Publications

The control of porcine reproductive and respiratory syndrome virus (PRRSV) hinges on monitoring and surveillance. The objective of this study was to assess PRRSV RNA detection by RT-PCR in tongue tips from dead suckling piglets compared to serum samples, processing fluids, and family oral fluids. Tongue tips and serum samples were collected from three PRRSV-positive breeding herd farms (farms A, B, and C) of three different age groups: newborns (<24 h), processing (2 to 7 days of age), and weaning (18 to 22 days of age). Additionally, processing fluids and family oral fluids were collected from 2-7 days of age and weaning age, respectively. In farms A and B, PRRSV RNA was detected in tongue tips from all age groups (100 and 95%, respectively). In addition, PRRSV RNA was detected in pooled serum samples (42 and 27%), processing fluids (100 and 50%), and family oral fluids (11 and 22%). Interestingly, the average Ct value from tongue tips was numerically lower than the average Ct value from serum samples in the newborn age. In farm C, PRRSV RNA was only detected in serum samples (60%) and family oral fluids (43%), both from the weaning age. Further, no PRRSV RNA was detected in tongue tips when pooled serum samples from the same age group tested PRRSV RNA-negative. Taken together, these results demonstrate the potential value of tongue tips for PRRSV monitoring and surveillance.

Introduction The porcine reproductive and respiratory syndrome virus (PRRSV) continues to challenge swine production in the US and most parts of the world. Effective PRRSV surveillance in swine herds can be challenging, especially because the virus can persist and sustain a very low prevalence. Although weaning-age pigs are a strategic subpopulation in the surveillance of PRRSV in breeding herds, very few sample types have been validated and characterized for surveillance of this subpopulation. The objectives of this study, therefore, were to compare PRRSV RNA detection rates in serum, oral swabs (OS), nasal swabs (NS), ear-vein blood swabs (ES), and family oral fluids (FOF) obtained from weaning-age pigs and to assess the effect of litter-level pooling on the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) detection of PRRSV RNA. Methods Three eligible PRRSV-positive herds in the Midwestern USA were selected for this study. 666 pigs across 55 litters were sampled for serum, NS, ES, OS, and FOF. RT-qPCR tests were done on these samples individually and on the litter-level pools of the swabs. Litter-level pools of each swab sample type were made by combining equal volumes of each swab taken from the pigs within a litter. Results Ninety-six piglets distributed across 22 litters were positive by PRRSV RT-qPCR on serum, 80 piglets distributed across 15 litters were positive on ES, 80 piglets distributed across 17 litters were positive on OS, and 72 piglets distributed across 14 litters were positive on NS. Cohen's kappa analyses showed near-perfect agreement between all paired ES, OS, NS, and serum comparisons (). The serum RT-qPCR cycle threshold values (Ct) strongly predicted PRRSV detection in swab samples. There was a ≥ 95% probability of PRRSV detection in ES-, OS-, and NS pools when the proportion of positive swab samples was ≥ 23%, ≥ 27%, and ≥ 26%, respectively. Discussion ES, NS, and OS can be used as surveillance samples for detecting PRRSV RNA by RT-qPCR in weaning-age pigs. The minimum number of piglets to be sampled by serum, ES, OS, and NS to be 95% confident of detecting ≥ 1 infected piglet when PRRSV prevalence is ≥ 10% is 30, 36, 36, and 40, respectively.

Human seasonal H3 clade 3C3a influenza A viruses (IAV) were detected four times in U.S. pigs from commercial swine farms in Michigan, Illinois, and Virginia in 2019. To evaluate the relative risk of this spillover to the pig population, whole genome sequencing and phylogenetic characterization were conducted, and the results revealed that all eight viral gene segments were closely related to 2018-2019 H3N2 human seasonal IAV. Next, a series of

A porcine reproductive and respiratory syndrome virus (PRRSV) type 2 (PRRSV-2) isolate was obtained from lung samples collected from a 4.5-month-old pig at a wean-to-finish site in Indiana, USA, although no gross or microscopic lesions suggestive of PRRSV infection were observed in the lung tissue. Phylogenetic and molecular evolutionary analyses based on the obtained virus sequences indicated that PRRSV USA/IN105404/2021 was a natural recombinant isolate from Ingelvac PRRS® MLV and Prevacent® PRRS, which are PRRSV-2-modified live virus vaccines commercially available in the United States. This study is the first to report the detection of a PRRSV-2 recombinant strain consisting entirely of two modified live virus vaccine strains under field conditions. Based on clinical data and the absence of lung lesions, this PRRSV-2 recombinant strain was not virulent in swine, although its pathogenicity needs to be confirmed by clinical trials.

