This study evaluates an aerial and ground imaging platform for assessment of canopy development in a wheat field. The dependence of two canopy traits, height and vigour, on fertilizer treatment was observed in a field trial comprised of ten varieties of spring wheat. A custom-built mobile ground platform (MGP) and an unmanned aerial vehicle (UAV) were deployed at the experimental site for standard red, green and blue (RGB) image collection on five occasions. Meanwhile, reference field measurements of canopy height and vigour were manually recorded during the growing season. Canopy level estimates of height and vigour for each variety and treatment were computed by image analysis. The agreement between estimates from each platform and reference measurements was statistically analysed. Estimates of canopy height derived from MGP imagery were more accurate (RMSE = 3.95 cm, R${}^2$ = 0.94) than estimates derived from UAV imagery (RMSE = 6.64 cm, R${}^2$ = 0.85). In contrast, vigour was better estimated using the UAV imagery (RMSE = 0.057, R${}^2$ = 0.57), compared to MGP imagery (RMSE = 0.063, R${}^2$ = 0.42), albeit with a significant fixed and proportional bias. The ability of the platforms to capture differential development of traits as a function of fertilizer treatment was also investigated. Both imaging methodologies observed a higher median canopy height of treated plots compared with untreated plots throughout the season, and a greater median vigour of treated plots compared with untreated plots exhibited in the early growth stages. While the UAV imaging provides a high-throughput method for canopy-level trait determination, the MGP imaging captures subtle canopy structures, potentially useful for fine-grained analyses of plants.

As the global population and resource scarcity simultaneously increase, the pressure on plant breeders and growers to maximise the effectiveness of their operations is immense. In this article, we explore the usefulness of image-based data collection and analysis of field experiments consisting of multiple field sites, plant varieties, and treatments. The goal of this approach is to determine whether the noninvasive acquisition and analysis of image data can be used to find relationships between the canopy traits of field experiments and environmental factors. Our results are based on data from three field trials in 2016, 2017, and 2018 in South Australia. Image data were supplemented by environmental data such as rainfall, temperature, and soil composition in order to explain differences in growth and the development of plants across field trials. We have shown that the combination of high-throughput image-based data and independently recorded environmental data can reveal valuable connections between the variables influencing wheat crop growth; meanwhile, further studies involving more field trials under different conditions are required to test hypotheses and draw statistically significant conclusions. This work highlights some of the more responsive traits and their dependencies.

This study compares parametric statistical time series models, such as autoregressive moving average (ARMA), with nonparametric artificial neural networks, specifically long short‐term memory (LSTM) models, for univariate forecasting. Two time series are analyzed separately: wind power output from the Clements Gap wind farm and the regional electricity price for South Australia. One‐step‐ahead forecast performance is evaluated using normalized mean bias error (NMBE), normalized mean absolute error (NMAE), and normalized root mean square error (NRMSE). Three LSTM models were examined: a manually tuned model, a structurally equivalent model implemented in a different library, and a model with automated hyperparameter tuning. While LSTM models achieved competitive performance, statistical models often performed equally well or better. For price forecasts, the manually tuned LSTM achieved the lowest NMBE (), while ARMA(3,1) had the best NMAE (0.0253) and AR(6) the best NRMSE (0.220). For wind forecasts, the manually tuned LSTM again performed best overall (NMBE: 0.00034, NMAE: 0.143, NRMSE: 0.225), while the equivalent library model performed worst. These results highlight the need to subject nonparametric LSTM models to more rigorous and systematic evaluation relative to their parametric statistical counterparts.

Radioresistant cancers often exhibit upregulated DNA damage response (DDR) proteins including ataxia telangiectasia mutated (ATM), recovering more readily from radiation-induced DNA double-strand breaks (DSBs). ATM inhibitors (ATMi) are therefore being explored as adjuvants in radiotherapy to both enhance radiosensitivity and minimise normal tissue complications. The molecule AZD1390 has been developed as an inhibitor of ATM and is undergoing assessment in clinical trials. However, traditional markers of DNA DSB repair, such as γH2AX, represent stages of DDR downstream from the action of ATM. The ATMi AZD1390, developed by AstraZeneca, ultimately prevents phosphorylation of the H2AX histone variant. In order to quantify ATMi AZD1390 action, the novel and highly complementary assay SensiTive Recognition of Individual DNA Ends (STRIDE) was employed to directly quantify DSBs in comparison to γH2AX after X-ray irradiation of 4T1 cells. Findings revealed that ATM inhibition via AZD1390 delays DSB repair initiation and appears to play a greater role in suppressing DDR beyond ATM inhibition alone. In X-ray irradiated conditions, obfuscation of DSBs commenced between 30- to 45-minutes post-irradiation without AZD1390 versus 45 to 60-minutes for cells pretreated with AZD1390 and entirely prevented γH2AX in the majority of cells. STRIDE and γH2AX exhibited almost no co-localization indicating that they provide distinct and complementary information. DSB formation was also assessed in cells fixed pre-insult to minimise any biological response, providing unprecedented assessment of DSB formation. These results highlight the potential of STRIDE to accurately measure DNA damage response kinetics, paving the way for more precise mechanistic studies into the role of ATM in radiotherapy.