Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
Browse
11 results
Search Results
Item Predicting resilience of migratory birds to environmental change.(National Academy of Sciences, 2024-05-07) Lisovski S; Hoye BJ; Conklin JR; Battley PF; Fuller RA; Gosbell KB; Klaassen M; Benjamin Lee C; Murray NJ; Bauer S; Kareiva PThe pace and scale of environmental change represent major challenges to many organisms. Animals that move long distances, such as migratory birds, are especially vulnerable to change since they need chains of intact habitat along their migratory routes. Estimating the resilience of such species to environmental changes assists in targeting conservation efforts. We developed a migration modeling framework to predict past (1960s), present (2010s), and future (2060s) optimal migration strategies across five shorebird species (Scolopacidae) within the East Asian-Australasian Flyway, which has seen major habitat deterioration and loss over the last century, and compared these predictions to empirical tracks from the present. Our model captured the migration strategies of the five species and identified the changes in migrations needed to respond to habitat deterioration and climate change. Notably, the larger species, with single or few major stopover sites, need to establish new migration routes and strategies, while smaller species can buffer habitat loss by redistributing their stopover areas to novel or less-used sites. Comparing model predictions with empirical tracks also indicates that larger species with the stronger need for adaptations continue to migrate closer to the optimal routes of the past, before habitat deterioration accelerated. Our study not only quantifies the vulnerability of species in the face of global change but also explicitly reveals the extent of adaptations required to sustain their migrations. This modeling framework provides a tool for conservation planning that can accommodate the future needs of migratory species.Item Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures.(Springer Nature Limited, 2021-05-25) Hunter LB; Baten A; Haskell MJ; Langford FM; O'Connor C; Webster JR; Stafford KSleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of this study was to determine if data from more easily applied non-invasive devices assessing neck muscle activity and heart rate (HR) alone could be used to differentiate between sleep stages. We developed, trained, and compared two machine learning models using neural networks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle activity and HR data collected from 12 cows in two environments. Using k-fold cross validation we compared the success of the models to the gold standard, Polysomnography (PSG). Overall, both models learned from the data and were able to accurately predict sleep stages from HR and muscle activity alone with classification accuracy in the range of similar human models. Further research is required to validate the models with a larger sample size, but the proposed methodology appears to give an accurate representation of sleep stages in cattle and could consequentially enable future sleep research into conditions affecting cow sleep and welfare.Item A proposed framework to establish in vitro-in vivo relationships using gastric digestion models for food research(The Royal Society of Chemistry, 2024-10-21) Nadia J; Roy D; Montoya CA; Singh H; Acevedo-Fani A; Bornhorst GMIn vitro digestion methods have been utilized in food research to reduce in vivo studies. Although previous studies have related in vitro and in vivo data, there is no consensus on how to establish an in vitro-in vivo relationship (IVIVR) for food digestion. A framework that serves as a tool to evaluate the utility and limitations of in vitro approaches in simulating in vivo processes is proposed to develop IVIVRs for food digestion, with a focus on the gastric phase as the main location of food structural breakdown during digestion. The IVIVR consists of three quantitative levels (A, B, and C) and a qualitative level (D), which relate gastric digestion kinetic data on a point-to-point basis, parameters derived from gastric digestion kinetic data, in vitro gastric digestion parameters with in vivo absorption or appearance parameters, and in vitro and in vivo trends, respectively. Level A, B, and C IVIVRs can be used to statistically determine the agreement between in vitro and in vivo data. Level A and B IVIVRs can be utilized further evaluate the accuracy of the in vitro approach to mimic in vivo processes. To exemplify the utilization of this framework, case studies are provided using previously published static and dynamic gastric in vitro digestion data and in vivo animal study data. Future food digestion studies designed to establish IVIVRs should be conducted to refine and improve the current framework, and to improve in vitro digestion approaches to better mimic in vivo phenomena.Item Impact of infectious diseases on wild bovidae populations in Thailand: insights from population modelling and disease dynamics.