Co-composting aquaculture sludge at full-scale: Two case studies evaluating process performance and compost quality - Christine Lepine; Abhinav Choudhury; Kaid Ryland; Jacob Bell; Christopher Good (Aquacultural Engineering, 2026)

Feasibility of integrating multi-vessel membrane biological reactors within a recirculating aquaculture system to treat and reuse discharged water - Curtis Crouse; John Davidson; Brian Vinci; Christine Lepine; Megan Murray; Christopher Good (Aquacultural Engineering, 2026)

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

SNAPSHOT USA 2024: Year 6 of the Coordinated National Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Daniel J. Herrera; Camilla Price; Phuong‐Thao Nguyen‐Nu; Katrina Adamczyk; Peter D. Alexander; David Allen; Jesse M. Alston; Diego F. Alvarado‐Serrano; et al. (Global Ecology and Biogeography, 2026)
Motivation SNAPSHOT USA is an annual, multi‐contributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2024 contains 377,427 records of camera trap image sequence data and 3127 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in 49 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1 August and 19 December in 2024. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2024 comprises two .csv files. The original data can be found within the SNAPSHOT USA 2024 project on the Wildlife Insights platform.

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.

Characteristics of aquaculture sludge produced from two different fish diets and its impact on biogas production - Abhinav Choudhury; Christine Lepine; Michaela Hersom; Christopher Good (Aquacultural Engineering, 2025)

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.

SNAPSHOT USA 2019–2023: The First Five Years of Data From a Coordinated Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Benjamin R. Goldstein; Christopher Pate; Paula Castiblanco; Maggie E. Abell; Jessie Adley; Briana Agenbroad; Adam A. Ahlers; Peter D. Alexander; et al. (Global Ecology and Biogeography, 2025)
Motivation SNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1st August and 29th December each year from 2019 to 2023. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform.

Evaluating the suitability of nitrate-nitrogen levels for post-smolt Atlantic salmon Salmo salar production in RAS with assistance from heart rate bio-loggers - John Davidson; Curtis Crouse; Christine Lepine; Christopher Good (Aquacultural Engineering, 2024)

Towards sustainable water disinfection with peracetic acid in aquaculture: A review - Dibo Liu; David L. Straus; Lars‐Flemming Pedersen; Christopher Good; Carlo C. Lazado; Thomas Meinelt (Reviews in Aquaculture, 2024)
Peracetic acid (PAA) has a long history as an efficacious and eco‐friendly disinfectant. It was first synthesised in 1902, and since then a wide range of applications has been developed in various industries. Aquaculture is a more recent industry wherein the potential of PAA is significant. As the global demand for sustainable development increases, there has likewise been growing interest in using PAA in aquaculture as an alternative to less environmentally friendly practices. PAA has no carcinogenic risk to humans (unlike formalin), has negligible harmful by‐products (unlike chlorine‐based disinfectants) and with appropriate precautions, the risks of causing severe human health damage is easier to control than ozone. Fish show strong physiological recovery and adaptation to PAA, whereas susceptible life stages of pathogens are highly vulnerable, enabling a safe and efficacious disinfection of the entire culture water and not the flow‐restricted disinfection by such processes as ultraviolet radiation or ozone. The effective concentration of PAA against many fish pathogens is usually below 2 mg L −1 , which is tolerable for most fish, and it has very low environmental risk due to rapid degradation. However, such degradation and the hydrodynamics in production‐scale aquaculture systems complicate the practical use of PAA. In this review, we summarise key results of safe concentrations of PAA and its effectiveness specifically for fish farmers. We also outline major difficulties and possible solutions for practical uses of PAA. We intend to bring global attention to this compound and inspire future possibilities for its sustainable use as a water disinfectant in aquaculture.

MortCam: An Artificial Intelligence-aided fish mortality detection and alert system for recirculating aquaculture - Rakesh Ranjan; Kata Sharrer; Scott Tsukuda; Christopher Good (Aquacultural Engineering, 2023)

Sprint cycling rate of torque development associates with strength measurement in trained cyclists - Shannon Connolly; Peter Peeling; Martyn J. Binnie; Paul S. R. Goods; Christopher Latella; Janet L. Taylor; Anthony J. Blazevich; Wouter P. Timmerman; Chris R. Abbiss (European Journal of Applied Physiology, 2023)
Purpose A cyclist’s rate of force/torque development (RFD/RTD) and peak force/torque can be measured during single-joint or whole-body isometric tests, or during cycling. However, there is limited understanding of the relationship between these measures, and of the mechanisms that contribute to each measure. Therefore, we examined the: (i) relationship between quadriceps central and peripheral neuromuscular function with RFD/RTD in isometric knee extension, isometric mid-thigh pull (IMTP), and sprint cycling; and (ii) relationship among RFD/RTD and peak force/torque between protocols. Methods Eighteen trained cyclists completed two familiarisation and two experimental sessions. Each session involved an isometric knee extension, IMTP, and sprint cycling protocol, where peak force/torque, average and peak RFD/RTD, and early (0–100 ms) and late (0–200 ms) RFD/RTD were measured. Additionally, measures of quadriceps central and peripheral neuromuscular function were assessed during the knee extension. Results Strong relationships were observed between quadriceps early EMG activity (EMG 50 /M) and knee extension RTD ( r or ρ = 0.51–0.65) and IMTP late RFD ( r = 0.51), and between cycling early or late RTD and peak twitch torque ( r or ρ = 0.70–0.75). Strong-to-very strong relationships were observed between knee extension, IMTP, and sprint cycling for peak force/torque, early and late RFD/RTD, and peak RFD/RTD ( r or ρ = 0.59–0.80). Conclusion In trained cyclists, knee extension RTD or IMTP late RFD are related to measures of quadriceps central neuromuscular function, while cycling RTD is related to measures of quadriceps peripheral neuromuscular function. Further, the strong associations among force/torque measures between tasks indicate a level of transferability across tasks.

Effects of image data quality on a convolutional neural network trained in-tank fish detection model for recirculating aquaculture systems - Rakesh Ranjan; Kata Sharrer; Scott Tsukuda; Christopher Good (Computers and Electronics in Agriculture, 2023)

Transformation and segregation of GFP fluorescence and glyphosate resistance in horseweed (Conyza canadensis) hybrids - Matthew D. Halfhill; Laura L. Good; Chhandak Basu; Jason Burris; Christopher L. Main; Thomas C. Mueller; C. Neal Stewart (Plant Cell Reports, 2007)

Transposable Element Orientation Bias in the Drosophila melanogaster Genome - Asher D. Cutter; Jeffrey M. Good; Christopher T. Pappas; Matthew A. Saunders; Dean M. Starrett; Travis J. Wheeler (Journal of Molecular Evolution, 2005)