Stratya2D: Enhancing kinematic backstripping through image-based 2D horizon integration - Harikrishnan Nalinakumar; Patrick Makuluni; Juerg Hauser; Stuart R. Clark (Computers & Geosciences, 2026)

Quantifying the basin dynamics of the Proterozoic Carrara Sub-basin to understand the assembly and evolution of the Australian continent - Harikrishnan Nalinakumar; Stuart R. Clark (Gondwana Research, 2025)

Global Marine Flyways Identified for Long‐Distance Migrating Seabirds From Tracking Data - Joanne M. Morten; Ana P. B. Carneiro; Martin Beal; Anne‐Sophie Bonnet‐Lebrun; Maria P. Dias; Marie‐Morgane Rouyer; Autumn‐Lynn Harrison; Jacob González‐Solís; Victoria R. Jones; Virginia A. Garcia Alonso; Michelle Antolos; Javier A. Arata; et al. (Global Ecology and Biogeography, 2025)
Aim To identify the broad‐scale oceanic migration routes (‘marine flyways’) used by multiple pelagic, long‐distance migratory seabirds based on a global compilation of tracking data. Location Global. Time Period 1989–2023. Major Taxa Studied Seabirds (Families: Phaethontidae, Hydrobatidae, Diomedeidae, Procellariidae, Laridae and Stercorariidae). Methods We collated a comprehensive global tracking dataset that included the migratory routes of 48 pelagic and long‐distance migrating seabird species across the Atlantic, Indian, Pacific and Southern Oceans. We grouped individuals that followed similar routes, independent of species or timings of migration, using a dynamic time warping clustering approach. We visualised the routes of each cluster using a line density analysis and used knowledge of seabird spatial ecology to combine the clusters to identify the broad‐scale flyways followed by most pelagic migratory seabirds tracked to‐date at an ocean‐basin scale. Results Six marine flyways were identified across the world's oceans: the Atlantic Ocean Flyway, North Indian Ocean Flyway, East Indian Ocean Flyway, West Pacific Ocean Flyway, Pacific Ocean Flyway and Southern Ocean Flyway. Generally, the flyways were used bidirectionally, and individuals either followed sections of a flyway, a complete flyway, or their movements linked two or more flyways. Transhemispheric figure‐of‐eight routes in the Atlantic and Pacific oceans, and a circumnavigation flyway in the Southern Ocean correspond with major wind ‐ driven ocean currents. Main Conclusions The marine flyways identified demonstrate that pelagic seabirds have similar and repeatable migration routes across ocean‐basin scales. Our study highlights the need to account for connectivity in seabird conservation and provides a framework for international cooperation.

Monitoring slope stabilization of a reactivated landslide in the Three Gorges Reservoir Region (China) with multi-source satellite SAR and optical datasets - Jianming Kuang; Linlin Ge; Alex Hay-Man Ng; Stuart R. Clark; Sadra Karimzadeh; Masashi Matsuoka; Zheyuan Du; Qi Zhang (Landslides, 2024)

Improved Fracture Segmentation from Unwrapped Drill-Core Images Using an Innovative Two-Stage Segmentation Approach - Fatimah Alzubaidi; Harikrishnan Nalinakumar; Stuart R. Clark; Jan Erik Lie; Peyman Mostaghimi; Ryan T. Armstrong (Mathematical Geosciences, 2023)
While machine learning (ML) provides a great tool for image analysis, obtaining accurate fracture segmentation from high-resolution core images is challenging. A major reason is that the segmentation quality of large and detailed objects, such as fractures, is limited by the capacity of the segmentation branch. This challenge can be seen in the Mask Region-based Convolutional Neural Network (Mask R-CNN), which is a common and well-validated instance segmentation model. This study proposes a two-stage segmentation approach using Mask R-CNN to improve fracture segmentation from unwrapped-core images. Two CNN models are used: the first model processes full-size unwrapped-core images to detect and segment fractures; the second model performs a more detailed segmentation by processing smaller regions of the images that include the fractures detected by the first model. In addition, the procedure uses a new architecture of Mask R-CNN with a point-based rendering (PointRend) neural network module that can increase segmentation accuracy. The method is evaluated on approximately 47 m of core from four boreholes and results in an improvement to previous fracture segmentation methods. It achieves an increase in the average intersection over union of approximately 27% from the baseline (one-stage segmentation with standard Mask R-CNN). The enhanced fracture segmentation provides a mean for obtaining an accurate fracture aperture with an average error of less than 1 mm, which represents a reduction of 0.5 mm from the baseline method. This work presents a novel contribution towards developing an ML-based workflow for core-image analysis.

