of African and European descent

Genotype and task influence stinging response thresholds of honeybee (Apis mellifera L.) worker

High-Level Systemic Expression of Conserved Influenza Epitope in Plants on the Surface of Rod-Shaped Chimeric Particles

Recombinant viruses based on the cDNA copy of the tobacco mosaic virus (TMV) genome carrying different versions of the conserved M2e epitope from influenza virus A cloned into the coat protein (CP) gene were obtained and partially characterized by our group previously; cysteines in the human consensus M2e sequence were changed to serine residues. This work intends to show some biological properties of these viruses following plant infections. Agroinfiltration experiments on Nicotiana benthamiana confirmed the efficient systemic expression of M2e peptides, and two point amino acid substitutions in recombinant CPs significantly influenced the symptoms and development of viral infections. Joint expression of RNA interference suppressor protein p19 from tomato bushy stunt virus (TBSV) did not affect the accumulation of CP-M2e-ser recombinant protein in non-inoculated leaves. RT-PCR analysis of RNA isolated from either infected leaves or purified TMV-M2e particles proved the genetic stability of TMV‑based viral vectors. Immunoelectron microscopy of crude plant extracts demonstrated that foreign epitopes are located on the surface of chimeric virions. The rod‑shaped geometry of plant-produced M2e epitopes is different from the icosahedral or helical filamentous arrangement of M2e antigens on the carrier virus-like particles (VLP) described earlier. Thereby, we created a simple and efficient system that employs agrobacteria and plant viral vectors in order to produce a candidate broad-spectrum flu vaccine

Immunogenicity and Cross Protection in Mice Afforded by Pandemic H1N1 Live Attenuated Influenza Vaccine Containing Wild-Type Nucleoprotein

Since conserved viral proteins of influenza virus, such as nucleoprotein (NP) and matrix 1 protein, are the main targets for virus-specific CD8+ cytotoxic T-lymphocytes (CTLs), we hypothesized that introduction of the NP gene of wild-type virus into the genome of vaccine reassortants could lead to better immunogenicity and afford better protection. This paper describes in vitro and in vivo preclinical studies of two new reassortants of pandemic H1N1 live attenuated influenza vaccine (LAIV) candidates. One had the hemagglutinin (HA) and neuraminidase (NA) genes from A/South Africa/3626/2013 H1N1 wild-type virus on the A/Leningrad/134/17/57 master donor virus backbone (6 : 2 formulation) while the second had the HA, NA, and NP genes of the wild-type virus on the same backbone (5 : 3 formulation). Although both LAIVs induced similar antibody immune responses, the 5 : 3 LAIV provoked greater production of virus-specific CTLs than the 6 : 2 variant. Furthermore, the 5 : 3 LAIV-induced CTLs had higher in vivo cytotoxic activity, compared to 6 : 2 LAIV. Finally, the 5 : 3 LAIV candidate afforded greater protection against infection and severe illness than the 6 : 2 LAIV. Inclusion in LAIV of the NP gene from wild-type influenza virus is a new approach to inducing cross-reactive cell-mediated immune responses and cross protection against pandemic influenza

Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada

<div><p>Influenza A virus commonly circulating in swine (IAV-S) is characterized by large genetic and antigenic diversity and, thus, improvements in different aspects of IAV-S surveillance are needed to achieve desirable goals of surveillance such as to establish the capacity to forecast with the greatest accuracy the number of influenza cases likely to arise. Advancements in modeling approaches provide the opportunity to use different models for surveillance. However, in order to make improvements in surveillance, it is necessary to assess the predictive ability of such models. This study compares the sensitivity and predictive accuracy of the autoregressive integrated moving average (ARIMA) model, the generalized linear autoregressive moving average (GLARMA) model, and the random forest (RF) model with respect to the frequency of influenza A virus (IAV) in Ontario swine. Diagnostic data on IAV submissions in Ontario swine between 2007 and 2015 were obtained from the Animal Health Laboratory (University of Guelph, Guelph, ON, Canada). Each modeling approach was examined for predictive accuracy, evaluated by the root mean square error, the normalized root mean square error, and the model’s ability to anticipate increases and decreases in disease frequency. Likewise, we verified the magnitude of improvement offered by the ARIMA, GLARMA and RF models over a seasonal-naïve method. Using the diagnostic submissions, the occurrence of seasonality and the long-term trend in IAV infections were also investigated. The RF model had the smallest root mean square error in the prospective analysis and tended to predict increases in the number of diagnostic submissions and positive virological submissions at weekly and monthly intervals with a higher degree of sensitivity than the ARIMA and GLARMA models. The number of weekly positive virological submissions is significantly higher in the fall calendar season compared to the summer calendar season. Positive counts at weekly and monthly intervals demonstrated a significant increasing trend. Overall, this study shows that the RF model offers enhanced prediction ability over the ARIMA and GLARMA time series models for predicting the frequency of IAV infections in diagnostic submissions.</p></div

Legislative Documents

Also, variously referred to as: Senate bills; Senate documents; Senate legislative documents; legislative documents; and General Court documents

Prospective simulated counts of weekly and monthly submissions and positive submissions for IAV.

<p>Counts were predicted for the last three years. The autoregressive integrated moving average (ARIMA) is shown in red, the generalized linear autoregressive moving average (GLARMA) in blue, and the random forest (RF) in green. The actual observations are represented in black.</p

Confusion matrix for predicted monthly positive submissions with the prospective autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA) and random forest (RF) time series models.

<p>Confusion matrix for predicted monthly positive submissions with the prospective autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA) and random forest (RF) time series models.</p

Predictive accuracy evaluated via the root mean square error (RMSE) of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series models.

<p>Predictive accuracy evaluated via the root mean square error (RMSE) of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series models.</p

Retrospective predicted counts of weekly and monthly submissions and positive submissions for IAV.

<p>The autoregressive integrated moving average (ARIMA) is shown in red, the generalized linear autoregressive moving average (GLARMA) in blue, and the random forest (RF) in green. The actual observations are represented by black lines.</p