Multi-Scale Simulation Modeling for Prevention and Public Health Management of Diabetes in Pregnancy and Sequelae

Diabetes in pregnancy (DIP) is an increasing public health priority in the Australian Capital Territory, particularly due to its impact on risk for developing Type 2 diabetes. While earlier diagnostic screening results in greater capacity for early detection and treatment, such benefits must be balanced with the greater demands this imposes on public health services. To address such planning challenges, a multi-scale hybrid simulation model of DIP was built to explore the interaction of risk factors and capture the dynamics underlying the development of DIP. The impact of interventions on health outcomes at the physiological, health service and population level is measured. Of particular central significance in the model is a compartmental model representing the underlying physiological regulation of glycemic status based on beta-cell dynamics and insulin resistance. The model also simulated the dynamics of continuous BMI evolution, glycemic status change during pregnancy and diabetes classification driven by the individual-level physiological model. We further modeled public health service pathways providing diagnosis and care for DIP to explore the optimization of resource use during service delivery. The model was extensively calibrated against empirical data.Comment: 10 pages, SBP-BRiMS 201

Genome-enabled phylogeographic investigation of the quarantine pathogen Ralstonia solanacearum race 3 biovar 2 and screening for sources of resistance against its core effectors.

Phylogeographic studies inform about routes of pathogen dissemination and are instrumental for improving import/export controls. Genomes of seventeen isolates of the bacterial wilt and potato brown rot pathogen Ralstonia solanacearum race 3 biovar 2 (R3bv2), a select agent in the USA, were thus analyzed to get insight into the phylogeography of this pathogen. Thirteen of fourteen isolates from Europe, Africa, and Asia were found to belong to a single clonal lineage while isolates from South America were genetically diverse and carried ancestral alleles at the analyzed genomic loci consistent with a South American origin of R3bv2. The R3bv2 isolates share a core repertoire of thirty-one type III-secreted effector genes representing excellent candidates to be targeted with resistance genes in breeding programs to develop durable disease resistance. Towards this goal, 27 R3bv2 effectors were tested in eggplant, tomato, pepper, tobacco, and lettuce for induction of a hypersensitive-like response indicative of recognition by cognate resistance receptors. Fifteen effectors, eight of them core effectors, triggered a response in one or more plant species. These genotypes may harbor resistance genes that could be identified and mapped, cloned and expressed in tomato or potato, for which sources of genetic resistance to R3bv2 are extremely limited.National Science Foundatio

Extension of the bayesian alphabet for genomic selection

<p>Abstract</p> <p>Background</p> <p>Two Bayesian methods, BayesC<it>π </it>and BayesD<it>π</it>, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability <it>π </it>that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.</p> <p>Results</p> <p>Estimates of <it>π </it>from BayesC<it>π</it>, in contrast to BayesD<it>π</it>, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesC<it>π </it>than for BayesD<it>π</it>, and longest for our implementation of BayesA.</p> <p>Conclusions</p> <p>Collectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesC<it>π </it>has merit for routine applications.</p

Interrogation of an Enzyme Library Reveals the Catalytic Plasticity of Naturally Evolved [4+2] Cyclases

\ua9 2023 The Authors. ChemBioChem published by Wiley-VCH GmbH.Stereoselective carbon-carbon bond forming reactions are quintessential transformations in organic synthesis. One example is the Diels-Alder reaction, a [4+2] cycloaddition between a conjugated diene and a dienophile to form cyclohexenes. The development of biocatalysts for this reaction is paramount for unlocking sustainable routes to a plethora of important molecules. To obtain a comprehensive understanding of naturally evolved [4+2] cyclases, and to identify hitherto uncharacterised biocatalysts for this reaction, we constructed a library comprising forty-five enzymes with reported or predicted [4+2] cycloaddition activity. Thirty-one library members were successfully produced in recombinant form. In vitro assays employing a synthetic substrate incorporating a diene and a dienophile revealed broad-ranging cycloaddition activity amongst these polypeptides. The hypothetical protein Cyc15 was found to catalyse an intramolecular cycloaddition to generate a novel spirotetronate. The crystal structure of this enzyme, along with docking studies, establishes the basis for stereoselectivity in Cyc15, as compared to other spirotetronate cyclases

Using the Pareto principle in genome-wide breeding value estimation

Genome-wide breeding value (GWEBV) estimation methods can be classified based on the prior distribution assumptions of marker effects. Genome-wide BLUP methods assume a normal prior distribution for all markers with a constant variance, and are computationally fast. In Bayesian methods, more flexible prior distributions of SNP effects are applied that allow for very large SNP effects although most are small or even zero, but these prior distributions are often also computationally demanding as they rely on Monte Carlo Markov chain sampling. In this study, we adopted the Pareto principle to weight available marker loci, i.e., we consider that x% of the loci explain (100 - x)% of the total genetic variance. Assuming this principle, it is also possible to define the variances of the prior distribution of the 'big' and 'small' SNP. The relatively few large SNP explain a large proportion of the genetic variance and the majority of the SNP show small effects and explain a minor proportion of the genetic variance. We name this method MixP, where the prior distribution is a mixture of two normal distributions, i.e. one with a big variance and one with a small variance. Simulation results, using a real Norwegian Red cattle pedigree, show that MixP is at least as accurate as the other methods in all studied cases. This method also reduces the hyper-parameters of the prior distribution from 2 (proportion and variance of SNP with big effects) to 1 (proportion of SNP with big effects), assuming the overall genetic variance is known. The mixture of normal distribution prior made it possible to solve the equations iteratively, which greatly reduced computation loads by two orders of magnitude. In the era of marker density reaching million(s) and whole-genome sequence data, MixP provides a computationally feasible Bayesian method of analysis

Best Linear Unbiased Prediction of Genomic Breeding Values Using a Trait-Specific Marker-Derived Relationship Matrix

With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest

The role of positive goal engagement in increased mental well-being among individuals with chronic non-cancer pain.

