Editorial: Aquaculture-aided approaches for conservation and sustainable fisheries - Matthew D. Taylor; Alistair Becker; Edward V. Camp; Luis Henríquez-Antipa; Neil R. Loneragan; Kai Lorenzen (Fisheries Research, 2026)

Lung function and inflammation in healthy young adults after 6.6 hours of 0.07 ppm ozone exposure - Edward R. Pennington; Jairus C. Pulczinski; Martin W. Case; Jacob S. Griffin; Neil E. Alexis; Ilona Jaspers; Carole Robinette; Ann Chelminski; Andrea Davis; Neena Miller; David B. Peden; Andrew J. Ghio; et al. (Environmental Research, 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

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.

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.

Environmental drivers of tropospheric bromine and mercury variability in coastal East Antarctica - Neil C. Page; Jenny A. Fisher; Stephen R. Wilson; Robyn Schofield; Robert G. Ryan; Sean Gribben; Andrew R. Klekociuk; Grant C. Edwards; Anthony Morrison (Atmospheric Environment, 2025)

Detrending tree-ring widths in closed-canopy forests for climate and disturbance history reconstructions - Daniel L. Druckenbrod; Edward R. Cook; Neil Pederson; Dario Martin-Benito (Dendrochronologia, 2024)

The Occurrence of Karenia species in mid-Atlantic coastal waters: Data from the Delmarva Peninsula, USA - Jennifer L. Wolny; Edward B. Whereat; Todd A. Egerton; Leah A. Gibala-Smith; John R. McKay; Judith M. O'Neil; Catherine E. Wazniak; Margaret R. Mulholland (Harmful Algae, 2024)

Determining the sustainability of legal wildlife trade - Alice Hughes; Mark Auliya; Sandra Altherr; Brett Scheffers; Jordi Janssen; Vincent Nijman; Chris R. Shepherd; Neil D'Cruze; Emerson Sy; David P. Edwards (Journal of Environmental Management, 2023)

PALEO‐PGEM‐Series : A spatial time series of the global climate over the last 5 million years ( Plio‐Pleistocene ) - Elisa Barreto; Philip B. Holden; Neil R. Edwards; Thiago F. Rangel (Global Ecology and Biogeography, 2023)
Motivation Climate change plays an important role in the generation and maintenance of biodiversity by driving processes such as diversification and range shifts. As a result, biodiversity patterns are often found to carry the imprints of palaeoclimatic changes. However, we still know little about the spatial and temporal variation in climate over the scale of millennia affecting eco‐evolutionary dynamics, mostly because of the scarcity of user‐friendly and freely available spatio‐temporal palaeoclimate series at such temporal scales. Here, we address this gap by presenting PALEO‐PGEM‐Series, a global spatio‐temporal dataset of the last 5 Myr, with 1 kyr resolution, spatially downscaled from emulations performed with the intermediate‐complexity atmosphere–ocean general circulation model PALEO‐PGEM. PALEO‐PGEM‐Series holds the potential to advance our understanding of the mechanisms behind the strong relationship between biodiversity and climate, a pressing need given projected biodiversity responses to anthropogenic climatic change. Main Types of Variables Contained Spatio‐temporal series of monthly temperature and precipitation and 17 derived bioclimatic variables over the Pliocene–Pleistocene, along with standard error estimates from multiple runs of the emulator. Spatial Location and Grain Global landmasses, at 1° × 1°. Time Period and Grain Last 5 Myr at 1000 year resolution. Major Taxa and Level of Measurement Not applicable. Software Format Tab‐delimited text files and accompanying R code to derive bioclimatic variables.

