Biology subjects

Linhares, D.

Publications and source records attributed to Linhares, D..

3 recordsLinked to original sources

bioRxiv2026

Evaluation of gaseous ozone technology for decontaminating porcine reproductive and respiratory syndrome virus (PRRSV) and porcine epidemic diarrhea virus (PEDV) from non-porous surface in truck cabins

Porcine reproductive and respiratory syndrome virus (PRRSV) and porcine epidemic diarrhea virus (PEDV), both economically detrimental to the swine industry, can be spread through contaminated truck cabins, posing significant transport biosecurity risks. This study evaluated the effectiveness of gaseous ozone technology in decontaminating PRRSV and PEDV on non-porous surfaces in truck cabins. An incomplete factorial study comprising 26 treatment groups, including controls, and ozone treatments at three capacities (30, 38, and 68 g/h) was conducted over varied exposure times (0.5, 1, or 2 hours) in a climate-controlled truck cabin, where PRRSV or PEDV contaminated rubber coupons were subjected to treatments under continuous environmental monitoring, followed by virus elution and titration in cell cultures. Statistical analyses included regression models and ANOVA with Tukey's comparisons, standard deviations for ozone machines, and Pearson correlations between ozone concentration and environmental factors. Ozone treatments showed variable results with less than two-log viral titer reduction. Ozone concentrations varied across identical setups (SD: 4.75 ppm for 30 g/h, 5.43 ppm for 38 g/h machines) during the first 30 minutes, while across the entire study period, ozone concentration showed weak negative correlation with virus titer (p > 0.05), moderate positive correlation with temperature (r = 0.5, p < 0.0001), and moderate negative correlation with humidity (r = -0.56, p < 0.0001). In conclusion, ozone treatments showed inconsistent and limited effectiveness in PRRSV and PEDV decontamination, influenced by temperature and humidity under the tested conditions.

microbiology↗

bioRxiv2025

Linking Molecular Tension and Cellular Tractions: A Multiscale Approach to Focal Adhesion Mechanics

Focal adhesions (FAs) are mechanosensitive structures that mediate force transmission between cells and the extracellular matrix. While Traction Force Microscopy (TFM) quantifies cellular tractions exerted on deformable substrates, Forster Resonance Energy Transfer (FRET)-based tension probes, such as Vinculin Tension Sensors (VinTS), measure molecular-scale forces within FA proteins. Despite their potential synergy, these methods have rarely been combined to explore the interplay between molecular tension and cellular tractions. Here, we introduce a framework integrating TFM and VinTS to investigate FA mechanics across scales. At cell level, tractions and vinculin tension increased with substrate stiffness. At FA level, vinculin tension correlated with vinculin density, while tractions scaled with FA area, total vinculin content and vinculin density. Direct comparison of tractions to tension revealed a complex, heterogenous relationship between these forces, possibly linked to diverse cell and FA maturation states. Sub-FA analysis revealed conserved spatial patterns, with tension and traction increasing towards the cell periphery. This multiscale approach provides insights into the multiscale dynamics of FA mechanotransduction, bridging the gap between molecular forces and cellular mechanics.j

biophysics↗

bioRxiv2024

Phylogenetic-based methods for fine-scale classification of PRRSV-2 ORF5 sequences: a comparison of their robustness and reproducibility

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 >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 >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 <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.

bioinformatics↗