ChemCentre

Killer whales (Orcinus orca) are cosmopolitan apex predators that interact with numerous species, including sharks, skates, and rays (subclass Elasmobranchii). Interactions between killer whales and elasmobranchs have garnered attention from the scientific community, but the dynamics of these events are poorly understood, as interactions are often challenging to observe. In this study, we collated imagery of interactions between killer whales and elasmobranchs to create the first image-based repository of interspecific interactions between marine predators. A total of 320 photos and videos from 82 unique interactions were collected from 12 countries. A minimum of 18 elasmobranch species across 16 genera were identified from the imagery. Furthermore, 10 different interaction types were documented, encompassing both predatory and non-predatory behavior. This dataset can provide insight into various research topics, including killer whale predation techniques, elasmobranch behavior, and the identification of individual killer whales that specialize in elasmobranch prey. As a new and openly accessible resource, this dataset provides the foundation to support future ecological and behavioral research on killer whale and elasmobranch interactions.

• Existing un-trended climatology forecasts of seasonal streamflow are prone to bias when trends are present in the streamflow record. • Trended climatology forecasts can remove bias, but are usually worse than un-trended climatology forecasts. • Selective adoption of trended climatology forecasts, based on observed trend significance, can lead to meaningful improvements. Probabilistic climatology forecasts of seasonal streamflow up to 12 months ahead are incredibly useful for water resource planning, informing risk-based management strategies. However, the assumption of a stationary distribution for such climatology forecasts fails to account for observed trends present in many streamflow gauge records, which can result in unnecessary bias and excessive spread in the forecasts. For example, where drying trends have led to reduced flow, water resources may be overestimated based on a higher historical mean flow. While trended climatology forecasts are sometimes far better than un-trended climatology forecasts, most of the time they are worse, in part due to the additional parameter uncertainty from trend estimation. Here we present an approach to selectively adopt trended climatology forecasts based on observed trends in the training period. We use a high-quality hydrological station network covering a broad range of hydroclimate conditions to compare trended and un-trended climatology and evaluate our proposed approach to selective adoption of trended climatology. We found trended climatology forecasts provided high skill relative to the un-trended climatology baseline when negative trends were present, especially when those trends were significant. We also observed negative skill in around half the cases, especially where positive trends were present, highlighting the necessity for selective application. We recommend the adoption of trended climatology for seasonal streamflow forecasting whenever there is a fair chance of a negative trend present (and not when a positive trend is present), particularly in applications that require conservative estimates of long-term flows (e.g., allocation of annual water accounts).

Honey bee colonies adapt their foraging behaviours to the availability of floral resources to meet their nutritional needs. However, it is unknown if the nutritional quality of stored or supplemented pollen can influence the floral choices of bees during commercial crop pollination. The foraging behaviour of bees from 40 colonies was studied during avocado pollination in southern Western Australia. A pollen database of the orchard was built and used to assess the floral preference of the bees. Pollen collectors and nectar foragers showed different foraging behaviour as indicated by their Dominance Candidate Index (DCI). The foraging choices were partially affected by the type of supplemented pollen that consisted of agricultural and forest species. Aside from nutritional cues, floral source abundance and attractiveness played a role in influencing the foraging behaviour for pollen and nectar. Both pollen and nectar foragers chose a sub-set of flowers available at the avocado orchard. Foraging bees respond strongly to various environmental cues that potentially underlie energetic investment and food reward principals.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-13981-6.

Laboratory automation has transformed bioanalytical research, yet smaller research laboratories face challenges in adopting such technologies due to limited resources, time, and technical expertise, while already facing complex bioanalytical methods. To address these barriers, we developed a robotic-arm-based camera detection system featuring two software applications designed to simplify laboratory automation. Both applications use fiducial markers (Augmented Reality University of Cordoba (ArUco)), for object detection. The first application creates a 3D digital model of the robot’s environment using ArUco markers and a Python-based Open Computer Vision (OpenCV) simulated stereo vision setup, enabling automated computer-aided design (CAD) in FreeCAD. This facilitates safe and efficient robot arm navigation. The second application integrates a deep learning neural network for automated digital display recognition, achieving an in-house error rate of 1.69%, comparable to manual readings. By leveraging low-cost hardware and open-source software available on GitHub, the system is accessible to smaller research facilities, reducing programming complexity and enabling broader adoption of laboratory automation in bioanalytical workflows. This work demonstrates an affordable and effective solution for integrating robotic arms into scientific workflows, enhancing reproducibility and efficiency in bioanalytical research.

Low carbohydrate diets (LCD) have shown efficacy in managing clinical outcomes in type 2 diabetes (T2D). Incorporating digital tools into health care provides an adjunct treatment modality, to educate patients and provide clinical support. This study examines the effect of a mobile health (mHealth) LCD application (app) on glycaemic profile, blood pressure and weight status, in people with T2D. The study is an online single-arm, pre-post study that recruited people with T2D from around Australia, referred via registered supporting general practitioners (GPs). The intervention (Defeat Diabetes app) provides education and resources on the use of a LCD for ongoing management of T2D. After 3 months, our cohort of 99 participants (mean age 59 ± 11 years, 55 females) showed reduced dietary carbohydrate intake as a proportion of overall energy (−14%kJ/day, 95% CI: −17 to −11). Improvement in the primary outcome HbA1c (−1.0%, 95% CI: −1.3 to −0.7), was associated with reduction in dietary carbohydrate intake. Systolic blood pressure (SBP) improved (−6 mmHg, 95% CI: −10 to −1), while 21 participants reduced their diabetes medication dose with two participants discontinuing all diabetes medication. These findings demonstrate that people with T2D receiving LCD education and resources through the Defeat Diabetes app for 3 months improved their glycaemic profile and SBP despite decreased overall medication usage in almost one third of the study sample prescribed medication at baseline.

Presented on Thursday 23 May: Session 24 Natural hydrogen’s viability as a sustainable energy source will be enhanced through comprehensive geological modelling. This paper integrates existing open-source data to delve into the geological aspects of natural hydrogen exploration and proposes possible workflows. Geological and geophysical modelling entails characterising subsurface formations conducive to natural hydrogen generation and trapping. Utilising geological surveys, field observation, geophysical seismic and gravity data, alongside existing well logs, this analysis looks to identify regions with favourable geological conditions for the generation, migration and structures for accumulation of natural hydrogen. Furthermore, understanding the subsurface geology aids in the development of safe and efficient extraction techniques. By incorporating geological modelling into the evaluation of natural hydrogen, this paper provides a comprehensive overview of its potential as a sustainable energy solution. Leveraging existing open-source data alongside geological insights ensures a robust foundation for decision-making in exploration, production, storage, and utilisation strategies. This integrated approach empowers stakeholders to make informed choices in shaping a greener, more sustainable energy landscape. To access the Oral Presentation click the link on the right. To read the full paper click here

For illicit and psychoactive drugs which remain in the water phase, better removal was linked to improved biodegradation conditions.

The use of chemometric methods in the analysis process of fire and explosive evidence allows for enhanced detection and classification of target analytes to be achieved. There is a vast amount of research into the application of chemometrics in the analysis of ignitable liquid and explosive residues throughout forensic fire and explosion investigations. This chapter provides an overview of research that focuses on discrimination and classification, as well as the use of experimental design to optimise sampling, storage, and analysis protocols for ignitable liquid and explosive residues. The research discussed demonstrates the usefulness of chemometrics as a tool for the efficient detection and classification of forensic evidence. Further research in this area is needed so that chemometrics may be used for future method development and identification and classification of ignitable liquid and explosive residues in real-life criminal casework.