Abstract The 2009 H1N1 pandemic (pdm09) lineage is a major component of the H1 influenza A virus (IAV) that causes seasonal outbreaks annually. Since its introduction in the 2009–10 season, this lineage has evolved into distinct, successive clades in humans. Predicting the fitness of influenza clades is essential to forecasting future prevalence, providing a critical opportunity to develop a response to mitigate infection. The relative fitness of pdm09 lineages was retrospectively inferred via relative reproduction rate (RRe) through RelRe, a programme that implements a renewal equation to estimate the relative difference in reproduction number between cocirculating clades. For this analysis, pdm09 lineage sequences from the USA, collected from 2017 to 2023 in both human and swine hosts, were downloaded from public databases. Clade designations were assigned using Nextclade. Human case count data were divided by each influenza season, and the RRe was estimated at 3-month intervals. The RRe was then used to forecast clade frequency 90 days into the future, and the predictions were compared to the historical data. The highest predicted frequency at 90 days corresponded to the most frequently detected lineage in 9 out of 13 predictions (69%). The pdm09 lineage plays an important role at the human–swine influenza interface. Bayesian inference using both human and swine data indicated unequal transmission rates of the pdm09 lineage, with 53–79 noted transmissions from human to swine and 0–2 in reverse using the available genetic data. Metadata analysis revealed that new clades of pdm09 in humans were typically detected in swine as early as ~8–20 months after clade emergence in humans. Understanding RRe and the fitness of contemporary IAV strains enables the identification of high-risk reverse-zoonotic strains and provides critical time for responding to emergent human clades.
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.
ABSTRACT A Nextclade data set for PRRSV-1 ORF5 based on a global nomenclature for standardized lineage classification was developed. This tool enables rapid sequence analysis, visualization, and comparison with reference strains and vaccines. By providing accessibility, it facilitates broader adoption of PRRSV-1 classification frameworks for research and surveillance.
Influenza A viruses (IAV) in swine constitute a major economic burden to an important global agricultural sector, impact food security, and are a public health threat. Despite significant improvement in surveillance for IAV in swine over the past 10 years, sequence data have not been integrated into a systematic vaccine strain selection process for predicting antigenic phenotype and identifying determinants of antigenic drift.
ABSTRACT Existing genetic classification systems for porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2), such as restriction fragment length polymorphisms and sub-lineages, are unreliable indicators of close genetic relatedness or lack sufficient resolution for epidemiological monitoring routinely conducted by veterinarians. Here, we outline a fine-scale classification system for PRRSV-2 genetic variants in the United States. Based on >25,000 U.S. open reading frame 5 (ORF5) sequences, sub-lineages were divided into genetic variants using a clustering algorithm. Through classifying new sequences every 3 months and systematically identifying new variants across 8 years, we demonstrated that prospective implementation of the variant classification system produced robust, reproducible results across time and can dynamically accommodate new genetic diversity arising from virus evolution. From 2015 to 2023, 118 variants were identified, with ~48 active variants per year, of which 26 were common (detected >50 times). Mean within-variant genetic distance was 2.4% (max: 4.8%). The mean distance to the closest related variant was 4.9%. A routinely updated webtool ( https://stemma.shinyapps.io/PRRSLoom-variants/ ) was developed and is publicly available for end users to assign newly generated sequences to a variant ID. This classification system relies on U.S. sequences from 2015 onward; further efforts are required to extend this system to older or international sequences. Finally, we demonstrate how variant classification can better discriminate between previous and new strains on a farm, determine possible sources of new introductions into a farm/system, and track emerging variants regionally. Adoption of this classification system will enhance PRRSV-2 epidemiological monitoring, research, and communication, and improve industry responses to emerging genetic variants. IMPORTANCE The development and implementation of a fine-scale classification system for PRRSV-2 genetic variants represent a significant advancement for monitoring PRRSV-2 occurrence in the swine industry. Based on systematically applied criteria for variant identification using national-scale sequence data, this system addresses the shortcomings of existing classification methods by offering higher resolution and adaptability to capture emerging variants. This system provides a stable and reproducible method for classifying PRRSV-2 variants, facilitated by a freely available and regularly updated webtool for use by veterinarians and diagnostic labs. Although currently based on U.S. PRRSV-2 ORF5 sequences, this system can be expanded to include sequences from other countries, paving the way for a standardized global classification system. By enabling accurate and improved discrimination of PRRSV-2 genetic variants, this classification system significantly enhances the ability to monitor, research, and respond to PRRSV-2 outbreaks, ultimately supporting better management and control strategies in the swine industry.
Nexus format phylogenetic tree. Hemagglutinin H1 Subtype Reference Sequences. Reference sequences used for determining the clade an H1 subtype hemagglutinin sequence falls into. (TXT 21 kb)
We evaluated an active participatory design for the regional surveillance of notifiable swine pathogens based on testing 10 samples collected by farm personnel in each participating farm. To evaluate the performance of the design, public domain software was used to simulate the introduction and spread of a pathogen among 17,521 farms in a geographic region of 1,615,246 km
