There is some evidence that ATP binding cassette (ABC) transporters play a role in resistance to anthelmintics, particularly against macrocyclic lactones. Some anthelmintics, including ivermectin (IVM), have been shown to induce transcription of multiple ABC transporters in nematodes; however, the effects of monepantel (MPL) on transcription of these transporter genes has not been studied. Larvae of two MPL-susceptible isolates of Haemonchus contortus were exposed to MPL at two concentrations (2.5 and 250 μg/ml) for periods of 3, 6 and 24 h. Transcription levels of sixteen ABC transporter genes were measured at the end of the incubation periods. The consequences of MPL exposure were examined by measuring rhodamine-123 efflux from the larvae, and their sensitivity to subsequent treatment with IVM or levamisole. Multiple ABC transporter genes showed significantly higher transcription in both worm isolates following exposure to MPL at 250 μg/ml for 3, 6 or 24 h, particularly the P-glycoprotein (P-gp) genes pgp-11, pgp-12 and pgp-14. Of these, only pgp-11 maintained the elevated levels 24 h after the end of the drug exposure period. In contrast, there was only a single instance of low-level upregulation as a result of exposure to MPL at 2.5 μg/ml. Larvae exposed to MPL at 250 μg/ml showed an increased efflux of rhodamine-123 and a proportion of the larval population showed an ability to subsequently tolerate higher concentrations of IVM in migration assays. There was no increased tolerance to IVM following pre-exposure to MPL at 2.5 μg/ml. Exposure of H. contortus larvae to 250 μg/ml MPL results in increased transcription of multiple transporter genes and increased R-123 efflux. The subsequent ability of a proportion of the larvae to tolerate IVM suggests a protective role of ABC transporters across different chemical entities. However, these observations were only made at a concentration of MPL well above that experienced by parasitic life stages in vivo, and hence their significance remains unclear.

The present study aimed to investigate the development of resistance to the pyrimidine derivative dicyclanil and the neonicotinoid imidacloprid in the sheep blowfly, Lucilia cuprina. We applied laboratory-based insecticide selection pressure to a susceptible strain and measured resistance patterns that emerged to all of the currently-available blowfly control chemicals. The dynamics of resistance emergence was quite different for the two compounds. Resistance to imidacloprid emerged after 4 rounds of selection, increased up to 9 rounds before then remaining relatively constant at almost 10-fold until the termination of the experiment after 15 rounds. Dicyclanil resistance did not emerge until the 13th round of selection, and then increased to approximately 3.5-fold at 15 rounds. This faster development of resistance to imidacloprid is in agreement with observations of resistance emergence in field populations of the blowfly over the last 25 years. The imidacloprid-selected strain also developed resistance towards dicyclanil (3.1-fold). Similarly, the dicyclanil-selected strain also showed resistance to imidacloprid (4.8-fold). This level of resistance to imidacloprid in the dicyclanil-selected strain (4.8-fold) was slightly higher than the level of resistance to dicyclanil itself (3.5-fold). The study provides insights into the dynamics of resistance emergence to different compounds in the sheep blowfly and describes cross-resistance patterns with important implications for chemical rotation strategies. In demonstrating the emergence of resistance to two chemical classes after exposure to single classes, this study illustrates the need to examine the potential impact of resistance mechanisms that can act across multiple insecticide classes when designing insecticide-use strategies for parasite control.

Prioritizing cows for closer observation around calving is important for livestock management, but reliable same-day calving detection from behavior alone remains challenging. We examine whether daily 06:00–06:00 behavior summaries derived from ear-tag accelerometers can support strictly causal, calving-oriented triage in grazing cattle. Because birth annotations provide only daily-resolution supervision, we frame the task as weakly supervised day-level risk assessment rather than precise event timing. Therefore, we train a compact weakly supervised multilayer-perceptron daily-risk model using an exactly-one likelihood over a two-day candidate window comprising the day before birth and the recorded day of birth. The model uses current behavior summaries, deviations from cow-specific causal behavior baselines for the current and previous retained days, a history-gap variable, and missingness indicators. We evaluate the model using cow-grouped nested cross-validation on data from 134 calving cows and 4,891 cow-days. At the primary base operating point, pooled out-of-fold predictions show a measurable but modest signal, with F1 score 0.277, MCC 0.244, AUPRC 0.231, and candidate-window detection 47.0%. Comparator methods recover similar signals with different detection–burden trade-offs: a supervised positive-bag random forest improves F1 score and candidate-window detection, but increases non-candidate false-alert burden. A higher-threshold Alert state reduces the retained non-candidate false-alert rate to 0.0121, but lowers recall to 0.194. Applying the frozen deployment model to an independent non-calving cohort yields low daily Watch/Alert rates but non-negligible cow-level exposure. Overall, daily behavior summaries are better suited to lightweight herd-level risk stratification and human-in-the-loop monitoring than to reliable standalone calving detection.

We investigated whether daily behavior summaries derived from a behavior classifier applied to ear-tag accelerometer data could support calving-oriented monitoring under field conditions. Data came from a seedstock operation in subtropical Australia with grazing and feedlot components. Because exact birth times were unavailable, we used weak supervision over two candidate days: the farm-recorded calf-observation date and the previous day. A neural network model using an exactly-one formulation over this candidate window generated an uncalibrated compatibility score from daily behavior durations and cow-specific baseline-deviation features only after each 06:00–06:00 interval was complete. Cow-grouped nested cross-validation used 134 calving cows and 4891 cow-days. At the selected operating point, the model achieved 47.0% candidate-window coverage, an F1 score of 0.277, an AUPRC of 0.231 with a candidate-day prevalence reference of 0.055, and a 2.94% pooled false-alert rate on non-candidate days. Random forest and an at-least-one neural network formulation achieved higher candidate-window coverage at their selected thresholds but greater false-alert burden outside the candidate window. We also characterized out-of-domain output burden in an independent cohort containing no calving events: 8 of 1547 scored cow-days were Alert and 7 of 31 cows received at least one Alert. These findings indicate useful but modest calving-associated information in daily behavior summaries and support prospective evaluation for management triage. However, the present study did not establish intervention lead time. More precise event timing and external calving cohorts are needed to establish advance-warning value and transferability.