Root Characteristics Vary with Depth Across Four Lowland Seasonal Tropical Forests - Amanda L. Cordeiro; Daniela F. Cusack; Lee H. Dietterich; William C. Hockaday; Karis J. McFarlane; Vinothan Sivapalan; Alexandra Hedgpeth; Avishesh Neupane; Lily Colburn; Weronika Konwent; Gabriel Oppler; Jacqueline C. Reu; et al. (Ecosystems, 2024)

Tropical root responses to global changes: A synthesis - Daniela Yaffar; Laynara F. Lugli; Michelle Y. Wong; Richard J. Norby; Shalom D. Addo‐Danso; Marie Arnaud; Amanda L. Cordeiro; Lee H. Dietterich; Milton H. Diaz‐Toribio; Ming Y. Lee; Om Prakash Ghimire; Chris M. Smith‐Martin; et al. (Global Change Biology, 2024)
Tropical ecosystems face escalating global change. These shifts can disrupt tropical forests' carbon (C) balance and impact root dynamics. Since roots perform essential functions such as resource acquisition and tissue protection, root responses can inform about the strategies and vulnerabilities of ecosystems facing present and future global changes. However, root trait dynamics are poorly understood, especially in tropical ecosystems. We analyzed existing research on tropical root responses to key global change drivers: warming, drought, flooding, cyclones, nitrogen (N) deposition, elevated (e) CO 2 , and fires. Based on tree species‐ and community‐level literature, we obtained 266 root trait observations from 93 studies across 24 tropical countries. We found differences in the proportion of root responsiveness to global change among different global change drivers but not among root categories. In particular, we observed that tropical root systems responded to warming and eCO 2 by increasing root biomass in species‐scale studies. Drought increased the root: shoot ratio with no change in root biomass, indicating a decline in aboveground biomass. Despite N deposition being the most studied global change driver, it had some of the most variable effects on root characteristics, with few predictable responses. Episodic disturbances such as cyclones, fires, and flooding consistently resulted in a change in root trait expressions, with cyclones and fires increasing root production, potentially due to shifts in plant community and nutrient inputs, while flooding changed plant regulatory metabolisms due to low oxygen conditions. The data available to date clearly show that tropical forest root characteristics and dynamics are responding to global change, although in ways that are not always predictable. This synthesis indicates the need for replicated studies across root characteristics at species and community scales under different global change factors.

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.

Using the Red Band Difference Algorithm to Detect and Monitor a Karenia spp. Bloom Off the South Coast of Ireland, June 2019 - Catherine Jordan; Caroline Cusack; Michelle C. Tomlinson; Andrew Meredith; Ryan McGeady; Rafael Salas; Clynton Gregory; Peter L. Croot (Frontiers in Marine Science, 2021)
During the months of May, June, July and August 2019 the Red Band Difference algorithm was tested over Irish waters to assess its suitability for the Irish harmful algal bloom alert system. Over the 4 weeks of June an extensive localised surface phytoplankton bloom formed in the Celtic Sea, south of Ireland. Satellite imagery from the Sentinel-3a’s Ocean and Land Colour Instrument, processed using the Red Band Difference algorithm detected the bloom in surface shelf waters and helped monitor its movement. Daily satellite images indicated that the bloom appeared at the sea surface on the 2nd June 2019 and peaked in size and surface abundance in offshore shelf waters within 4 weeks, remnants remained at the surface into July. A particle tracking approach was used to replicate oceanic circulation patterns in the vicinity of the observed algal bloom and estimate its trajectory. The initial horizontal distribution of particles in the tracking model were based on a satellite imagery polygon of the bloom when it first appeared in surface waters. Good agreement was observed between satellite imagery of the bloom and the particle tracking model. In situ sampling efforts from a research cruise and the national inshore phytoplankton monitoring programme confirmed that Karenia mikimotoi was the causative organism of the bloom. This pilot study shows great potential to use the Red Band Difference algorithm in the existing Irish harmful algal bloom alert system. In addition, satellite ocean colour data combined with particle tracking model estimates can be a useful tool to monitor high biomass harmful algal bloom forming species, such as Karenia mikimotoi , in surface coastal waters around Ireland and elsewhere.

The Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP): A Platform for Integrated Multidisciplinary Ocean Science - Bernadette M. Sloyan; Rik Wanninkhof; Martin Kramp; Gregory C. Johnson; Lynne D. Talley; Toste Tanhua; Elaine McDonagh; Caroline Cusack; Eleanor O’Rourke; Evin McGovern; Katsuro Katsumata; Steve Diggs; et al. (Frontiers in Marine Science, 2019)

Evolving and Sustaining Ocean Best Practices and Standards for the Next Decade - Jay Pearlman; Mark Bushnell; Laurent Coppola; Johannes Karstensen; Pier Luigi Buttigieg; Francoise Pearlman; Pauline Simpson; Michele Barbier; Frank E. Muller-Karger; Cristian Munoz-Mas; Peter Pissierssens; Cyndy Chandler; et al. (Frontiers in Marine Science, 2019)

Anniston community health survey: Follow-up and dioxin analyses (ACHS-II)—methods - Linda S. Birnbaum; N. D. Dutton; C. Cusack; S. T. Mennemeyer; M. Pavuk (Environmental Science and Pollution Research, 2016)

Ocean modelling for aquaculture and fisheries in Irish waters - T. Dabrowski; K. Lyons; C. Cusack; G. Casal; A. Berry; G. D. Nolan (Ocean Science, 2016)
The Marine Institute, Ireland, runs a suite of operational regional and coastal ocean models. Recent developments include several tailored products that focus on the key needs of the Irish aquaculture sector. In this article, an overview of the products and services derived from the models are presented. The authors give an overview of a shellfish model developed in-house and that was designed to predict the growth, the physiological interactions with the ecosystem, and the level of coliform contamination of the blue mussel. As such, this model is applicable in studies on the carrying capacity of embayments, assessment of the impacts of pollution on aquaculture grounds, and the determination of shellfish water classes. Further services include the assimilation of the model-predicted shelf water movement into a new harmful algal bloom alert system used to inform end users of potential toxic shellfish events and high biomass blooms that include fish-killing species. Models are also used to identify potential sites for offshore aquaculture, to inform studies of potential cross-contamination in farms from the dispersal of planktonic sea lice larvae and other pathogens that can infect finfish, and to provide modelled products that underpin the assessment and advisory services on the sustainable exploitation of the resources of marine fisheries. This paper demonstrates that ocean models can provide an invaluable contribution to the sustainable blue growth of aquaculture and fisheries.

Refined radiation hybrid map of mouse Chromosome 17 - Leonard C. Schalkwyk; Michael Weiher; Michelle Kirby; Brian Cusack; Heinz Himmelbauer; Hans Lehrach (Mammalian Genome, 1998)

Effects of media composition on substrate removal by pure and mixed bacterial cultures - C. P. Leslie Grady; Leslie Cordone; Laura Cusack (Biodegradation, 1993)