Improved flux-surface parameterization through constrained nonlinear optimization

Parameterization of magnetic flux-surfaces is often used for magnetohydrodynamic stability analysis and microturbulence modeling in tokamaks. Shape parameters for such local parameterization of a (numerical) equilibrium are traditionally computed analytically using geometrically derived quantities. However, often the shape is approximated by the average of values for different sections of the flux-surface contour or a truncated series, which does not guarantee an optimal fit. Here, instead nonlinear least squares optimization is used to compute these parameters, with a weighted sum of squared error cost function that is robust to outliers. This method results in a lower total absolute error for both the parameterization of the flux-surface contour and the poloidal magnetic field density than current methods for several parameterizations based on the well-known "Miller geometry."Furthermore, rapid convergence of shape parameters is achieved, no approximate geometric measurements of the contour are needed, and the method is applicable to any analytical shape parameterization. Validation with local, linear gyrokinetic simulations using these optimized shape parameters showed reduced root mean square errors in both the growth rate and frequency spectra when compared with simulations based on numerical equilibria. In particular, the popular Turnbull-Miller parameterization benefits from this approach, extending its usability closer toward the last-closed flux-surface for cases with minor up-down asymmetry.</p

Available energy of trapped electrons in Miller tokamak equilibria

Available energy (\AE{}), which quantifies the maximum amount of thermal energy that may be liberated and converted into instabilities and turbulence, has shown to be a useful metric for predicting saturated energy fluxes in trapped-electron-mode-driven turbulence. Here, we calculate and investigate the \AE{} in the analytical tokamak equilibria introduced by \citet{Miller1998NoncircularModel}. The \AE{} of trapped electrons reproduces various trends also observed in experiments; negative shear, increasing Shafranov shift, vertical elongation, and negative triangularity can all be stabilising, as indicated by a reduction in \AE{}, although it is strongly dependent on the chosen equilibrium. Comparing \AE{} with saturated energy flux estimates from the \textsc{tglf} model, we find fairly good correspondence, showcasing that \AE{} can be useful to predict trends. We go on to investigate \AE{} and find that negative triangularity is especially beneficial in vertically elongated configurations with positive shear or low gradients. We furthermore extract a gradient threshold-like quantity from \AE{} and find that it behaves similarly to gyrokinetic gradient thresholds: it tends to increase linearly with magnetic shear, and negative triangularity leads to an especially high threshold. We next optimise the device geometry for minimal \AE{} and find that the optimum is strongly dependent on equilibrium parameters, e.g. magnetic shear or pressure gradient. Investigating the competing effects of increasing the density gradient, the pressure gradient, and decreasing the shear, we find regimes that have steep gradients yet low \AE{}, and that such a regime is inaccessible in negative-triangularity tokamaks.Comment: 31 pages, 16 figure

Annotations for Rule-Based Models

The chapter reviews the syntax to store machine-readable annotations and describes the mapping between rule-based modelling entities (e.g., agents and rules) and these annotations. In particular, we review an annotation framework and the associated guidelines for annotating rule-based models of molecular interactions, encoded in the commonly used Kappa and BioNetGen languages, and present prototypes that can be used to extract and query the annotations. An ontology is used to annotate models and facilitate their description

Profiling of external metabolites during production of hantavirus nucleocapsid protein with recombinant Saccharomyces cerevisiae

Recombinant strains of Saccharomyces cerevisiae, producing hantavirus Puumala nucleocapsid protein for diagnostics and as a candidate vaccine were analyzed for uptake and excretion of intermediary metabolites during process optimization studies of fed-batch bioreactor cultures. Concentrations of glucose, maltose, galactose, pyruvate, acetaldehyde, ethanol, acetate, succinate and formaldehyde (used as a selection agent) were measured in the culture medium in order to find a metabolite pattern, indicative for the physiological state of the producer culture. When the inducer galactose was employed as a growth substrate, the metabolite profile of recombinant yeast cells was different from those of the non-recombinant original strain which excreted considerable amounts of metabolites with this substrate. In contrast, galactose-induced heterologous gene expression was indicated by the absence of excreted intermediary metabolites, except succinate. A model strain expressing a GFP fusion of hantavirus nucleocapsid protein differed in the excretion of metabolites from strains without GFP. In addition, the influence of alkali ions, employed for pH control is also demonstrated

Microbial catabolic activities are naturally selected by metabolic energy harvest rate

The fundamental trade-off between yield and rate of energy harvest per unit of substrate has been largely discussed as a main characteristic for microbial established cooperation or competition. In this study, this point is addressed by developing a generalized model that simulates competition between existing and not experimentally reported microbial catabolic activities defined only based on well-known biochemical pathways. No specific microbial physiological adaptations are considered, growth yield is calculated coupled to catabolism energetics and a common maximum biomass-specific catabolism rate (expressed as electron transfer rate) is assumed for all microbial groups. Under this approach, successful microbial metabolisms are predicted in line with experimental observations under the hypothesis of maximum energy harvest rate. Two microbial ecosystems, typically found in wastewater treatment plants, are simulated, namely: (i) the anaerobic fermentation of glucose and (ii) the oxidation and reduction of nitrogen under aerobic autotrophic (nitrification) and anoxic heterotrophic and autotrophic (denitrification) conditions. The experimentally observed cross feeding in glucose fermentation, through multiple intermediate fermentation pathways, towards ultimately methane and carbon dioxide is predicted. Analogously, two-stage nitrification (by ammonium and nitrite oxidizers) is predicted as prevailing over nitrification in one stage. Conversely, denitrification is predicted in one stage (by denitrifiers) as well as anammox (anaerobic ammonium oxidation). The model results suggest that these observations are a direct consequence of the different energy yields per electron transferred at the different steps of the pathways. Overall, our results theoretically support the hypothesis that successful microbial catabolic activities are selected by an overall maximum energy harvest rate

