From urban biosolids to biochar and nanobiochar: A scoping review of environmental applications and circular resource recovery - Sheila Abreu Mourão; Alexandre Santos Pimenta; José Cola Zanuncio; Fernanda Helfer; Ivan Gratchev; Ruby Naomi Michael (Journal of Environmental Management, 2026)

Abiotic factors influencing the health of mature Araucaria heterophylla (Norfolk Island Pine trees) in urban coastal parks - Anna Petrova; Chris Pratt; Ruby Naomi Michael (Environmental Management, 2026)
Tree decline can significantly impact both the aesthetic value and ecological function of urban forests. Understanding the abiotic factors influencing tree health is essential for developing effective management strategies. This study investigated the decline of heritage Araucaria heterophylla (Norfolk Island Pine) in urban areas of the Gold Coast, Queensland, Australia, with a focus on identifying underlying abiotic causes. General assessment was conducted along a 5-km coastal strip (including 946 trees); climate conditions on the Gold Coast and Norfolk Island were compared; and 38 selected trees of varying health status were examined in detail. This included soil physicochemical and nutrient analysis, as well as foliar nutrient profiling. Results showed that 15.8% of the 946 initially assessed trees exhibited symptoms of decline, such as dieback, browning of foliage tips, extensive defoliation and shortened foliage length. Mean temperatures on the Gold Coast were consistently higher than those in the species’ native range and the current decline followed a period of elevated temperatures and reduced rainfall. Declining trees ( n = 32) from the subset population were found to be growing in compacted soils, with significantly elevated foliar sodium and lower foliar carbon levels compared to healthy trees ( n = 6), indicating possible disruption of nutrient regulation. Half of the trees in the advanced stage of decline were located less than 5 m away from carparks (50%), which was higher when compared with Moderate Decline (18.7%) and very healthy Benchmarking trees (0%). Most trees in the advanced decline category were not mulched and had turfgrass as the primary groundcover (62.5%), whereas trees in moderate decline were more commonly associated with turfgrass combined with fallen branchlets (43.75%). In addition, all Benchmarking trees were located in sandy soils, whereas declining trees were more commonly associated with loam soils, which are more susceptible to compaction. Key abiotic stressors identified were extreme weather and proximity to urban infrastructure. Management recommendations include improving root zone conditions by providing irrigation during dry periods, mulching, implementing routine health monitoring to support early intervention and, for new planting, increasing distances from hard infrastructure.

Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration - Nikhil N. Chaudhari; Owen M. Vega; Phoebe Imms; Jaron M. Kawamura; Nahian F. Chowdhury; Tamara Jafar; Andrei Irimia; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; et al. (GeroScience, 2026)
Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves many neuroanatomic features whose effects on brain structure remain unexplored. To address this challenge, we trained interpretable deep neural networks (DNNs) to estimate brain age (BA) from T 1 -weighted ( T 1 w) MRI. By identifying MRI features unapparent to humans, DNNs can find aging-related structural alterations above and beyond cortical thickness and atrophy. Using a novel approach to DNN interpretability, we mapped brain aging progression in 25,539 cognitively normal UK Biobank adults aged 45–83 years. Cortical aging is found to involve anatomic features becoming prominent during the 50s within frontolateral, mesolimbic, cuneal, and occipitotemporal regions. From these foci, aging-related features propagate to adjacent areas at rates peaking in the 60s. Cortical thinning and atrophy do not trend closely with neurodegeneration, but important DNN-identifiable anatomic features have spatiotemporal dynamics that match those of amyloid or tau. Our results challenge the assumption that MRI cannot map anatomic features trending with neurodegeneration. Interpretable DNNs can empower MRI to quantify anatomic aging as a process of spatial feature expansion from focal regions into nearby structures in the sequence of neurodegenerative pathology. Traditional morphometrics explain only ∼1% of variance in DNN-identifiable features, which clarifies why the former are insensitive to anatomic changes involving neurodegeneration. Our results conceptualize brain aging in the context of spatial and temporal parallels between anatomic senescence and neuropathology. These findings may help to map cognitively normal adults’ neurodegenerative anatomy even without PET measurements. Graphical Abstract

