Biology subjects

Dowsey, A. W.

Publications and source records attributed to Dowsey, A. W..

6 recordsLinked to original sources

bioRxiv2026

Local Temperature and Humidity are Associated with Proportion of Antimicrobial-Resistant Escherichia coli isolates in Farm Environments: Considerations for On-Farm Surveillance

Evidence suggests that increased local temperatures are associated with higher prevalence of antimicrobial resistance (AMR) in environmental bacteria. This study investigates the association between local climate and the proportion of antimicrobial-resistant Escherichia coli isolated from 2,766 farm environment samples from 53 English dairy farms. To do this, a non-linear Bayesian model that specifically accounts for decreased test sensitivity at low E. coli abundance was developed and used to estimate the proportion of isolates resistant to four antimicrobials (amoxicillin, cephalexin, streptomycin and tetracycline) from colony count data. Mean 7-day temperature and relative humidity at the farm location was modelled using a generalised additive model formulation. A higher proportion of E. coli isolates were resistant to cephalexin and streptomycin in samples collected from adult cow collecting yards, than heifer housing sheds. In contrast, a greater proportion of E. coli isolates from heifer housing sheds were resistant to amoxicillin and tetracycline. Evidence that local temperature is associated with an increase in the proportion of E. coli isolates resistant to streptomycin (20{degrees}C increase associated with a 5.0-fold increase; 95% CI: 1.03-33.0) and tetracycline (2.6-fold increase; 90% CI: 1.1-5.2) was observed. Additionally, relative humidity was associated with an increase in the proportion of isolates resistant to amoxicillin streptomycin and tetracycline. The influence of weather on the proportion of antimicrobial-resistant E. coli varied between samples collected from adult animals in collecting yards and heifers in housing sheds. These findings highlight the importance of considering weather conditions, sample characterises and seasonality when designing on-farm AMR surveillance systems. ImportanceUnderstanding how environmental conditions are associated with variability in AMR prevalence is critical for developing robust livestock AMR surveillance and anticipating the potential effects of climate change. The non-linear Bayesian modelling approach developed here adjusts for E. coli abundance associated variability in test sensitivity, enabling the influence of risk factors associated with the proportion of antimicrobial-resistant E. coli within samples to be more accurately estimated. Applying this approach to 2,766 faecal samples from 53 dairy farms in Southwest England indicated that the proportion of antimicrobial-resistant E. coli generally increased under warmer and wetter conditions. These findings suggest that environmental conditions can influence the prevalence of AMR E. coli in dairy farm environments and demonstrate the importance of accounting for weather related variability in livestock AMR surveillance. Adjusting for these associations in livestock AMR surveillance could improve the accuracy of modelling AMR trends and strengthen the assessment of climate-associated AMR risks.

microbiology↗

bioRxiv2024

Accelerometer-derived classifiers for early detection of degenerative joint disease in cats

Decreased mobility is a clinical sign of degenerative joint disease (DJD) in cats, which is highly prevalent, with 61% of cats aged six years or older showing radiographic evidence of DJD. Radiographs can reveal morphological changes and assess joint degeneration, but they cannot determine the extent of pain experienced by cats. Additionally, there is no universal objective assessment method for DJD-associated pain in cats. Developing an accurate evaluation model could enable earlier treatment, slow disease progression, and improve cats well-being. This study aimed to predict early signs of DJD in cats using accelerometers and machine learning techniques. Cats were restricted to indoors or limited outdoor access, including being walked on a lead or allowed into enclosed areas for short periods. Fifty-six cats were fitted with collar-mounted sensors that collected accelerometry data over 14 days, with data from 51 cats included in the analysis. Cat owners assessed their cats mobility and assigned condition scores, validated through clinical orthopaedic examinations. The study group comprised 24 healthy cats (no owner-reported mobility changes) and 27 unhealthy cats (owner-reported mobility changes, suggestive of early DJD). Data were segmented into 60-second windows centred around peaks of high activity. Using a Support Vector Machine (SVM) algorithm, the model achieved 78% (confidence interval: 0.65, 0.88) area under the curve (AUC), with 68% sensitivity (0.64, 0.77) at 75% specificity (0.68, 0.79). These results demonstrate the potential of accelerometry and machine learning to aid early DJD diagnosis and improve management, offering significant advances in non-invasive diagnostic techniques for cats.

