Less than 3% of the United States population lives on what is today defined as farms. The question has been raised, Why spend time on agricultural education (ag ed) in schools when such a small population would use it? The answer should be as simple as, Do you eat food and care about how it is produced? As witnessed in the press, an increasing number of people are interested in the food they eat. Unfortunately, their judgment of food is based on very little knowledge of the production, handling, processing and commercialization of this food. Returning to the question Is agricultural education necessary?, the answer should be It's a requirement. No one academic discipline has and will continue to experience the kinds of pressure to explain to the public the practical relationships among food, consumers and biotechnology like the educators involved in agriculture. Yet many agriculture educators find themselves illequipped to communicate the science and technical knowledge needed to meet these present and future challenges in agriculture. The biotechnology program at Iowa State University (ISU) since 1994 has concentrated on helping classroom ag ed teachers meet these challenges. In 1992, ISU biotechnology director Dr. Walter Fehr convened a group of secondary, extension and college educators to the first Biotechnology Education Council meeting on campus in Ames, Iowa. In those early meetings it became apparent from research and the members' own experiences that in order to get biotechnology integrated into K- 12 school curriculums, the program would have to overcome three major hurdles. 1. Educators lacked the content and technical knowledge to feel comfortable about integrating biotechnology into their curriculums. 2. There was a serious shortage of money for supplies, equipment and release time for educators to obtain training. 3. There was little time during the day and in classrooms to prepare and present biotechnology. With the securing of a Roy J. Carver Charitable Trust grant and industry support, ISU launched the Biotechnology Public Education program in 1994. The philosophy then continues today -teachersteach-teachers biotechnology. The program would be centered around the inquiry-based or hands-on pedagogy of instruction. Each set of classroom activities would be tested by teachers and would focus on the basic scientific principles and technical skills of biotechnology. Fifteen teachers, each representing an Area Education Agency (AEA), were trained by ISU faculty and staff and were given the title Master Teachers. This was later expanded to include the seven regional extension areas in Iowa. The original activities were DNA extraction, DNA fingerprinting (restriction analysis) and bacterial transformation. The master teachers' responsibilities were to hold workshops in their regions to train other educators in the preparation and delivery of these activities. These educators, in turn, would receive free supplies, equipment and technical support to help them integrate the biotechnology activities into their curriculums. Many of the first workshops were hosted in one of the regional Area Education Agencies or Local Education Agencies (LEA) in the state. Targeted educators taught science (biology), ag ed, family and consumer sciences or were extension educators. Later, workshops were moved to ISU's campus and divided into three specific workshops to better address the different needs of each discipline. All the activities have been tested by educators to meet their needs, particularly the need to fit activities into a 45-minute classroom period. For activities that take longer than 45 minutes, stopping points were built into the procedures. This helped alleviate the concern of time to do some of the activities. …
Porcine reproductive and respiratory syndrome (PRRS) is one of the most challenging diseases for swine production. The PRRS virus (PRRSV) is an RNA virus that replicates via an RNA-dependent RNA polymerase (RDRP) mechanism, which is prone to high mutation rates. Recombinations are characterized by the exchange of genetic material across two or more viruses. Modified live virus (MLV) vaccines produce an immune response to PRRSV after replicating in pigs, similar to natural exposure. Here, we report the emergence of an MLV-like recombinant strain, its associated production impact, and its disappearance trajectory from a breeding herd. The emergent virus was identified and successfully eliminated from a 9248-sow breed-to-wean herd. Accidental usage of two distinct MLVs in the herd led to the recombination and emergence of a new strain. The clinical presentation was mild compared to current wild-type strains, with the associated production loss amounting to 549 weaned piglets per 1000 sows. Production levels returned to normal within 7 weeks. Transitory, no significative production loss in the wean-to-market phase was identified. Immunization of the herd and tightening of biosecurity and biocontainment practices were able to eliminate the virus from the herd, without evidence of broad regional spread.