Introduction In swine disease surveillance, obtaining labeled data for supervised learning models can be challenging because many farms lack standardized diagnostic routines and consistent health monitoring systems. Unsupervised learning is particularly suitable in such scenarios because it does not require labeled data, allowing for detecting anomalies without predefined labels. This study evaluates the effectiveness of unsupervised machine learning models in detecting anomalies in productivity indicators in swine breeding herds. Methods Anomalies, defined as deviations from expected patterns, were identified in indicators such as abortions per 1000 sows, prenatal losses, preweaning mortality, total born, liveborn, culled sows per 1000 sows, and dead sows per 1000 sows. Three unsupervised models - Isolation Forest, Autoencoder, and K-Nearest Neighbors (KNN) - were applied to data from two swine production systems. The herd-week was used as the unit of analysis, and anomaly scores above the 75th percentile were used to flag anomalous weeks. A permutation test assessed differences between anomalous and non-anomalous weeks. Performance was evaluated using F1-score, precision, and recall, with true anomalous weeks defined as those coinciding with reported health challenges, including porcine reproductive and respiratory syndrome (PRRS) and Seneca Valley virus outbreaks. A total of 8,044 weeks were analyzed. Results The models identified 336 anomalous weeks and 1,008 non-anomalous weeks in Production System 1, and 1,675 anomalous weeks and 5,025 non-anomalous weeks in Production System 2. The results from the permutation test revealed significant differences in productivity indicators between anomalous and non-anomalous weeks, especially during PRRS outbreaks, with more subtle changes observed during Seneca Valley virus outbreaks. The models performed well in detecting the PRRSV anomaly, achieving perfect precision (100%) across all models for both production systems. For anomalies like SVV the models showed lower performance compared to PRRSV. Discussion These findings suggest that unsupervised machine learning models are promising tools for early disease detection in swine herds, as they can identify anomalies in productivity data that may signal health challenges.

The aim of this study was to characterize the behavior of 2 commercially available internal teat sealants containing bismuth subnitrate (SOa and ORBb) during the dry- and postfresh periods in dairy cattle. Digital radiography was used to monitor location and percentage of teat cistern fill at dry-off and during the dry period. Teat-level health parameters including infection status at dry-off and post-fresh as well as incidence of clinical mastitis were also assessed. Radiographic imaging revealed that 21.1% of teats had sealant present in the gland cistern at dry-off (SO= 23.6%, ORB = 18.5%) which increased to 55.9% (SO = 59.6%, ORB = 52.2%) at pre-fresh imaging. Quarters with sealant that had migrated into the gland shed noticeable amounts of sealant for twice as long (mean 14.34 milkings), compared to quarters that did not have sealant in the gland (mean: 7.12, P &lt; 0.001). There was no correlation between sealant location and intramammary infection at dry-off or post-fresh, nor did sea ant location impact clinical mastitis in the first 120 days in milk. Milk leakage after dry-off also was not correlated with an increased risk of intramammary infection during the dry or post-fresh periods. There was no difference between treatments in any of the parameters evaluated. Prolonged sealant shedding is a common reason that producers cite for not utilizing internal teat sealant products in their dry-off protocols. Further research is indicated to determine the optimal amount of sealant required to provide protection while minimizing post-fresh shedding.

Abstract Disease management and epidemiological investigations of porcine reproductive and respiratory syndrome virus-type 2 (PRRSV-2) often rely on grouping together highly related sequences. In the USA, the last five years have seen a major paradigm shift within the swine industry when classifying PRRSV-2, beginning to move away from RFLP (restriction fragment length polymorphisms)-typing and adopting the use of phylogenetic lineage-based classification. However, lineages and sub-lineages are large and genetically diverse, and the rapid mutation rate of PRRSV coupled with the global prevalence of the disease has made it challenging to identify new and emerging variants. Thus, within the lineage system, a dynamic fine-scale classification scheme is needed to provide better resolution on the relatedness of PRRSV-2 viruses to inform disease management and monitoring efforts and facilitate research and communication surrounding circulating PRRSV viruses. Here, we compare potential fine-scale systems for classifying PRRSV-2 variants (i.e., genetic clusters of closely related ORF5 sequences at finer scales than sub-lineage) using a database of 28,730 sequences from 2010 to 2021, representing &gt;55% of the U.S. pig population. In total, we compared 140 approaches that differed in their tree-building method, criteria, and thresholds for defining variants within phylogenetic trees using TreeCluster . Three approaches produced epidemiologically meaningful variants (i.e., ≥5 sequences per cluster), and resulted in reproducible and robust outputs even when the input data or input phylogenies were changed. In the three best performing approaches, the average genetic distance amongst sequences belonging to the same variant was 2.1 – 2.5%, and the genetic divergence between variants was 2.5-2.7%. Machine learning classification algorithms were also trained to assign new sequences to an existing variant with &gt;95% accuracy, which shows that newly generated sequences could be assigned without repeating the phylogenetic and clustering analyses. Finally, we identified 73 sequence-clusters (dated &lt;1 year apart with close phylogenetic relatedness) associated with circulation events on single farms. The percent of farm sequence-clusters with an ID change was 6.5-8.7% for our best approaches. In contrast, ∼43% of farm sequence-clusters had variation in their RFLP-type, further demonstrating how our proposed fine-scale classification system addresses shortcomings of RFLP-typing. Through identifying robust and reproducible classification approaches for PRRSV-2, this work lays the foundation for a fine-scale system that would more reliably group related field viruses and provide better improved clarity for decision-making surrounding disease management.