animal behavior and cognition↗

bioRxiv2023

Five inhibitory receptors display distinct vesicular distributions in T cells

T cells can express multiple inhibitory receptors. Upon induction of T cell exhaustion in response to persistent antigen, prominently in the anti-tumor immune response, many are expressed simultaneously. Key inhibitory receptors are CTLA-4, PD-1, LAG3, TIM3 and TIGIT, as investigated here. These receptors are important as central therapeutic targets in cancer immunotherapy. Inhibitory receptors are not constitutively expressed on the cell surface, but substantial fractions reside in intracellular vesicular structures. It remains unresolved to which extent the subcellular localization of different inhibitory receptors is distinct. Using quantitative imaging of subcellular distributions and plasma membrane insertion as complemented by proximity proteomics and a biochemical analysis of the association of the inhibitory receptors with trafficking adaptors, the subcellular distributions of the five inhibitory receptors were discrete. The distribution of CTLA-4 was most distinct with preferential association with lysosomal-derived vesicles and the sorting nexin 1/2/5/6 transport machinery. With a lack of evidence for the existence of specific vesicle subtypes to explain divergent inhibitory receptor distributions, we suggest that such distributions are driven by divergent trafficking through an overlapping joint set of vesicular structures. This extensive characterization of the subcellular localization of five inhibitory receptors in relation to each other lays the foundation for the molecular investigation of their trafficking and its therapeutic exploitation.

immunology↗

bioRxiv2020

Accurate and interpretable prediction of poor health in small ruminants with accelerometers and machine learning

Assessment of the health status of individual animals is a key step in the timely and targeted treatment of infections, which is critical in the fight against anthelmintic and antimicrobial resistance. The FAMACHA scoring system has been used successfully to detect anaemia caused by infection with the parasitic nematode Haemonchus contortus in small ruminants and is an effective way to identify individuals in need of treatment. However, assessing FAMACHA is labour-intensive and costly as individuals must be manually examined at frequent intervals. Here, we used accelerometers to measure the individual activity of extensively grazing small ruminants (sheep and goats) exposed to natural Haemonchus contortus worm infection in southern Africa over long time scales (13+ months). When combined with machine learning, this activity data can predict poorer health (increases in FAMACHA score), as well as those individuals that respond to treatment, all with precision up to 83%. We demonstrate that these classifiers remain robust over time. Interpretation of trained classifiers reveals that poorer health significantly affects the night-time activity levels in the sheep. Our study thus reveals behavioural patterns across two small ruminant species, which lowcost biologgers can exploit to detect subtle changes in animal health and enable timely and targeted intervention. This has real potential to improve economic outcomes and animal welfare as well as limit the use of anthelmintic drugs and diminish pressures on anthelmintic resistance in both commercial and resource-poor communal farming.

animal behavior and cognition↗

bioRxiv2020

mzMLb: a future-proof raw mass spectrometry data format based on standards-compliant mzML and optimized for speed and storage requirements

With ever-increasing amounts of data produced by mass spectrometry (MS) proteomics and metabolomics, and the sheer volume of samples now analyzed, the need for a common open format possessing both file size efficiency and faster read/write speeds has become paramount to drive the next generation of data analysis pipelines. The Proteomics Standards Initiative (PSI) has established a clear and precise XML representation for data interchange, mzML, receiving substantial uptake; nevertheless, storage and file access efficiency has not been the main focus. We propose an HDF5 file format mzMLb that is optimised for both read/write speed and storage of the raw mass spectrometry data. We provide extensive validation of write speed, random read speed and storage size, demonstrating a flexible format that with or without compression is faster than all existing approaches in virtually all cases, while with compression, is comparable in size to proprietary vendor file formats. Since our approach uniquely preserves the XML encoding of the metadata, the format implicitly supports future versions of mzML and is straightforward to implement: mzMLbs design adheres to both HDF5 and NetCDF4 standard implementations, which allows it to be easily utilised by third parties due to their widespread programming language support. A reference implementation within the established ProteoWizard toolkit is provided.

bioinformatics↗

bioRxiv2018

Regional protein expression in human Alzheimer’s brain correlates with disease severity

Alzheimers disease (AD) is a progressive neurodegenerative disorder that currently affects 36 million people worldwide with no effective treatment available. Development of AD follows a distinctive pattern in the brain and is poorly modelled in animals. Therefore, it is vital to widen both the spatial scope of the study of AD and prioritise the study of human brains. Here we show that functionally distinct human brain regions show varying and region-specific changes in protein expression. These changes provide novel insights into the progression of disease, novel AD-related pathways, the presence of a gradient of protein expression change from less to more affected regions, and the presence of a protective protein expression profile in the cerebellum. This spatial proteomics analysis provides a framework which can underpin current research and opens new avenues of interest to enhance our understanding of molecular pathophysiology of AD, provides new targets for intervention and broadens the conceptual frameworks for future AD research.

neuroscience↗