Pathobiologyhttps://aurora.auburn.edu/handle/11200/442472026-07-14T12:52:09Z2026-07-14T12:52:09ZBehavioral components define operational suitability metric for detection dog successhttps://aurora.auburn.edu/handle/11200/508242026-07-01T11:47:29ZBehavioral components define operational suitability metric for detection dog success
To ensure effective development and assessment of detection dogs, Auburn University Canine Performance Sciences program has implemented a structured behavioral evaluation at four developmental milestones. At each timepoint, trainers scored candidate detection dogs on 11 subtests evaluating detection performance and environmental soundness using behavioral component (BC) scores. Additionally, trainers rated each dog’s overall suitability for operational detection work using a singular global score. To investigate the relative importance of different metrics in predicting final outcomes, we leveraged a longitudinal dataset collected from 304 dogs. Using a machine learning classifier, we discovered that suitability (S) scores at 12-months of age were highly predictive of training outcomes, achieving a prediction accuracy of 74.3%. The S scores appear to reflect a synthesis of subtest scores across relevant behavioral domains, illustrated by their correlative structures. To determine if S scores could be replicated using only BC scores, we estimated computationally synthesized suitability (CSS) scores at each timepoint using five significant BC scores as predictors in a linear model. Strong correlations were observed between trainer-reported suitability and CSS, with spearman’s correlation coefficients exceeding 0.84 at all timepoints, demonstrating CSS scores’ excellent ability to predict suitability. As predictors of training outcome, trainer-reported S scores demonstrated high accuracy, while CSS scores exhibited high precision (76.7%). Notably, both suitability metrics increase over time in dogs deemed operationally capable but not in unsuccessful dogs. Our findings provide novel insights into the importance of data-driven approaches by combining expert trainer global assessments of suitability and BC scores with computational modeling in detection dog development.
Identification of transcriptome markers in blue catfish cryopreserved sperm to predict male reproductive performance for catfish aquaculturehttps://aurora.auburn.edu/handle/11200/506842025-05-22T17:28:00ZIdentification of transcriptome markers in blue catfish cryopreserved sperm to predict male reproductive performance for catfish aquaculture
In hybrid catfish aquaculture, hatch success varies significantly using cryopreserved sperm from different males, but current phenotypic assays only explain a fraction of observed variations. We profiled the sperm transcriptome of 30 blue catfish (Ictalurus furcatus) males, whose cryopreserved sperm yielded a hatch success from 14 to 63%. Transcriptome study through RNA sequencing identified 3,850 differentially expressed genes (DEGs) between high-hatch-success (HHS) and low‑hatch‑success (LHS) groups (log2(fold change) > 2 and FDR < 0.05). More than 95% of these DEGs were retained in fresh sperm, demonstrating that intrinsic molecular differences, rather than cryopreservation, drive fertility variation. In LHS sperm, upregulation of mitophagy, autophagy, RNA degradation, and cilium‑assembly pathways was observed, consistent with cellular stress and motility deficiencies. Spearman correlation across all males identified 27 annotated DEGs strongly associated with hatch success, of which the positive marker trpm4a and negative markers kmt2b and zbtb33 were validated by qRT‑PCR (P < 0.05). A random‑forest regression model combining the six most informative transcriptomic markers with testosterone level and initial motility achieved high predictivity with ρ = 0.77 between predicted and observed hatch success (P < 0.001), outperforming models based on either data type alone. These findings establish a compact panel that can be implemented as a multiplex qPCR assay to guide broodstock selection, reduce gamete wastage, and enhance fry output. Our work continues to build the foundation to extend marker validation across hatcheries, explore causal mechanisms via functional genomics, and incorporate epigenetic features to further refine predictive accuracy. Ultimately, this framework will support profitable and sustainable catfish aquaculture operations.
Videos associated with “Protocol for synchronizing waveforms and videos from electropenetrography studies of arthropods”https://aurora.auburn.edu/handle/11200/506802025-05-22T17:47:57ZVideos associated with “Protocol for synchronizing waveforms and videos from electropenetrography studies of arthropods”
Electropenetrography (EPG) is a non-invasive technique for quantifying intra-tissue host-arthropod interactions and behaviors. To use EPG on new species, waveforms must be correlated with behaviors and biological activities. Synchronizing observed waveforms with video recordings of the arthropods during EPG can aid in defining the biological meanings of the waveforms. Our procedure for using Observer XT Behavioral Coding Software to synchronize EPG waveforms with video recordings is published as a “Protocol for synchronizing waveforms and videos from electropenetrography studies of arthropods.” This article references videos in the expected outcomes that are archived here.
Inter-evaluator bias and applicability of feline body condition score from visual assessmenthttps://aurora.auburn.edu/handle/11200/506522024-11-04T14:22:55ZInter-evaluator bias and applicability of feline body condition score from visual assessment
Background: Body Condition Score (BCS) is an effective tool for assessing body weight and overall body composition.
Objectives: To determine whether BCS can be accurately assessed solely from photographs of cats, and to evaluate inter-evaluator bias in visually assessed BCS.
Animals: Thirty-eight client-owned cats enrolled during routine wellness checks.
Methods: A set of cat images collected from online sources was administered as a quiz to nine evaluators. To validate the results, BCS was clinically assessed for 38 enrolled cats through palpation by one evaluator, and also visually assessed by all nine evaluators using only cat photographs.
Results: Inter-evaluator bias was found to be relatively low in BCS assessment based on animal images (mean±SE=0.35±0.03). In the validation set of 38 client-owned cats, the visual assessment of BCS deviated from the clinically assessed BCS by 0.61±0.04, which was slightly higher than the deviation observed in the mock image set. In both scenarios, majority voting among nine evaluators achieved the highest accuracy, demonstrating its effectiveness in reducing evaluator bias. Inter-evaluator bias caused a 14% misclassification between ideal and overweight BCS, but only 1% between ideal and obese, indicating minimal bias in diagnosing feline obesity.
Conclusions and clinical importance: The ability to accurately assess BCS through photographic evaluation will enhance remote consultations in telemedicine and support large-scale epidemiological studies on feline obesity. This study has developed a tool for evaluating and minimizing inter-evaluator bias in BCS assessments across diverse practitioners and settings, thereby improving the consistency and comparability of large feline obesity research.