Sex and APOE genotype specific brain regional vulnerability to Alzheimer's Disease - Qi Zeng; Minghui Wang; Erming Wang; Xianxiao Zhou; Peng Xu; Vahram Haroutunian; Dongming Cai; Bin Zhang; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; et al. (GeroScience, 2026)
Alzheimer’s disease (AD) disproportionately affects women and carriers of the apolipoprotein E ε4 allele (APOE4), yet little is known about how sex and APOE interact to influence white matter (WM) integrity during disease progression. We integrated diffusion MRI and matched blood transcriptomic data to investigate these interactions and their underlying biological mechanisms. WM microstructure was quantified using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), and regional vulnerability was assessed with a composite vulnerability score (CVS) derived from associations between diffusion features and AD severity across clinical traits in each of the four sex-APOE groups (female or male, with or without APOE4). Brain parcellation with the Eve atlas revealed regions consistently affected across sex-APOE groups (e.g., parahippocampal and superior temporal gyri) and regions specific to individual groups (e.g., the cingulum in females with APOE4 and the middle frontal gyrus in males without APOE4). Gene co-expression network analysis of the matched blood expression data identified gene subnetworks linked to group-specific regional vulnerability, including a muscle tissue morphogenesis module regulated by NEURL1B and HIST1H2BN associated with middle frontal gyrus vulnerability. These findings demonstrate that sex and APOE genotype jointly shape region-specific WM vulnerability and its molecular signatures in AD. Understanding these interactions provides novel mechanistic insights and may inform precision approaches to drug development, biomarker discovery, and clinical trial design for AD.

What Kills Mature Street and Park Trees in Cities? Systematic Quantitative Review of Published Case Studies - Anna Petrova; Ruby Naomi Michael; Chris Pratt (Environmental Management, 2025)
Street and park trees often endure harsher conditions, including increased temperatures and drier soil and air, than those found in urban or natural forests. These conditions can lead to shorter lifespans and a greater vulnerability to dieback. This literature review aimed to identify confirmed causes of street and park tree dieback in urban areas from around the world. Peer-reviewed case studies related to urban tree decline were scanned for the words “urban”, “city”, “cities”, “tree*”, “decline”, “dieback”, “mortality”, and “survival”. From an initial pool of 1281 papers on Web of Science and 1489 on Scopus, 65 original peer-reviewed research papers were selected for detailed analysis. Out of all species reported to decline, 46 were native, while non-natives were represented by 35 species. The most commonly affected trees were Platanus, Fraxinus, Acer, and Ficus . Most studies were conducted in Mediterranean, humid subtropical, and humid continental climates, with the greatest representation from the United States, followed by Australia, Brazil, Iran, Italy, and Russia. Many authors focused on either biotic or abiotic causes of dieback; some explored both, and some also discussed underlying environmental and urban stresses as potential predisposing factors. The majority (81% of the papers) concluded that a decline was caused by either an arthropod or a microorganism. Overall, it was suggested that changing management strategies to improve water availability and soil health might help with tree resilience. Additionally, regular monitoring and research, along with improving tree species selection and implementing biological and chemical control methods, can help prevent or slow down tree decline. Increasing awareness and adopting preventative approaches could help to extend the lifespan of street and park trees in urban environments and mitigate some of the biological threats, especially considering the challenges we may be facing due to the changing climate.

Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learning - Marthe Mieling; Mushfa Yousuf; Nico Bunzeck; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; Susan Landau; Monica Rivera-Mindt; Leslie M. Shaw; Edward B. Lee; et al. (GeroScience, 2025)
Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer’s disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, those with mild cognitive impairment (MCI) and AD dementia patients. Features included structural MRI, CSF status, demographics, and genetic data. Analyses comprised one cross-sectional multi-class classification (CN vs. MCI vs. AD dementia, n = 568) and two longitudinal binary-class classifications (CN-to-MCI converters vs. CN stable, n = 92; MCI-to-AD converters vs. MCI stable, n = 378). All classifications achieved 70–77% accuracy and 61–83% precision. Key features were CSF status, hippocampal volume, entorhinal thickness, and amygdala volume, with a clear dissociation: hippocampal properties contributed to the conversion to MCI, while the entorhinal cortex characterized the conversion to AD dementia. The findings highlight explainable, trajectory-specific insights into AD progression.