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Ecosystem Controls on Methylmercury Production by Periphyton Biofilms in a Contaminated Stream: Implications for Predictive Modeling
Periphyton biofilms produce a substantial fraction of the overall monomethylmercury (MMHg) flux in East Fork Poplar Creek, an industrially contaminated, freshwater creek in Oak Ridge, Tennessee. We examined periphyton MMHg production across seasons, locations, and light conditions using mercury stable isotopes. Methylation and demethylation rate potentials (km, trans av and kd, trans av, respectively) were calculated using a transient availability kinetic model. Light exposure and season were significant predictors of km, trans av, with greater values in full light exposure and in the summer. Season, light exposure, and location were significant predictors of kd, trans av, which was highest in dark conditions, in the spring, and at the upstream location. Light exposure was the controlling factor for net MMHg production, with positive production for periphyton grown under full light exposure and net demethylation for periphyton grown in the dark. Ambient MMHg and km, trans av were significantly correlated. Transient availability rate potentials were 15 times higher for km and 9 times higher for kd compared to full availability rate potentials (km, full av and kd, full av) calculated at 1 d. No significant model for the prediction of km, full av or kd, full av could be constructed using light, season, and location. In addition, there were no significant differences among treatments for the full availability km, full av, kd, full av, or net MMHg calculated using the full availability rate potentials. km, full av was not correlated with ambient MMHg concentrations. The present results underscore the importance of applying transient availability kinetics to MMHg production data when estimating MMHg production potential and flux.
Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model
PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy of this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.
Predictive capability of a leaf optical meter for determining leaf pigment status during senescence
We conducted an experiment to assess the predictive capability of a leaf optical meter for determining leaf pigment status of Acer mono Maxim., A. ginnala Maxim., Quercus mongolica Fisch., and Cornus alba displaying a range of visually different leaf colors during senescence. Concentrations of chlorophyll (Chl) a, Chl b, and total Chl decreased while the concentration of carotenoids (Car) remained relatively static for all species as leaf development continued from maturity to senescence. C. alba exhibited the lowest average concentration of total Chl, Chl a, and Car, but the highest relative anthocyanin concentration, while Q. mongolica exhibited the highest total Chl, Chl b, and the lowest relative anthocyanin concentration. A. mono exhibited the highest Chl a and Car concentrations. The relationships between leaf pigments and the values measured by the optical meter generally followed an exponential function. The strongest relationships between leaf pigments and optical measurements were for A. mono, A. ginnala, and Q. mongolica (R 2 ranged from 0.64 to 0.95), and the worst relationship in C. alba (R 2 ranged from 0.13 to 0.67). Moreover, optical measurements were more strongly related to Chl a than to Chl b or total Chl. Optical measurements were not related to Car or relative anthocyanin concentrations. We predicted that weak relationships between leaf pigments and optical measurements would occur under very low Chl concentrations or under very high anthocyanin concentrations; however, these factors could not explain the weak relationship between Chl and optical measurements observed in C. alba. Overall, our results indicated that optical meter can accurately estimate leaf pigment concentrations during leaf senescence — a time when pigment concentrations are dynamically changing — but that the accuracy of the estimate varies across species. Future research should investigate how species-specific leaf traits may influence the accuracy of pigment estimates derived from optical meters.
Predicting CaO-(MgO)-Al2O3-SiO2 glass reactivity in alkaline environments from force field molecular dynamics simulations
In this investigation, force field-based molecular dynamics (MD) simulations have been employed to generate detailed structural representations for a range of amorphous quaternary CaO-MgO-Al{sub 2}O{sub 3}-SiO{sub 2} (CMAS) and ternary CaO-Al{sub 2}O{sub 3}-SiO{sub 2} (CAS) glasses. Comparison of the simulation results with select experimental X-ray and neutron total scattering and literature data reveals that the MD-generated structures have captured the key structural features of these CMAS and CAS glasses. Based on the MD-generated structural representations, we have developed two structural descriptors, specifically (i) average metal oxide dissociation energy (AMODE) and (ii) average self-diffusion coefficient (ASDC) of all the atoms at melting. Both structural descriptors are seen to more accurately predict the relative glass reactivity than the commonly used degree of depolymerization parameter, especially for the eight synthetic CAS glasses that span a wide compositional range. Hence these descriptors hold great promise for predicting CMAS and CAS glass reactivity in alkaline environments from compositional information.