(The Royal Society, 2024-07-03) Horpiencharoen W; Marshall JC; Muylaert RL; John RS; Hayman DTSThe wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times in a model gaur (Bos gaurus) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, haemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.Item Mathematical modelling supports the existence of a threshold hydrogen concentration and media-dependent yields in the growth of a reductive acetogen.(Springer Nature Limited, 2020-05-01) Smith NW; Shorten PR; Altermann E; Roy NC; McNabb WCThe bacterial production of acetate via reductive acetogenesis along the Wood-Ljungdahl metabolic pathway is an important source of this molecule in several environments, ranging from industrial bioreactors to the human gastrointestinal tract. Here, we contributed to the study of reductive acetogens by considering mathematical modelling techniques for the prediction of bacterial growth and acetate production. We found that the incorporation of a hydrogen uptake concentration threshold into the models improves their predictions and we calculated this threshold as 86.2 mM (95% confidence interval 6.1-132.6 mM). Monod kinetics and first-order kinetics models, with the inclusion of two candidate threshold terms or reversible Michaelis-Menten kinetics, were compared to experimental data and the optimal formulation for predicting both growth and metabolism was found. The models were then used to compare the efficacy of two growth media for acetogens. We found that the recently described general acetogen medium was superior to the DSMZ medium in terms of unbiased estimation of acetogen growth and investigated the contribution of yeast extract concentration to acetate production and bacterial growth in culture. The models and their predictions will be useful to those studying both industrially and environmentally relevant reductive acetogenesis and allow for straightforward adaptation to similar cases with different organisms.Item Modelling Lassa virus dynamics in West African Mastomys natalensis and the impact of human activities.(The Royal Society, 2024-07-24) John RS; Fatoyinbo HO; Hayman DTSLassa fever is a West African rodent-borne viral haemorrhagic fever that kills thousands of people a year, with 100 000 to 300 000 people a year probably infected by Lassa virus (LASV). The main reservoir of LASV is the Natal multimammate mouse, Mastomys natalensis. There is reported asynchrony between peak infection in the rodent population and peak Lassa fever risk among people, probably owing to differing seasonal contact rates. Here, we developed a susceptible-infected-recovered ([Formula: see text])-based model of LASV dynamics in its rodent host, M. natalensis, with a persistently infected class and seasonal birthing to test the impact of changes to seasonal birthing in the future owing to climate and land use change. Our simulations suggest shifting rodent birthing timing and synchrony will alter the peak of viral prevalence, changing risk to people, with viral dynamics mainly stable in adults and varying in the young, but with more infected individuals. We calculate the time-average basic reproductive number, [Formula: see text], for this infectious disease system with periodic changes to population sizes owing to birthing using a time-average method and with a sensitivity analysis show four key parameters: carrying capacity, adult mortality, the transmission parameter among adults and additional disease-induced mortality impact the maintenance of LASV in M. natalensis most, with carrying capacity and adult mortality potentially changeable owing to human activities and interventions.Item Examination of hydrogen cross-feeders using a colonic microbiota model(BioMed Central Ltd, 2021-12) Smith NW; Shorten PR; Altermann E; Roy NC; McNabb WCBACKGROUND: Hydrogen cross-feeding microbes form a functionally important subset of the human colonic microbiota. The three major hydrogenotrophic functional groups of the colon: sulphate-reducing bacteria (SRB), methanogens and reductive acetogens, have been linked to wide ranging impacts on host physiology, health and wellbeing. RESULTS: An existing mathematical model for microbial community growth and metabolism was combined with models for each of the three hydrogenotrophic functional groups. The model was further developed for application to the colonic environment via inclusion of responsive pH, host metabolite absorption and the inclusion of host mucins. Predictions of the model, using two existing metabolic parameter sets, were compared to experimental faecal culture datasets. Model accuracy varied between experiments and measured variables and was most successful in predicting the growth of high relative abundance functional groups, such