Neoblast-like stem cells of Fasciola hepatica.

The common liver fluke (Fasciola hepatica) causes the disease fasciolosis, which results in considerable losses within the global agri-food industry. There is a shortfall in the drugs that are effective against both the adult and juvenile life stages within the mammalian host, such that new drug targets are needed. Over the last decade the stem cells of parasitic flatworms have emerged as reservoirs of putative novel targets due to their role in development and homeostasis, including at host-parasite interfaces. Here, we investigate and characterise the proliferating cells that underpin development in F. hepatica. We provide evidence that these cells are capable of self-renewal, differentiation, and are sensitive to ionising radiation- all attributes of neoblasts in other flatworms. Changes in cell proliferation were also noted during the early stages of in vitro juvenile growth/development (around four to seven days post excystment), which coincided with a marked reduction in the nuclear area of proliferating cells. Furthermore, we generated transcriptomes from worms following irradiation-based ablation of neoblasts, identifying 124 significantly downregulated transcripts, including known stem cell markers such as fgfrA and plk1. Sixty-eight of these had homologues associated with neoblast-like cells in Schistosoma mansoni. Finally, RNA interference mediated knockdown of histone h2b (a marker of proliferating cells), ablated neoblast-like cells and impaired worm development in vitro. In summary, this work demonstrates that the proliferating cells of F. hepatica are equivalent to neoblasts of other flatworm species and demonstrate that they may serve as attractive targets for novel anthelmintics.

Identifying animal behaviours from accelerometers: Improving predictive accuracy of machine learning by refining the variables selected, data frequency, and sample duration.

Observing animals in the wild often poses extreme challenges, but animal-borne accelerometers are increasingly revealing unobservable behaviours. Automated machine learning streamlines behaviour identification from the substantial datasets generated during multi-animal, long-term studies; however, the accuracy of such models depends on the qualities of the training data. We examined how data processing influenced the predictive accuracy of random forest (RF) models, leveraging the easily observed domestic cat (Felis catus) as a model organism for terrestrial mammalian behaviours. Nine indoor domestic cats were equipped with collar-mounted tri-axial accelerometers, and behaviours were recorded alongside video footage. From this calibrated data, eight datasets were derived with (i) additional descriptive variables, (ii) altered frequencies of acceleration data (40 Hz vs. a mean over 1 s) and (iii) standardised durations of different behaviours. These training datasets were used to generate RF models that were validated against calibrated cat behaviours before identifying the behaviours of five free-ranging tag-equipped cats. These predictions were compared to those identified manually to validate the accuracy of the RF models for free-ranging animal behaviours. RF models accurately predicted the behaviours of indoor domestic cats (F-measure up to 0.96) with discernible improvements observed with post-data-collection processing. Additional variables, standardised durations of behaviours and higher recording frequencies improved model accuracy. However, prediction accuracy varied with different behaviours, where high-frequency models excelled in identifying fast-paced behaviours (e.g. locomotion), whereas lower-frequency models (1 Hz) more accurately identified slower, aperiodic behaviours such as grooming and feeding, particularly when examining free-ranging cat behaviours. While RF modelling offered a robust means of behaviour identification from accelerometer data, field validations were important to validate model accuracy for free-ranging individuals. Future studies may benefit from employing similar data processing methods that enhance RF behaviour identification accuracy, with extensive advantages for investigations into ecology, welfare and management of wild animals.

Host movement dominates the predicted effects of climate change on parasite transmission between wild and domestic mountain ungulates.

Climate change is shifting the transmission of parasites, which is determined by host density, ambient temperature and moisture. These shifts can lead to increased pressure from parasites, in wild and domestic animals, and can impact the effectiveness of parasite control strategies. Understanding the interactive effects of climate on host movement and parasite life histories will enable targeted parasite management, to ensure livestock productivity and avoid additional stress on wildlife populations. To assess complex outcomes under climate change, we applied a gastrointestinal nematode transmission model to a montane wildlife-livestock system, based on host movement and changes in abiotic factors due to elevation, comparing projected climate change scenarios with the historic climate. The wildlife host, Alpine ibex (Capra ibex ibex), undergoes seasonal elevational migration, and livestock are grazed during the summer for eight weeks. Total parasite infection pressure was more sensitive to host movement than to the direct effect of climatic conditions on parasite availability. Extended livestock grazing is predicted to increase parasite exposure for wildlife. These results demonstrate that movement of different host species should be considered when predicting the effects of climate change on parasite transmission, and can inform decisions to support wildlife and livestock health.