High summer temperatures are associated with poorer performance of underyearling Atlantic salmon ( Salmo salar ) in upland streams - Fionn R. Bernthal; Ben W. Seaman; Edward Rush; John D. Armstrong; Darryl McLennan; Keith H. Nislow; Neil B. Metcalfe (Journal of Fish Biology, 2023)
Future warming scenarios are predicted to result in an increased frequency of high, and potentially stressful, temperatures in aquatic ecosystems. Here we examined whether the performance of wild underyearling Atlantic salmon ( Salmo salar ) in Scottish streams stocked with identical egg densities was influenced by thermal stress. Biomass and density declined with degree hours exceeding 23°C, indicating apparent mortality or emigration as a possible result of exposure to high temperatures. These results strengthen the need for further action such as riparian tree planting to reduce stream summer temperatures.

Estimated life-history traits and movements of the Caribbean reef shark (Carcharhinus perezi) in The Bahamas based on tag-recapture data - Brendan S. Talwar; Darcy Bradley; Christopher Berry; Mark E. Bond; Ian A. Bouyoucos; Annabelle M. L. Brooks; Candace Y. A. Fields; Austin J. Gallagher; Tristan L. Guttridge; Annie E. Guttridge; Neil Hammerschlag; Ian Hamilton; et al. (Marine Biology, 2022)

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer - Kaitlin H. Wade; James Yarmolinsky; Edward Giovannucci; Sarah J. Lewis; Iona Y. Millwood; Marcus R. Munafò; Fleur Meddens; Kimberley Burrows; Joshua A. Bell; Neil M. Davies; Daniela Mariosa; Noora Kanerva; et al. (Cancer Causes & Control, 2022)
Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.

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.

The dangers of misrepresenting wildlife trade: response to Natusch et al. 2021 - David P. Edwards; Neil D'Cruze; Sandra Altherr; Alice Hughes; Jordi Janssen; Vincent Nijman; Stesha A. Pasachnik; Brett R. Scheffers; Chris R. Shepherd; Emerson Sy; Mark Auliya (Conservation Biology, 2021)

Riparian populations of minnesota reed canarygrass (Phalaris arundinacea) are most likely native, based on SNPs (DArTseqLD) - Andrzej K. Noyszewski; Neil O. Anderson; Alan G. Smith; Andrzej Kilian; Diana Dalbotten; Emi Ito; Anne Timm; Holly Pellerin; Barbora Kubátová; Tereza Kávová; Vojtěch Januš; Vladislav Čurn; et al. (Wetlands Ecology and Management, 2021)
The native vs. exotic status of reed canarygrass (RCG), a major invasive species of Minnesota wetlands, is unknown. The aim of this study was to investigate this native vs. exotic status to enhance its management. Genetic comparison of wild RCG populations from six Minnesota and six Czech Republic rivers was performed. A total of 2521 polymorphic SNP markers (single nucleotide polymorphisms) were used to evaluate 478 RCG samples across all collections. In the PCoA, all (n = 256) tested extant wild, riparian RCG genotypes from six Minnesota Rivers and six Czech Republic Rivers were genetically distinct, although some SNPs were common in both populations since they are the same species. DAPC analysis also resulted in the formation of two primary clusters separating the Minnesota Rivers and Czech Republic Rivers riparian samples, with little overlap; STRUCTURE analysis also supported this clustering with k = 4 groups as it separated the Czech Republic Rivers populations into three groups, along with Minnesota Rivers. The uniformity of PCoA, DAPC, STRUCTURE, and Evanno results indicates the distinct separation of Minnesota Rivers and Czech Republic Rivers populations. Portions of the genome (specific SNPs) are preserved or in common across continents, as indicated by STRUCTURE similarities. Nonetheless, overall significant SNP differences between the continents indicate that the Minnesota riparian populations are distinct enough from the European (Czech) collections to be delineated as native N. American RCG. PCoA of all the Minnesota RCG collections clustered Minnesota Rivers, Herbarium, Extant Herbarium, Research Field and Native Field collections together. STRUCTURE analysis (k = 2; Evanno) divided these Minnesota collections from the Commercial Field and Cultivars collections. There are two genetically distinct groups of RCG in Minnesota and since the Minnesota Rivers, the Research Field, the Native Field and pre-1930 herbaria collections clustered together, they are most likely native N. American types. Analysis of molecular variance (AMOVA) indicated that the genetic variation was more significant within, rather than among, the RCG populations. Native, historic herbaria types cluster together with all wild RCG river populations in Minnesota, all of which were distinct from those in Central Europe, suggesting native RCG type persistence in N. America. Also, cultivated forage types of RCG are distinct from wild RCG Minnesota river populations. The SNP genetic data shows that riparian Minnesota RCG populations are native. These data will facilitate future management strategies to control RCG as a native, but invasive, species.

SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States - Michael V. Cove; Roland Kays; Helen Bontrager; Claire Bresnan; Monica Lasky; Taylor Frerichs; Renee Klann; Thomas E. Lee; Seth C. Crockett; Anthony P. Crupi; Katherine C. B. Weiss; Helen Rowe; et al. (Ecology, 2021)
With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14‐week period (17 August–24 November of 2019). We sampled wildlife at 1,509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian’s eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the United States. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban–wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot‐usa, as will future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species‐specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.

Changes in gastrointestinal cell integrity after marathon running and exercise-associated collapse - Edward Walter; Oliver R. Gibson; Michael Stacey; Neil Hill; Iain T. Parsons; David Woods (European Journal of Applied Physiology, 2021)

Elevated carbon dioxide decreases the adverse effects of higher temperature and drought stress by mitigating oxidative stress and improving water status in Arabidopsis thaliana - Mohammad I. Abo Gamar; Anna Kisiala; R. J. Neil Emery; Edward C. Yeung; Sophia L. Stone; Mirwais M. Qaderi (Planta, 2019)

Characterisation of the effect of day length, and associated differences in dietary intake, on the gut microbiota of Soay sheep - Nadine A. Thomas; Andrea M. Olvera-Ramírez; Leticia Abecia; Clare L. Adam; Joan E. Edwards; Georgina F. Cox; Patricia A. Findlay; Elodie Destables; Tracy A. Wood; Neil R. McEwan (Archives of Microbiology, 2019)

Toward the Integrated Marine Debris Observing System - Nikolai Maximenko; Paolo Corradi; Kara Lavender Law; Erik Van Sebille; Shungudzemwoyo P. Garaba; Richard Stephen Lampitt; Francois Galgani; Victor Martinez-Vicente; Lonneke Goddijn-Murphy; Joana Mira Veiga; Richard C. Thompson; Christophe Maes; et al. (Frontiers in Marine Science, 2019)

Securing a Just Space for Small-Scale Fisheries in the Blue Economy - Philippa J. Cohen; Edward H. Allison; Neil L. Andrew; Joshua Cinner; Louisa S. Evans; Michael Fabinyi; Len R. Garces; Stephen J. Hall; Christina C. Hicks; Terry P. Hughes; Svein Jentoft; David J. Mills; et al. (Frontiers in Marine Science, 2019)

Multi-level emulation of complex climate model responses to boundary forcing data - Giang T. Tran; Kevin I. C. Oliver; Philip B. Holden; Neil R. Edwards; András Sóbester; Peter Challenor (Climate Dynamics, 2019)