Predictive coarse-graining
We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a probabilistic coarse-to-fine map. This corresponds to a directed probabilistic model where the coarse variables play the role of latent generators of the fine scale (all-atom) data. From an information-theoretic perspective, the framework proposed provides an improvement upon the relative entropy method and is capable of quantifying the uncertainty due to the information loss that unavoidably takes place during the coarse-graining process. Furthermore, it can be readily extended to a fully Bayesian model where various sources of uncertainties are reflected in the posterior of the model parameters. The latter can be used to produce not only point estimates of fine-scale reconstructions or macroscopic observables, but more importantly, predictive posterior distributions on these quantities. Predictive posterior distributions reflect the confidence of the model as a function of the amount of data and the level of coarse-graining. The issues of model complexity and model selection are seamlessly addressed by employing a hierarchical prior that favors the discovery of sparse solutions, revealing the most prominent features in the coarse-grained model. A flexible and parallelizable Monte Carlo – Expectation–Maximization (MC-EM) scheme is proposed for carrying out inference and learning tasks. A comparative assessment of the proposed methodology is presented for a lattice spin system and the SPC/E water model.
Development of multilayer perceptron networks for isothermal time temperature transformation prediction of U-Mo-X alloys
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Finite-element model to predict roll-separation force and defects during rolling of U-10Mo alloys
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Predicting zeolites’ stability during the corrosion of nuclear waste immobilization glasses: Comparison with glass corrosion experiments
During the long-term corrosion of nuclear waste glasses under nuclear waste disposal conditions, the precipitation of zeolitic phases has been linked to a delayed acceleration in glass corrosion (known as “Stage III”). Hence, predicting the thermodynamic propensity for zeolites to form upon the dissolution of nuclear waste glasses is key to ensure their long-term performance. Here, we compile a unified, internally-consistent thermodynamic database “clay20” to estimate the stability of clay and feldspar phases relevant to nuclear waste immobilization glasses, including beidellite(Mg, Ca, Na, K), kaolinite, montmorillonite(Mg, Ca, Na, K), nontronite(Mg, Ca, Na, K), saponite(Ca, Na, K), and albite. Based on this, we report a geochemical modeling method allowing us to predict the stability of secondary phases (including zeolites, calcium–silicate–hydrate gels, and clays) upon the dissolution of nuclear waste immobilization glasses. We show that this approach offers a realistic description of the stability of the secondary phases forming during the dissolution of two archetypical model nuclear glasses (namely, the International Simple Glass, ISG, and WVUTh-203) under conditions relevant to nuclear waste disposal (T = 90°C, p = 1 bar) as a function of pH. We find that the formation of silica and clay secondary phases is thermodynamically favored at low pH (pH < 10), whereas, in contrast, zeolite (analcime) and calcium–silicate–hydrate phases are favored at high pH (pH > 10.5). This suggests that thermodynamics (i.e., not solely kinetics) plays a key role in determining the range of solution pH wherein stage III corrosion may occur, i.e., when zeolite formation is favored.
Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX
Structure determination remains a challenge for many biologically important proteins. In particular, proteins that adopt multiple conformations often evade crystallization in all biologically relevant states. Although computational de novo protein folding approaches often sample biologically relevant conformations, the selection of the most accurate model for different functional states remains a formidable challenge, in particular, for proteins with more than about 150 residues. Electron paramagnetic resonance (EPR) spectroscopy can obtain limited structural information for proteins in well-defined biological states and thereby assist in selecting biologically relevant conformations. The present study demonstrates that de novo folding methods are able to accurately sample the folds of 192-residue long soluble monomeric Bcl-2-associated X protein (BAX). The tertiary structures of the monomeric and homodimeric forms of BAX were predicted using the primary structure as well as 25 and 11 EPR distance restraints, respectively. The predicted models were subsequently compared to respective NMR/X-ray structures of BAX. EPR restraints improve the protein-size normalized root-mean-square-deviation (RMSD100) of the most accurate models with respect to the NMR/crystal structure from 5.9 Å to 3.9 Å and from 5.7 Å to 3.3 Å, respectively. Additionally, the model discrimination is improved, which is demonstrated by an improvement of the enrichment from 5% to 15% and from 13% to 21%, respectively.
A unified conceptual framework for prediction and control of microbiomes
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Predicting the temperature dependence of self-diffusion behavior in Ni-Cr alloys via molecular dynamics
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Bridging a gap between continuum-QCD and ab initio predictions of hadron observables
Within contemporary hadron physics there are two common methods for determining the momentum-dependence of the interaction between quarks: the top-down approach, which works toward an ab initio computation of the interaction via direct analysis of the gauge-sector gap equations; and the bottom-up scheme, which aims to infer the interaction by fitting data within a well-defined truncation of those equations in the matter sector that are relevant to bound-state properties. We unite these two approaches by demonstrating that the renormalisation-group-invariant running-interaction predicted by contemporary analyses of QCD's gauge sector coincides with that required in order to describe ground-state hadron observables using a nonperturbative truncation of QCD's Dyson–Schwinger equations in the matter sector. This bridges a gap that had lain between nonperturbative continuum-QCD and the ab initio prediction of bound-state properties.
