Comparative immunogenicity of M. hyopneumoniae NrdF encoded in different expression systems delivered orally via attenuated S. typhimurium aroA in mice.

The Mycoplasma hyopneumoniae ribonucleotide reductase R2 subunit (NrdF) gene fragment was cloned into eukaryotic and prokaryotic expression vectors and its immunogenicity evaluated in mice immunized orally with attenuated Salmonella typhimurium aroA CS332 harboring either of the recombinant expression plasmids. We found that NrdF is highly conserved among M. hyopneumoniae strains. The immunogenicity of NrdF was examined by analyzing antibody responses in sera and lung washes, and the cell-mediated immune (CMI) response was assessed by determining the INF-[gamma] level produced by splenocytes upon in vitro stimulation with NrdF antigen. S. typhimurium expressing NrdF encoded by the prokaryotic expression plasmid (pTrcNrdF) failed to elicit an NrdF-specific serum or secretory antibody response, and IFN-[gamma] was not produced. Similarly, S. typhimurium carrying the eukaryotic recombinant plasmid encoding NrdF (pcNrdF) did not induce a serum or secretory antibody response, but did elicit significant NrdF-specific IFN-[gamma] production, indicating induction of a CMI response. However, analysis of immune responses against the live vector S. typhimurium aroA CS332 showed a serum IgG response but no mucosal IgA response in spite of its efficient invasiveness in vitro. In the present study we show that the DNA vaccine encoding the M. hyopneumoniae antigen delivered orally via a live attenuated S. typhimurium aroA can induce a cell-mediated immune response. We also indicate that different live bacterial vaccine carriers may have an influence on the type of the immune response induced.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.