Spatial reconstruction of Scottish summer temperatures from tree rings - Miloš Rydval; Björn E. Gunnarson; Neil J. Loader; Edward R. Cook; Daniel L. Druckenbrod; Rob Wilson (International Journal of Climatology, 2017)
A detailed understanding of past temporal patterns and spatial expression of temperature variations is important to place recent anthropogenic climate change into a longer term context. In order to fill the current gap in our understanding of northwest European temperature variability, point‐by‐point principal component regression was used to reconstruct a spatial field of 0.5° temperature grids across Scotland. A sequence of reconstructions utilizing several combinations of detrending and disturbance correction procedures, and a selection of tree‐ring parameters [including ring width ( RW ), maximum latewood density ( MXD ) and blue intensity ( BI )] was used in an evaluation of reconstruction skill. The high resolution of the reconstructed field serves also as a diagnostic tool to spatially assess the temperature reconstruction potential of local chronologies. Best reconstruction results, reaching calibration r 2 = 65.8% and verification r 2 = 63.7% in central Scotland over the 1901–1976 period, were achieved using disturbance‐corrected and signal‐free detrended RW chronologies merged with BI data after low‐pass (high‐pass) filtering the RW ( BI ) chronologies. Calibration and verification r 2 > 50% was attained for central, north and east Scotland, >40% in west and northwest, and >30% in southern Scotland with verification of nearly all grids showing some reconstruction skill. However, the full calibration potential of reconstructions outside central Scotland was reduced either due to residual disturbance trends undetected by the disturbance correction procedure or due to other climatic or non‐climatic factors which may have adversely affected the strength of the climate signal.

Worldwide impacts of climate change on energy for heating and cooling - Maryse Labriet; Santosh R. Joshi; Marc Vielle; Philip B. Holden; Neil R. Edwards; Amit Kanudia; Richard Loulou; Frédéric Babonneau (Mitigation and Adaptation Strategies for Global Change, 2015)

Cellular therapy in combination with cytokines improves survival in a xenograft mouse model of ovarian cancer - Susan B. Ingersoll; Sarfraz Ahmad; Hasina C. McGann; Robert K. Banks; Nicole M. Stavitzski; Milan Srivastava; Ghazanfar Ali; Neil J. Finkler; John R. Edwards; Robert W. Holloway (Molecular and Cellular Biochemistry, 2015)

A continental‐scale analysis of feral cat diet in Australia - Tim S. Doherty; Robert A. Davis; Eddie J. B. van Etten; Dave Algar; Neil Collier; Chris R. Dickman; Glenn Edwards; Pip Masters; Russell Palmer; Sue Robinson (Journal of Biogeography, 2015)
Aim Reducing the impacts of feral cats ( Felis catus ) is a priority for conservation managers across the globe, and success in achieving this aim requires a detailed understanding of the species’ ecology across a broad spectrum of climatic and environmental conditions. We reviewed the diet of the feral cat across Australia and on Australian territorial islands, seeking to identify biogeographical patterns in dietary composition and diversity, and use the results to consider how feral cats may best be managed. Location Australia and its territorial islands. Methods Using 49 published and unpublished data sets, we modelled trophic diversity and the consumption of eight food groups against latitude, longitude, mean temperature, precipitation, environmental productivity and climate‐habitat regions. Results We recorded 400 vertebrate species that feral cats feed on or kill in Australia, including 28 IUCN Red List species. We found evidence of continental‐scale prey‐switching from rabbits to small mammals, previously recorded only at the local scale. The consumption of arthropods, reptiles, rabbits, rodents and medium‐sized native mammals varied with different combinations of latitude, longitude, mean annual precipitation, temperature and environmental productivity. The frequency of rodents and dasyurids in cats’ diets increased as rabbit consumption decreased. Main conclusions The feral cat is an opportunistic, generalist carnivore that consumes a diverse suite of vertebrate prey across Australia. It uses a facultative feeding strategy, feeding mainly on rabbits when they are available, but switching to other food groups when they are not. Control programmes aimed at culling rabbits could potentially decrease the availability of a preferred food source for cats and then lead to greater predation pressure on native mammals. The interplay between cat diet and prey species diversity at a continental scale is complex, and thus cat management is likely to be necessary and most effective at the local landscape level.

Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME - Patricio Godoy; Nicola J. Hewitt; Ute Albrecht; Melvin E. Andersen; Nariman Ansari; Sudin Bhattacharya; Johannes Georg Bode; Jennifer Bolleyn; Christoph Borner; Jan Böttger; Albert Braeuning; Robert A. Budinsky; et al. (Archives of Toxicology, 2013)

No evidence for cumulative effects in a Dnmt3b hypomorph across multiple generations - Neil A. Youngson; Trevor Epp; Amity R. Roberts; Lucia Daxinger; Alyson Ashe; Edward Huang; Krystal L. Lester; Sarah K. Harten; Graham F. Kay; Timothy Cox; Jacqueline M. Matthews; Suyinn Chong; et al. (Mammalian Genome, 2013)

Clusters of genes encoding fructan biosynthesizing enzymes in wheat and barley - Bao-Lam Huynh; Diane E. Mather; Andreas W. Schreiber; John Toubia; Ute Baumann; Zahra Shoaei; Nils Stein; Ruvini Ariyadasa; James C. R. Stangoulis; James Edwards; Neil Shirley; Peter Langridge; et al. (Plant Molecular Biology, 2012)

Evaluation of Fluorescent Phosphatidylserine Substrates for the Aminophospholipid Flippase in Mammalian Cells - Bryan A. Smith; Edward J. O’Neil; Andrew J. Lampkins; James R. Johnson; Jung-Jae Lee; Erin L. Cole; Bradley D. Smith (Journal of Fluorescence, 2012)

Pacific Rim Forestry / Silviculture and Forest Ecology - Vishal Singh Negi; Archana Pal; Ratnesh Singh; Dulal Borthakur; Archana Pala; Vishal Singh Negi; Dulal Borthakur; Isabel Rushanaedy; Tyler Jones; Nicklos Dudley; Dulal Borthakur; Jannai Yafuso; et al. (Journal of Forestry, 2011)
Leucaena leucocephala (leucaena) is an important agroforestry tree of tropics and subtropics due to its protein rich foliages, ability to grow on poor soils, and high level of tolerance to various stresses including drought and diseases. We hypothesized that leucaena have a unique set of genes which confer tolerance to various abiotic and biotic stresses and these genes are either absent or not expressed in other legumes that are susceptible to these stress conditions. Interspecies suppression subtractive hybridization (iSSH) was performed to identify some unique genes from leucaena using cDNAs from leucaena and Acacia confusa (acacia) as the tester and driver, respectively. We identified 100 leucaena genes showing homology to various plant genes with known functions, of which 23 genes had homology to various stress-related proteins including chitinase, PR-10 protein, wound-stress protein, type-2 metallothionein, thaumatin-like protein, disease resistance response protein, cysteine proteinase, and kunitz trypsin inhibitor. We also identified 50 leucaena genes which either had homology to hypothetical proteins (HyPs) or had no homology at all. Putative functions of 15 HyPs were predicted using conserved domain and PSI-BLAST analyses. Linear motif analyses identified 17 HyPs with MAPK recognition motif, which is involved in signaling pathways, including stress responses. This study identified 23 known and some putative unique stress-related genes from leucaena which may serve as a useful source of stress-tolerance genes for tree and crop improvement in the future.

Precalibrating an intermediate complexity climate model - Neil R. Edwards; David Cameron; Jonathan Rougier (Climate Dynamics, 2011)

Poster abstracts - J. Ferrie; M. Shipley; F. Cappuccio; E. Brunner; M. Miller; M. Kumari; M. Marmot; A. Coenen; J. L. Castillo; F. Araya; G. Bustamante; L. Montecino; et al. (Sleep and Biological Rhythms, 2007)

The coupling of optimal economic growth and climate dynamics - Olivier Bahn; Laurent Drouet; Neil R. Edwards; Alain Haurie; Reto Knutti; Socrates Kypreos; Thomas F. Stocker; Jean-Philippe Vial (Climatic Change, 2006)

Uncertainties due to transport-parameter sensitivity in an efficient 3-D ocean-climate model - Neil R. Edwards; Robert Marsh (Climate Dynamics, 2005)

16S rDNA library-based analysis of ruminal bacterial diversity - Joan E. Edwards; Neil R. McEwan; Anthony J. Travis; R. John Wallace (Antonie van Leeuwenhoek, 2004)