Rapid and reliable identification of the hemagglutinin (HA) and neuraminidase (NA) genetic clades of an influenza A virus (IAV) sequence from swine can inform control measures and multivalent vaccine composition. Current approaches to genetically characterize HA or NA sequences are based on nucleotide similarity or phylogenetic analyses. Public databases exist to acquire IAV genetic sequences for comparison, but personnel at the diagnostic or production level have difficulty in adequately updating and maintaining relevant sequence datasets for IAV in swine. Further, phylogenetic analyses are time intensive, and inference drawn from these methods is impacted by input sequence data and associated metadata. We describe here the use of the IAV multisequence identity tool as an integrated public webpage located on the Iowa State University Veterinary Diagnostic Laboratory (ISU-VDL) FLUture website: https://influenza.cvm.iastate.edu/. The multisequence identity tool uses sequence data derived from IAV-positive cases sequenced at the ISU-VDL, employs a BLAST algorithm that identifies sequences that are genetically similar to submitted query sequences, and presents a tabulation and visualization of the most genetically similar IAV sequence and associated metadata from the FLUture database. Our tool removes bioinformatic barriers and allows clients, veterinarians, and researchers to rapidly classify and identify IAV sequences similar to their own sequences to augment interpretation of results.
Alzheimer’s Disease (AD), affecting over 55 million people globally, demands reliable diagnostic tools. Single-model approaches using CNNs and traditional ML face critical limitations. This study proposes two frameworks: a stacking-CNN ensemble (VGG-16, ResNet-101, DenseNet-121) and two voting ML ensembles (Voting[all]: KNN, RF, SVC, LR, XGBoost; Voting[few]: KNN, RF, XGBoost). Evaluated on 6,400 MRIs, Voting[few] achieved the highest classification metrics (97.8% accuracy; 0.984 MCC; 93.8% F1macro), outperforming individual CNNs, validated through Friedman-Nemenyi tests. Results suggest, in this context, that simpler ML models might better capture the inherent characteristics of MRI data for AD diagnosis.
The diversity of the 8 genes of influenza A viruses (IAV) in swine reflects introductions from nonswine hosts and subsequent antigenic drift and shift. Here, we curated a data set and present a pipeline that assigns evolutionary lineage and genetic clade to query gene segments.
Genetically distinct clades of influenza A virus (IAV) in swine undermine efforts to control the disease. Swine producers commonly use vaccines, and vaccine strains are selected by identifying the most common hemagglutinin (HA) gene from viruses detected in a farm or a region.
Influenza A virus (IAV) infection is a recognized cause of acute respiratory disease in pigs that can culminate in the decline of performance due to increasing feed conversion and costs of antimicrobial drugs to control secondary infections. Biosecurity practices are the key to prevent transmission of highly contagious agents. The aim of this study was to assess the effect of biosecurity practices on IAV seroprevalence through a cross-sectional study carried out in 404 sows from 21 herds. An indirect ELISA was used to detect antibodies against a nucleoprotein of IAV. To evaluate IAV subtypes (H1N1pdm09, H1N2 and H3N2), all samples positive by ELISA were tested using the hemagglutination inhibition assay (HI). Prevalence ratios (PR) estimates were calculated using multivariate Poisson regression accounted with survey weights. Sixty-four percent (261/404) of sows were positive in the rNP-ELISA and the estimated prevalence was 63.9% (95% CI 55%–73%). All farms had at least one seropositive sow; the frequency of IAV subtypes found in seropositive sows was 51.9% for H1N1pdm09, 38.1% for codetection H1N1pdm09 and H1N2, 8.6% for H1N2, and 0.6% for codetection H1N1pdm09 and H3N2, and 19 herds presented coinfection of H1N1 pdm09 and H1N2. Variables significantly associated with IAV seroprevalence found in the final model were 'bird-proof net' (PR = 0.75; 95% CI: 0.65–0.86) and 'gilt acclimatization unit' (PR = 0.57, 95% CI: 0.50–0.66), showing a protective effect against IAV seroprevalence, and 'external replacement', which had a positive effect on IAV seroprevalence (PR = 1.38, 95% CI: 1.17–1.64). This study suggests that preventing contact among wild species and swine and using an adaptation area for animals before entry into the herd can be strategies to control the influenza virus in breeding herds.