O objetivo deste trabalho foi avaliar e classificar as principais causas de morte de leitões com até seis dias de idade, em um sistema intensivo de suínos confinados (SISCO), localizado na região

Abstract Introduction: Identifying characteristics that account for variability in reproductive performance among sow farms is challenging through simple field observation. Unsupervised learning effectively clusters farms by their similarities, revealing relationships among various multidimensional characteristics and management practices. This study aimed to classify farms based on farm-level characteristics and identify drivers associated with reproductive performance. Methods: An observational study was conducted across 29 breeding herds from the same company in southern Brazil, examining ten characteristics related to infrastructure and management practices. These included the presence of trees and grass around gestation and farrowing barns, automated control of temperature and lighting in gestation and lactation facilities, barn orientation (east-west), as well as management practices such as encouraging sows to stand during lactation, bump-feeding, and grouping sows by body condition score before moving them into gestation pens. A K-means cluster analysis was performed to group farms based on these variables. The method assessed the homogeneity of characteristics using the Ward-D2 method through a binary distance matrix. The performance data was used to assess differences in performance between clusters. Reproductive indicators were analyzed using the Kruskal-Wallis or ANOVA test. The frequency of farm characteristics between clusters was analyzed using Fisher’s exact test, and all analyses were performed in R software. Results: The model suggested two clusters, Cluster 1 (C1) with 17 farms and Cluster 2 (C2) with 12. The farms averaged 1,269 sows (450–3,024) in C1 and 1,264 sows (330–3,000) in C2. Farrowing rate (FR) did not differ significantly (P=0.21) between Clusters 1 and 2 (90.0% vs. 88.6%, respectively); however, the coefficient of variation of FR was lower in C1 (3.39% vs. 4.31%). No difference in the percentage of return to estrus (P=0.56) was observed between clusters (C1: 5.10%; C2: 5.57%). The total number of piglets born and born alive also did not differ between clusters. However, pre-weaning mortality of piglets was lower in C1 (5.82%) compared to C2 (7.00%; P=0.03). The C1 had a higher percentage of farms with trees and grass surrounding the gestation and farrowing barns than C2 (P&amp;lt; 0.01). Additionally, C1 had a lower percentage of farms that used bump-feeding and encouraged sows to stand during lactation than C2 (P≤0.05). Trees and grass around facilities may affect barn temperature, ventilation, and less exposure to dust and airborne pathogens. Bump-feeding reduces feed intake during lactation. Both factors influence CV of FR and pre-weaning mortality. Sows standing more frequently could increase piglet crushing. Table 1 shows the frequency of farm characteristics across clusters. Conclusion: Cluster 1 demonstrated, on average, better reproductive performance, distinguishing itself from Cluster 2 by having more farms with trees and grass around gestation and lactation barns and fewer farms using bump-feeding or encouraging sows to stand during lactation.

A risk-based approach to animal selection for sampling enhances pathogen detection by increasing the probability of selecting an animal harboring the pathogen while requiring a smaller sample size. Postmortem tongue fluids (TF) have emerged as a promising risk-based approach, with a PRRSV RNA positivity rate similar to serum, processing fluids, and family oral fluids. Thus, this study assessed the effect of stillborn presence, litter size, and PRRSV RNA detection by RT-qPCR in stillborn TF on the probability of having viremic piglets within the litter. Samples from 130 litters were collected within 12 hours after farrowing from two breeding herds. TF and intracardiac blood were collected from stillborns, and tail blood swabs were collected from liveborn littermates within the selected litters. Samples were individually tested for PRRSV RNA detection by RT-qPCR. Litters with ≤ 11 liveborn piglets were defined as small. Generalized linear regression models were used to evaluate the litter size, presence of stillborns, and stillborn PRRSV results on the probability that a litter or at least one liveborn littermate would test PRRSV-positive. The live piglets' mean positivity within the litter was 5.0%, while the total born was 4.6%. Litters with at least one stillborn had 12.5 times higher odds of having a PRRSV-positive result, and 4.8 times higher odds of having at least one viremic liveborn piglet. In small litters, the odds of having a PRRSV-positive result increased 12.2 times, whereas the odds of having a viremic liveborn littermate increased 10.8 times. When the stillborn TF was positive, the odds of having a viremic liveborn littermate increased 17.6 times. In conclusion, stillborn TFs were a reliable indicator of PRRSV status among litters. Liveborn piglets from litters with PRRSV-positive stillborn TF or small litters had greater odds of testing PRRSV-positive. Therefore, stillborn TF collection and targeting small litters improve PRRSV detection and support farrowing room biocontainment strategies.