Serum biomarkers for diagnosis and prediction of type 1 diabetes
Type 1 diabetes (T1D) culminates in the autoimmune destruction of the pancreatic ß-cells, leading to insufficient production of insulin and development of hyperglycemia. Serum biomarkers including glucose, glycated molecules, c-peptide, and autoantibodies have been well established to diagnose T1D from other subtypes of diabetes. However, these molecules often mark a late stage of the disease when ~90% of the pancreatic insulin-producing ß-cells have already been lost. With the prevalence of T1D increasing worldwide and because of the physical and psychological burden induced by this disease, there is a great need for prognostic biomarkers to predict T1D development or progression. This would allow us to identify individuals at high risk for early prevention and intervention. Therefore, considerable efforts have been dedicated to the understanding of disease etiology and the discovery of novel biomarkers in the last few decades. The advent of high-throughput and sensitive ‘-omics’ technologies for the study of proteins, nucleic acids, and metabolites have allowed large scale profiling of protein expression and gene changes in T1D patients relative to disease-free controls. In this review, we briefly discuss the classical diagnostic biomarkers of T1D and mainly focus on the novel biomarkers that are identified as markers of ß-cell destruction and screened with the use of state-of-the-art ‘-omics’ technologies.
Prediction of the Thermal Runaway Limit and Optimal Operation of Heat Transfer-Limited, Fixed-Bed Reactor Systems
Here, we derive a new prediction for thermal runaway starting from the alpha model for fixed-bed reactor systems. This method accounts for thermal resistance internal to the reactor tube and the radial temperature gradients that result. To showcase our method, we compare its predictions to other common criteria for thermal runaway using o-xylene oxidation as the example chemistry. Even in systems where internal heat transfer is negligible, the empirical practical design criterion for thermal runaway is inaccurate. For cases where internal heat transfer is relevant, our runaway limit is more stringent than limits derived from simpler 1-D models. To augment our work, we optimize the product yield with the thermal runaway constraint using orthogonal collocation. Using the alpha model, the results illustrate that the thermal runaway limit can be accurately determined using either numerical or analytical methods.
Firefly Algorithm Applied to Noncollinear Magnetic Phase Materials Prediction
In most noncollinear crystal magnets, the number of metastable states is quite large and any calculation that tries to predict the ground state can fall into one of the possible metastable phases. In this work, we generalize the population based meta-heuristic firefly algorithm to the problem of the noncollinear magnetic phase ground state prediction within density functional theory (DFT). We extend the different steps in the firefly algorithm to this specific problem by using polarized constrained DFT calculations, whereby using Lagrange multipliers the directions of the atom magnetic moments remain fixed. By locking the directions of the magnetic moments at each search iteration, the method allows one to explore the entire Born–Oppenheimer energy surface of existing and physically plausible noncollinear configurations present in a crystal. Finally, we demonstrate that the number of minima can be large, which restrains the use of exhaustive searches.
Comparing measurements of limiting current of electrolytes with theoretical predictions up to the solubility limit
Imposing a steady ionic current through an electrolyte results in the formation of salt concentration gradients that compromise battery performance. The limiting current is usually defined as the current at which the salt concentration at the cathode approaches zero. Higher currents cannot be imposed on the cell as larger concentration gradients are unsustainable. We study the limiting current in electrolytes comprising a perfluorinated oligomer, C8-DMC, and lithium bis-(fluorosulfonyl)imide salt in symmetric lithium cells. The time-dependence of the potential, which increases as salt concentration gradients develop, was also measured. Both steady-state and transient behaviors are modeled using Newman's concentrated solution theory; transport and thermodynamic parameters needed to perform the calculations were measured independently. The limiting current is a nonmonotonic function of salt concentration in both theory and experiment. The model shows that at low salt concentrations (below 0.88 mol/kg solvent), the concentration at the cathode approaches zero at limiting current. In contrast, at high salt concentrations (above 0.88 mol/kg solvent), the concentration at the anode approaches the solubility limit (2.03 mol/kg solvent). The experimentally determined salt concentration at which the limiting current is maximized is in excellent agreement with theoretical predictions made without resorting to any adjustable parameters.