Biology subjects

Parnell, A.

Publications and source records attributed to Parnell, A..

3 recordsLinked to original sources

bioRxiv2026

Acoustic detection of a rarely vocalising invasive mammal from sparse data

O_LIMonitoring and limiting the spread of invasive species on islands requires efficient detection and population estimation methods. However, elusive species can be difficult to monitor using traditional methods, making autonomous approaches such as camera trapping and acoustic monitoring increasingly valuable. C_LIO_LIOn the island of Okinawa, Japan, the small Indian mongoose ( Urva auropunctata) threatens many native species since its introduction in 1910. Listed among the worlds worst invasive species, effective monitoring of U. auropunctata in Okinawa is critical. The Okinawa Environmental Observation Network (OKEON) uses camera traps to detect U. auropunctata, but success depends on precise placement. Though OKEON also includes a high-resolution acoustic monitoring programme, no audio classification model currently exists for U. auropunctata. Developing such a model could improve substantially our capacity to detect and manage the species. C_LIO_LIUsing sparse U. auropunctata vocalisations collected from camera trap videos, we built a lightweight Convolutional Neural Network distilled from a more complex model for classifying contact calls and alarm calls of U. auropunctata. Our distilled model performed similarly to the full model at detecting vocalisations from training data, but was considerably faster. C_LIO_LIWe applied the distilled classifier to [~]486 hrs of audio collected over eight years from southern Okinawa, where we successfully detected U. auropunctata a handful of times in each year of recording. In spite of strong model performance on test data, our model did not transfer well to unseen data, perhaps owing to the rarity of U. auropunctata calls and consequent small training dataset size, limiting its utility for ecological monitoring. C_LIO_LIPractical implication. The use of sparse audio data from camera trap videos to train an acoustic classifier had limited utility to detect the rarely vocalising U. auropunctata from passive acoustic monitoring data. We provide several recommendations for enhancing classifier performance to provide robust actionable insights into the distribution and spread of U. auropunctata, and aid targeted conservation efforts for Okinawas threatened biodiversity. C_LI

ecology↗

bioRxiv2026

A stem cell secretome delays functional decline and supresses inflammation in two distinct models of neurodegeneration.

Alzheimers disease (AD) is a progressive neurodegenerative disorder with a rapidly increasing global prevalence. Current pharmacological interventions offer symptomatic relief but do not modify disease progression. Secretome-based therapeutics have emerged as a potential disease-modifying strategy, given their capacity to influence multiple pathological pathways, including amyloid burden, reactive gliosis, and neuronal survival. Early clinical studies support the safety and potential efficacy of these approaches, indicating mechanisms involving neuroprotection, neurodegeneration, and modulation of neuroinflammation, processes central to AD pathology. In the present study, we investigated the therapeutic efficacy of multipotent stromal cell (MSC)-derived secretomes produced by a specific platform (Secretomix) in two distinct mouse models of neurodegenerative disease: An AD model characterized by amyloid pathology, and a motor neurone disease (MND) model exhibiting TDP-43 protein aggregation. Administration of the MSC secretome resulted in a positive modulation of the behavioural phenotype in the AD model, and reduction in the rate of decline of motor co-ordination (attenuated the progression of motor deficits) in the MND model. In the latter, these functional benefits were accompanied by a measurable reduction in neuroinflammatory responses but without direct alteration of standard neuropathological markers. Additionally, ex vivo assays using human peripheral blood demonstrated broad anti-inflammatory activity of the MSC secretome, providing a potential mechanistic basis for the in vivo observations. Collectively, these findings support further investigation of MSC-derived secretomes as a promising therapeutic approach for neurodegenerative disorders, with relevance across proteinopathies characterised by distinct molecular pathways. Significance StatementHere we demonstrate the efficacy of a stem cell secretome in ameliorating cognitive and behavioural phenotypes in different models of neurodegeneration. These models represent distinct neuropathological features that are unaffected by stem cell secretome treatment but share common features of modulation of inflammation post stem cell secretome treatment. This study highlights the therapeutic potential of stem cell secretomes in the treatment of neurodegenerative conditions with an already existing neuropathology.

neuroscience↗

bioRxiv2021

Bayesian Additive Regression Trees for Genotype by Environment Interaction Models - AMBARTI

We propose a new class of models for the estimation of genotype by environment (GxE) interactions in plant-based genetics. Our approach, named AMBARTI, uses semi-parametric Bayesian additive regression trees to accurately capture marginal genotypic and environment effects along with their interaction in a cut Bayesian framework. We demonstrate that our approach is competitive or superior to similar models widely used in the literature via both simulation and a real world dataset. Furthermore, we introduce new types of visualisation to properly assess both the marginal and interactive predictions from the model. An R package that implements our approach is available at https://github.com/ebprado/ambarti.

bioinformatics↗