Untethered from earthly constraints: A spatial seven-day ahead machine-learning forest fuel moisture forecasting system, independent of real-time sensor networks - Thomas Keeble; Christopher Sean Lyell; Tim Gazzard; Thomas James Duff; Gary Sheridan (Environmental Modelling & Software, 2026)

Medically Tailored Grocery Delivery for Food Pantry Clients with Diabetes - Eliza Short; Ji Li; James P. Selig; Kelsey Bounds; Brett Rowland; Bonnie Faitak; Jazmin Lane; Rosana Solis-Garcia; Holly C. Felix; Krista Langston; Pearl A. McElfish; Juan Mejia-Otera; et al. (Journal of Nutrition Education and Behavior, 2025)

Network of networks: Time series clustering of AmeriFlux sites - David E Reed; Housen Chu; Brad G. Peter; Jiquan Chen; Michael Abraha; Brian Amiro; Ray G. Anderson; M. Altaf Arain; Paulo Arruda; Greg A. Barron-Gafford; Carl Bernacchi; Daniel P. Beverly; et al. (Agricultural and Forest Meteorology, 2025)

Forecasting dead fuel moisture content below forest canopies – A seven-day forecasting system - Christopher Sean Lyell; Usha Nattala; Thomas Keeble; Elena M. Vella; Rakesh Chandra Joshi; Zaher Joukhadar; Jonathan Garber; Simon J Mutch; Tim Gazzard; Tom Duff; Gary Sheridan (Agricultural and Forest Meteorology, 2024)

Portable gas chromatography–mass spectrometry method for the in-field screening of organic pollutants in soil and water at pollution incidents - Denise Duff; Chris Lennard; Yarong Li; Christopher Doyle; Katelyn J. Edge; Ian Holland; Kevin Lothridge; Paul Johnstone; Paul Beylerian; Val Spikmans (Environmental Science and Pollution Research, 2023)
Environmental pollution incidents generate an emergency response from regulatory agencies to ensure that the impact on the environment is minimised. Knowing what pollutants are present provides important intelligence to assist in determining how to respond to the incident. However, responders are limited in their in-field capabilities to identify the pollutants present. This research has developed an in-field, qualitative analytical approach to detect and identify organic pollutants that are commonly detected by regulatory environmental laboratories. A rapid, in-field extraction method was used for water and soil matrices. A coiled microextraction (CME) device was utilised for the introduction of the extracted samples into a portable gas chromatography–mass spectrometry (GC–MS) for analysis. The total combined extraction and analysis time was approximately 6.5 min per sample. Results demonstrated that the in-field extraction and analysis methods can screen for fifty-nine target organic contaminants, including polyaromatic hydrocarbons, monoaromatic hydrocarbons, phenols, phthalates, organophosphorus pesticides, and organochlorine pesticides. The method was also capable of tentatively identifying unknown compounds using library searches, significantly expanding the scope of the methods for the provision of intelligence at pollution incidents of an unknown nature, although a laboratory-based method was able to provide more information due to the higher sensitivity achievable. The methods were evaluated using authentic casework samples and were found to be fit-for-purpose for providing rapid in-field intelligence at pollution incidents. The fact that the in-field methods target the same compounds as the laboratory-based methods provides the added benefit that the in-field results can assist in sample triaging upon submission to the laboratory for quantitation and confirmatory analysis.

Prediction of disability-free survival in healthy older people - Johannes Tobias Neumann; Le T. P. Thao; Anne M. Murray; Emily Callander; Prudence R. Carr; Mark R. Nelson; Rory Wolfe; Robyn L. Woods; Christopher M. Reid; Raj C. Shah; Anne B. Newman; Jeff D. Williamson; et al. (GeroScience, 2022)
Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people. Trial registration Clinicaltrials.gov (NCT01038583)

Multiple primary malignancies associated with a germline SMARCB1 pathogenic variant - Judith A. Eelloo; Miriam J. Smith; Naomi L. Bowers; John Ealing; Paul Hulse; James P. Wylie; Patrick Shenjere; Noel W. Clarke; Calvin Soh; Richard W. Whitehouse; Mark Jones; Christopher Duff; et al. (Familial Cancer, 2019)

Imaging changes associated with cognitive abnormalities in Parkinson’s disease - Yuko Koshimori; Barbara Segura; Leigh Christopher; Nancy Lobaugh; Sarah Duff-Canning; Romina Mizrahi; Clement Hamani; Anthony E. Lang; Kelly Aminian; Sylvain Houle; Antonio P. Strafella (Brain Structure and Function, 2015)