Prediction of enteric methane emissions by sheep using an intercontinental database - Alejandro Belanche; Alexander N. Hristov; Henk J. van Lingen; Stuart E. Denman; Ermias Kebreab; Angela Schwarm; Michael Kreuzer; Mutian Niu; Maguy Eugène; Vincent Niderkorn; Cécile Martin; Harry Archimède; et al. (Journal of Cleaner Production, 2023)

Neurostructural and Neurophysiological Correlates of Multiple Sclerosis Physical Fatigue: Systematic Review and Meta-Analysis of Cross-Sectional Studies - Paula M. Ellison; Stuart Goodall; Niamh Kennedy; Helen Dawes; Allan Clark; Valerie Pomeroy; Martin Duddy; Mark R. Baker; John M. Saxton (Neuropsychology Review, 2022)
Fatigue is one of the most debilitating symptoms for people with multiple sclerosis (PwMS). By consolidating a diverse and conflicting evidence-base, this systematic review and meta-analysis aimed to gain new insights into the neurobiology of MS fatigue. MEDLINE, ProQuest, CINAHL, Web of Science databases and grey literature were searched using Medical Subject Headings. Eligible studies compared neuroimaging and neurophysiological data between people experiencing high (MS-HF) versus low (MS-LF) levels of perceived MS fatigue, as defined by validated fatigue questionnaire cut-points. Data were available from 66 studies, with 46 used for meta-analyses. Neuroimaging studies revealed lower volumetric measures in MS-HF versus MS-LF for whole brain (22.74 ml; 95% CI: -37.72 to -7.76 ml; p = 0.003), grey matter (18.81 ml; 95% CI: 29.60 to 8.03 ml; p < 0.001), putamen (0.40 ml; 95% CI: 0.69 to 0.10 ml; p = 0.008) and acumbens (0.09 ml; 95% CI: 0.15 to 0.03 ml; p = 0.003) and a higher volume of T1-weighted hypointense lesions (1.10 ml; 95% CI: 0.47 to 1.73 ml; p < 0.001). Neurophysiological data showed reduced lower-limb maximum voluntary force production (19.23 N; 95% CI: 35.93 to 2.53 N; p = 0.02) and an attenuation of upper-limb (5.77%; 95% CI:8.61 to 2.93%; p < 0.0001) and lower-limb (2.16%; 95% CI:4.24 to 0.07%; p = 0.04) skeletal muscle voluntary activation, accompanied by more pronounced upper-limb fatigability (5.61%; 95% CI: -9.57 to -1.65%; p = 0.006) in MS-HF versus MS-LF. Results suggest that MS fatigue is characterised by greater cortico-subcortical grey matter atrophy and neural lesions, accompanied by neurophysiological decrements, which include reduced strength and voluntary activation. Prospero registration Prospero registration number: CRD42016017934

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)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Multiple environmental controls on phytoplankton growth strategies determine adaptive responses of the N : P ratio - Stuart J. Daines; James R. Clark; Timothy M. Lenton (Ecology Letters, 2014)
The controls on the ‘Redfield’ N : P stoichiometry of marine phytoplankton and hence the N : P ratio of the deep ocean remain incompletely understood. Here, we use a model for phytoplankton ecophysiology and growth, based on functional traits and resource‐allocation trade‐offs, to show how environmental filtering, biotic interactions, and element cycling in a global ecosystem model determine phytoplankton biogeography, growth strategies and macromolecular composition. Emergent growth strategies capture major observed patterns in marine biomes. Using a new synthesis of experimental RNA and protein measurements to constrain per‐ribosome translation rates, we determine a spatially variable lower limit on adaptive rRNA:protein allocation and hence on the relationship between the largest cellular P and N pools. Comparison with the lowest observed phytoplankton N : P ratios and N : P export fluxes in the Southern Ocean suggests that additional contributions from phospholipid and phosphorus storage compounds play a fundamental role in determining the marine biogeochemical cycling of these elements.

Environmental selection and resource allocation determine spatial patterns in picophytoplankton cell size - James R. Clark; Timothy M. Lenton; Hywel T. P. Williams; Stuart J. Daines (Limnology and Oceanography, 2013)
Here we describe a new trait‐based model for cellular resource allocation that we use to investigate the relative importance of different drivers for small cell size in phytoplankton. Using the model, we show that increased investment in nonscalable structural components with decreasing cell size leads to a trade‐off between cell size, nutrient and light affinity, and growth rate. Within the most extreme nutrient‐limited, stratified environments, resource competition theory then predicts a trend toward larger minimum cell size with increasing depth. We demonstrate that this explains observed trends using a marine ecosystem model that represents selection and adaptation of a diverse community defined by traits for cell size and subcellular resource allocation. This framework for linking cellular physiology to environmental selection can be used to investigate the adaptive response of the marine microbial community to environmental conditions and the adaptive value of variations in cellular physiology.

Notes on recent literature - W. Stuart; W. M. Peacock; W. S. Baird; W. Saxby Blair; R. H. Helmer; W. H. Hicks; W. R. Leslie; B. F. Lutman; H. H. Mann; S. D. Nagpurkar; G. S. Kulkarni; R. S. Kasargode; et al. (American Journal of Potato Research, 1924)