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol.Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the Computational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation

Detection and characteristics of microvascular obstruction in reperfused acute myocardial infarction using an optimized protocol for contrast-enhanced cardiovascular magnetic resonance imaging

Several cardiovascular magnetic resonance imaging (CMR) techniques are used to detect microvascular obstruction (MVO) after acute myocardial infarction (AMI). To determine the prevalence of MVO and gain more insight into the dynamic changes in appearance of MVO, we studied 84 consecutive patients with a reperfused AMI on average 5 and 104 days after admission, using an optimised single breath-hold 3D inversion recovery gradient echo pulse sequence (IR-GRE) protocol. Early MVO (2 min post-contrast) was detected in 53 patients (63%) and late MVO (10 min post-contrast) in 45 patients (54%; p = 0.008). The extent of MVO decreased from early to late imaging (4.3 ± 3.2% vs. 1.8 ± 1.8%, p < 0.001) and showed a heterogeneous pattern. At baseline, patients without MVO (early and late) had a higher left ventricular ejection fraction (LVEF) than patients with persistent late MVO (56 ± 7% vs. 48 ± 7%, p < 0.001) and LVEF was intermediate in patients with early MVO but late MVO disappearance (54 ± 6%). During follow-up, LVEF improved in all three subgroups but remained intermediate in patients with late MVO disappearance. This optimised single breath-hold 3D IR-GRE technique for imaging MVO early and late after contrast administration is fast, accurate and allows detection of patients with intermediate remodelling at follow-up

Active restoration accelerates the carbon recovery of human modified-tropical forests

More than half of all tropical forests are degraded by human impacts, leaving them threatened with conversion to agricultural plantations and risking substantial biodiversity and carbon losses. Restoration could accelerate recovery of aboveground carbon density (ACD), but adoption of restoration is constrained by cost and uncertainties over effectiveness. We report a long-term comparison of ACD recovery rates between naturally regenerating and actively restored logged tropical forests. Restoration enhanced decadal ACD recovery by more than 50%, from 2.9 to 4.4 megagrams per hectare per year. This magnitude of response, coupled with modal values of restoration costs globally, would require higher carbon prices to justify investment in restoration. However, carbon prices required to fulfill the 2016 Paris climate agreement [$40 to$80 (USD) per tonne carbon dioxide equivalent] would provide an economic justification for tropical forest restoration

A combined genome-wide linkage and association approach to find susceptibility loci for platelet function phenotypes in European American and African American families with coronary artery disease

<p>Abstract</p> <p>Background</p> <p>The inability of aspirin (ASA) to adequately suppress platelet aggregation is associated with future risk of coronary artery disease (CAD). Heritability studies of agonist-induced platelet function phenotypes suggest that genetic variation may be responsible for ASA responsiveness. In this study, we leverage independent information from genome-wide linkage and association data to determine loci controlling platelet phenotypes before and after treatment with ASA.</p> <p>Methods</p> <p>Clinical data on 37 agonist-induced platelet function phenotypes were evaluated before and after a 2-week trial of ASA (81 mg/day) in 1231 European American and 846 African American healthy subjects with a family history of premature CAD. Principal component analysis was performed to minimize the number of independent factors underlying the covariance of these various phenotypes. Multi-point sib-pair based linkage analysis was performed using a microsatellite marker set, and single-SNP association tests were performed using markers from the Illumina 1 M genotyping chip from deCODE Genetics, Inc. All analyses were performed separately within each ethnic group.</p> <p>Results</p> <p>Several genomic regions appear to be linked to ASA response factors: a 10 cM region in African Americans on chromosome 5q11.2 had several STRs with suggestive (p-value < 7 × 10^-4) and significant (p-value < 2 × 10^-5) linkage to post aspirin platelet response to ADP, and ten additional factors had suggestive evidence for linkage (p-value < 7 × 10^-4) to thirteen genomic regions. All but one of these factors were aspirin <it>response </it>variables. While the strength of genome-wide SNP association signals for factors showing evidence for linkage is limited, especially at the strict thresholds of genome-wide criteria (N = 9 SNPs for 11 factors), more signals were considered significant when the association signal was weighted by evidence for linkage (N = 30 SNPs).</p> <p>Conclusions</p> <p>Our study supports the hypothesis that platelet phenotypes in response to ASA likely have genetic control and the combined approach of linkage and association offers an alternative approach to prioritizing regions of interest for subsequent follow-up.</p

Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network

Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i) a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii) new quantitative predictions to be derived, (iii) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv) the key features of the model to be extracted that can be used as a guidance to design future experiments