E-Nose for gas detection at vehicle exhaust Using supervised learning algorithm

Abstract: An electronic nose is an intelligent system used to monitor the gases. The system is designed to detect the pollution at vehicle exhaust. The system informs the user about the concentration of CO and HC. It also displays whether pollution is under control or not. Commercial gas sensors having low power consumption are used in the design. For data acquisition, a micro-controller is used. Data processing is done using supervised learning of Artificial Neural Network (ANN). The results of ANN training are given, which is obtained using MATLAB. The system is calibrated using the actual field readings of PUC machines available. Five Different ANN training methods are also compared based on errors. GUI developed displays concentrations of CO and HC, a conclusive message and bars indicating present gas level

Mitigating the Impact of Emerging Animal Infectious Disease Threats: First Emerging Animal Infectious Diseases Conference (EAIDC) Report

From 29 November to 1 December 2021, an “emerging animal infectious disease conference (EAIDC)” was held at the Pennsylvania State University. This conference brought together distinguished thought leaders in animal health, veterinary diagnostics, epidemiology and disease surveillance, and agricultural economics. The conference’s primary objective was to review the lessons learned from past experiences in dealing with high-consequence animal infectious diseases to inform an action plan to prepare for future epizootics and panzootics. Invited speakers and panel members comprised world-leading experts in animal infectious diseases from federal state agencies, academia, professional societies, and the private sector. The conference concluded that the biosecurity of livestock operations is critical for minimizing the devastating impact of emerging animal infectious diseases. The panel also highlighted the need to develop and benchmark cutting-edge diagnostics for rapidly detecting pathogens in clinical samples and the environment. Developing next-generation pathogen agnostic diagnostics will help detect variants of known pathogens and unknown novel pathogens. The conference also highlighted the importance of the One Health approach in dealing with emerging animal and human infectious diseases. The recommendations of the conference may be used to inform policy discussions focused on developing strategies for monitoring and preventing emerging infectious disease threats to the livestock industry

Development and Validation of Indirect Enzyme-Linked Immunosorbent Assays for Detecting Antibodies to SARS-CoV-2 in Cattle, Swine, and Chicken

Multiple domestic and wild animal species are susceptible to SARS-CoV-2 infection. Cattle and swine are susceptible to experimental SARS-CoV-2 infection. The unchecked transmission of SARS-CoV-2 in animal hosts could lead to virus adaptation and the emergence of novel variants. In addition, the spillover and subsequent adaptation of SARS-CoV-2 in livestock could significantly impact food security as well as animal and public health. Therefore, it is essential to monitor livestock species for SARS-CoV-2 spillover. We developed and optimized species-specific indirect ELISAs (iELISAs) to detect anti-SARS-CoV-2 antibodies in cattle, swine, and chickens using the spike protein receptor-binding domain (RBD) antigen. Serum samples collected prior to the COVID-19 pandemic were used to determine the cut-off threshold. RBD hyperimmunized sera from cattle (n = 3), swine (n = 6), and chicken (n = 3) were used as the positive controls. The iELISAs were evaluated compared to a live virus neutralization test using cattle (n = 150), swine (n = 150), and chicken (n = 150) serum samples collected during the COVID-19 pandemic. The iELISAs for cattle, swine, and chicken were found to have 100% sensitivity and specificity. These tools facilitate the surveillance that is necessary to quickly identify spillovers into the three most important agricultural species worldwide