Influenza A Virus (IAV) causes respiratory disease in swine and is a zoonotic pathogen. Uncontrolled IAV in swine herds not only affects animal health, it also impacts production through increased costs associated with treatment and prevention efforts. The Iowa State University Veterinary Diagnostic Laboratory (ISU VDL) diagnoses influenza respiratory disease in swine and provides epidemiological analyses on samples submitted by veterinarians. To assess the incidence of IAV in swine and inform stakeholders, the ISU FLUture website was developed as an interactive visualization tool that allows the exploration of the ISU VDL swine IAV aggregate data in the clinical diagnostic database. The information associated with diagnostic cases has varying levels of completeness and is anonymous, but minimally contains: sample collection date, specimen type, and IAV subtype. Many IAV positive samples are sequenced, and in these cases, the hemagglutinin (HA) sequence and genetic classification are completed. These data are collected and presented on ISU FLUture in near real-time, and more than 6,000 IAV positive diagnostic cases and their epidemiological and evolutionary information since 2003 are presented to date. The database and web interface provides rapid and unique insight into the trends of IAV derived from both large- and small-scale swine farms across the United States of America. ISU FLUture provides a suite of web-based tools to allow stakeholders to search for trends and correlations in IAV case metadata in swine from the ISU VDL. Since the database infrastructure is updated in near real-time and is integrated within a high-volume veterinary diagnostic laboratory, earlier detection is now possible for emerging IAV in swine that subsequently cause vaccination and control challenges. The access to real-time swine IAV data provides a link with the national USDA swine IAV surveillance system and allows veterinarians to make objective decisions regarding the management and control of IAV in swine. The website is publicly accessible at http://influenza.cvm.iastate.edu .
Foot and mouth disease (FMD) is a highly infectious disease that affects cloven-hoofed livestock and wildlife. FMD has been a problem for decades, which has led to various measures to control, eradicate and prevent FMD by National Veterinary Services worldwide. Currently, the identification of areas that are at risk of FMD virus incursion and spread is a priority for FMD target surveillance after FMD is eradicated from a given country or region. In our study, a knowledge-driven spatial model was built to identify risk areas for FMD occurrence and to evaluate FMD surveillance performance in Rio Grande do Sul state, Brazil. For this purpose, multi-criteria decision analysis was used as a tool to seek multiple and conflicting criteria to determine a preferred course of action. Thirteen South American experts analyzed 18 variables associated with FMD introduction and dissemination pathways in Rio Grande do Sul. As a result, FMD higher risk areas were identified at international borders and in the central region of the state. The final model was expressed as a raster surface. The predictive ability of the model assessed by comparing, for each cell of the raster surface, the computed model risk scores with a binary variable representing the presence or absence of an FMD outbreak in that cell during the period 1985 to 2015. Current FMD surveillance performance was assessed, and recommendations were made to improve surveillance activities in critical areas.
H3.2010.2 is a new phylogenetic clade of H3N2 circulating in swine that became established after the spillover of a human seasonal H3N2 from the 2016–2017 influenza season. The novel H3.2010.2 transmitted and adapted to the swine host and demonstrated reassortment with internal genes from strains endemic to pigs, but it maintained human-like HA and NA.
Abstract Sequencing and phylogenetic classification have become a common task in human and animal diagnostic laboratories. It is routine to sequence pathogens to identify genetic variations of diagnostic significance and to use these data in real-time genomic contact tracing and surveillance. Under this paradigm, unprecedented volumes of data are generated that require rapid analysis to provide meaningful inference. We present a machine learning logistic regression pipeline that can assign classifications to genetic sequence data. The pipeline implements an intuitive and customizable approach to developing a trained prediction model that runs in linear time complexity, generating accurate output more rapidly than other classification methods. Our approach was benchmarked against porcine respiratory and reproductive syndrome virus (PRRSv) and swine H1 influenza A (IAV) datasets. Trained classifiers were tested against sequences and simulated datasets that artificially degraded sequence quality at 0, 10, 20, 30, and 40%. When applied to a poor-quality sequence data, the classifier achieved between >85% to 95% accuracy for the PRRSv and the swine H1 IAV HA dataset and this increased to near perfect accuracy when using the full dataset. The model also identifies amino acid positions used to determine genetic clade identity through a feature selection ranking within the model. These positions can be mapped onto a maximum-likelihood phylogenetic tree, allowing for the inference of clade defining mutations. Our approach is implemented as a python package with code available at https://github.com/flu-crew/classLog .