Individuals with chronic pain commonly report significant functional impairment and reduced quality of life. Despite this, little is known about psychological processes and mechanisms underpinning enhancements in well-being within this population. The study aimed to investigate whether (1) increased levels of pain intensity and interference were associated with lower levels of mental well-being, (2) increased positive goal engagement was associated with higher levels of mental well-being and (3) whether the relationships between pain characteristics and mental well-being were mediated by increased positive goal engagement. A total of 586 individuals with chronic pain participated in the cross-sectional, online study. Participants completed self-report measures to assess pain intensity and interference, mental well-being and goal motivation variables. Results showed that pain interference and positive goal engagement were associated with mental well-being. Moreover, the relationship between pain interference and mental well-being was partially mediated by positive goal engagement. The results provide tentative evidence for the protective role of positive goal engagement in enabling individuals with chronic pain to maintain a sense of mental well-being. The study develops the biopsychosocial model of chronic pain by examining the roles and relationships of relevant yet previously unexplored psychological constructs. The promotion of mental well-being through the enhancement of positive goal engagement is discussed, offering a platform for further research and clinical interventions

A Validated Genome Wide Association Study to Breed Cattle Adapted to an Environment Altered by Climate Change

Continued production of food in areas predicted to be most affected by climate change, such as dairy farming regions of Australia, will be a major challenge in coming decades. Along with rising temperatures and water shortages, scarcity of inputs such as high energy feeds is predicted. With the motivation of selecting cattle adapted to these changing environments, we conducted a genome wide association study to detect DNA markers (single nucleotide polymorphisms) associated with the sensitivity of milk production to environmental conditions. To do this we combined historical milk production and weather records with dense marker genotypes on dairy sires with many daughters milking across a wide range of production environments in Australia. Markers associated with sensitivity of milk production to feeding level and sensitivity of milk production to temperature humidity index on chromosome nine and twenty nine respectively were validated in two independent populations, one a different breed of cattle. As the extent of linkage disequilibrium across cattle breeds is limited, the underlying causative mutations have been mapped to a small genomic interval containing two promising candidate genes. The validated marker panels we have reported here will aid selection for high milk production under anticipated climate change scenarios, for example selection of sires whose daughters will be most productive at low levels of feeding

The merger that led to the formation of the Milky Way's inner stellar halo and thick disk

The assembly process of our Galaxy can be retrieved using the motions and chemistry of individual stars. Chemo-dynamical studies of the nearby halo have long hinted at the presence of multiple components such as streams, clumps, duality and correlations between the stars' chemical abundances and orbital parameters. More recently, the analysis of two large stellar surveys have revealed the presence of a well-populated chemical elemental abundance sequence, of two distinct sequences in the colour-magnitude diagram, and of a prominent slightly retrograde kinematic structure all in the nearby halo, which may trace an important accretion event experienced by the Galaxy. Here report an analysis of the kinematics, chemistry, age and spatial distribution of stars in a relatively large volume around the Sun that are mainly linked to two major Galactic components, the thick disk and the stellar halo. We demonstrate that the inner halo is dominated by debris from an object which at infall was slightly more massive than the Small Magellanic Cloud, and which we refer to as Gaia-Enceladus. The stars originating in Gaia-Enceladus cover nearly the full sky, their motions reveal the presence of streams and slightly retrograde and elongated trajectories. Hundreds of RR Lyrae stars and thirteen globular clusters following a consistent age-metallicity relation can be associated to Gaia-Enceladus on the basis of their orbits. With an estimated 4:1 mass-ratio, the merger with Gaia-Enceladus must have led to the dynamical heating of the precursor of the Galactic thick disk and therefore contributed to the formation of this component approximately 10 Gyr ago. These findings are in line with simulations of galaxy formation, which predict that the inner stellar halo should be dominated by debris from just a few massive progenitors.Comment: 19 pages, 8 figures. Published in Nature in the issue of Nov. 1st, 2018. This is the authors' version before final edit

Elevated CO2 degassing rates prevented the return of Snowball Earth during the Phanerozoic

The Cryogenian period (~720–635 Ma) is marked by extensive Snowball Earth glaciations. These have previously been linked to CO₂ draw-down, but the severe cold climates of the Cryogenian have never been replicated during the Phanerozoic despite similar, and sometimes more dramatic changes to carbon sinks. Here we quantify the total CO₂ input rate, both by measuring the global length of subduction zones in plate tectonic reconstructions, and by sea-level inversion. Our results indicate that degassing rates were anomalously low during the Late Neoproterozoic, roughly doubled by the Early Phanerozoic, and remained comparatively high until the Cenozoic. Our carbon cycle modelling identifies the Cryogenian as a unique period during which low surface temperature was more easily achieved, and shows that the shift towards greater CO₂ input rates after the Cryogenian helped prevent severe glaciation during the Phanerozoic. Such a shift appears essential for the development of complex animal life