as the Bacteroides, and short chain fatty acid (SCFA) production. Two versions of the colonic model were developed: one representing the colon with sequential compartments and one utilising a continuous spatial representation. When applied to the colonic environment, the model predicted pH dynamics within the ranges measured in vivo and SCFA ratios comparable to those in the literature. The continuous version of the model simulated relative abundances of microbial functional groups comparable to measured values, but predictions were sensitive to the metabolic parameter values used for each functional group. Sulphate availability was found to strongly influence hydrogenotroph activity in the continuous version of the model, correlating positively with SRB and sulphide concentration and negatively with methanogen concentration, but had no effect in the compartmentalised model version. CONCLUSIONS: Although the model predictions compared well to only some experimental measurements, the important features of the colon environment included make it a novel and useful contribution to modelling the colonic microbiota.Item Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach(Elsevier Inc, 2018-03-29) Perez-Acle T; Fuenzalida I; Martin AJM; Santibañez R; Avaria R; Bernardin A; Bustos AM; Garrido D; Dushoff J; Liu JHComputational simulation is a widely employed methodology to study the dynamic behavior of complex systems. Although common approaches are based either on ordinary differential equations or stochastic differential equations, these techniques make several assumptions which, when it comes to biological processes, could often lead to unrealistic models. Among others, model approaches based on differential equations entangle kinetics and causality, failing when complexity increases, separating knowledge from models, and assuming that the average behavior of the population encompasses any individual deviation. To overcome these limitations, simulations based on the Stochastic Simulation Algorithm (SSA) appear as a suitable approach to model complex biological systems. In this work, we review three different models executed in PISKaS: a rule-based framework to produce multiscale stochastic simulations of complex systems. These models span multiple time and spatial scales ranging from gene regulation up to Game Theory. In the first example, we describe a model of the core regulatory network of gene expression in Escherichia coli highlighting the continuous model improvement capacities of PISKaS. The second example describes a hypothetical outbreak of the Ebola virus occurring in a compartmentalized environment resembling cities and highways. Finally, in the last example, we illustrate a stochastic model for the prisoner's dilemma; a common approach from social sciences describing complex interactions involving trust within human populations. As whole, these models demonstrate the capabilities of PISKaS providing fertile scenarios where to explore the dynamics of complex systems.Item A multi-objective genetic algorithm to find active modules in multiplex biological networks(PLOS, 2021-08-30) Novoa-Del-Toro EM; Mezura-Montes E; Vignes M; Térézol M; Magdinier F; Tichit L; Baudot A; Jensen PThe identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease.Item Inflammation and the Association of Vitamin D and Depressive Symptomatology.(MDPI (Basel, Switzerland), 2021-06) Dogan-Sander E; Mergl R; Willenberg A; Baber R; Wirkner K; Riedel-Heller SG; Röhr S; Schmidt FM; Schomerus G; Sander CDepression and vitamin D deficiency are major public health problems. The existing literature indicates the complex relationship between depression and vitamin D. The purpose of this study was to examine whether this relationship is moderated or mediated by inflammation. A community sample (n = 7162) from the LIFE-Adult-Study was investigated, for whom depressive symptoms were assessed via the German version of CES-D scale and serum 25-hydroxyvitamin D (25(OH)D) levels and inflammatory markers (IL-6 and CRP levels, WBC count) were quantified. Mediation analyses were performed using Hayes’ PROCESS macro and regression analyses were conducted to test moderation effects. There was a significant negative correlation between CES-D and 25(OH)D, and positive associations between inflammatory markers and CES-D scores. Only WBC partially mediated the association between 25(OH)D levels and depressive symptoms both in a simple mediation model (ab: −0.0042) and a model including covariates (ab: −0.0011). None of the inflammatory markers showed a moderation effect on the association between 25(OH)D levels and depressive symptoms. This present work highlighted the complex relationship between vitamin D, depressive symptoms and inflammation. Future studies are needed to examine the effect of vitamin D supplementation on inflammation and depressive symptomatology for causality assessment.