihood approach is used since the full likelihood is unavailable either analytically or numerically. In addition to latitude, longitude, and elevation, the Southern Oscillation Index (SOI) is incorporated into the parameters of the marginal GEV models. We find that, in a spatial context, the ENSO has a significant influence on the extreme precipitation in California by shifting the location parameter of the GEV distributions, with higher values of the SOI corresponding to lower maximum winter daily precipitation. The joint spatial model is used to assess risks concerning joint extremal events at network of sites with spatial dependence properly accounted for. The probability of extremal events occurring at multiple sites in the same season is found to be much higher than what would be expected under the independence assumption.
Abstract. Understanding extreme precipitation is very important for Ethiopia, which is heavily dependent on lowproductivity rainfed agriculture but lacks structural and nonstructural water regulating and storage mechanisms. There has been an increasing concern about whether there is an increasing trend in extreme precipitation as the climate changes. Existing analysis of this region has been descriptive, without taking advantage of the advances in extreme value modeling. After reviewing the statistical methodology on extremes, this paper presents an analysis based on the generalized extreme value modeling with daily time series of precipitation records at Debre Markos in the Northwestern Highlands of Ethiopia. We found no strong evidence to reject the null hypothesis that there is no increasing trend in extreme precipitation at this location.
Evaluation of candidate surrogate endpoints using individual patient data from multiple clinical trials is considered the gold standard approach to validate surrogates at both patient and trial levels. However, this approach assumes the availability of patient-level data from a relatively large collection of similar trials, which may not be possible to achieve for a given disease application. One common solution to the problem of too few similar trials involves performing trial-level surrogacy analyses on trial sub-units (e.g., centers within trials), thereby artificially increasing the trial-level sample size for feasibility of the multi-trial analysis. To date, the practical impact of treating trial sub-units (centers) identically to trials in multi-trial surrogacy analyses remains unexplored, and conditions under which this ad hoc solution may in fact be reasonable have not been identified. We perform a simulation study to identify such conditions, and demonstrate practical implications using a multi-trial dataset of patients with early stage colon cancer.
Evaluation of surrogate endpoints using patient-level data from multiple trials is the gold standard, where multi-trial copula models are used to quantify both patient-level and trial-level surrogacy. While limited consideration has been given in the literature to copula choice (e.g., Clayton), no prior consideration has been given to direction of implementation (via survival versus distribution functions). We demonstrate that evenwith the “correct” copula family, directional misspecification leads to biased estimates of patient-level and trial-level surrogacy. We illustrate with a simulation study and a re-analysis of disease-free survival as a surrogate for overall survival in early stage colon cancer.
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